94 results about "Function optimization" patented technology

The invention relates to a model predictive control charging optimization method based on a dynamic power state, which initiates a'dynamic power state collaborative optimization 'mechanism, takes a real-time power upper limit as an active optimization target instead of a fixed constraint condition, and breaks through the technical bottleneck of power limitation passive response in a traditional charging strategy. The method specifically comprises the following steps: constructing an electric-thermal-aging multi-physics field coupling model of the lithium ion battery, updating electric-thermal characteristic parameters in real time through an online parameter identification algorithm, and synchronously estimating a core temperature and an aging state in combination with a double-Kalman filtering state observer; innovatively establishing a four-dimensional objective function optimization model containing a dynamic power state, and performing multi-objective collaborative optimization on a power upper limit, a charging speed, a capacity fading rate and a current fluctuation rate; and designing a dynamic rolling optimization algorithm based on a model prediction control framework, and solving the optimal charging current meeting the dynamic power distribution requirement of the power grid in real time under the hard constraint of ensuring the maximum core temperature and terminal voltage.

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

The invention discloses an automatic driving carrying equipment scheduling system and method for an industrial robot. The system comprises a plurality of automatic driving carrying devices, a central dispatching device and corresponding communication modules. The system adopts a multi-agent reinforcement learning framework and combines a graph neural network processing environment topological structure to realize dynamic path planning and multi-device collaborative scheduling; integrating an energy consumption prediction mechanism based on a Kalman filter, and bringing energy consumption factors into a task allocation decision; meanwhile, a fault-tolerant management mechanism based on a distributed account book and federated learning is established, and fault detection and rapid recovery are achieved. The technical modules are deeply coupled, and a unified collaborative optimization framework is formed through reward function design, utility function optimization and fault probability calculation of multi-agent reinforcement learning. According to the method, the problems of poor dynamic environment adaptability, isolated decision making of each module, extensive energy consumption management and the like in the prior art are effectively solved, and the overall efficiency, energy efficiency and reliability of a scheduling system are remarkably improved.

The invention provides a social robot detection method and system, computer equipment and a storage medium, and belongs to the technical field of social robot detection.The method comprises the steps that user multi-source data on a social platform and the social relation between users are collected; a hybrid encoder is adopted to capture local dependence and global time sequence dynamic states of behaviors, and user behavior characteristics are generated; aggregating structure attention features between the initial node features of the target account and the initial node features of the social relation account to obtain multi-modal relation aggregated structure features; introducing a multi-modal adversarial training strategy, adaptively adjusting the disturbance intensity of each modal based on gradient sensitivity, and aligning the characterization of the clean sample and the adversarial sample in combination with a content discriminator and a behavior discriminator; and outputting a target user classification result through the multi-task target function optimization model. According to the method, the problems of insufficient modal fusion and insufficient adversarial robustness of an existing method can be effectively solved, and the accuracy and stability of social robot detection in a complex and adversarial scene are improved.

The invention provides a cascade flow field prediction method and device based on sparse promotion modal feature prediction, and belongs to the technical field of gas turbine flow field prediction. According to the method, a flow field snapshot is decomposed into a DMD mode, a mode amplitude matrix and a DMD eigenvalue matrix, and a flow field reconstruction expression is constructed; constructing an objective function, including promoting a sparse term to produce a more sparse solution; the optimal position of the non-zero modal amplitude is solved based on target function optimization, and a modal order corresponding to the non-zero modal amplitude forms a dominant modal representing the dynamic characteristics of the flow field; the modal amplitude and the DMD characteristic value of the unknown working condition are predicted based on the modal amplitude and the DMD characteristic value of the dominant modal of the flow field under the known working condition, and then the flow field of the unknown working condition is predicted by using the inverse process of the DMD. According to the method, control over the number of sparse modes is added into the target function, the mode sparsity is enhanced, and then the mode subset which has remarkable influence on the flow field is better identified.

The invention discloses a trajectory planning method based on double sampling optimization, and belongs to the technical field of automatic driving. The method aims at solving the technical problems that in a multi-branch unstructured scene, a traditional planning method is prone to falling into local optimum, planning fails or calculation time is too long, and system reliability is affected. According to the scheme, sampling optimization is carried out on a guide path and a smooth path at the same time, an improved WRRT *-S algorithm is adopted for searching, and an optimal guide path is selected in combination with a risk potential field, path distance cost and steering cost; generating a smooth path candidate cluster by using a quintic Bezier curve, and selecting an optimal smooth path through multi-objective function optimization of path smoothness, economy, collision penalty and the like; and discretizing a speed planning problem into a nonlinear planning problem for solving by taking the maximum speed as a constraint and utilizing a Gaussian pseudo-spectral method, so as to realize collaborative optimization of the path and the speed. According to the invention, the vehicle can plan a safe, stable and efficient track in a complex scene.

The invention discloses a photovoltaic power prediction method based on an adaptive modified quantile regression neural network. Feature extraction is realized by constructing a double-flow hybrid neural network so as to improve prediction accuracy, a branch, combined with a multi-head attention mechanism, of the convolutional neural network is responsible for extracting long-term features, a branch of a bidirectional gating circulation unit focuses on identifying short-term fluctuation, and the double-flow hybrid neural network is combined with quantile regression. In order to solve the problems of quantile crossing and non-differentiable zero point of a loss function, a self-adaptive correction marble loss function is provided, and smooth function optimization is introduced to ensure monotone increasing of predicted quantiles and whole-domain differentiable of the loss function. According to the method, point prediction, interval prediction and probability density prediction can be realized, the prediction effect is verified through a multi-dimensional evaluation index, potential information of photovoltaic power is fully mined, and the method has practical engineering application value.

The invention provides an unmanned excavator track planning and control method, and relates to the field of artificial intelligence vehicle control. According to the system, aiming at engineering requirements of linear excavation and the like, an excavator working device is simplified into a four-degree-of-freedom mechanical arm, and a target excavation track is planned through an RRT-Connect algorithm and an S-type speed interpolation function; constructing the nonlinear system into a second-order linear system, and identifying model parameters on line by adopting a recursive least square method; and then an augmentation system containing error integration is constructed, model prediction control is combined, and high-precision trajectory tracking is realized through discretization processing, objective function optimization and a QP solver. The problem that a traditional control strategy is insufficient in precision is solved, the operation precision and adaptability of the unmanned excavator under complex working conditions are improved, the method is suitable for scenes such as groove excavation and slope leveling, and intelligent development of the excavator is promoted.

The invention discloses a numerical control machine tool spindle bearing feature extraction method based on improved FMD, and belongs to the technical field of rotating machine fault diagnosis. The method aims at solving the problems that traditional feature mode decomposition is high in parameter dependency and fault feature extraction is difficult under the noise background. The core of the method is that firstly, noise is added into a collected bearing vibration signal, and an ETO-FMD model is input; secondly, using an exponential trigonometric function optimization algorithm to take weighted envelope spectrum kurtosis as a fitness function, performing adaptive global optimization on the mode number M of the FMD and the length L of a filter, and automatically obtaining an optimal parameter combination; and finally, calculating a weighted envelope spectrum kurtosis value of each IMF component after FMD decomposition, and screening out the most critical component to perform signal reconstruction so as to realize accurate extraction of fault features. According to the method, the limitation of manually setting parameters is overcome, the accuracy, the adaptability and the robustness of feature extraction are remarkably improved, and the method is suitable for diagnosis of various faults of the spindle bearing of the high-end numerical control machine tool.

The invention belongs to the field of artificial intelligence, and discloses a distributed data acquisition system based on multi-agent collaborative decision and a construction method, and the method comprises the steps: 1, collecting heterogeneous data of production equipment, an environment sensor, a man-machine interaction terminal and an enterprise information system in real time through the deployment of a distributed data acquisition network, cleaning, de-noising and standardizing the edge computing nodes to generate a structured data stream; 2, automatically extracting features and laws of structured data through a multi-dimensional engine analysis model, and completing semantic alignment and fusion of heterogeneous data; 3, multiple agents are adopted to form empirical data according to a deterministic strategy gradient model in combination with a multi-dimensional reward function optimization model; and step 4, performing incremental learning on the empirical data to optimize the anomaly detection model, realizing multi-dimensional information visualization display of first-line decision and center command, and deploying corresponding functional agents according to the multi-dimensional information. The problems that a multi-agent system is low in control precision and low in efficiency are solved.

The invention discloses a railway construction management multi-objective equilibrium optimization method based on an improved MOPSO algorithm, and the method comprises the steps: 1) constructing four objective function optimization models on the premise of meeting the basic constraint conditions of a railway construction project, and enabling each objective function optimization model to correspond to an optimization objective; 2) according to the four objective function optimization models constructed in the step 1), constructing a multi-objective equilibrium optimization model; and 3) performing iterative solution on the multi-objective equilibrium optimization model constructed in the step 2) by using an improved multi-objective particle swarm optimization MOPSO algorithm to obtain an optimal solution set of the project under equilibrium in four aspects of construction period, resource, cost and safety. According to the method, the convergence speed, the diversity maintenance capability and the constraint processing flexibility of the MOPSO algorithm can be remarkably improved while the four optimization objectives of the construction period, the resources, the cost and the safety of the railway construction project are effectively considered, and an efficient and reliable multi-objective comprehensive decision support tool is provided for railway construction management.

The invention discloses a multi-scene agent training method and system fused with a large language model, and relates to the field of agent training, and the method comprises the steps: constructing a three-dimensional state space of an unmanned plane agent, and carrying out the standardization of the three-dimensional state space; initializing the pre-training large language model to obtain task scene adaptation parameters; configuring an intelligent agent based on the task scene adaptation parameters; training the intelligent agent in stages by adopting a hierarchical reinforcement learning algorithm, and recording a training log in real time; optimizing a reward function based on the training log and generating a new reward value; the new reward value is input into a large language model for defect recognition, and a quantitative adjustment suggestion is generated; feeding the quantitative adjustment suggestions back to a training process and a reward function optimization process, and performing iterative training until convergence; and executing a cooperative task in multiple scenes based on the finally trained agent. According to the method, intelligent agent training is realized by fusing a large language model, and the problems of high cross-scene reconstruction cost, low cooperation efficiency and lack of dynamic optimization of training of a traditional intelligent agent are effectively solved.

The invention discloses an AGV global and local fusion navigation method and system based on LT-TD3 reinforcement learning, and the method comprises the steps: extracting environment features based on laser radar data through obstacle edge detection, channel recognition and open space detection, dynamically generating a navigation target point set, and selecting an optimal navigation point through a distance weighted evaluation strategy; a multi-modal input processing mechanism is adopted to fuse laser radar data, target position information and a historical action sequence, and a state vector is formed after processing of a convolutional neural network and a gating circulation unit; a state value network, a double-Q network and a strategy network are adopted for cooperative work, and in combination with Expectile regression, a mixed target updating mechanism and composite strategy target function optimization, continuous linear speed and angular speed control instructions are output; and collision detection and intelligent control right switching are combined to realize AGV intelligent safety navigation, so that the problem that traditional reinforcement learning is easy to fall into local optimum is solved, and the phenomenon of navigation instability caused by distribution offset is effectively relieved.

The invention provides a rock mass fracture sub-pixel level identification and parameter extraction method and system, and belongs to the technical field of computer vision and digital image processing. According to the method, a multi-task convolutional neural network is constructed, and a segmentation probability matrix and a boundary signed distance field matrix are synchronously generated; in combination with energy function optimization, sub-pixel-level fracture boundary extraction is realized; a skeleton line is extracted by adopting constrained Delaunay triangulation and direction weight optimization, and topological distortion is avoided; and finally, geometric parameters are calculated based on the high-precision boundary and skeleton data. The system comprises an image processing module, a boundary optimization module, a skeleton extraction module and a parameter calculation module, and realizes integration of identification and parameter extraction. According to the rock mass fracture sub-pixel-level identification and parameter extraction method and system, the problems of low precision, skeleton distortion and flow splitting of a traditional method are solved, and the precision and reliability of rock mass fracture identification are improved.

This invention discloses a water treatment dosing control method and system based on quadratic programming, belonging to the field of water treatment process control and optimization technology. It collects historical water treatment operation data and constructs a mechanistic feature set, using the mechanistic feature set as the independent variable and turbidity reduction as the target variable. The turbidity reduction is used to represent the change in flocculation or sedimentation of suspended impurities in the water, and a full-variable regression model is constructed. Variables in the mechanistic feature set are screened, and the full-variable regression model is optimized using a stepwise regression method to obtain a simplified prediction model. Real-time influent water quality parameters are acquired and input into the simplified prediction model. The process of maximizing turbidity reduction in the simplified prediction model is transformed into minimizing a convex loss function, and the convex loss function is iteratively optimized using a hierarchical constrained projection gradient descent method to obtain the optimal dosing scheme. Through mechanism-driven modeling, convex function optimization, and hierarchical constrained projection, intelligent, efficient, and reliable control of the dosing process is achieved.

The invention discloses a task-oriented dialogue system reward function optimization method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: acquiring an expert dialogue track from a dialogue system data set, extracting a state-action-reward triple, training an initial reward function by using a maximum entropy inverse reinforcement learning framework, and initializing a strategy network; an Actor-Critic reinforcement learning algorithm is adopted to train a strategy network, and a suboptimal trajectory is collected to dynamically update a reward function; and taking the dynamic reward function as a unified evaluation signal, and optimizing the strategy network to form a dialogue strategy model. Through dynamic reward function optimization, manual rule dependence is reduced, the generalization ability, the task completion rate and the stability of a dialogue system are improved, and the method is suitable for complex dialogue scenes in multiple fields.

The application provides a light steel hot deformation constitutive optimization method based on a neural network and precipitation strengthening coupling, and relates to the technical field of metal material hot working and constitutive model establishment; the neural network model automatically learns the nonlinear relationship among the rheological stress, temperature, strain rate and strain, and introduces a precipitation strengthening correction term in a stress prediction term, which is used for representing the competition effect of cutting mechanism and bypass mechanism; through engineering design of precipitation characteristic parameters, base training and strengthening parameter calibration are performed on the neural network model; a damage function optimization algorithm based on a dislocation slip mechanism is further introduced, so that the double constraints of prediction accuracy and physical consistency are realized; the application can realize high-precision flow stress prediction of light steel under different deformation temperatures and strain rates, significantly improves the generalization and physical interpretability of the model, and provides a new modeling approach and theoretical support for constitutive modeling and microstructure and performance control of light steel and other precipitation strengthening type high-strength alloys.

The invention discloses a construction settlement dynamic monitoring method based on data analysis, and relates to the technical field of construction engineering monitoring and data-driven modeling, and the method comprises the steps: combining a causal diagram modeling method with a multi-source data analysis mechanism, achieving the structural modeling and dynamic correlation recognition of multi-level observation variables in a construction settlement region, and achieving the dynamic monitoring of the construction settlement. According to the method, a causal structure is updated in real time under multi-source heterogeneous data flow through a fast conditional independence test algorithm in combination with a modular skeleton diagram updating mechanism, logic consistency of a monitoring model is kept, and a multi-objective function optimization algorithm is combined with DAG constraint and a direction confidence coefficient matrix, so that a multi-source heterogeneous data flow is optimized. According to the method, automatic judgment and global optimal causal direction reasoning of conflict causal relationships are achieved, a priori knowledge attenuation function is combined with a data-driven confidence fusion model, time sequence dynamic evaluation of causal edge reliability is achieved, and self-evolution of a knowledge system is achieved.

The invention provides an integrated topological optimization photonic device reverse design method. Maxwell equation solution and objective function optimization are put in the same position. And the optimization of a target function is realized while the Maxwell equation is solved. Compared with a traditional topological optimization method which needs to solve a Maxwell equation set for multiple times, the integrated topological optimization algorithm put forward by the invention has the advantages that the Maxwell equation and the objective function are placed at the same position, the limitation that one of the Maxwell equation and the objective function must be established constantly is relaxed, Maxwell equation solving and objective function optimization are realized at the same time, the Maxwell equation does not need to be solved again after parameters are updated each time, and the optimization efficiency of the Maxwell equation and the objective function is improved. And a large amount of computing resources required by topological optimization are greatly saved. The photonic device designed by the invention has the advantages of small physical size, suitability for large-scale integration and the like.

The embodiment of the invention discloses a casting cleaning production line intelligent scheduling control method based on multi-agent reinforcement learning, and the method comprises the steps: enabling each processing station and a carrying execution unit to make a decision according to an environment state through introducing a multi-agent reinforcement learning frame based on an established state space for casting cleaning production line scheduling, and achieving the intelligent scheduling control of a casting cleaning production line. Through global value function optimization, beat coordination and resource dynamic allocation between stations are realized, workpiece waiting time and equipment idle time are reduced, fixed beat or manual intervention is eliminated, and the overall utilization rate of a production line is improved. Based on the setting of the reward function, the system can automatically optimize the task allocation logic according to the actual operation state, manual adjustment of beat parameters is not needed, the debugging and maintenance workload of the system is remarkably reduced, and manual dependence and debugging cost are reduced.

The invention relates to the technical field of chip design, and provides a core particle screening method and device based on orthogonal combination and function optimization in order to solve the problems of insufficient stability, universality and efficiency in the prior art, and the method comprises the steps of sub-table management, missing value deletion, parameter standardization and normalization and other data preprocessing; taking the function set of each core particle as a set, and calculating a Jaccard similarity coefficient between any two core particles as a function overlap ratio; comprehensively considering the total function coincidence degree, the total gain of the system and the total power consumption, and constructing a multi-objective optimization model based on function optimization by introducing weights; solving the multi-objective optimization model by adopting an incremental simulated annealing algorithm to obtain an optimal solution; and verifying the optimal solution obtained by the incremental simulated annealing algorithm by using disturbance test and random comparison. According to the technical scheme of the method, the minimum complete orthogonal core particle set can be screened out, so that the stability and efficiency of a general system are improved to the maximum extent.

The invention discloses a hybrid strategy optimization method and system for a refrigerating unit under multiple working conditions, and the method comprises the steps: dividing a control strategy of the refrigerating unit into a plurality of independent control units according to key parameters, such as the temperatures of a valve, a motor and a water inlet pipe, based on historical operation data, and carrying out the clustering statistics through a dominance analysis mechanism; and establishing an advantage mapping relation between the control unit and the working condition change and the global influence. Working condition characteristics are collected in real time, and optimal strategy matching under multiple targets is achieved. When working condition drifting occurs, neural network parameters are finely adjusted by combining attention weighting and a transfer learning mechanism, and the adaptability under a new working condition is improved. Meanwhile, a small-amplitude disturbance and reward function optimization mechanism is adopted, the optimal combination of the control units is automatically explored, and dynamic balance of energy consumption and engineering response is achieved. According to the method, the energy-saving, intelligent and self-adaptive levels of the refrigerating unit system are improved, and the method has continuous self-learning and global optimization capabilities and is suitable for intelligent energy management and control under complex and changeable working conditions.

The application discloses a picture quality improving method based on a guided feature separation, which comprises a training process and an inference process, wherein a feature rough extraction module is used to extract features after fusing input foreground and background data, and the features are given to a guided feature separation module; in the training process, parameters need to be transmitted to a function optimization module; the guided feature separation module uses a guided filtering technology to quickly identify and separate foreground and background features, and the separated features are given to a mask synthesis module; in the training process, parameters need to be transmitted to the function optimization module; the mask synthesis module is responsible for fusing the separated features and outputting the features to a data outflow module; in the training process, parameters need to be transmitted to the function optimization module; the function optimization module firstly performs stage training on parameters of the feature rough extraction module, and then performs optimization on the feature rough extraction, the guided feature separation and the mask synthesis module together, and finally all optimized modules are used in the inference process.

The invention discloses a new energy electric power automatic transaction method, device, equipment and product. The method comprises the following steps: acquiring historical new energy output data, historical electric power spot market clearing price data, historical irradiance data and historical wind speed data; using the first neural network model to predict the current clearing price of the electric power spot market; using the second neural network model to predict the current new energy output; establishing a multi-objective function optimization model; and resolving the multi-objective function optimization model to obtain transaction declaration electric quantity, and automatically declaring the transaction declaration electric quantity in the electric power transaction. According to the invention, the method can determine the automatic declared transaction electric quantity in the power transaction through the prediction of the corresponding future condition based on the two neural network models, has the dynamic future prediction capability, can dynamically adjust the automatic declared transaction electric quantity in the power transaction, and effectively improves the transaction efficiency and benefits.

The application discloses a networking radar jamming and mutual jamming elimination method based on decentralized Q learning, first, according to the phased array radar signal processing flow, a target echo signal, a jamming signal and a mutual jamming signal model are established, second, the conditions of radar jamming and mutual jamming are analyzed, a multi-radar frequency domain resource scheduling optimization model based on the SIJNR criterion is established, then each radar is regarded as an intelligent agent, the multi-radar cooperative anti-jamming and anti-mutual jamming process is constructed into a generalized Markov decision process, and the frequency domain resource scheduling optimization model is converted into a value function optimization model, finally, the DQJIE algorithm is used to solve the problem, and the frequency agility strategy of each radar is obtained. The method constructs the multi-radar cooperative anti-jamming and anti-mutual jamming process into a generalized Markov decision process, effectively suppresses the mutual jamming in the system while resisting the frequency sweeping jamming, and effectively improves the networking radar detection performance.

This invention discloses a risk control system and method for bank loan business based on big data analysis, belonging to the field of financial risk control technology. The method includes: collecting and preprocessing real-time behavioral data to obtain a user behavior feature set; based on the user behavior feature set, calling a behavior graph modeling engine to perform structured graph construction, matching it with a risk anchor rule base, identifying potential risk patterns, and labeling initial anchor risk tags; using a value function optimization method to correct the deviation between the initial risk anchor tags and actual default records, and combining user historical behavior sample data and loan feedback data to predict the risk level score of each user's current behavior; predicting the probability distribution of future migration to a default state through user risk level scores and historical state evolution data; assessing the current loan business risk by combining the potential losses under each behavioral path, and generating risk control strategies through risk level mapping rules and a strategy decision engine.

The invention relates to the field of network optimization, and provides an FTTR intelligent networking method and system based on multi-link collaborative optimization in order to realize multi-link collaborative scheduling, which realizes multi-link collaborative scheduling by defining a comprehensive performance objective function based on multi-link perception and construction of a collaborative matrix reflecting a mutual influence relationship between links. The bandwidth utilization rate and the coverage quality of the FTTR network are effectively improved, and the networking effects of high bandwidth, low time delay and high stability are achieved. In the comprehensive performance objective function optimization process, a hybrid mechanism combining reinforcement learning and a genetic algorithm is adopted, the genetic algorithm is responsible for periodic global topology exploration, reinforcement learning is responsible for real-time local strategy fine adjustment, the genetic algorithm and the reinforcement learning operate in a layered cooperation mode, and both global search efficiency and local dynamic adjustment capability are considered.

The invention discloses a helmet lock remote control method and system based on the Internet, and relates to the technical field of Internet of Things control and intelligent decision, and the method comprises the steps: achieving the construction of multi-dimensional risk features of a user state through the combination of a context information tuple and a cloud agent cooperation mechanism and intuitive fuzzy modeling; the sensitivity of the system to user behavior differences is improved, unification of risk dynamic assessment and probability prediction is achieved through combination of an L1 kernel estimation function and an intuitionistic fuzzy entropy weight calculation mechanism, the generalization performance of the model is improved, and through combination of a grey correlation analysis model and improved intuitionistic fuzzy distance measurement, the accuracy of the system is improved. According to the method, multi-dimensional similarity calculation and risk grade division among user states are realized, the relativity of risk judgment is achieved, a continuous function optimization method of triangular fuzzy weighted geometric averaging is combined with a credibility driven fusion mechanism, a dynamic fusion decision of risk probability and similarity information is realized, and the reliability of risk judgment is improved. And the decision stability of the system in a complex scene is improved.

The invention discloses a general T-Student kernel function-based bamboo strip broken simple data registration splicing method, which comprises the following steps of: taking to-be-spliced bamboo strip broken simple data as input of a point cloud splicing model of the bamboo strip broken simple to obtain a splicing result of the spliced bamboo strip broken simple; the model comprises a geometric feature extraction module, a texture feature extraction module and a joint optimization registration module. The geometric feature extraction module constructs a multi-scale geometric descriptor fused with region connected graph convolution by calculating a local normal vector, and generates a geometric label; a texture feature extraction module extracts a texture gradient vector and generates a binary direction mask; and the joint optimization registration module integrates the outputs of the first two modules, constructs a multi-modal error function fusing geometric-texture errors, performs weighted optimization by adopting a generalized T-Student kernel function, and finally outputs a splicing result through iterative calculation. According to the method, multi-scale geometric features, texture direction constraints and robust kernel function optimization are combined, and high-precision automatic splicing of the bamboo strips is achieved.