897results about "Non-linear system models" patented technology

The invention provides an intelligent inspection path planning method and system based on reinforcement learning, and relates to the technical field of path planning, comprising the steps of constructing a scene understanding model and a dynamic interaction model, and forming a cognitive enhanced digital twin system. Metaheuristic search and social learning mechanisms are fused, and the inspection efficiency, energy consumption and risk are balanced through a multi-target conflict coordinator. The group behavior emergence prediction model achieves dynamic prediction of a collaborative mode, the federated learning framework achieves aggregation of experience knowledge, a group evolution strategy with high adaptability is generated, and closed-loop optimization from task execution to cognitive enhancement is achieved through a robustness evaluation mechanism fed back in real time and a task allocation optimizer based on an attention mechanism. And the inspection efficiency and adaptability are improved.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention relates to the technical field of safety monitoring, and particularly discloses an embankment danger rapid identification method and system, and the method comprises the steps: collecting multi-modal data in real time through arranging a multi-source sensor network; according to the phase space trajectory, extracting a Lyapunov exponent spectrum, correlating the dimension and the Kolmogorov entropy, and forming a structure response chaos degree index; calculating a hydrogeological coupling coefficient in combination with multi-scale decomposition and mutual information analysis; and fusing the two into a three-dimensional dangerous case feature tensor, inputting the three-dimensional dangerous case feature tensor into a pre-training model based on a deep convolutional neural network and a long-short-term memory network, realizing intelligent discrimination of high, medium and low risk levels, generating an adaptive monitoring instruction for a low-risk working condition, outputting a risk evolution trend map, and supporting closed-loop management and control.

The invention discloses an artificial intelligence-based bid invitation file error content optimization method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the causal inference through employing a CIRCA frame, constructing a causal Bayesian network, introducing the Chen's three-dimensional chaotic disturbance, optimizing the edge probability, recognizing potential error nodes through a GNN model, generating a semantic embedding vector through employing SBERT, and carrying out the calculation of the error content of a bid invitation file. The method comprises the following steps of: mapping to a Riemannian manifold, realizing embedded dimension reduction through Riemannian principal component analysis, determining an optimal path leading to an ideal state by using a geodesic path algorithm, and calculating a candidate fitness score by constraining an Abbe structure and conflict strength. According to the method, causal reasoning and chaotic disturbance are combined, so that the accuracy of error positioning and the reliability of classification judgment in the bid invitation file are improved, and the semantic error recognition capability and the global optimality of a correction path are improved through semantic manifold modeling and geodesic path optimization.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

A system and method for automatic generation and automatic conduct of a chaos experiment in distributed applications includes a software application having a layer and a chaos configuration, a chaos engine having a chaos orchestrator, a chaos prompt generator having a prompt template composer, and a chaos engineering large language model. The chaos engineering large language model is configured to recommend chaos experiments for the chaos engine to process and to inject into the software application. The chaos engineering large language model is also configured to patch the software application in accordance with objectives and parameters developed in the system and method. The system and method may be automated through a trigger. The trigger may be the chaos engineering large language model recommending one or more chaos experiments to the chaos engine. The trigger may also be the implementation of one or more patches to the software application.

The invention relates to a ship port resource coordinated scheduling method and device, and belongs to the technical field of port resource optimized scheduling, and the method comprises the steps: building a target function with the maximization of the anchor ground utilization rate, the maximization of the berth utilization rate and the minimization of the ship waiting time as targets; solving the objective function by using MILP and a particle swarm algorithm to obtain an initial scheduling scheme meeting constraint conditions; the initial scheduling scheme comprises anchor ground distribution, berth distribution, berthing time and leaving time; clustering the initial scheduling scheme based on the multi-core weighted distance of the initial scheduling scheme to obtain a clustered scheduling scheme; and optimizing the clustered scheduling scheme based on a game theory model to obtain a target scheduling scheme. According to the ship and port resource coordinated scheduling method provided by the invention, global optimization of ship berth allocation, channel use and anchor ground scheduling is realized, and high efficiency and safety of ship operation are ensured while channel resource conflicts are reduced.

The invention provides a pelican optimization strategy-based mobile edge computing unloading method, and aims to solve the problems of low computing task unloading efficiency and poor system performance in application scenes such as Internet of Things and smart cities. By introducing chaotic mapping and a flight strategy, a traditional pelican optimization algorithm is improved, and the global search ability and the ability of jumping out of a local optimal solution of the algorithm are enhanced. A calculation unloading model of a multi-user multi-edge server is constructed, and delay and energy consumption in the data transmission and calculation process are comprehensively considered. Experimental results show that the method can effectively reduce the time delay and energy consumption of the system, optimize resource allocation and improve the overall performance of the system. Especially in a multi-user and multi-task complex scene, the method disclosed by the invention is excellent in performance, and a new solution is provided for the calculation unloading problem in the mobile edge calculation.

The invention discloses a big language model-based RAG enterprise data hierarchical decentralized management method, which relates to the technical field of data security and access control, and comprises the following steps of: acquiring a voice audio stream and a lip-shaped three-dimensional track point set, loading permission baseline parameters, and generating a permission token; the permission token comprises a quantum watermark seed, a role identifier, a permission token aging stamp and a permission level; based on the permission level and the role identifier, obtaining permission data blocks through a retrieval enhancement generation framework, dividing levels according to permission baseline parameters, generating retrieval keywords based on the permission token, establishing a hierarchical index, matching the permission data blocks, performing role identifier matching on the permission data blocks, then reordering, and generating a response text; converting the quantum watermark seed into a binary stream, and embedding the binary stream into the response text to obtain a watermark response text; according to the method, through multi-modal biological feature fusion and Shannon entropy calculation, the problem that the authority decision is lack of dynamic risk perception is solved.

The invention discloses a mine intelligent ventilation combined optimal adjustment and optimal control method and system. The method comprises the steps that S1, multi-source mine ventilation data are collected through a distributed multi-mode sonic sensor by adopting a gridding deployment strategy; s2, performing fusion optimization on the multi-source mine ventilation data by using a trust function theory to obtain comprehensive state data; s3, on the basis of the comprehensive state data, obtaining optimal preliminary regulation and control actions of different mine equipment through a self-adaptive reinforcement learning model; and S4, based on the optimal preliminary regulation and control action, introducing a roadway structure safety degree as a dynamic constraint factor, combining with an improved pigeon inspired optimization algorithm to obtain a final regulation and control action, and realizing underground ventilation control based on the final regulation and control action. The safety degree of the roadway structure is incorporated into a fitness function, so that the safety of the roadway structure is fully considered while the ventilation effect and the energy consumption are optimized by the algorithm.

The invention belongs to the field of uncertainty quantification and robustness design optimization of aero-engine air-cooled turbines, and particularly discloses a one-dimensional uncertainty design optimization method and system for a reverse cooling turbine based on a one-dimensional aerodynamic analysis method for the reverse cooling turbine. Forming an augmented space by the optimization variables and the uncertainty parameters, and generating a sample set; establishing a Kriging global agent model, and generating an uncertainty parameter sample set; an ASPC model is established, and one-dimensional uncertainty quantification of the reverse cooling turbine is completed; an NSGA-II multi-objective optimization algorithm is coupled, and one-dimensional robustness design optimization of the reverse cooling turbine is completed. The one-dimensional uncertainty design optimization method and system framework of the reverse cooling turbine are provided and established, aerodynamic performance analysis of the reverse cooling turbine can be completed through simple parameters, a geometric entity is not needed, and tasks such as one-dimensional uncertainty quantification and robustness design optimization of the reverse cooling turbine can be achieved.

The invention discloses an engine turbine shaft sequential optimization design method based on ensemble learning, and the method comprises the steps: carrying out the parametric modeling of an aero-engine turbine shaft through employing a finite element method, and determining a design variable; determining an optimized objective function and constraint conditions based on the turbine shaft structure; constructing a training point set by using the target function and the constraint condition, and establishing an ensemble learning agent model by using the training point set through an ensemble learning algorithm; executing a sequential optimization design process based on the ensemble learning agent model; searching a current optimal sequence design point by utilizing an intelligent algorithm and a sequential updating criterion, and judging whether the sequence design point meets a threshold value requirement or not; if the current optimal sequence design point meets the threshold requirement, outputting the current optimal sequence design point as a design result; otherwise, adding the sequence design points as new training points into the training point set to update the training point set, updating the ensemble learning agent model by using the updated training point set, and iteratively calculating the sequence design points.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention provides a multi-sensor electric power intelligent monitoring system, and belongs to the field of electric power monitoring. The system comprises a data processing system, a monitoring center server and a plurality of edge computing nodes deployed on different power devices, and the data processing system comprises a multi-source data acquisition module used for acquiring operation data of the power devices; the fault diagnosis module is used for constructing a distributed knowledge graph according to the basic data and the historical operation data of the power equipment, and storing the constructed knowledge graph in each edge computing node and the monitoring center server in a scattered manner so as to perform preliminary diagnosis on the fault of the power equipment; the early warning module is used for mining a data feature association relationship through a quantum heuristic algorithm and establishing a dynamic early warning mechanism; and the life prediction module is used for predicting the residual life of the power equipment based on the chaos theory and the phase-space reconstruction technology. According to the invention, the problems of low data processing efficiency, inaccurate fault diagnosis, untimely early warning and the like in traditional power monitoring are solved.

The invention provides a dangerous chemical safety management method based on a GIS, and relates to the technical field of dangerous chemical supervision, and the method comprises the following steps: S100, constructing a dangerous chemical space information model, and integrating multi-source data to a unified GIS platform; s200, establishing a standardized dangerous chemical geographic information database; s300, risk grade evaluation is carried out based on the AHP and a fuzzy comprehensive evaluation model; s400, implementing dynamic monitoring and updating spatial data in real time; s500, constructing a visual management platform based on a WebGIS (Web Geographic Information System); s600, generating an emergency response strategy and supporting multi-department collaborative scheduling; and S700, outputting a safety management comprehensive analysis report containing trend prediction and optimization suggestions. According to the method, a multi-factor space risk model is constructed by introducing a geographic information system (GIS). Compared with a traditional risk judgment method depending on a static table and qualitative experience, the method realizes dynamic and quantitative risk identification based on spatial dimensions, and the scientificity and precision of risk identification are greatly improved.

The invention relates to the technical field of power quality monitoring, and discloses a method for identifying power quality disturbance characteristics of a distributed photovoltaic interference power supply, which comprises the following steps: S1, data acquisition and preprocessing: acquiring a power quality signal of a photovoltaic system and carrying out denoising, normalization and time alignment; and S2, carrying out disturbance signal adaptive decomposition based on improved variational mode decomposition, and automatically selecting a mode number by utilizing information geometric optimization. An improved variational mode decomposition method is adopted, information geometric optimization and Bayesian optimization are combined, self-adaptive mode decomposition of disturbance signals is achieved, the optimal mode number and penalty factors can be automatically determined through improved variational mode decomposition, decomposition errors caused by manual parameter setting in traditional variational mode decomposition are avoided, and the method is suitable for large-scale popularization and application. Compared with a fixed parameter variational mode decomposition method in the prior art, the method has the advantages that the analysis capability of complex disturbance signals is improved, and the disturbance mode in the photovoltaic system is accurately decomposed.

The invention discloses an ultrasonic guided wave signal noise reduction method based on HHO-SVMD and singular spectrum analysis, and the method comprises the steps: obtaining a to-be-processed ultrasonic guided wave signal, optimizing a penalty factor of variation mode decomposition (SVMD) through employing an improved Harris eagle algorithm, initializing a population through Circle chaotic mapping, introducing chaotic disturbance and weight, and carrying out the noise reduction of the to-be-processed ultrasonic guided wave signal. Determining an optimal alpha value and decomposing the signal into an optimal modal component; calculating a kurtosis value of each modal component, and screening effective components containing damage information according to a threshold value; singular spectrum analysis denoising is carried out on the effective components, and a self-adaptive window mechanism is introduced to dynamically adjust the window length and the truncation strength; reconstructing the de-noised effective component to obtain a de-noised signal; according to the method, the parameter optimization precision and efficiency are improved, damage characteristics and noise are effectively separated, different dominant frequency signals are adapted, the damage characteristics can still be reserved in a low-signal-to-noise-ratio environment, the noise reduction effect of ultrasonic guided wave signals and the damage detection reliability are improved, and the method is suitable for nondestructive detection of components such as ultra-long small-diameter heat absorption pipes.

The invention discloses a traditional Chinese medicine pesticide residue-oriented risk assessment method and system, particularly relates to the technical field of traditional Chinese medicine quality control, and is used for solving the problem of inaccurate assessment caused by neglecting the influence of a processing technology on pesticide form transformation and synergistic toxicity of multiple pesticides in the prior art. The method comprises the following steps: acquiring pesticide residue detection data and processing technological parameters; determining a conversion form and a conversion rate by associating a pesticide chemical conversion database; quantifying a toxicity weight based on biological network topology data and a graph convolution model; correcting the actual residual quantity by combining the chaos theory and the recurrent neural network with environmental disturbance; dynamically optimizing the weight aiming at the synergistic effect of multiple pesticide residues; generating cross-batch risk early warning in combination with historical data; through multi-dimensional dynamic coupling analysis, the pesticide residue toxicity evaluation precision is remarkably improved, and the safety risk of pesticide residues of traditional Chinese medicinal materials is reduced.

The invention provides an intelligent call center resource optimization method and system based on predictive analysis, and relates to the technical field of call center management, and the method comprises the steps: obtaining to-be-mediated customer data, mediator data and external economic indicators; processing according to the to-be-conciliated customer data to obtain a customer emotion feature matrix; performing feature selection and quantitative evaluation processing according to the mediator data to obtain a mediator capability feature matrix; performing scene simulation according to the external economic indexes, the customer emotion feature matrix and the mediator ability feature matrix to obtain a mediation effect score list; performing time sequence analysis and behavior pattern recognition according to the historical mediation information to obtain a prediction result; and performing optimization according to the mediation effect score list and the prediction result to obtain a final mediator scheduling scheme. According to the method, emotion analysis, behavior pattern recognition and scheduling optimization technologies are integrated, the processing efficiency of the mediation request is improved by using data analysis and a prediction model, the waiting time of a customer is shortened, and resource allocation is optimized.

The invention discloses a determination and early warning method and system for mercury content in food raw materials and a storage medium, and relates to the technical field of food safety detection.The method comprises the steps that a surface enhanced Raman spectrum of a food raw material sample is obtained, preprocessing and data enhancement are carried out, spectral characteristics of enhanced Raman spectrum data are extracted, and a detection result is obtained; the optimal spectral feature combination is optimized to generate spectrogram features, a feature parameter combination is extracted based on the spectrogram features, and the feature parameter combination and the mercury content samples with different concentrations are synthesized to construct a data set; constructing a recognition determination model, training by utilizing the data set, collecting the surface enhanced Raman spectrum of the food raw material to be detected, and extracting spectrogram characteristics as model input to perform qualitative and quantitative analysis on the mercury content; when the mercury content concentration in the quantitative analysis result is larger than a preset mercury content threshold value, abnormal early warning information is generated. The rapid detection method for the mercury content in the food raw materials is constructed by using the surface enhanced Raman spectroscopy, and the accuracy of mercury content determination is improved.

The invention discloses a multi-modal large model training data acquisition method and system, and relates to the technical field of data acquisition optimization, and the method comprises the steps: building a causal graph adjacency matrix based on a cleaning alignment data set, carrying out the anti-fact intervention after recognizing a prejudice variable, and generating an anti-fact sample set; combining the anti-fact sample set and the cleaning alignment data set into an enhanced data set; performing cross-modal anti-long-tail compensation on the enhanced data set to obtain a balanced data set; based on the image data and the text data in the balance data set, depth separable convolution feature extraction and BERT semantic coding are carried out respectively, cross-modal fusion is carried out, and fusion features are output; through the steps of depth separable convolution, BERT coding, quantum latent variable evolution and cross-modal semantic verification, the generalization ability, robustness and social adaptability of the multi-modal large model in a complex and real scene are significantly improved.

The invention provides a rolling bearing fault diagnosis algorithm based on an improved VMD optimized CNN-GRU neural network. The rolling bearing fault diagnosis algorithm aims at solving the problem that early faults of a rolling bearing are difficult to recognize under complex working conditions. The method comprises the steps of providing an OCSSA algorithm fusing an eagle algorithm and a Cauchy variation strategy, realizing adaptive optimization of VMD parameters, remarkably relieving modal aliasing, and improving signal decomposition precision; according to the method, the CNN-GRU end-to-end deep diagnosis framework is constructed, time-frequency fusion features are automatically extracted, time sequence dependence is modeled, and dependence on artificial features is reduced; the model performance is verified on a CWRU bearing data set, the average recognition accuracy rate reaches 98.67%, compared with a mainstream model, the precision and operation efficiency are obviously improved, and the good generalization ability and engineering application potential are achieved.

The invention discloses an internet integral dynamic incentive mechanism design method based on reinforcement learning. The method comprises the following steps: S1, constructing a user behavior data set; s2, generating a situation label by adopting a natural language processing technology; s3, calculating a reward value by using a context-adaptive reward algorithm; s4, dynamically adjusting the reward strategy through the reward value and the user behavior feedback in combination with the task participation condition of the user and a feedback loop; s5, predicting a future behavior trend of the user by using the gating cycle unit model, and generating a behavior prediction result of the user; s6, dynamically optimizing the reward strategy in combination with a chaos search strategy and a wolf pack optimization algorithm to obtain an optimized reward strategy; and S7, evaluating effectiveness of rewards and user interest changes, obtaining a final global reward strategy based on an A3C algorithm, and realizing real-time dynamic adjustment of an internet integral incentive mechanism. According to the method, a situation self-adaptive reward algorithm, an optimization technology and the like are utilized, and the dynamic adjustment of an internet integral incentive strategy is realized.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention discloses a dynamic correction method for digital twinborn model parameters of line equipment of MPC, which belongs to the technical field of power system automation, and comprises the following steps: acquiring original working condition data through distributed edge nodes, extracting load fluctuation, environmental disturbance and mechanical spectrum key features, forming chaotic feature vectors, and carrying out dynamic correction on the chaotic feature vectors; according to the method, the complex interaction is quantified through the multi-physics field coupling entropy source formula, the equipment state evolution process can be described more accurately, the sensitivity of the system to the initial disturbance is further evaluated through introduction of the Lyapunov index, the potential instability risk is warned in advance, and in the power transformer overheating fault prediction process, the method has the advantages of being high in reliability and high in reliability. According to the method, thermal-electric coupling abnormity can be detected several hours ahead of time, sudden shutdown is avoided, the safety and reliability of equipment operation are remarkably improved, real-time self-adaptive correction of parameters is achieved through double-channel parallel calculation, and the adaptability to complex working conditions is remarkably improved.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

The invention discloses a method and a device for filling and quantifying multi-source data of a power system based on a bimodal hybrid algorithm in extreme weather, which belong to the technical field of power distribution networks and are based on a reverse chaos whale transfer algorithm and combined with meteorological characteristics to dynamically adjust and construct a load prediction model to fill missing data and output distribution parameters and weight coefficients. Meanwhile, a meteorological condition matrix is introduced, a Gaussian mixture model is constructed, and the uncertainty of data is quantized and filled through a Monte Carlo subset deduction algorithm. The method effectively solves the problems that a traditional method is low in processing efficiency and large in error in extreme weather, experiments show that the method is high in convergence speed and high in estimation precision, the accuracy and reliability of power system data are remarkably improved, and guarantee is provided for stable operation of a power dispatching system.

The invention provides a hydrological ensemble forecasting method and system based on space-time adaptive generative adversarial-random differential disturbance integration and a medium. The method comprises the steps of multi-source data fusion and standardization processing, spatial-temporal feature extraction, chaotic initial field generation, differential generative adversarial network construction, SDE disturbance field and physical constraint design, integrated forecast generation and statistical post-processing, and dynamic updating and set performance verification after a new observation value arrives. By coupling data driving and a physical mechanism, unified modeling of uncertainty propagation and spatial-temporal heterogeneity in hydrological forecasting is realized, and a high-resolution, reliable and uncertain set forecasting result is provided for hydrological forecasting.

The invention discloses a 3D digital human facial expression synthesis method based on audio driving and a visualization system. The method comprises the following steps: acquiring a data set; inputting the original audio in the data set into an audio encoder in the model, and extracting audio features; inputting the audio features into a KAN-based decoder in the model to generate 3D digital human facial expression actions; inputting 3D digital human facial expression actions into a graph encoder in the model, and extracting lip features; collaborative optimization and model training of multi-modal features are realized by constructing a joint loss function fusing audio features, 3D facial expression parameters and lip features; and inputting an original audio to be tested into the trained model, and generating corresponding 3D digital human facial expression actions through the audio encoder and the KAN decoder in sequence. According to the invention, the generated 3D digital human facial expression action is more vivid and closer to the real character expression.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.