351 results about "Metacomputing" patented technology

The invention provides an elevator energy recovery application method and system based on a super capacitor, and relates to the technical field of energy recovery, and the method comprises the steps: collecting elevator operation data and super capacitor state data, constructing a state space and an action space, and constructing a reward function according to peak-valley electricity price economic benefits, super capacitor life loss and energy utilization efficiency; training a deep reinforcement learning model based on the double-Q network to obtain an energy recovery optimization strategy; when the elevator is braked, regenerative braking energy is stored in the super capacitor; when the elevator is driven, the stored energy is released for traction; the optimal storage allocation scheme of the multiple elevator rooms is calculated through the virtual energy storage unit, hierarchical control is implemented based on an optimization strategy and the allocation scheme, the upper layer adopts fuzzy self-adaptive weight to determine the energy storage priority, and the lower layer adopts a distributed algorithm to calculate the energy allocation coefficient and the target power; and the charge-discharge rate is updated according to the health state of the supercapacitor.

The invention discloses a power distribution cabinet bus temperature change analysis and early warning method and system, and relates to the technical field of power distribution cabinets, and the method comprises the steps: collecting the temperature data of a three-phase bus of a power distribution cabinet, dividing a temperature sequence into time-space units, and calculating the spatial characteristics and time characteristics of each time-space unit; performing frequency domain analysis on the temperature data of the space-time unit, decomposing the temperature data to obtain a low-frequency component, an intermediate-frequency component and a high-frequency component, extracting sub-components of different time scales, and calculating a time coupling feature, a space coupling feature and a physical coupling feature; fault-free data is collected to establish a normal mode library, historical fault data is collected to establish a fault mode library, the deviation degree of features and a baseline is calculated in real time, and marking and feedback updating are carried out on potential new faults; on the basis of distributed monitoring, temperature abnormal points are positioned, reconstruction errors of all scales are calculated, the abnormal origin scale is determined, and graded early warning is triggered, so that the problem that a traditional monitoring method is insufficient in fault recognition capability is solved.

To provide a technique for supporting structural calculation of a deck plate used in a roof structure, which can reduce the complexity of management by a user.SOLUTION: An information processing system 1 includes an information processing terminal 12 having a display device 305 and a server 11 connected to the information processing terminal 12 via a communication network 13A. The server 11 includes: a reception unit 21 configured to receive deck plate information 200; a storage unit 20; a calculation unit 22 configured to perform structural calculation of the deck plate based on the deck plate information 200 and store calculation results in the storage unit 20; and an output unit 23 configured to transmit the calculation results to the information processing terminal 12 and cause the display device 305 to display the calculation results. The deck plate information 200 includes information 200C on material, cross-sectional performance, beam support method, support distance, and acting load of the deck plate.SELECTED DRAWING: Figure 2

The invention relates to the technical field of hydraulic engineering and geotechnical engineering, in particular to a parameter updating method and device for a rock-fill dam deformation analysis model.The method comprises the steps that a plurality of Duncan-Zhang E-B model parameter combinations of an indoor triaxial experiment of a target rock-fill dam project are obtained to construct a copula multivariate joint distribution model; sampling a parameter combination sample of the target rock-fill dam project, and inputting the parameter combination sample into the finite element deformation analysis model to generate a finite element calculation sample; training a pre-constructed deep neural network by using the parameter combination sample and the finite element calculation sample to generate a deep learning agent model; and fusing the copula multivariate joint distribution model, the deep learning agent model and an NSGA-III multi-objective optimization algorithm to search for final model parameters for updating the rock-fill dam deformation analysis model generation by generation. Therefore, the problem that an existing dam project parameter updating method neglects complex correlation existing between different parameters is solved.

The invention discloses a laser cladding temperature field analysis method based on a finite element simulation method. A metal flat plate is used as a research object. ANSYS software is adopted for simulation. A laser cladding process is simulated by constructing a finite element model and a refined mesh in combination with a moving Gaussian heat source and birth-death unit technology, and simulated laser heating is applied to the surface of a part as a thermal load. The ANSYS processing unit calculates a simulation result, and the postprocessor displays temperature field distribution. Simulated and actual coating cross section molten pool shapes are compared, it is found that the size of a simulation model is highly consistent with the size of an actual molten pool, the maximum error is only 9%, and the reliability of numerical simulation is verified. According to the method, a solid theoretical basis and an effective simulation means are provided for the metal laser cladding process, the process research level is improved, and the development cost is reduced. The method has an important value for practical engineering application, and provides a scientific basis for optimizing the laser cladding technology.

The invention discloses a control system and method for an automatic beer production line, relates to the technical field of automatic control, is used for solving the problems that state judgment is lagged and abnormal trend is difficult to identify in time, identifies electronic tags of key equipment through an RFID reader-writer, imports models and serial numbers into a database, and automatically associates operation parameters. The distributed sensors collect temperature, pressure, flow and vibration data in real time, the analysis unit calculates the operation deviation and the health degree, a multi-condition control strategy is configured through the programmable logic controller based on the health degree, a time window and a task priority algorithm are combined, real-time data and historical data are compared to recognize abnormity, and the real-time data and the historical data are analyzed. And a fuzzy control algorithm is fused to generate an adjustment instruction, the equipment state is automatically adjusted, time sequence modeling is utilized to analyze whole-process data, a start-stop strategy is formulated, energy distribution and process optimization are performed, and intelligent scheduling and self-adaptive optimization of the beer production line are realized.

The invention provides a distributed network data integrity verification method and device based on a block chain. The method comprises the steps that original data is divided into m data blocks, each data block comprises n data units, the initial hash value of each data block is calculated, and the initial hash value is uploaded to a block chain network; uploading the data block to be verified to the block chain network, and recalculating the hash value of the data block; preliminarily verifying the integrity of each data block according to the determined verification parameter and a preset threshold value, and judging whether the data block meets a preliminary integrity condition or not; for the data blocks meeting the preliminary integrity condition, constructing a ternary tree according to the hash value of each data unit; judging whether the data in the data block meets a complete condition or not; and when all the segmented data blocks meet a complete integrity condition, judging that the original data is complete. The problems that a traditional data integrity verification method is low in efficiency and prone to being attacked are effectively solved.

Provided in the embodiments of the present application are a server system, a resource scheduling method for a server system, and a chip and a chiplet. The server system comprises: a multivariate computing resource pool, a data storage resource pool, a switching module and a control module, wherein the multivariate computing resource pool comprises a general computing resource pool and a heterogeneous computing resource pool, the general computing resource pool comprising a group of general computing units, and the heterogeneous computing resource pool comprising a group of heterogeneous computing units, and the multivariate computing resource pool is connected to the data storage resource pool via the switching module by means of a cache consistency bus inside the server system; the data storage resource pool comprises data storage resources allowed to be shared by multivariate computing resources in the multivariate computing resource pool; and the control module is configured to perform computing power scheduling on the multivariate computing resources in the multivariate computing resource pool, and dynamically allocate the data storage resources in the data storage resource pool to the multivariate computing resources in the multivariate computing resource pool.

The invention relates to the technical field of intelligent supply chain management and multi-modal artificial intelligence crossing, and discloses a multi-modal large model-based supply chain toughness evaluation system, which comprises a data acquisition module, a multi-modal processing module, a toughness evaluation module, a cloud edge collaboration module and a decision support module, the data acquisition module is used for acquiring supply chain texts, images and time sequence heterogeneous data; the multi-modal processing module is used for realizing dynamic feature fusion through term matrix enhanced Transform; the toughness evaluation module quantifies toughness scores based on a three-level index system and a fuzzy matter element calculation engine; the cloud edge collaboration module realizes edge end real-time reasoning through a lightweight model; the decision support module optimizes node reinforcement and path planning based on network topology and real-time weather data. According to the method, the problems of insufficient data isomerism processing, single evaluation dimension and lagging real-time response in the prior art are solved.

The embodiment of the invention provides a BIM-based bridge model intelligent construction method and system, and belongs to the field of BIM-based bridge model intelligent construction. The method comprises the steps that bridge model construction related data are collected and preprocessed, and a component library is established according to the preprocessed bridge model construction related data; defining geometric linkage rules and topological connection logic of components in the component library, and generating an initial BIM bridge model through a generative adversarial network; performing lightweight finite element calculation on the initial BIM bridge model to obtain an optimized BIM bridge model; and performing standard conflict detection and conflict parameter adjustment on the optimized BIM bridge model to obtain a final bridge model. According to the method, the parameter relation matrix and the dynamic linkage mechanism are constructed, the generative adversarial network is introduced to generate the complex bridge type, and the graph convolutional network is introduced into the generative adversarial network, so that reasonable bridge type topology and dynamic parameter linkage are guaranteed.

The invention relates to a server-side satellite precision product anomaly detection method and system and a terminal, specifically, a previous time window of a current epoch is obtained, the time window comprises a plurality of epochs, the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated, and the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated; normalizing the residual mean value and the root-mean-square to obtain the characteristics of the satellite in epochs; acquiring features of all the satellites in all epochs in the previous time window to form a feature set, performing outlier detection on the feature set by adopting an LOF method, and determining the LOF score of the satellite at the current epoch moment by utilizing the features of the current epoch of the satellite and the feature set after removing the satellite of which the LOF score is greater than a preset value from the feature set; the LOF scores of the satellites are sent to the PPP user side, when the PPP user side carries out ambiguity fixing, all the satellites are tried to be fixed firstly, if fixing fails, the satellite with the maximum LOF value is removed from the ambiguity set, fixing is tried again, and continuous circulation is carried out till the ambiguity is fixed successfully.

The invention discloses a multi-disaster-type disaster internet-of-things time sequence adaptive anomaly detection method and system, and relates to the field of internet of things, and the method comprises the steps: S1, constructing an anomaly detection model; s2, acquiring a training data set; s3, training an anomaly detection model; s4, acquiring to-be-detected data; s5, analyzing the reconstruction structure of the to-be-detected data; s6, analyzing an abnormal score; the emergency disaster early warning system comprises an acquisition unit, a storage unit, a calculation unit and an early warning unit. A multi-scale time convolutional network and a self-adaptive spectrum feature module are fused, the characteristics of time sequence data in a time domain and a frequency domain are deeply mined, a gating memory mechanism is introduced, normal time-frequency features in the data can be accurately captured and enhanced, and the recognition capability is improved; by adding the radial basis function layer, the detection capability of the model on tiny anomalies is remarkably improved, so that the model can detect tiny abnormal changes more accurately.

The invention discloses a hydrodynamic analysis method for a floating structure with multi-body and multi-coupling characteristics, which comprises the following steps of: acquiring geometric characteristics of each floating body, and dividing boundary element computational grids; wave excitation force, additional mass and radiation damping of each floating body are calculated, and a mass matrix and a rigidity matrix of each floating body are obtained; determining a connection mode and a coupling factor of the floating body; according to a connection mode and coupling characteristics of the system, establishing displacement connection conditions among the floating bodies; constructing a motion constraint matrix according to the displacement continuity condition and the motion constraint relation; determining a frequency domain motion equation of the multi-floating-body system according to a Lagrange multiplier method; and solving the motion equation to obtain the motion response of the multi-floating-body system, and further systematically analyzing the wave energy capture power according to the coupling characteristics of the system. According to the analysis method, the problem that the motion response of each degree of freedom cannot be accurately described when the hydrodynamic response characteristics of the complex multi-body system are obtained through an existing simulation method is solved.

The invention relates to the technical field of tiny fault detection, and discloses an intelligent tiny fault detection method and system based on empowerment divergence, and the detection method comprises the steps: collecting offline production data at each moment in an industrial operation process; constructing a principal component analysis model and extracting a principal component; calculating a data score matrix; performing JS divergence test under a sliding window strategy; performing local outlier abnormal value detection on the sequence of the JS divergence statistics through a local outlier factor weighting method, and introducing an obtained local outlier weight factor into the JS divergence statistics; fitting data distribution through a smooth kernel function; calculating the weighted JS divergence statistic of the online production data, and judging whether a fault occurs or not; according to the method, JS divergence calculation of local outlier factor empowerment is introduced, and a principal component analysis technology is combined, so that an efficient and stable tiny fault detection model is constructed, and the detection capability of tiny faults under specific conditions or in a local area is improved.

The invention belongs to the field of hardware neural network design, and particularly discloses a CMOS-based attractor recurrent neural network system and an implementation method thereof.A neuron calculation module receives an external input high-level signal and generates an initial synaptic high-level signal; integration and activation calculation are carried out based on the current output by the synaptic calculation module, and whether a synaptic high-level signal is generated or not is determined according to an activation state; the synaptic storage module writes the non-zero weight into a synaptic weight SRAM (Static Random Access Memory) array; the synaptic calculation module performs product operation on the synaptic high-level signal and the synaptic weight in the synaptic weight SRAM array, and outputs current; when the STDP updating module is in a neuron training mode, the STDP updating module updates the synaptic weight SRAM array according to the synaptic high-level signal; and the group state module outputs a neuron sequence number corresponding to the maximum activation frequency. A cyclic connection structure of the attractor neural network is formed, and the real-time cognitive behavior requirement of the brain is met.

The invention relates to the technical field of building energy consumption analysis, and discloses a building energy consumption and carbon neutralization analysis system based on big data analysis, which comprises an acquisition unit, an analysis unit, a calculation unit, a deficit unit, a judgment unit and a carbon neutralization management unit, and is used for generating a carbon neutralization management instruction of a target object according to a carbon neutralization urgency index, performance degradation of a target object along with increase of service life is captured, an analysis unit combines real-time carbon emission data of a power grid, generates a dynamic carbon track slope and reflects a carbon emission change trend in real time, a calculation unit performs segment processing on operation duration based on big data, and a complete-cycle carbon track vector is constructed. A healthy deficit degree and a stepped urgency index are generated through big data analysis of the deficit unit and the judgment unit, and finally a management instruction is output by the management unit, so that the problem of insufficient tracking of full-life-cycle energy consumption attenuation and carbon emission increment of a target object by traditional analysis is effectively solved, and the accuracy of long-term carbon neutralization planning is greatly improved.

The invention relates to the technical field of power distribution management, and discloses a novel power distribution network aggregation resource power prediction method and system, and the system comprises a meteorological data unit, a mutual information analysis unit, a load data unit, a load analysis unit, and a neural unit. According to the novel power prediction method and system for the aggregated resources of the power distribution network, various types of weather forecast data are obtained through a weather data unit, mutual information between actually measured wind power and weather factors and mutual information between different weather factors are calculated through a mutual information analysis unit, and then feature sorting is carried out through an incremental search method; a load data unit obtains load data and communication people flow data, space-time grid clustering and space-time coupling are carried out, user load classification, load grading and communication people flow and load relevance are analyzed, and a neural unit learns and predicts resident user loads and resident area distribution transformer loads; a load increase and decrease target is formulated, power prediction is more accurate, and the utilization rate of aggregation resources is high.

The invention relates to the technical field of point cloud semantic segmentation, and discloses a point cloud segmentation method and system based on point cloud serialization and a Mama network. Specifically, a geometric distance matrix and a semantic distance matrix are calculated for a plurality of representative point units obtained by sampling on an original point cloud, and a path sequence is generated based on a fused comprehensive distance matrix. And traversing the path sequence by adopting a bidirectional scanning strategy to generate a forward sequence and a reverse sequence, extracting features from the forward sequence and the reverse sequence by utilizing a Mama network, cascading or fusing the extracted features, and finally performing classification based on the fused features. According to the method, better long-range dependency and local context modeling effects can be obtained, the comprehensive expression capability of detail features and global context in sparse and irregular scenes is enhanced, and the calculation and memory overhead required for global dependency capture of large-scale point clouds is greatly reduced.

The invention provides a mixed modeling method for the pumping and printing process of 3D printing concrete. According to the method, intelligent aggregate is introduced, real-time monitoring and data collection of particle motion behaviors in the 3D concrete pumping and printing process are achieved, and comparative analysis is conducted on collected multi-dimensional dynamic data of particle speed, acceleration and collision frequency and discrete element-computational fluid mechanics (DEM-CFD) coupling simulation results; therefore, efficient inversion and accurate calibration of input parameters of the DEM-CFD coupling simulation model are achieved, and compared with unobservability and uncertainty of the particle flow state in the traditional concrete pumping process, the method can achieve transparency in the particle movement process; the parameter inversion method provided by the invention is based on fitting optimization of actually measured particle motion data and a numerical simulation result, so that manual intervention and experimental overhead are remarkably reduced.

The invention provides a test method and device of a simulation model and electronic equipment, and can be applied to the technical field of machinery and the technical field of simulation test. The test method of the simulation model comprises the steps that a to-be-tested simulation model is obtained, the to-be-tested simulation model is used for simulating the state change generated by the package of a target object under the action of external force impact, and the to-be-tested simulation model comprises an impact simulation body and a package simulation body; performing first finite element calculation on the structure of the packaging simulation body according to the simulation parameter set to obtain a first stress-strain relation of the packaging simulation body in a static compression process; the first stress-strain relation is converted according to packaging simulation parameters of the packaging simulation body, a second stress-strain relation of the packaging simulation body in the dynamic compression process is obtained, and the simulation parameter set comprises the packaging simulation parameters; and performing second finite element calculation on the structure of the package simulation body by using the second stress-strain relationship to obtain a motion state test result of the package simulation body in the dynamic compression process.

The invention, which relates to the technical field of mine geology and urban safety, discloses a refined collaborative prediction method and system for subsidence of a global goaf, comprising the steps of establishing a hierarchical storage system, and correcting a working face calculation boundary; extracting the maximum influence radius of all the working faces and four-dimensional coordinates of the mining boundary of each working face, and calculating to obtain a global prediction range to generate a global surface prediction grid; point set insertion and Delaunay triangulation are adopted to obtain an effective triangular unit, a local influence domain range of the effective triangular unit is calculated, and an influenced sub-grid is screened from the global surface prediction grid; and calculating a predicted point movement deformation value based on a parallel computing architecture, accumulating the predicted point movement deformation value to a global result array through a coordinate index mechanism, synthesizing a deformation component in a specific direction, generating a visual result, and performing statistical analysis. According to the method, the problems of poor adaptability and low efficiency of a traditional scheme are effectively solved, and the prediction precision and efficiency are remarkably improved.

The invention discloses a self-adaptive system for various gas sensors and a parameter calculation method of the self-adaptive system, and belongs to the technical field of gas detection. The system comprises a front-end polarity adaptation unit, a current-voltage conversion unit, an intelligent neural network calculation unit U5 and an analog-to-digital conversion unit U4, wherein the front-end polarity adaptation unit is used for being connected with a gas sensor and receiving a current signal of the gas sensor; the signal subjected to front-end polarity matching and current-voltage conversion is subjected to signal filtering and signal gain amplification through the intelligent neural network calculation unit U5, analog quantity and digital quantity conversion is performed through the analog-to-digital conversion unit U4, the detected gas voltage value of the gas sensor is obtained, and the intelligent neural network calculation unit U5 calculates the current gas concentration. The multi-gas detection device adapts to gas sensors of different manufacturers, different principles and different measuring ranges, and intelligent self-adaption of multi-gas detection is achieved on the premise that the product cost is not increased; and accurate concentrations can be calculated for different gases.

The invention discloses a machine tool dynamic characteristic finite element calculation method based on bolting joint surface parametric modeling, and belongs to the technical field of bolting joint surface and machine tool overall modeling and simulation. According to the method, a gantry machine tool reduced scale model is taken as an example, contact parameter calculation is conducted on bolting joint surfaces based on the fractal theory, then finite element parametric modeling is conducted on the bolting joint surfaces through an abaqus basic connector unit, and dynamic performance analysis of the whole machine is completed. Compared with a test method, the accuracy of the method is proved. According to the method, more accurate and more efficient calculation of the dynamic performance of the machine tool is realized, and a theoretical basis is provided for subsequently exploring the influence of each combination surface on the dynamic performance of the whole machine.

The invention relates to the technical field of rolling mill automatic control, in particular to a special rolling mill controller without a PLC framework and an integrated control system, and aims to solve the problems that in rolling of a rolling mill, the sampling rate difference of pressure and thickness sensors and material plastic deformation accumulation cause the time sequence dislocation of a rolling force peak value and a thickness valley value, the collaborative utilization of data by a thickness control algorithm is influenced, and the cost is reduced. In order to solve the problems that rolling force, thickness data and roller pulse signals are collected through a special controller, and a rolling length observation benchmark is converted; the dynamic phase compensation unit calculates a plastic deformation cumulant, after triggering compensation, a basic phase difference is established based on a sampling rate difference, a compensation coefficient is dynamically increased, a compensation data segment is generated through linear interpolation, and a time sequence is forcibly aligned; and the integrated control unit inputs the alignment data into a thickness control algorithm, the rolling force serves as feedforward, the thickness data serves as feedback, a precise roller gap adjusting instruction is generated, and the material thickness rolling precision is improved.

The invention discloses a 3D modeling calculation auxiliary system based on natural language driving, in the system, a basic information input unit is used for receiving a modeling request input by a user, and the modeling request comprises description of specific scene modeling requirements of the user in a natural language form; the semantic recognition unit performs semantic recognition on natural language input information to extract structural modeling calculation parameters, and performs standardized output; a corresponding modeling tool flow is called through a 3D modeling unit according to the semantic recognition result of the large model, and parametric modeling is carried out; the finite element calculation unit can perform data interaction with the 3D modeling unit, geometric information established by the 3D modeling unit is input into finite element calculation software, calculation parameter structures serve as other parameters to be input into the finite element calculation software, corresponding finite element models are established according to the calculation parameter structures, and solving calculation is performed. According to the system, a hybrid architecture of natural language semantic link parameterized workflow is adopted, so that the learning cost of parameterized modeling, finite element calculation and special calculation requirements can be effectively reduced, and the efficiency of modeling and calculation is improved.

The invention belongs to the technical field of optoelectronic equipment testing, and particularly relates to a method for testing irradiation intensity of an area light source device, which comprises the following steps of: firstly, dividing the area light source device into a plurality of test areas, and numbering each area; secondly, acquiring the irradiation intensity of each test area in real time by using a high-precision optical sensor array; then, the collected analog signals are converted into digital signals through an A / D conversion circuit, and the digital signals are transmitted to a data processing unit; then, the data processing unit calculates the irradiation intensity value of each area, and analyzes the uniformity and consistency of the overall irradiation intensity distribution; and finally, displaying a test result through an upper computer control system, and generating an irradiation intensity distribution diagram. Through the high-precision sensor array and the data processing technology, the irradiation intensity distribution of the area light source device can be rapidly and accurately tested, a reliable basis is provided for light source performance optimization, and the method has the advantages of being efficient, accurate and high in practicability.

A method may include: receiving a prompt and generated text from the LLM; computing an original token probability distribution for each token in the prompt and in the generated text; receiving a token position probability distribution for each token position in the generated text from the LLM; identifying keywords in the prompt; perturbing embedding vectors for the keywords used by the LLM by adding noise to the embedding vectors; computing a perturbed probability distribution for the perturbed embedding vectors by providing the perturbed embedding vectors as an input to a neural network used by the LLM, wherein the neural network returns a perturbed token probability distribution; evaluating a divergence between the original token probability distribution and the perturbed token probability distribution; identifying semantically meaningful tokens in the generated text; calculating a mean of divergences for the semantically meaningful tokens; and classifying the LLM based on the mean of divergences.

The present invention provides an action classification apparatus (10) including: an extraction unit (11) that extracts, from a moving image, a plurality of human movements presented in any number of frames; a time-series feature value computation unit (12) that computes a time-series feature value for the any number of frames by computing, for each of the extracted human movements, a feature value of a human pose in each of the any number of frames; a similarity computation unit (13) that computes a similarity between a plurality of the time-series feature values; and a classification unit (14) that classifies the plurality of extracted human movements, based on the similarity.

The invention relates to a real-time path planning method and system based on conflict resolution, equipment and a medium. The method comprises the following steps: preprocessing original observation data to obtain an evidence unit of each sensor; calculating a total conflict amount and a sensor pair contribution matrix based on an evidence unit, updating the credibility of each sensor and selecting a fusion operator to obtain fusion evidence and a conflict degree after fusion; a short-term time sequence is generated according to the fusion evidence and the conflict degree after fusion, the expected occupation probability and the expected conflict trend of each grid unit are predicted, and a time sequence conflict map is obtained; according to the time sequence conflict map, conflict-level adjacent grid unit aggregation fuzziness and area-level conflict characteristics are carried out; and inputting the fusion evidence, the conflict fluctuation degree, the expected stable time and the region-level conflict feature as cost and constraint into a real-time local path planner to obtain a control decision. By adopting the method, the problem of path decision oscillation caused by multi-source sensor conflicts can be relieved.

The invention discloses a seismic simulation method and system fusing an implicit iterative graph network and a spectral element method. The method comprises the following steps: acquiring acceleration data of a small time step node; performing grid sparse division and interpolation mapping processing, and projecting to a low-dimensional manifold to obtain large-time-step node acceleration data and a low-order unit calculation force matrix of the current frame; obtaining a physical relation sparse matrix I, a physical relation sparse matrix II and sparse matrix topological relation information of the physical relation sparse matrix I and the physical relation sparse matrix II through a processing method of Jacobian iteration and projection to a low-dimensional manifold; obtaining a seismic simulated diagram neural network model; and predicting by using the seismic simulation diagram neural network model, and predicting the acceleration by using the large time step of the sparse unit nodes projected back to the target area by using the projection matrix. According to the method provided by the invention, the mapping between the high-order unit small time step and the low-order unit large time step is established through the fitting capability of the neural network, so that the calculation time is greatly shortened, and the earthquake simulation is quickly carried out.