225 results about "Response analysis" patented technology

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The invention relates to the technical field of water photovoltaic flexible support pile foundation construction data processing, in particular to a process for a water photovoltaic flexible support pile foundation, which comprises the following steps: fusing meteorological, hydrological and pile deformation multi-source data through a dynamic weight distribution mechanism, and generating a space-time associated dynamic load spectrum by adopting wavelet packet transformation and entropy feature extraction; a collaborative analysis architecture of a finite element simulation and machine learning agent model is constructed, an anchoring depth mapping relation and a real-time design parameter adjustment instruction are output in parallel, and macroscopic fluid parameters and microscopic material degradation characteristics are associated through a data cascade interaction correction mechanism; full-life-cycle data version management is achieved through the block chain technology, and finite element model re-calibration and protection strategy dynamic updating are triggered. The dynamic load response analysis precision and long-term stability of the pile foundation are improved, and the deformation coordination requirement of the flexible support in the complex water area environment is met.

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent diagnosis system and method for a comprehensive energy station, and the system comprises a dynamic trajectory capture module, a lag response analysis module, a mismatch behavior diagnosis module and a fault chain positioning module. According to the method, a dynamic time warping algorithm is adopted to process sliding window time sequence data and calculate track slope difference, capture precision of instantaneous disturbance characteristics is enhanced, a grey correlation model is utilized to analyze track matching degree and set a dynamic lag threshold value, and a continuous non-convergence condition trigger mechanism is combined to improve hidden fault identification sensitivity. Multi-parameter range calculation and direction consistency check integrate start-stop frequency and load rate change trend, reduce misjudgment risk caused by single parameter fluctuation, construct a time sequence alignment axis and calculate delay response joint probability distribution, realize time-space association of historical data and real-time monitoring, accurately locate a coupling relationship of a multi-source fault chain, and improve the reliability of the multi-source fault chain. The abnormal state early warning capability is enhanced, and the fault positioning efficiency is optimized.

The invention relates to the field of bridge structure dynamic response analysis, and discloses a windmill bridge coupling response analysis method. According to the method, wind speed, wind direction and vehicle speed data are collected, and a data set is constructed by combining finite element and CFD coupling numerical simulation; a parallel encoder is adopted to fuse Transform feature extraction and LSTM time sequence processing to generate a hybrid prediction response; constructing a physical constraint and composite loss function based on a train-bridge motion equation, and optimizing neural network parameters through a subtraction average strategy; and finally, predicting dynamic response through forward propagation and verifying physical consistency to form a model optimization closed loop. According to the method, a deep learning method and physical equation constraints are fused, the analysis precision and calculation efficiency of windmill bridge coupling response are remarkably improved, and a more reliable dynamic evaluation means is provided for bridge wind resistance design.

The invention discloses a pile foundation integrity detection method which comprises the following steps: applying a variable amplitude sound wave excitation sequence to a pile foundation structure, collecting acoustic response signals under different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting material rheological characteristics; performing frequency dispersion analysis and hysteretic characteristic analysis on the nonlinear dynamic response data, extracting a stress memory dissipation coefficient and a nonlinear memory capacity index, and establishing a time-varying characteristic curve; according to an abnormal change interval and a memory capacity fading rate of the time-varying characteristic curve, determining a defect position and a defect type, and obtaining an evolution state parameter of the defect; and based on the historical change trend of the evolution state parameters, the material degradation rate and the structure damage development rate are calculated, the health state index of the pile foundation structure is predicted, and a full life cycle performance evolution curve is formed. Through stress memory activation and nonlinear dynamic response analysis, the detection method considering the rheological property of the material is established, and the performance evolution rule of the pile foundation in the whole life cycle can be accurately evaluated.

The invention relates to the field of water quality monitoring analysis, in particular to a multi-modal water quality monitoring data intelligent processing method and system. The method comprises the following steps: obtaining a water quality monitoring parameter stream and a water quality monitoring image, and carrying out adaptive processing to obtain a filtering optimization parameter stream and an optimization monitoring image; performing water quality state evolution perception according to the adaptive filtering optimization parameter flow, and constructing a water quality state evolution graph; and performing biological index response analysis and water quality ecological influence evaluation based on the water quality state trend evolution graph to obtain a water quality evaluation report. And carrying out pollutant spatial distribution change analysis based on the optimized monitoring image, and executing final water quality monitoring evaluation according to a water quality evaluation report. The invention provides efficient and accurate water quality status analysis.

The invention discloses a multi-dimensional natural resource intelligent monitoring method and system based on big data analysis, and relates to the technical field of resource monitoring, and the method comprises the steps: calculating the information sharing degree between different data streams, constructing an abnormal feature credibility distribution map, and carrying out the calculation of the abnormal feature credibility distribution map; and inputting the abnormal feature credibility distribution graph and the multi-source auxiliary information into an integrated classifier, carrying out joint analysis and comprehensive research and judgment on multi-dimensional information to output a target list, constructing an event response relation graph reflecting a monitoring task priority and an execution logic relation according to the attribute features of each element in the target list, and carrying out event response analysis on the monitoring task priority and the execution logic relation. Wherein each node represents a to-be-monitored target, an edge represents a dependency relationship between tasks, and an optimal monitoring execution path is planned for various intelligent monitoring carriers by using an improved A algorithm integrated with a multi-factor cost function. Through natural resource monitoring of multi-modal data interference correction, intelligent classification and path optimization and dynamic response, the monitoring quality and the execution efficiency are effectively improved.

The invention relates to the technical field of environmental protection, in particular to a meteorological element-driven pollutant contribution degree evaluation method and system, and the method comprises the following steps: recognizing regional meteorological characteristics, calculating a wind field disturbance intensity index, predicting a pollutant propagation path, and analyzing the relation between a turbulence period and pollutant concentration attenuation. The retention time of various pollutants is predicted, the influence of humidity on the pollutants is evaluated, and the contribution degree of the pollution source is evaluated in combination with the emission characteristics of the pollution source and meteorological data. According to the method, the meteorological sudden change characteristic index is constructed, the identification capability of abnormal meteorology on the pollution process is optimized, the dynamic prediction precision of the pollutant propagation path is enhanced by combining the wind field disturbance intensity index, the judgment accuracy of the pollution residence time is improved by utilizing the relationship between the turbulence period and attenuation, and the humidity and particle size response analysis is adopted; and the evaluation of the effect of humidity on pollution is refined, and the multi-source emission characteristics and meteorological factors are fused, so that the accurate identification and source-based analysis of the pollution contribution degree are realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a call service quality auditing method, device, equipment and medium, and the method comprises the steps: collecting call audio data, and translating the call audio data into call text data; identifying a client intention in the call text data by using an intention identification model, and generating a client intention result; using an emotion recognition model to recognize customer emotion, and generating an emotion recognition result; using the service response analysis model to identify service behavior performance, and generating a service response analysis result; and generating an audit report containing service quality analysis content based on the client intention result, the emotion recognition result and the service response analysis result. According to the method, the client intention, the emotional state and the service response behavior in the call content are processed in a unified manner, the unstructured call information is converted into the structured evaluation data, automatic, multi-dimensional and deep auditing analysis of the service quality is realized, and the auditing efficiency and accuracy are improved.

The invention relates to the technical field, in particular to a credit-based IT equipment lease management system, which comprises a credit state recognition module, an equipment matching and screening module, a lease term behavior monitoring module, an equipment state rechecking module and a lease permission backtracking module. According to the method, the dynamic credit label is formed by extracting the credit score change trend of the service party and combining the task emergency degree, intelligent judgment on resource priority configuration is achieved, and in the equipment screening link, a scheduling combination is accurately constructed through purpose consistency comparison and historical adaptation data screening, the matching efficiency is improved, and the matching efficiency is improved. In the operation process, multi-dimensional behavior data is collected and equipment self-inspection information is combined to realize joint identification of abnormal use, interface response analysis is used to guide servicing task assignment, and a closed-loop treatment system of pre-rental evaluation, in-rental monitoring and post-rental management and control is constructed by combining service party score fluctuation and equipment credit bearing limit.

The invention discloses a rapid calculation method based on a non-coordination and reanalysis theory and a computer device. The invention relates to the technical field of structural design optimization, and solves the problem that it is difficult to realize efficient structural response analysis while ensuring the calculation precision in the prior art. The method comprises the following steps: selecting a structural member of a to-be-calculated structural response and discretizing the structural member; preprocessing the data, and determining a coarse and fine grid index set corresponding to interface fine grid nodes; solving a unit shape function, and embedding the shape function into a body stiffness matrix according to a degree-of-freedom position to serve as an interface constraint condition; calculating a variable element stiffness matrix, and completing the construction of a modified model stiffness matrix; carrying out iterative calculation to obtain a basis vector; and calculating and solving the structural response of the modified model. According to the method, the efficient response analysis of the non-coordinated variable structure is realized on the premise of ensuring the calculation precision.

The invention discloses a dynamic response regulation and control and vibration reduction system and method for a floating type photovoltaic platform in the technical field of offshore new energy equipment, and aims to improve the anti-wave stability and structural safety of the platform under complex sea conditions. The system comprises a platform main body, a monitoring unit, a response analysis and early warning unit and a regulation and control execution unit, the monitoring unit is arranged on the platform main body and is used for collecting real-time sea condition data; the response analysis and early warning unit is in communication connection with the monitoring unit and is used for performing response analysis on the real-time sea condition data and sending an early warning signal to the regulation and control execution unit when the abnormal working condition of the platform main body is identified; and the regulation and control execution unit is used for carrying out regulation and control and vibration reduction on the platform main body according to the received early warning signal. According to the method, the assembly fatigue can be obviously relieved, the cable damage probability is reduced, the service life of the platform is prolonged, and good engineering adaptability and intelligent expansion potential are achieved.

The invention provides a deep sea temporary platform large-diameter steel casing dynamic response analysis method, and relates to the technical field of deep sea engineering, and the method comprises the steps: building a three-dimensional explicit dynamic coupling model according to the typhoon frequent generation characteristics of a target sea area and steel casing structure parameters; according to the three-dimensional explicit dynamic coupling model, the wave load is decomposed, the vibration hammer load effect, the ocean current load effect and the internal wave current load effect are integrated, and a comprehensive dynamic load set of the steel casing is determined. By establishing a precise model, integrating multi-source loads, identifying a risk area, monitoring stress in real time and optimizing design parameters based on data, self-adaptive matching of steel casing design and a complex marine environment is achieved, the structural safety of the deep sea temporary platform steel casing is effectively improved, and the cost is reduced.

The invention relates to the technical field of data processing, in particular to an engineering geological survey data processing method and system, and the method comprises the following steps: obtaining hole site space positioning and lithologic boundary information, collecting a structure segment sequence, extracting resistance change direction classification disturbance behaviors, recognizing node sequence conflicts, and judging hydrological turning positions. Identifying the corresponding relation between the structural section and the water level track, and generating a structural section water level merging mapping table. According to the method, the spatial information, the lithology identification, the sampling level and the penetration resistance of the drilling structure sections are collected and serialized, disturbance identification and non-sampling section classification are performed based on the resistance change trend between the sections, and node conflicts are identified in combination with the numbering relation and the lithology combination; continuous modeling and classified merging of geological information on the spatial level are achieved, the linkage adaptability of data in a multi-dimensional analysis scene is enhanced, misjudgment interference caused by information isolation is avoided, and dynamic recognition and hydrological response analysis of a geological structure are supported.

The invention discloses a dynamic response analysis method of a floating breakwater anchor chain system. The dynamic response analysis method comprises the following steps: determining parameters of a floating box and an anchor chain system in the floating breakwater system; a sea condition environment where the system is located is given; motion of the anchor chain unit is described, and a global position vector of any point P on the axis of the anchor chain unit is given; constructing an expression of an anchor chain unit kinetic energy and mass matrix; calculating strain energy and elastic force of the anchor chain unit; calculating the environmental load borne by the floating breakwater anchor chain system; assembling a system mass matrix and a constraint equation of the system; establishing a dynamic equation set of the system, and correcting to obtain a first-order differential equation set; performing simulation to extract displacement, speed and acceleration data of the top end of the anchor chain rope, and substituting the data as external constraint conditions of the top end of the anchor chain into the dynamic equation set; and performing dynamic solution on the first-order differential equation set. According to the invention, the accuracy of dynamic response calculation is improved.

The invention discloses a method for constructing an evaluation model for the influence of climate change on biodiversity, and particularly relates to the technical field of ecological modeling and climate response analysis. The method comprises the following steps: generating a high-resolution characteristic grid based on microclimate disturbance data and a remote sensing image, extracting ecological plaques and establishing a heterogeneity spatial index model, constructing a connectivity map and a coupling response path library, extracting a typical species response mode by using nonlinear dimension reduction and spectral clustering, and performing dynamic fitting in combination with biological survey data. And finally, generating a diversity attenuation trend prediction curve and a risk thermodynamic diagram, identifying a biodiversity collapse critical point and outputting an intervention priority, and the method can be widely applied to regional ecological early warning and protection planning.

The invention provides a sudden cardiac death high-risk group multi-modal data intelligent analysis and prediction method and system, and relates to the technical field of medical health monitoring, and the method comprises the steps: collecting multi-modal data, projecting an electrocardiogram to a three-dimensional coordinate system, extracting potential trajectory features, and calculating a space-time discrete index; performing dynamic response analysis on the blood pressure data, and matching with a space-time discrete index to calculate a cardiovascular coupling coefficient; and performing segmented correction according to the motion state data, monitoring a coupling coefficient deviation condition in real time, calculating a risk degree and generating an early warning. According to the method, heart abnormity can be found in advance, and sudden cardiac death can be effectively prevented.

The invention discloses a rock mass structural surface identification method and system based on adaptive neighborhood and multi-seed-point growth, and relates to the technical field of geotechnical engineering, geological exploration and mining engineering, and the method comprises the steps: carrying out the adaptive non-uniform downsampling of three-dimensional point cloud data based on local feature entropy; retaining key geometric features of the structural surface while compressing the data volume; fusing the normal vector and the local point cloud density to carry out multi-feature classification; an improved region growing algorithm supporting parallel growth of multiple seed points is adopted, and a structural surface is accurately recognized; automatically extracting occurrence, spacing, trace length and roughness parameters of each structural surface; performing full-automatic advantage grouping on the occurrence through a DP-DBSCAN hybrid clustering algorithm; and constructing a three-dimensional discrete fracture network model based on the statistical parameters, and integrating the three-dimensional discrete fracture network model to discrete element software for mechanical response analysis. According to the method, full-process automation of rock mass structural surface recognition is realized, and the processing efficiency, the recognition precision and the engineering practicability are remarkably improved.

The invention discloses a layered foundation construction optimization method based on dynamic loading and intelligent inversion, and relates to the technical field of intelligent geotechnical engineering. In order to solve the problem that comprehensive analysis and optimization design of multiple layers of soil bodies are difficult to carry out in the prior art, the invention provides the following technical scheme: the layered foundation construction optimization method based on dynamic loading and intelligent inversion is characterized by comprising the following steps: S1, applying a dynamic load on a known layer of soil body; s2, acquiring response data, including acceleration, displacement and stress, of the soil body under the action of the dynamic load through a sensor; s3, deducing the dynamic response of the lower-layer soil body by using an inversion algorithm based on the acquired response data; s4, analyzing dynamic response characteristics of each layer of soil body; and S5, optimizing the compactness and the elastic modulus of the soil layer. The method can improve the accuracy and efficiency of soil dynamic response analysis, is suitable for mechanical property optimization in complex foundation engineering, and has wide engineering application value.

The invention relates to the technical field of intelligent management, and particularly discloses a building intelligent management method and system based on a BIM technology, and the method comprises the steps: firstly constructing a BIM model of a building, inputting the equipment operation and maintenance information of building equipment into the BIM model, and collecting the operation state data of the building equipment in real time through a sensor group, a deep learning algorithm is introduced, feature extraction and deep interaction response analysis are performed on the real-time operation state data and the historical operation and maintenance information of the building equipment, and the historical operation and maintenance data and the real-time state of the equipment are combined to mine a potential fault mode of the equipment, so that intelligent fault diagnosis of the building equipment is realized; and the health state of the equipment is visually displayed in the BIM model through color coding. In this way, the operation and maintenance efficiency and the fault diagnosis accuracy of the building equipment can be improved, potential fault risks can be found and early warned in time, and meanwhile the intelligent level of building management is enhanced.

The invention relates to the technical field of engineering safety management, and particularly discloses an engineer safety management method and system based on big data, and the method comprises the steps: collecting all data of a construction site in real time; performing space-time alignment and fusion processing on the collected data to generate multi-modal fusion data; training a multi-modal risk assessment model based on the obtained construction big data; performing construction risk feature extraction on the collected data based on the multi-modal risk assessment model, and outputting construction risk features; and performing safety response analysis on the multi-modal fusion data based on the construction risk features, and generating a safety response result. All data of a construction site are fused, and a multi-modal data set is constructed, so that full-dimensional monitoring of the construction site is realized; a big data training model is matched based on construction types, and space-time correlation analysis of risk features is achieved; a differentiated early warning mechanism for personnel, equipment and environment is generated, multi-agent co-processing is supported, and accident loss is reduced.

An emergency response data system (ERDS) integrates an AI agent into an emergency management application to generate emergency response analytics to facilitate operational efficiency of one or more ECCs. The ERDS provides an emergency management application operable by an emergency communications center (ECC) computing system to display an emergency management user interface (UI) at one or more ECC. The ERDS stores, in one or more data structures, historical emergency response analytics. The ERDS receives, with the emergency management UI, user instructions related to the historical emergency response analytics. The ERDS provides the user instructions to the emergency response AI agent. The ERDS generates, with the AI agent, textual or visual representations of the historical emergency response analytics in response to the user instructions. The ERDS displays, with the emergency management UI, the textual or visual representations of the historical emergency response analytics to facilitate operational efficiency of ECCs.

The invention provides a tree wind-induced response evaluation and analysis method, and relates to the technical field of disaster prevention and reduction engineering, and the method comprises the steps: carrying out the construction of a geometric model for a target tree; on the basis of the geometric model, CFD analog simulation is carried out, and wind pressure time history data and crown load data of CFD grid points of the branches under different wind field conditions are obtained; finite element mesh generation is carried out on the geometric model, a finite element model is constructed, and mesh unit types in the model comprise hexahedron units, tetrahedron units and wedge-shaped units; the branch wind pressure time history data and the crown load data are mapped to the surface of a finite element model, structural finite element analysis is carried out, and cloud chart data of stress and displacement of the target tree changing along with time under different wind field conditions are obtained; and carrying out wind-induced response analysis and wind resistance evaluation on the target tree based on the cloud picture data and the wind-induced damage criterion of the tree. According to the technical scheme, a scientific basis can be provided for understanding the response behavior of the target tree under the strong wind condition.

The invention relates to the technical field of component quality control, and discloses a remote component quality control system based on digital twin and wireless communication. A component data acquisition module of the system obtains multi-dimensional data of a to-be-monitored component through a wireless sensor network, and a component identification code is associated to generate a quality basic data set; the regional association analysis module establishes association mapping of adjacent components according to a spatial topological relation to form a quality association partition map; the environmental response evaluation module analyzes the influence degree of environmental factors on the performance of the component and generates an environmental response analysis report; the defect positioning module identifies problem points and constructs a defect distribution map; and the quality early warning module integrates the problem points and the associated parameters, marks the position of the potential safety hazard, and outputs a quality evaluation and risk early warning report. The system realizes remote and comprehensive control of component quality, and assists in timely finding and dealing with quality problems.

【Solution means】The proxy device includes a request determination unit 105 that receives a request sent from a virtual browser and determines the security risk of the request URL specified in the request, and a request transfer unit 101 that transfers a request determined to be normal by the request determination unit 105 to a server 30 that operates the site to be analyzed, where the site to be analyzed is a target site where the service used on the site to be analyzed is specified, a response analysis unit 102 that receives and analyzes a response from the server 30, and a response return unit 103 that returns the response to the virtual browser. According to this, it is possible to prevent the analysis device 10 from receiving a response having a security risk.

The invention provides an intelligent response method and device for dam safety abnormity based on a large text model, and the method comprises the steps: collecting dam monitoring data in real time through an edge calculation node, and carrying out the abnormity detection based on an LSTM-Attention time sequence prediction model and a multi-physics coupling analysis model; generating a standardized text report containing exception description, time, position and associated parameters; the text report serves as input, intelligent response analysis is conducted through a text large model based on LoRA fine tuning, and emergency disposal suggestions conforming to industry specifications are generated; and performing knowledge base matching verification on the emergency disposal suggestions, screening out a high-confidence-coefficient response scheme based on a cosine similarity threshold, and automatically triggering an emergency response process when a dam break risk coefficient is detected to exceed a preset threshold. According to the method, full-process automation of dam safety monitoring from abnormal detection to emergency response can be realized, the response time is remarkably shortened to the minute level, and the early warning accuracy and decision scientificity are improved.

The application discloses an intelligent identification method for dominant seepage channels based on big data, which comprises the following steps: collecting static geological data, dynamic development data and dynamic monitoring data of an oil reservoir and performing data processing; performing data quality analysis on the processed data and establishing a sample database; performing inter-well connectivity analysis and inter-well pressure response analysis in a plane according to the sample database, and identifying dominant channels in the plane; performing interlayer production and absorption law research in a vertical direction according to the sample database, predicting interlayer liquid production and absorption ratios and water cut, and identifying dominant interlayer positions in the vertical direction; and comprehensively identifying dominant seepage channels between each layer of a production well and an injection well based on the identified dominant channels in the plane and the dominant interlayer positions in the vertical direction.

The invention belongs to the field of dam flood discharge safety, and provides a dam flood discharge safety protection method and system based on earthquake response analysis, and the method comprises the steps: S1, obtaining multi-source environment and structure data in real time; s2, preprocessing the multi-source environment and structure data to form a unified time sequence multi-dimensional data set; s3, simulating and analyzing the structural dynamic response of the dam and the flood discharge gate based on the unified time sequence multi-dimensional data set, and evaluating potential damage; s4, comprehensive risk assessment is carried out by combining the earthquake response analysis and damage assessment result, the hydro meteorological data and the flood discharge gate operation state data, and early warning information is generated; s5, generating a dynamically adaptive flood discharge gate opening and closing strategy according to the comprehensive risk assessment result and the early warning information; s6, executing the opening and closing strategy of the flood discharge gate, and monitoring the actual operation state of the flood discharge gate in real time; and carrying out comparative analysis on the actual running state of the flood discharge gate and the prediction strategy, and carrying out updating and optimization according to an analysis result.

The invention discloses a real-time monitoring control system based on a thermal flow sensor and a controller, and relates to the technical field of sensor monitoring. According to the real-time monitoring control system based on the thermal flow sensor and the controller, the method comprises the steps that S1, sensor monitoring data and historical statistical data are preprocessed; s2, lagging and response time dynamic characteristics are extracted; performing modal decomposition, phase estimation, peak calculation and steady-state noise analysis; s3, executing phase compensation, peak value recovery and enhancement compensation judgment; recording a residual sequence and a compensation parameter, and outputting optimized flow data; s4, constructing a credibility interval and dynamic consistency score synthesis control credibility mark; and executing security constraint and prediction filtering and archiving. The problems that an existing thermal flow sensor generates obvious phase lag and peak smoothing distortion due to the influence of thermal diffusion thermal inertia under the rapid fluctuation and pulsation working conditions, and the real-time monitoring precision and the closed-loop control stability are difficult to guarantee due to the lack of an online dynamic response analysis and compensation mechanism are solved.

The invention relates to the technical field of traffic management infrastructures, and particularly discloses a holographic traffic signal lamp system and method, and the method comprises the steps: collecting vibration wave data in real time through a vibration detector, and remotely transmitting the collected vibration wave data to a third-party platform; the method comprises the following steps: extracting vibration wave archived data marked as abnormal from a three-party data source as a data set for comparative analysis, and introducing a deep learning algorithm to carry out waveform feature extraction on vibration wave data and each abnormal vibration wave archived data; the vibration wave data and each abnormal vibration wave archived data are subjected to dynamic query response analysis based on fluctuation characteristics, so that potential association between the real-time vibration wave data and the abnormal vibration wave archived data is mined, and intelligent earthquake early warning release is realized. In this way, the problem of false triggering caused by transient interference factors can be effectively solved, the accuracy and reliability of earthquake early warning are improved, and the situations of false alarm and missing alarm are reduced.