223 results about "Factor matrix" patented technology

The invention relates to the technical field of equipment health state prediction, in particular to a multi-source sensing driven equipment health prediction method and system. The method comprises the following steps: synchronously acquiring equipment temperature, vibration, current and acoustic data through a multi-source sensor, carrying out denoising and standardization processing, dynamically distributing each signal weight to adapt to an equipment operation stage, generating a high-dimensional dynamic feature vector, and embedding a historical smoothing mechanism to realize continuous updating; performing standardization and nonlinear mapping on the features, constructing a dynamic coupling factor matrix to quantify a cooperative relationship between the features, fusing interaction information and adaptively enhancing abnormal features; three-layer progressive health prediction from a local part, a middle-layer subsystem to global equipment is implemented based on coupling characteristics, a trend consistency verification mechanism is introduced, global and middle-layer prediction differences are quantified through residual errors, weights are adaptively corrected, and the equipment health state evolution trend and the risk level are output. According to the method, the multi-working-condition adaptability, the feature coupling sensitivity and the prediction result reliability are remarkably improved.

The invention relates to the technical field of electric power engineering purchase demand prediction, in particular to an electric power engineering purchase demand prediction system based on machine learning, and the system comprises the steps: obtaining historical purchase data, construction progress information and electric power engineering design parameters, carrying out the standard stage division and time alignment, and constructing a stage sequence model reflecting the material use rhythm; and a coupling factor matrix is generated based on the material co-occurrence frequency and the stage position relationship, and the modeling capability of the model for the material cooperation relationship is enhanced. And the stage time sequence features, the coupling information and the structured engineering parameter vectors are fused and input into a regression prediction model, so that accurate mapping of material demands and multi-dimensional engineering features is realized, and the purchase prediction precision in a target period is improved. A deviation sequence is constructed based on historical prediction errors, and error correction is performed through a feedforward neural network, so that prediction accuracy and response capability are effectively improved, and resource waste and construction delay are reduced.

The invention discloses an intelligent regulation and control method for a power battery positive electrode binder production process, and the method comprises the steps: synchronously collecting multi-dimensional process parameters such as temperature, viscosity and the like and performance indexes such as particle size distribution, bonding strength and the like, carrying out the normalization, denoising and time sequence alignment processing, and fusing hydrodynamic simulation and historical data to construct an initial parameter coupling model; identifying a parameter influence weight through an attention mechanism neural network, generating a decoupling factor matrix to reconstruct a parameter space, and establishing a virtual control channel; executing constrained gradient descent multi-objective optimization in the channel, generating a regulation and control instruction, and reversely mapping the regulation and control instruction into an equipment executable parameter; according to the method, performance indexes and optimization targets after regulation and control are continuously compared, model parameters are updated, retraining is triggered when deviation exceeds a threshold value, regulation and control precision and process stability are guaranteed, and the real-time performance of parameter regulation and control, the collaborative optimization capability and the quality consistency of the production process are remarkably improved.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention discloses a multi-drive cooperative control method for high-precision electric drive assembly equipment, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of insufficient cooperative control precision of a multi-axis system under parameter mismatch and disturbance. By constructing a multi-source data fusion and dynamic coupling analysis mechanism, the system collects the motion state and control parameter data of multiple driving shafts, generates a multi-shaft collaborative dynamic response sequence, and calculates the dynamic coupling degree between the shafts to identify the uncertainty mode of the system; positioning a performance degradation shaft section by combining the temperature field and the vibration signal characteristics, and generating a system stability evaluation coefficient; and establishing a space mapping relation to construct a dynamic compensation factor matrix, and generating a control parameter adjusting quantity meeting a collaborative matching condition through fuzzy rule reasoning. According to the method, control instability caused by traditional global adjustment is avoided, precise cooperative control of the multi-axis system is achieved, and the control precision, operation stability and reliability of equipment under the dynamic working condition are remarkably improved.

The invention relates to the technical field of bridge engineering detection and repair, in particular to a bridge crack repair effect prediction method and system based on performance simulation, and the method comprises the following steps: obtaining basic data and environmental load data of a target bridge, and matching at least two candidate repair schemes from a preset repair scheme library; constructing an initial performance model before repair, and calculating an initial bearing capacity loss rate and a crack propagation rate; quantifying the interaction influence of the multi-modal data through a coupling factor matrix, and iteratively calculating the repair condition of a set time period after repair to obtain an initial simulation result; and collecting real-time monitoring data in a set time period in the repairing process and after repairing, and inputting the real-time monitoring data into a preset LSTM adaptive correction model to obtain a corrected simulation result. According to the method, the long-term repair condition after repair can be accurately calculated, the accuracy of a prediction result and the environmental adaptability are remarkably improved, and a scientific basis is provided for long-term service performance pre-judgment.

The invention provides a wind tunnel structure sensitive reflection settlement position intelligent identification method, and belongs to the technical field of wind tunnels. Vibration sensors and displacement sensors are arranged at key positions of a wind tunnel structure to form a monitoring network, collected signals are preprocessed, and a settlement factor matrix is established; a dynamic load matrix is constructed to describe composite load distribution, a vibration burr identification matrix is established, real signals and noise are separated by adopting wavelet transformation, a slow settlement trend matrix is constructed to extract a long-term change rule, and a least square optimization algorithm is adopted to jointly solve each matrix parameter to establish a settlement position identification function. The settlement three-dimensional position coordinate is determined according to the multi-sensor data fusion result, the structure safety state is evaluated through the settlement risk coefficient, and the technical problems that the wind tunnel structure settlement position recognition precision is insufficient, and a real settlement signal and a noise interference signal cannot be effectively distinguished are solved.

The invention relates to an intelligent driving control method of a vehicle and the vehicle, and belongs to the technical field of intelligent driving, and the method comprises the following steps: obtaining an original data stream of a vehicle sensor network, and carrying out feature extraction on a dynamic feature vector; state vectors of the traffic participants are extracted according to the dynamic feature vectors, edges between the two traffic participants are constructed, a traffic participant interaction graph is constructed, and an asymmetric factor matrix is calculated; acquiring a historical scene data set of the vehicle, calculating correction similarity, and calculating a weight coefficient; performing weighted fusion on the weight coefficient and the historical scene data set to obtain an enhanced data set; and performing updating training on the pre-trained intelligent driving decision model according to the enhanced data set to obtain an updated intelligent driving decision model, generating an intelligent driving decision, and controlling vehicle operation, thereby realizing more accurate characterization of a dynamic game relationship in a complex traffic scene, and improving the accuracy of the dynamic game relationship. And the intelligent driving decision model can continuously adapt to an asymmetric interaction mode effect in a real scene.

The invention discloses an energy storage system intelligent energy management control method and system, and relates to the technical field of energy storage system intelligent energy management control, and the method comprises the steps: obtaining an energy storage unit operation state parameter and an environment disturbance factor matrix through a multi-source data collection device, and carrying out the data preprocessing; and constructing a long-short-term memory neural network model, dynamically adjusting a prediction time window of the model and optimizing a target function weight coefficient based on the load predicted by the model, and determining a charging and discharging control instruction. And updating and optimizing the target function weight coefficient and the charging and discharging strategy library through a reinforcement learning algorithm to realize self-adaptive optimization adjustment of the charging and discharging strategy. According to the method, high-precision energy management and intelligent optimization control of the energy storage system in a complex environment are realized. The load prediction accuracy and the energy utilization rate of the system are improved, the aging rate of the battery is reduced, the service life of the battery is prolonged, the adjusting capacity of the energy storage system is improved, and the overall stability of the energy storage system in dynamic change is enhanced.

The invention relates to the technical field of wireless communication, and discloses a wireless communication adaptive method and system based on a data transmission state, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the multi-source data, obtaining a CSI compression matrix, predicting the channel coherence time, dynamically adjusting the CSI sampling interval, and defining a load-channel coupling factor; obtaining cross-layer data based on the CSI compression matrix, performing fusion through a rotation matrix to obtain a fusion matrix, and extracting a physical layer fusion feature and an application layer fusion feature to calculate a multi-target state score; establishing a 5G power compensation mechanism based on cross-layer data, calculating a four-dimensional influence tensor, performing tensor decomposition and optimal action selection, and decomposing T into a core tensor and a factor matrix; selecting an optimal parameter combination through modular product calculation; based on cross-layer data, a quantum entanglement feedback mechanism is introduced, data is fed back, entanglement state association cross-layer indexes are designed, a quantum gate is adjusted through entanglement state design, and a model is updated in real time in combination with incremental learning.

The invention relates to the technical field of computer data processing, and discloses a building material supplier dynamic evaluation and recommendation system based on big data, and the system comprises a data fusion module which integrates multi-source heterogeneous data to generate a unified data set; the tensor modeling module is used for constructing and decomposing a five-dimensional space-time tensor to obtain a factor matrix and a dynamic weight; the causal correction module is used for establishing a causal graph based on the network relationship and eliminating hybrid deviation; the recommendation decision module is used for outputting a recommendation list through reinforcement learning in combination with the evaluation weight and the performance distribution; and the interpretable module is used for generating an interpretable report based on the factor and the causal path. According to the method, the technical scheme of multi-source heterogeneous data fusion and five-dimensional space-time tensor decomposition is adopted, and dynamic, multi-dimensional and relevance evaluation of supplier performance is realized by constructing a unified data structure including suppliers, time, static characteristics, context and cooperative relationships.

The invention relates to a quick mountain torrent disaster risk assessment method and system based on multi-source data fusion. The method comprises the following steps: performing space-time registration on multi-source data, constructing a dynamic factor matrix and a static geological parameter matrix, and calculating soil saturation; on the basis of the dynamic factor matrix, removing redundant factors, analyzing and generating a weight vector through an entropy method, on the basis of the effective factor set, the static geological parameter matrix and the soil saturation, correcting the rock-soil shear strength by quantifying the sediment deposition effect, obtaining the corrected rock-soil shear strength, and calculating the slope safety coefficient; and according to the coefficient, the effective factor set and the weight vector, generating a risk membership degree vector through fuzzy evaluation, calculating a risk feature value, identifying a disaster dominant type, and constructing a risk map. According to the method, through multi-source data space-time registration, redundant factor elimination, sediment deposition effect quantitative correction and the like, the efficiency and precision of mountain torrent disaster risk assessment are improved, and the support of an assessment result on disaster prevention and reduction decisions is enhanced.

The invention discloses a multi-dimensional layered current limiting method based on spatio-temporal feature fusion. The method comprises the following steps: acquiring a hardware limit speed, and generating a global quota based on a reinforcement learning model; calculating a variable coefficient, and if the variable coefficient exceeds a preset critical value, introducing a conservative coefficient and generating a weight factor through nonlinear function mapping to suppress the micro network jitter; constructing a priority factor matrix based on the service priority and the service type, and obtaining a service priority factor; correcting the average bandwidth of the node by integrating the multi-dimensional dynamic weight factor and the service priority factor to obtain a suggested speed, inputting a historical performance index into the LSTM model to predict the load of the next period, and if the historical performance index exceeds a critical value, triggering connection migration before executing current limiting to realize active avoidance; otherwise, comparing and selecting the minimum value of the global quota, the suggested speed and the hardware limit speed as the final current-limiting speed. The problems of current limiting strategy lag and wide oscillation can be solved, and the bandwidth utilization rate and the system stability are improved.

The invention relates to the technical field of power grid dispatching, in particular to a power grid dispatching operation ticket intelligent generation and risk pre-judgment system based on multi-modal data fusion. The method comprises the following steps: a weather-time window mapping unit associates and identifies weather data on that day, and judges the rationality of planned operation time and steps by establishing a mapping relationship between a meteorological condition and an operation time window and utilizing a meteorological influence factor matrix; after the historical operation ticket analysis unit confirms meteorological condition operation, information is extracted from the meteorological condition operation through the NLP technology, and a set of standard operation ticket template library is constructed; the four-dimensional risk association unit establishes a four-dimensional risk association model based on power safety regulations, historical accident cases and equipment defect data. According to the design of the invention, through multi-dimensional data fusion technologies such as meteorological-time window mapping, four-dimensional risk association analysis and dynamic topology simulation, safety risks existing in the operation process can be comprehensively identified and evaluated.

Disclosed in the present application are a compression and training method and apparatus for a defect detection model. The method comprises: obtaining, by means of segmentation labeling, a segmentation labeling factor matrix of each sample image; inputting each sample image into both a first defect detection model and a second defect detection model, and extracting first feature maps outputted by target convolutional layers in the first defect detection model and second feature maps outputted by corresponding target convolutional layers in the second defect detection model; and calculating, by using the segmentation labeling factor matrix, corrected distances between corresponding feature vectors of the first feature maps and the second feature maps, and calculating, as a first loss function, the sum of the corrected distances between all the feature vectors of the first feature maps and the second feature maps. The present embodiment can improve the accuracy of detecting tiny product appearance defects by means of a compressed defect detection model.

The invention provides a temperature compensation error elimination method in a full-automatic transformer transformation ratio test, and belongs to the technical field of transformers. A heat conduction coefficient matrix, a convective heat transfer coefficient matrix and a radiation form factor matrix are utilized to construct a temperature field dynamic evolution equation set, and a neural network compensation model combining a multi-layer perceptron architecture and an attention mechanism is adopted. A hierarchical fusion weight is dynamically adjusted based on a thermal gain stability matrix eigenvalue, a temperature gradient vector modulus length and a thermal capacity matrix condition number through a gating weight function, compensation parameters are adjusted in advance by using a temperature field prediction algorithm, and continuous improvement of measurement precision is realized through a temperature compensation effect evaluation mechanism and a dynamic parameter optimization strategy. The technical problems that the measurement precision is reduced and accurate temperature compensation cannot be realized due to temperature change in the transformation ratio test process of the transformer are solved.

The invention provides a settlement data over-limit adjustment method in a construction stage, and belongs to the technical field of wind tunnel construction. Monitoring points are installed at key positions of a wind tunnel structure foundation to establish a three-dimensional monitoring network; five special analysis matrixes including a shrinkage creep factor matrix, a load settlement gradient matrix, a sudden change settlement identification matrix, a multi-factor continuous coupling matrix and a construction process parameter matrix are constructed, and the independent contribution value of each deformation component is quantitatively separated through a settlement separation optimization equation set; a settlement overrun evaluation index calculation model is established for risk grade division, and corresponding load control measures, concrete curing condition optimization and construction process improvement operation are selected from a construction process parameter matrix according to an evaluation result. The technical problem that the contribution degree of each component of settlement deformation under the multi-factor coupling effect in the construction process cannot be accurately separated and quantitatively analyzed is solved.

The invention relates to the field of data processing, and provides a multi-dimensional feature driven B2B2C collaborative recommendation method and system. The method comprises the steps of performing multi-dimensional collection on B-end merchant features, C-end user features and commodity features through a heterogeneous data source interface to obtain standardized multi-dimensional features; performing dynamic weight learning on the standardized multi-dimensional feature data set through a multi-head self-attention mechanism to obtain a fusion feature vector; performing three-layer cooperative matrix construction on the fusion feature vector based on tensor decomposition to obtain a multi-dimensional factor matrix; performing causal relationship modeling on the multi-dimensional factor matrix through a causal graph structure-based collaborative filtering algorithm to obtain a deep collaborative network model; and performing real-time recommendation of to-be-recommended items through the deep collaborative network model to obtain a personalized B2B2C recommendation list. According to the method, the complex mode in the business scene can be captured, and the accuracy of the personalized recommendation result is improved.

The invention relates to the technical field of logistics scheduling, and discloses an intelligent cross-border logistics scheduling optimization system, and the system comprises the steps: S1, obtaining cross-border logistics full-chain parameters in real time through a multi-source heterogeneous data collection module, and generating a logistics basic data set; s2, performing real-time risk quantification processing on the logistics basic data set based on a dynamic risk modeling engine, and constructing a logistics risk factor matrix; by constructing a multi-source heterogeneous data acquisition module and integrating the real-time transportation situation, policy compliance and global risk data of a cross-border logistics full chain, the problems of data islands and information lag in a traditional system are solved, comprehensive perception and deep fusion of logistics basic data are realized, a solid data support is provided for subsequent intelligent scheduling, and the real-time transportation situation, policy compliance and global risk data of the cross-border logistics full chain are integrated. And the perception capability of the system to a complex cross-border environment is improved.

The invention relates to the technical field of data processing, in particular to a dynamic weight matrix decomposition energy consumption prediction method and system, and the method comprises the steps: standardizing internal energy consumption data, and generating a standard data matrix and a sparse mask matrix; based on the mask matrix, processing the standard data matrix by adopting a dynamic regularization matrix decomposition technology, and determining a sparse internal factor matrix and a reconstructed data matrix; and smoothing the noise points by using a local weighted regression algorithm to generate a smooth data matrix. Standardizing the external influence factor data to form low-dimensional external factor embedding; and calculating dynamic influence weights of the external factors by adopting an attention mechanism algorithm, and fusing to generate weighted external factor representation. And when newly added energy consumption data is obtained, a final factor matrix is obtained through an incremental learning optimization algorithm. And outputting a future energy consumption prediction result based on the final factor matrix and the smooth data matrix, thereby effectively improving the accuracy, robustness and efficiency of energy consumption prediction in a complex scene.

The invention relates to the technical field of warehousing of bulk grains in a horizontal warehouse, discloses a warehousing method and system for bulk grains in a horizontal warehouse, and aims to solve the problems of uneven accumulation, potential safety hazards and low efficiency in an existing method. A three-dimensional warehouse body coordinate system is constructed through multi-dimensional analysis, and spatial density distribution is simulated; establishing a dynamic accumulation model based on density data to simulate a spatial-temporal trajectory, calculating acting force among particles through finite element analysis to obtain stress distribution, and constructing a stability evaluation system; the impurity rate is corrected through a coupling model by utilizing environment parameters of the internet of things, and a stability evaluation result is dynamically compensated by adopting a multi-factor matrix; a deep reinforcement learning algorithm is applied to optimize a control strategy, an intelligent model is deployed at an edge node, and a joint control automation system realizes closed-loop management. According to the bulk grain warehousing device, the uniformity, stability and efficiency of bulk grain warehousing operation are improved.

The invention belongs to the field of geological disaster early warning, and provides a landslide danger prediction system and method, and the method comprises the steps: carrying out the fusion and normalization processing of multi-source disaster-inducing factors, obtaining an impact factor matrix, and carrying out the weighted correction of the impact factor matrix; determining spatial association strength and semantic association degree between the nodes according to the association edges; adopting feature mapping, association weight calculation and information aggregation adaptive learning to obtain association strength and association features of the nodes; according to the association strength and the association features of the nodes, learning by adopting an association graph model to obtain a global prediction model, and optimizing the global prediction model; and predicting the target landslide area through the optimized global prediction model to obtain a prediction result, and carrying out danger grade division on the prediction result according to a preset probability threshold. The beneficial effect of the invention is that the precision of landslide risk prediction is improved.

The invention provides a multi-domain power grid data collaborative modeling method and system based on a tensor game diagram, and the method comprises the following steps: firstly constructing a six-dimensional enhanced tensor model, constructing a six-order tensor based on the number of nodes, timestamps and other six dimensions, and obtaining a kernel tensor and a factor matrix through CPD-Tucker mixed decomposition; a joint optimization objective function containing reconstruction errors, game equilibrium and privacy risks is constructed, and an optimization kernel tensor and factor matrix is solved; and finally, inputting an optimization result into the MGGCN, processing double targets through a leader branch (a physical topology adjacency matrix guarantees reliability) and a follower branch (a market transaction incidence matrix optimizes economic cost), and generating a collaborative decision result through multi-head attention fusion. According to the method, the problems of low multi-source heterogeneous data fusion efficiency and difficulty in considering cross-domain collaborative privacy security and dynamic optimization are solved, the new energy output prediction error can be reduced, and the data utility loss caused by global encryption is reduced.

The invention belongs to the technical field of battery health degree evaluation, and particularly relates to a battery health degree evaluation method and system, electronic equipment and a storage medium. Based on the original sample data of the battery health degree evaluation indexes, establishing a standardized decision matrix to eliminate the dimensional difference between the indexes; constructing a correlation matrix according to the matrix, and evaluating index correlation; extracting common factors of a correlation matrix, calculating a core quantitative index, determining a main factor by taking standard reaching of an accumulated variance contribution rate as a standard, realizing data dimension reduction and retaining original index core information; and calculating a main factor score in combination with the original data and the rotated factor matrix, performing weighting according to a variance contribution rate, and performing weighted summation to obtain a health degree comprehensive score. And the health difference of the batteries can be judged in an auxiliary manner through clustering analysis, and data support is provided for reasonable configuration and fine management of the batteries. According to the method, dimension reduction weighting is carried out on various related indexes through a factor analysis method, a result is objectively deduced based on data features, subjective influences are reduced, and evaluation scientificity and reliability are improved.

The invention relates to an entry and exit certificate verification method based on dynamic weight and multi-modal fusion, and belongs to the technical field of entry and exit certificate information processing and verification. The method comprises the following steps: collecting multi-source heterogeneous data of a certificate in three modes of visual reading, machine reading and chip reading; constructing a verification data graph taking the unique identification of the certificate as a core, and carrying out data association and alignment; based on a dynamic weight distribution model, performing weighted voting fusion on the homologous key fields, and performing semantic fusion on the heterologous complementary fields; and constructing a traceable verification decision factor matrix, judging a verification conclusion according to the content of the matrix, positioning a source when the verification conclusion is abnormal, and generating a structured verification record containing a complete decision path and a disposal scheme. Through the dynamic weight fusion and the decision factor matrix, the problem of splitting of the existing verification mode is effectively solved, and the accuracy and reliability of the verification result and the process traceability are remarkably improved.

The invention relates to the technical field of vacuum insulation manufacturing of liquid hydrogen storage and transportation equipment, and discloses a liquid hydrogen spherical tank vacuumizing and filtering multi-parameter cooperative control method and system, and the method comprises the steps: obtaining the temperature, vacuum degree and vibration monitoring data in a liquid hydrogen spherical tank, and space coordinates of the data; calculating a vacuum degree mean value and a vibration intensity mean value of each partition, and generating a multi-physical field heterogeneous partition topological structure; extracting a physical field coupling factor matrix by using a tensor decomposition algorithm, and identifying a dominant coupling relationship between physical fields; and scheduling the multi-parameter monitoring data to be transmitted in a shallow fading period according to the partition period quality label matrix, completing issuing of a scheduling cooperative control instruction before deep fading arrives, and outputting the control instruction to an execution mechanism. The overall performance and safety of the liquid hydrogen spherical tank vacuumizing and filtering multi-parameter cooperative control system are improved under the extreme environment of coupling influence of multiple physical fields such as a temperature field, a vacuum degree field and a vibration field.

The invention discloses an RIS-assisted millimeter wave satellite communication system channel estimation method based on PARAFAC. The method comprises the following steps: constructing an RIS-assisted millimeter wave satellite communication system cascade channel model; the method comprises the following steps: modeling received signals of an RIS-assisted satellite communication system into a third-order parallel factor (PARAFAC) model through data reconstruction based on a multi-dimensional structure of an RIS-assisted millimeter wave satellite communication system channel; and performing parallel factorization on received signals of the RIS-assisted satellite communication system by using an alternating optimization method to obtain an estimated value of a model factor matrix, and finally reconstructing an RIS-assisted satellite communication channel by using the estimated value of the factor matrix to obtain a channel estimated value. The RIS-assisted millimeter wave satellite communication system channel estimation method can accurately achieve RIS-assisted millimeter wave satellite communication system channel estimation, and the performance of the RIS-assisted millimeter wave satellite communication system channel estimation method is superior to that of a traditional least square method (LS)-based channel estimation method.

The invention discloses a distributed electrically-driven vehicle driving system failure fault-tolerant control method, which comprises the following steps of: acquiring fault factors of each electric wheel in real time based on a state monitor, and constructing a fault factor matrix; based on the fault factor matrix, establishing a dynamic unified model including longitudinal, lateral and yawing motion of a vehicle body and rotational motion of wheels; calculating and dynamically adjusting the target total driving force and the target yawing moment of the whole vehicle according to the current total driving force demand of the vehicle and the motion state of the vehicle on the basis of a dynamic unified model and the fault factor matrix; constructing an optimization model of an electric wheel torque distribution controller by taking the minimization of the sum of the tire attachment utilization rates as an optimization target; and solving the optimization model of the electric wheel torque distribution controller to obtain the optimal driving torque of each wheel, and outputting and executing the optimal driving torque.

The invention discloses an electric power intra-day price prediction method based on dynamic holiday weight and multi-source fusion, and the method comprises the steps: obtaining multi-source historical data, and carrying out the time synchronization processing; holiday and festival time information is acquired, and a dynamic weight is generated based on the influence of holidays and festivals and upstream and downstream dates on the power load and the electricity price; constructing a multi-dimensional predictive factor matrix based on the multi-source historical data after time synchronization processing; and according to the multi-dimensional predictive factor matrix, on the basis of a collaborative optimization multi-model combination comprising a feedforward neural network model and a bagged regression tree model, intra-day joint prediction is executed, the intra-day joint prediction refers to a process of predicting the power load and the electricity price hourly, and an hourly prediction result of the power load and the electricity price is output. A dynamic holiday weight mechanism is introduced, a multi-source fused high-dimensional predictive factor matrix is constructed, and a multi-model combined predictive strategy of collaborative optimization of a feedforward neural network and a bagged regression tree is adopted, so that the precision and stability of intra-day electricity price and load prediction of the electricity market are improved.

The invention belongs to the technical field of artificial intelligence education, and relates to an intelligent teaching assistance method and system based on a large language model. The method comprises the steps that text data, voice data and eye movement track data of students are acquired, feature extraction is carried out, and a three-dimensional cognitive state tensor model is constructed; constructing a cognitive state tensor decomposition constraint optimization function, performing tensor decomposition, and outputting a core tensor and a factor matrix; generating a teaching strategy, and defining a cross-modal kernel function to realize feature association; tensor decomposition parameters are dynamically adjusted; and outputting the updated teaching strategy. According to the method, the cognitive change trajectory of a learner can be captured from multiple dimensions, and the accuracy and comprehensiveness of cognitive state characterization are remarkably improved through a cross-modal kernel function and a gradient coupling mechanism; constructing a cognitive state tensor decomposition constraint optimization function to describe an evolution law of a cognitive state, and capturing a continuous change characteristic of the cognitive state along with time through a dynamic constraint condition of a time factor matrix.