92 results about "Differential equation models" patented technology

The invention provides a learning performance evaluation-driven teaching management method constructed based on a capability graph. The method comprises the following steps: S1, constructing a multi-dimensional capability graph; s2, a step of operating a multi-modal data acquisition system; s3, performing a dynamic capability value calculation model; s4, a step of constructing a real-time capability early warning system; s5, generating a self-adaptive teaching strategy; s6, a step of carrying out interdisciplinary ability association analysis; s7, generating a personalized learning path; s8, dynamically evaluating the teaching effect; and S9, dynamically optimizing the teaching resources. According to the method, the core problems of data lag, inaccuracy in intervention and one-sided evaluation in traditional education management are solved by constructing a dynamic capability evaluation model, and a quantifiable decision support system is provided for precise teaching. The innovation of the method is that differential equation modeling and reinforcement learning are combined, and continuous optimization and adaptive adjustment of the education process are realized.

The invention discloses a human pancreas islet function digital evaluation method and system based on an AI algorithm, and relates to the technical field of artificial intelligence medical evaluation, and the method comprises the steps: collecting and preprocessing multi-modal physiological data; extracting multi-source features to generate a low-dimensional input vector; performing dynamic weighted fusion based on a space-time attention mechanism; constructing a neural differential equation model in which a beta cell two-phase secretion mechanism is introduced, and outputting pancreas islet function evaluation indexes; and a dynamic evaluation report is generated through a visualization and early warning module. According to the method, the modeling accuracy of the pancreas islet function under the interference of complex behaviors can be improved, the individualized quantitative evaluation of function decline trend prediction and sensitivity scoring is realized, the clinical staging reference value and the model interpretation ability are enhanced, and the technical problems of low fusion precision, poor dynamic nature and lack of physiological constraints in the existing method are solved.

The invention provides a hydropower station AI supervision system and method based on a multi-modal large model, and relates to the technical field of intelligent hydropower. The system comprises a multi-modal data acquisition module, a cross-modal space-time alignment module, a multi-modal feature extraction module, a multi-modal large model processing module and an intelligent reasoning and decision module. A neural differential equation model is introduced to carry out space-time alignment on asynchronous sensing data, networks such as Vision Transformer, MelCNN, TCN and the like are utilized to extract multi-modal features, cross-modal fusion analysis is realized by combining a local and global attention mechanism and dynamic weight distribution, and equipment abnormality is further reasoned based on a reconstruction error, a mahalanobis distance and a knowledge graph and a maintenance strategy is generated. According to the method, high-precision anomaly detection, fault root cause positioning and dynamic maintenance optimization of key equipment of the hydropower station are realized, diagnosis errors caused by traditional manual inspection and data splitting are avoided, and the operation and maintenance intelligence level and the equipment operation reliability are improved.

The invention discloses a water quality prediction system and method based on machine learning, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-source monitoring data; the teacher model module is used for constructing a Shenchang differential equation model as a teacher model and outputting a prediction result of the target water quality index; the student model module is used for constructing a lightweight neural network model as a student model to perform structure optimization training; the trajectory evaluation module is used for calculating predicted trajectory similarity of the teacher model and the student model and optimizing structural parameters of the student model; the model reasoning module is used for calling the optimized student model to generate a prediction result of the target water quality index; and the result output module is used for outputting a prediction result and providing a display and storage interface. According to the method, high-precision, light-weight and sustainable self-adaptive prediction of water quality time sequence data is realized by fusing Shenchang differential modeling and a trajectory similar distillation optimization mechanism.

The invention discloses an industrial AI assistant cross-modal interaction method based on a dynamic knowledge graph, and the method comprises the following steps: collecting texts, voices, images and multi-source sensor data in an industrial system, carrying out the modal recognition, feature extraction and time alignment, and generating an event feature set and a state feature set with timestamps. Modeling a time dependency relationship between event types by constructing a multivariable Hawkes model, and outputting an event trigger sequence and trigger strength; and in combination with a neural controlled differential equation model, guiding the state to evolve along with time and jump at a specific moment to form a state evolution trajectory. Performing fusion coding on the event and the state, constructing a dynamic knowledge graph with a causal structure and semantic continuity, and generating interactive output based on context reasoning; and after system feedback is received, the triggering strength and the state track are updated, and continuous evolution of the knowledge graph and reverse optimization of model parameters are achieved.

The invention relates to the technical field of microbial ecological information processing, and discloses a microbiome functional activity prediction method and system, and the method comprises the steps: collecting dynamic environment factor data in soil in real time through a sensor, and carrying out the preprocessing of the dynamic environment factor data; establishing a partial differential equation model based on the preprocessed data; the partial differential equation model comprises a diffusion term, a reaction term and an environmental factor coupling term; optimizing the differential equation model parameters by using a neural network, and carrying out transfer learning training on the partial differential equation model parameters based on historical data; and predicting the activity grade and function of the microorganisms on the basis of the optimized model for the functional activity of the microorganisms. The adaptability and generalization ability of the model in different soil areas are remarkably improved, and closed-loop processing from function activity numerical value prediction to function classification output is achieved.

The invention discloses an image denoising method and system based on a differential equation and a deep learning network. The method comprises the following steps: inputting a noise image subjected to image preprocessing into a differential equation NODE model and an improved deep learning network UNet; extracting multi-level features of the noise image layer by layer through a multi-scale channel feature fusion module of an improved UNet network; attention is increased for shallow information in the network through an attention mechanism module; and carrying out continuous time dynamic modeling by using a NODE model, taking the improved UNet network as a neural network vector field in the NODE model, and simulating a continuous time evolution process through an ODE solver to obtain a clear image. Through the fusion of ODE modeling and the deep learning network, the dependence on large-scale annotation data is reduced, parameters are evolved and optimized by using continuous time characteristics, the generalization ability of the model to complex noise is improved, and a good denoising effect can be obtained under the condition of few sample training.

The invention relates to the technical field of detection and identification, and discloses a mobile ship dynamic tracking and locking method and system based on target detection and identification, and the method comprises the steps: constructing a manifold embedded network, and mapping the multi-modal features of a ship to a Riemannian manifold space; constructing a continuous evolution modeler of the Shenchang differential equation model, and predicting a continuous evolution trajectory of the features under time, view angle and scale changes; a multi-scale bridging network is constructed, and bidirectional conversion between long and short distance feature representations is realized; constructing a differential geometric attitude encoder, and representing a ship attitude in an SO (3) Lie group space; a feature memory and reconstruction mechanism is realized, and the problem of short-time disappearance target recovery is solved; the components are integrated to construct a unified tracking and locking system. According to the method, the problems of discretization processing limitation, feature representation fragmentation, cross-condition consistency deficiency and the like in the prior art are solved, remarkable technical effects are achieved in the aspects of cross-condition recognition capability, continuous feature evolution capability, short-time disappearance target recovery capability and the like, and the method is suitable for the scenes of Yangtze River shipping monitoring, maritime affair safety supervision and the like.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention provides a tobacco leaf drying kinetic model-based baking process parameter optimization method, and belongs to the technical field of electrical digital data process.The tobacco leaf drying kinetic model-based baking process parameter optimization method comprises the following steps of: firstly, establishing a tobacco leaf drying differential equation model and a curing barn computational fluid mechanics model; temperature and humidity field distribution is obtained through simulation analysis; calculating a tobacco leaf surface heat and mass transfer coefficient, and establishing a moisture migration prediction function; a moisture transfer optimization function is introduced to perform parameter preliminary screening, and a pre-trained quality evaluation neural network model is adopted to evaluate a parameter scheme; respectively optimizing three stages of a yellowing stage, a color fixing stage and a stem drying stage to form a parameter set; and finally, an optimal parameter combination is searched by applying a genetic algorithm, an optimization target is a comprehensive score of tobacco leaf quality consistency and energy consumption, real-time optimization of parameters in the baking process is realized, and the technical problem that tobacco leaf baking process parameters are difficult to accurately optimize aiming at different tobacco leaf characteristics in the prior art is solved.

The invention relates to the technical field of battery health management and prediction, in particular to a lithium battery capacity fading trend prediction method and system, and the method comprises the steps: enabling data driving features and electrochemical features to generate deep fusion features through a dual calibration mechanism; inputting the feature sequence into an electrochemical process perception model, and encoding the feature sequence into a potential state vector representing personalized decline; and inputting the vector as an initial condition into a neural differential equation model, and finally generating a continuous health state attenuation trajectory and obtaining a prediction result by learning a decline dynamic law and performing integral solution. According to the method, deep fusion is carried out on a physical mechanism and a data driving model, the defect that a traditional black box model is weak in generalization ability is overcome, long-term, high-precision and continuous prediction of the full-life-cycle decline trend can be achieved only through early weak signals of a battery, and the reliability and the practical value are high.

The invention discloses an integrated power box health state assessment method based on AI modeling. The method comprises the following steps: S1, constructing a multivariable time sequence; s2, modeling the multivariable time sequence trajectory by adopting a neural control differential equation model to obtain a hidden space trajectory; s3, performing dynamic modal decomposition on the hidden space trajectory, and constructing a modal health space; s4, adopting a manifold interpolation mixing method in the modal health space to generate an enhanced training sample set; s5, inputting the mixed mode vector in the enhanced training sample set into a health classification module; s6, inputting the hidden space trajectory into a time semantic playback module to generate a final prediction state trajectory; and S7, comparing the final predicted state trajectory with the actual historical state trajectory, and when the semantic difference value exceeds a set tolerance threshold value, generating an abnormal mark and executing a state rollback operation. According to the method, neural differential modeling, interpolation enhancement and time semantic playback are fused, and the health state of the integrated power box is accurately evaluated.

The invention discloses a lower limb vein thrombus formation risk prediction method based on machine learning. The method comprises the following steps: step 1, constructing a triaxial thrombus evolution dynamic container space; 2, generating a pre-thrombus micro-state orbital chain; 3, determining a thrombus formation critical mutation window; 4, introducing a causal stem budget at the thrombus formation critical mutation window, and constructing a natural evolution path and a controlled path; step 5, obtaining an orbit offset difference value; 6, calculating a dynamic orbit stability index; 7, inputting the pre-thrombus micro-state orbit chain and the dynamic orbit stability index into the improved PatchTST model, and introducing a flow continuity regulation operator into a reconfiguration module to obtain a corrected orbit stability index; and 8, outputting a thrombus formation risk prediction result. According to the method, the thrombus formation risk prediction is realized by constructing a triaxial thrombus evolution dynamic container space and combining a mental structure differential equation model and an improved PatchTST model.

The invention provides a speedometer sideslip online compensation integrated navigation method based on FPGA (Field Programmable Gate Array) hardware acceleration. Relates to the technical field of train navigation positioning. According to the SINS / DR integrated navigation system, an IMU inertial measurement unit is integrated in an SINS inertial navigation module and specifically comprises a three-axis gyroscope and a three-axis accelerometer, in SINS / DR integrated navigation, mapping from sensor data of the gyroscope, the accelerometer, an odometer and the like to a slip mode is achieved by adopting a Sheng differential equation model, and the SINS / DR integrated navigation system is obtained. Two speedometers are installed on non-steering wheels on the two sides respectively, a speedometer speed error model is established to compensate and correct the actual speed, and acceleration of a Kalman filtering algorithm is achieved through an FPGA.

The invention discloses an armored vehicle diesel engine digital twin model reduction method and system, and the method comprises the steps: building a diesel engine twin model which is a high-dimensional nonlinear system and comprises a thermodynamic equation of a diesel engine; operating the diesel engine twin model under different working conditions, collecting input and output data of variables, and performing standardization processing on the input and output data of the variables to obtain simulation data; a neural network is used for constructing a nerve differential equation model, simulation data is used for training the nerve differential equation model to obtain a reduced-order model, and the output of the diesel engine is predicted based on the obtained reduced-order model. Through Simulink modeling, neural network training, incremental learning and real-time data updating, a dynamic model of the diesel engine is effectively simplified, and it is ensured that high-precision prediction capacity is kept under different working conditions and operation environments. The system architecture supports real-time monitoring, online learning and self-adaptive adjustment, and high efficiency and stability of the diesel engine control system in practical application are guaranteed.

The invention discloses an equipment asset depreciation prediction method based on machine learning, and the method comprises the following steps: S1, collecting an asset original value, a depreciation code, an age limit, a starting date and a historical net value, and generating an asset state input sequence in a unified time domain through normalization; s2, inputting the normalized asset state into a Shenchang differential equation model, and constructing a differential system of the asset state evolving along with time; s3, identifying parameter changes in the asset depreciation period, extracting change time and amplitude, and constructing a disturbance event sequence; s4, constructing a time gating function and a disturbance coupling function according to the disturbance event sequence; s5, injecting the disturbance coupling function into a differential system, and resolving to obtain a depreciation evolution path; and S6, mapping the prediction path to an actual time interval, and outputting a depreciation trend prediction result. According to the invention, fine prediction of the depreciation trend of the equipment and dynamic response of strategy change are realized, and the intelligent level of asset management is improved.

The invention provides an oxidation kettle temperature prediction control method. The method comprises the following steps: S1, establishing a differential equation model; s2, converting the differential equation model into a discretized space prediction model, enabling the space prediction model to be equivalent to a first-order inertia plus lag system, determining state transition matrix parameters through system identification, and generating an oxidation kettle temperature prediction model; s3, obtaining a target function of a model prediction controller according to an error between the predicted temperature and the reference temperature; s4, improving an artificial bee colony algorithm by adopting a variation strategy of a differential evolution algorithm; s5, in the rolling optimization process of model prediction control, the improved artificial bee colony algorithm is adopted to update the optimal control input sequence in a rolling mode until the target function is minimized, and the optimal control input sequence is obtained; and S6, applying the optimal control input sequence to an oxidation kettle control system in real time, and dynamically adjusting the temperature of the oxidation kettle. The selection of the control input sequence is optimized, and the temperature control in the reaction process is more accurate and efficient.

The invention relates to the technical field of boiler control, in particular to a multivariable coupling thermal process regulation and control system and method for carbon pollution governing, and the method comprises the steps: collecting multi-source data such as acoustic emission, temperature, humidity and spectrum, and constructing a feature sequence through time mark alignment and wavelet packet enhancement; a heat value characterization quantity is predicted by using a Shenchang differential equation model fused with dynamic gating, a partition equivalent thermal network model is driven on this basis, and accurate prediction of a future time domain temperature field is realized by dynamically correcting thermal resistance and thermal capacity; based on the prediction result, a control instruction is solved through multi-objective optimization under the condition that the active temperature constraint is met; and in combination with heat flow density feedback, a layered reinforcement learning controller is adopted for online compensation of a pre-feedback instruction, and stable and efficient regulation and control of the boiler are achieved.

The invention relates to the technical field of intelligent robot inspection, and discloses a digital twinning-based intelligent AI inspection method, which comprises the following steps of: analyzing a multispectral image acquired by a sensor carried by a robot by utilizing a dynamic attention multilayer feature extraction network; generating an industrial facility abnormal coverage degree real-time distribution map; analyzing historical abnormal coverage data by using a space-time sequence contrast learning model, and generating abnormal coverage trend prediction; analyzing an abnormal coverage evolution process by using a physical constraint neural differential equation model, and generating an accurate abnormal evolution prediction result; generating an optimal inspection path by using a multi-objective optimization algorithm; dynamically updating the digital twin system, and generating an industrial facility maintenance optimization suggestion; according to the invention, through the dynamic attention multilayer feature extraction network and the illumination condition adaptive enhancement algorithm, the detection problem in the strong light and high reflection environment is effectively solved.

The invention relates to the technical field of multi-modal ophthalmology image analysis, and particularly discloses a multi-modal ophthalmology image analysis method, model and system based on optimal transmission image diffusion and a medium thereof.The method at least comprises the steps that S100, the distance between fundus color photo and OCT image modals is explicitly calculated through an optimal transmission algorithm, and the distance between fundus color photo and OCT image modals is calculated; carrying out collaborative evolution on the features in a continuous time domain in combination with a graph neural network and a Sheng differential equation model; and step S200, applying self-attention and cross attention in parallel, reserving modal specificity with low-layer fine granularity, and progressively fusing the color photo texture and the OCT depth structure in a high layer to realize cross-layer long-range dependent depth semantic fusion. The method not only promotes the leading-edge development of multi-modal medical image analysis, but also provides important scientific basis and technical support for constructing a high-precision and high-robustness intelligent ophthalmology diagnosis system.

The invention relates to the technical field of battery life prediction, and discloses a battery pack life prediction method and device and a storage medium, and the method comprises the steps: obtaining a standardized monitoring data matrix; generating a reconstructed electrochemical impedance spectroscopy data matrix according to the standardized monitoring data matrix and a preset impedance spectroscopy reconstruction algorithm; extracting a plurality of health factors according to a nonlinear manifold learning method; obtaining a predicted life end time point of the battery pack according to the target differential equation model; wherein the target differential equation model is obtained by performing constraint condition fusion, scale expansion, constraint condition intensity adjustment and control of balance of a known area and an unknown area on the initial neural differential equation model; the initial neural differential equation is used for describing the evolution dynamics of the health factors along with time. According to the method, high-precision, interpretable and high-adaptability battery life prediction is realized, and the method has important application value in the fields of electric automobiles, energy storage systems and electronic equipment.

The invention discloses a personnel state detection system and method based on a wifi router, and the method comprises the following steps: S1, deploying the wifi router, collecting subcarrier CSI which changes due to the disturbance of the movement of personnel, and forming a CSI signal sequence; s2, configuring a neural network classifier and loading parameters; s3, preprocessing the CSI signal sequence to obtain a stable CSI time sequence signal; s4, constructing a graph structure taking the subcarriers as graph nodes, and generating a spectrogram; s5, performing Laplace feature transformation on the spectrogram, and extracting a frequency domain feature vector of the spectrogram; s6, constructing a multi-scale Sheng differential equation model, and generating a state trajectory under each time scale; and S7, splicing the state tracks to form a state evolution sequence, inputting the state evolution sequence into a neural network classifier, and generating a state recognition result. According to the method, CSI is used for modeling personnel disturbance, multi-scale dynamic representation and accurate recognition are achieved, and the method is suitable for a non-contact personnel state sensing scene.

The invention relates to the technical field of geothermal pipe network monitoring, in particular to a geothermal pipe network intelligent monitoring method and system based on edge calculation. The method comprises the following steps: acquiring a sound pressure signal, a vibration signal and a pressure signal in real time, and performing first-order difference calculation based on the pressure signal to obtain a pressure difference change sequence; inputting the sound pressure signal and the vibration signal into a micro-seismic intelligent identification model, outputting a micro-seismic risk quantitative scoring index, generating an execution regulation and control instruction, and recording execution feedback data to form a micro-seismic scoring feedback data set; performing time sequence reconstruction on the pressure difference change sequence by using a neural differential equation model, identifying an abnormal propagation source point and a shortest propagation path, and constructing a water hammer propagation response structural body; and the microseismic score feedback data set and the water hammer propagation response structural body are transmitted to a cloud platform, and the state of the geothermal pipe network is monitored and early warned in real time. According to the invention, intelligent monitoring and tracking of the micro-seismic abnormal state and the water hammer effect of the geothermal pipe network are realized.

The invention discloses a multi-dimensional network intrusion behavior intelligent identification method based on deep learning. The method comprises the following steps: collecting network multi-dimensional data and generating a standardized network event set and a network control path input set; establishing a neural controlled differential equation model, and generating a continuous time context representation set through hidden state evolution; establishing a neuro-hox process identification model, performing intensity function modeling, and generating an event intensity prediction sequence set and an identification intermediate representation set; forming an intrusion behavior decision rule set and a reasoning configuration set through joint training; new network multi-dimensional data are collected, the reasoning configuration set operation model is loaded, and a new event intensity prediction sequence set and a new candidate trigger time set are output; and generating a network intrusion behavior recognition result set in combination with the intrusion behavior decision rule set. According to the method, time modeling and logical reasoning are fused, and high-precision intrusion identification is realized.

The invention discloses a carbon footprint real-time evaluation system based on the Internet of Things, and the system comprises the steps: collecting the high-frequency energy consumption and carbon emission activity data of industrial production equipment and environment monitoring equipment in real time, and forming a data sequence with a timestamp; performing anomaly detection, denoising and interpolation preprocessing on the data sequence; extracting high-dimensional continuous time features of the data sequence; predicting a carbon emission sequence based on a neural controlled differential equation model; time-varying carbon emission factors corresponding to the enterprise area and the power grid nodes are obtained in real time; and coupling the carbon emission sequence and the time-varying carbon emission factor sequence in a continuous time domain, calculating a real-time carbon footprint, and performing dynamic monitoring and visual display. According to the invention, high-precision real-time prediction and monitoring of the carbon footprint data are realized, and the real-time performance and accuracy of evaluation are improved.

This invention proposes a method for predicting the health status of lithium batteries based on multidimensional features and neural network constant differential equations. The method includes the following steps: S1, preprocessing the capacity data and charging stage operation data collected during lithium battery operation, and constructing features from historical health status data; S2, constructing multidimensional feature inputs for health status prediction based on the charging stage operation data; S3, inputting the multidimensional features into a gated recurrent unit network to fuse and encode the historical health status sequence and constant current charging stage features to obtain a potential feature representation characterizing the battery degradation state; S4, comparing the predicted health status value output by the neural network constant differential equation model with the corresponding actual health status value, calculating the prediction error, and evaluating the prediction accuracy. This application achieves high-precision prediction of lithium battery health status by integrating a multidimensional feature screening mechanism and a continuous-time state evolution modeling method.

The invention relates to the technical field of carbon emission prediction and early warning, and provides a multi-scale neural distribution prediction and hierarchical migration early warning method, which comprises the following steps: acquiring historical carbon emission data, respectively inputting a historical sequence and a to-be-predicted sequence into an energy consumption stochastic differential equation model and a carbon factor stochastic differential equation model, generating a multi-scale carbon emission path sample set through an independent random disturbance term; calculating a path-level suitability score of each path sample based on the standard-exceeding risk integral, the first standard-exceeding moment and the path fluctuation variance; layering the calibration data set into a plurality of working condition layers according to working condition labels, sharing distribution shape parameters among the working condition layers through a hierarchical Bayesian method, and regularizing quantiles of small sample working condition layers to obtain an early warning threshold value of each working condition layer; and selecting a corresponding early warning threshold value according to the current working condition label to compare and trigger early warning. According to the method, the accuracy of carbon emission distribution prediction and the robustness of an early warning system are improved, and the problem that the early warning threshold value is unstable under the small sample working condition is relieved.

The invention relates to the technical field of data processing, in particular to a drink sweetness preference dynamic prediction model and a construction method thereof, the dynamic prediction model establishes a coupling differential equation model of sweetness preference and multi-dimensional state factors, replaces a traditional single temperature factor method, and is more comprehensive in prediction dimension and higher in capability of coping with a complex environment. Meanwhile, by introducing a saturation effect mechanism, the model is prevented from outputting a prediction result of anti-physiological common sense under extreme conditions, so that the prediction result is more in line with human labor-saving and psychological response laws, thereby being more consistent with real behaviors of a user. The invention further provides a method for predicting the sweetness preference of the user beverage, the method can respond to changes of various environment and product factors dynamically and quantitatively in real time, accurate prediction of the sweetness preference of the user is achieved, data support is provided for personalized product recommendation or automatic production scheduling, and the method has good practical value.

The invention discloses a tourist emotion real-time perception and intervention method based on a multi-modal large model, and particularly relates to the technical field of computer perception. The method comprises the following steps: acquiring a facial micro-expression image sequence, a gait posture sequence, a phrase-level voice stream and a body surface infrared heat map of a tourist at a scenic spot sightseeing path node, constructing a current state feature vector set F, calculating an emotion weight coefficient based on a cross-modal attention mechanism, and generating an emotion fusion representation vector E; judging the current emotional risk level R of the tourist according to E, predicting an emotional fluctuation trend delta E in a future time period based on a Shenchang differential equation model in combination with a historical path trajectory and an environment state parameter, and judging whether a critical emotional transition risk exists or not; if the risk exists, an adaptive intervention strategy is matched and executed in real time; updating the emotional state vector E'again according to the tourist intervention response data; according to the invention, high-precision and dynamic tourist emotion management can be realized, and the intelligent level of scenic spot service and the tourist safety guarantee capability are effectively improved.

Disclosed in the present application are a neural signal generation method and system based on self-supervised learning, and a terminal. The method comprises: acquiring an original neural signal, and encoding the original neural signal to obtain a first neural signal set, wherein the first neural signal set comprises: first neural signals, first behavioral signals and first time signals; performing signal enhancement on the first neural signals corresponding to all the first time signals, so as to obtain a plurality of second enhanced neural signals; inputting the first neural signal set and all the second enhanced neural signals into an encoder based on a multi-head self-attention mechanism for encoding, so as to obtain a first latent variable; inputting the first latent variable into a neural ordinary differential equation model for training, so as to obtain a trained second latent variable; and acquiring Gaussian noise, combining the second latent variable with the Gaussian noise to obtain a combined variable, and inputting the combined variable into a generative adversarial network model to generate a high-dimensional pseudo-neural signal. Therefore, the robustness of a decoder can be improved.