57 results about "Meta-optimization" patented technology

The invention discloses a tunnel pipe roofing construction dynamic deviation correction method based on laser ranging and optical fiber sensing, and relates to the technical field of measurement and sensing, and the method comprises the steps: S1, collecting laser and optical fiber data, obtaining real strain through temperature coupling compensation, and calculating global pose parameters; s2, generating an eigenmode component based on the real strain and the global pose parameter, and screening and constructing an intelligent feature vector according to the contribution degree of the physical coupling feature; s3, constructing a physical information driving state estimation model in combination with pipe joint mechanical constraints, and outputting a pipe joint state estimation value in real time; s4, performing construction deviation prediction by using the pipe joint state estimation value, and generating an optimal control sequence and instruction; and S5, applying the control instruction to the construction device, and adaptively updating the key parameters of the system through a meta-optimization algorithm based on feedback. And through multi-modal data fusion and physical constraint intelligent prediction, high-precision deviation correction, closed-loop robust control and system self-adaptive optimization of the pipe joints are realized, and the intelligence and long-term precision of tunnel pipe curtain construction are improved.

The invention relates to a power distribution network frame optimization method based on a quantum annealing algorithm. The method comprises the following steps: constructing an objective function of power distribution network frame optimization based on actual power distribution network data and setting constraints; obtaining a quantum Hamiltonian function based on the objective function and the constraint; based on the quantum Hamiltonian function, obtaining a secondary unconstrained binary optimization objective function of power distribution network frame optimization; based on the quantum Hamiltonian function or the quadratic unconstrained binary optimization objective function, solving a power distribution network frame optimization optimal solution; and generating a power distribution network frame optimization strategy based on the optimal solution. Compared with the prior art, the method has the advantages of higher calculation efficiency and precision and the like.

The invention provides a query optimization method, electronic equipment, a storage medium and a program product. The method includes. Based on the left deep tree, the optimization problem of the table connection sequence in the database is converted into a secondary unconstrained binary optimization QUBO problem; the relationship in the left deep tree is a table in the database; solving a QUBO problem through a quantum solving method to obtain a connection relation of the tables in the left deep tree; and determining an optimized table connection sequence based on the connection relationship of the tables in the left deep tree.

The invention provides a construction method of a network intrusion model based on meta learning, comprising the following steps: a global server constructs a plurality of client nodes based on a federated learning framework, and constructs a node detection model in each client node; a client node collects local multivariate heterogeneous data at a fixed time interval, generates a simulated attack sample by using a GAN network, and then forms training data by using the simulated attack sample and a normal sample; performing iterative training on the node detection model by using the training data; performing meta-optimization on the trained node detection model by using an irrelevant meta-algorithm; and the global server dynamically allocates aggregation weights for each client node, and performs model aggregation in a weighted average manner to generate a global detection model. According to the method, the problems that the detection capability of a detection model on minority class attacks is insufficient and the detection model is difficult to quickly adapt to newly occurring novel network attacks due to the fact that the classes of model training samples are unbalanced in the prior art are solved.

A method and system for building and implementing a meta-machine learning (meta-ML) optimization engine for a neural network (NN) or a machine learning (ML) connective model. A computer processor may iteratively simulate a backpropagation algorithm by executing a sequence of optimization steps. At each optimization step a position of a loss function may be determined that may be closer than a previously determined position of the loss function to a local minimum. A computer processor may compute and store after each iteration a detachment of the loss function, learning rate, and optimal learning rate. A computer processor may train a machine learning connective model to model the optimal learning rates of the simulated backpropagation algorithm. The meta-ML optimization engine may be implemented for a NN or ML connective model by generating a modified backpropagation algorithm in which algorithmic features of gradient descent may be replaced by the meta-ML optimization engine.

The embodiment of the invention relates to a non-expert data assisted meta reinforcement learning frequency hopping interference resource allocation method. The method comprises the following steps: constructing a frequency hopping interference resource allocation problem according to a communication confrontation scene; modeling a frequency hopping interference resource allocation problem as a Markov decision process; randomly initializing the element strategy to obtain an initial strategy network; meta-training is carried out on the initial strategy network based on an MAML algorithm, and the meta-training comprises the steps that in the specific task adaptation stage, based on non-expert data, optimization training is carried out on the initial strategy network through a reinforcement learning loss function and a behavior cloning loss function, a specific task strategy is obtained, and reinforcement learning loss of all interference tasks is calculated; in the meta-optimization stage, the reinforcement learning loss of all interference tasks is minimized, and an optimized distribution strategy is obtained; and carrying out fine adjustment on the optimized distribution strategy according to the new interference task. According to the method, useful information is extracted from non-expert data in combination with reinforcement learning and behavior cloning, and efficient strategy exploration is realized.

The invention discloses a renewable energy source data clustering method and system based on an optical quantum computer, and the method comprises the steps: building a renewable energy source data clustering optimization problem based on a quadratic unconstrained binary optimization (QUBO) model based on a discrete optimization objective function, carrying out the matrix of the objective function, carrying out the clustering optimization of the objective function through a kernel method, and carrying out the clustering optimization of the objective function. The method comprises the following steps of: establishing a target function item, converting the target function item into a matrix form, constructing a matrix of a new clustering model, finally solving a new QUBO optimization problem by using CIM, and converting the new QUBO optimization problem into an optimal clustering result. According to the method, data similarity measurement is improved by constructing the QUBO model and combining a kernel method, and a renewable energy source data clustering problem is converted into a form suitable for CIM solution, so that the calculation bottleneck of a classical calculation method in high-dimensional data processing is avoided. According to the method, the nonlinear characteristic of the data can be kept, efficient clustering optimization calculation can be realized, and the calculation efficiency and the clustering precision are remarkably improved.

The invention relates to the technical field of finite element optimization, in particular to a performance optimization method and system based on finite elements, and the method comprises the steps: obtaining a finite element model of a gear in a transplanting mechanism of a rice transplanter, and dividing the finite element model into a plurality of grids; corresponding loads are applied to the finite element model according to simulation data of gears when the rice transplanter works, simulation indexes of all the grids are obtained, the grid sizes of all the grids are optimized based on the simulation indexes, and optimized target grids are output; determining a performance index of each target grid, optimizing design parameters of the target grids according to the performance indexes, and updating the finite element model based on the optimized design parameters to serve as a final finite element model of the gear; according to the method, through deep coupling of grid optimization and parameter optimization, the analysis precision and optimization efficiency of the gear can be improved.

Example systems perform complex optimization tasks with improved efficiency via neural meta-optimization of experts. In particular, provided is a machine learning framework in which a meta-optimization neural network can learn to fuse a collection of experts to provide a predicted solution. Specifically, the meta-optimization neural network can learn to predict the output of a complex optimization process which optimizes over outputs from the collection of experts to produce an optimized output. In such fashion, the meta-optimization neural network can, after training, be used in place of the complex optimization process to produce a synthesized solution from the experts, leading to orders of magnitude faster and computationally more efficient prediction or problem solution.

The invention relates to the technical field of data processing, in particular to a metal strip mine production scheduling optimization system based on multi-source data fusion, and the system comprises a data processing module which comprises a data collection unit for collecting metal strip mine data of a plurality of data sources; the model training module comprises a model generation unit which is used for carrying out model training according to the feature extraction unit so as to generate a genetic algorithm model; the production scheduling module is connected with the model training module and comprises an analysis unit which is connected with the model generation unit and is used for analyzing the metal strip mine data through a genetic algorithm model so as to output an analysis result; and the optimization module is respectively connected with the data processing module, the model training module and the production scheduling module, and is used for determining time alignment tolerances of different data sources according to the loss rate of the metal strip mine data. The optimization stability of metal strip mine production scheduling is improved.

An information processing device includes an optimization unit, a control unit, and a search unit. The optimization unit executes optimization calculation processing of obtaining an optimal solution by repeatedly executing solving processing of obtaining a solution to an optimization problem by using one or more set parameters. The control unit sets a calculation time of the optimization calculation processing for each of a plurality of search times obtained by dividing a specified time that is specified. The search unit repeatedly executes search processing of setting the parameter different from parameters of other search times for each of the plurality of search times, causing the optimization unit to execute the optimization calculation processing within the set calculation time by using the set parameter, and searching for an optimal value of the parameter by Bayesian optimization by using the optimal solution obtained by the optimization calculation processing.

One example method includes receiving a plurality of Quadratic Unconstrained Binary Optimization (QUBO) instances, the plurality of QUBO instances including at least first controllable features and their corresponding feature values and a first uncontrollable structure feature that defines a problem type of each of the plurality of QUBO instances. The first controllable features and the first uncontrollable structure feature of the received plurality of QUBO instances are used to train a QUBO generation machine-learning (ML) model to generate a QUBO instance. A QUBO feature set that includes second controllable features and their corresponding feature values and a second uncontrollable structure feature that defines a problem type of a new QUBO instance is received. The trained QUBO generation ML model generates the new QUBO instance that includes the second one or more controllable features and their corresponding feature values and the second uncontrollable structure feature.

The invention discloses a neural network reliability evaluation method based on self-attention and meta-learning. The method comprises the following steps: in a task specificity adaptation process, a self-attention mechanism is utilized to model association between a bit flipping fault position and a neural network reliability influence, and technologies such as sparse coding, label distribution smoothing and a sparse perception engine are combined to construct a neural network reliability evaluation model; in the meta-optimization process, initialization parameters are trained in task distribution formed by different neural network architectures, and the initialization parameters are applied to the evaluation model, so that the universality of the evaluation model among different tasks is improved; the method has high efficiency, accuracy and universality, and is suitable for various fault scenes and neural network architectures.

The invention discloses a novel finite element optimization solution algorithm, which comprises the following steps of: preprocessing a real image scanned by CT (Computed Tomography) to obtain an image gray value; reading an image gray value to obtain an image finite element model; establishing a mapping relationship between the unit node number and the overall degree-of-freedom number; constructing a Jacobian matrix and a geometric matrix according to the unit node numbers; calculating an element stiffness matrix and a stress function matrix corresponding to each material attribute; calculating an equivalent node force according to the element stiffness matrix, and assembling a node force vector; solving node displacement under a given boundary condition; according to the displacement field, stress components corresponding to all units under the given boundary condition are calculated and averaged, and finally the homogenized constitutive tensor is obtained. The invention provides a novel finite element optimization solution algorithm, a traditional finite element preprocessing process is simplified, the calculation efficiency is remarkably improved, and calculation resources are greatly saved.

The present disclosure discloses a louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, wherein a heat transfer unit is selected according to the louver fin micro-channel heat exchanger, the fins and the micro-channel of the heat transfer unit are modeled, the structural parameters of the heat transfer unit are extracted to establish sample space points, a parameter subset with the largest prognosis coefficient is selected, and a response surface model is established for each response variable; based on the response surface model, a genetic algorithm is applied to optimization within the limit interval and the constraint condition, the heat exchanger volume is reduced, the heat exchanger heat transfer capacity is increased, and the corresponding fan power is reduced; the output parameters of the response surface are used as a subset of the objective function of the genetic algorithm optimization, so as to reduce the fan power of the louver fin micro-channel heat exchanger and improve the heat transfer capacity under a certain capacity, and the corresponding Pareto frontier is obtained, and the optimal solution in the geometric parameter dimension region is given.

A method is disclosed for optimizing Quadratic Unconstrained Binary Optimization (QUBO) instances for efficient execution on a quantum computer. Initially, a first machine learning (ML) model receives a QUBO instance along with scaling and scheduling parameters. The first ML model transforms the QUBO instance into a scaled version and adjusts the scheduling parameters accordingly. Subsequently, a second ML model compares the scaled QUBO instance and parameters with those of a known efficiently executable QUBO instance on a quantum computer. Based on this comparison, the second ML model assigns a score to the scaled QUBO instance, indicating its efficiency for execution on the quantum computer. This method enables the optimization of QUBO instances for enhanced quantum computing performance.

The invention relates to the technical field of energy storage power station emergency disposal, in particular to an energy storage power station intelligent emergency disposal process automatic generation system and method.The system adopts a scheme of multi-modal fault understanding, large language model reasoning and multi-target optimization scheduling, receives multi-modal input such as texts, alarm codes, temperature / voltage / alarm marks and the like, and sends the multi-modal input to a server; the method comprises the following steps: firstly, taking GPT-4o as a scene vector, carrying out double-tower Transform coding on the GPT-4o as a scene vector, generating a candidate plan by combining the GPT-4o subjected to LoRA fine tuning with a knowledge graph, screening an optimal scheme through a safety, timeliness and cost ternary optimization function psi and a particle swarm optimization algorithm, and carrying out digital twinborn simulation verification and weekly-level closed-loop learning iteration; according to the method, a standardized plan is generated in 1.7 seconds, the compiling efficiency is improved by 103 times, the execution consistency is improved by 23%, the annual operation and maintenance accident loss is reduced by 18%, the monthly newly-added scene coverage rate is 92%, the deployment operation and maintenance confirmation rate in a 100 MWh energy storage station is 96%, and the energy storage safety operation and maintenance intelligent level is remarkably improved.

One example method includes receiving a Quadratic Unconstrained Binary Optimization (QUBO) problem that comprises a matrix that includes various cells having data. It is then determined that one or more of cells is missing data or has corrupted data. A machine learning (ML) model performs a denoising process that removes random noise from the one or more cells having the missing data or corrupted data. This results in data being imputed to the one or more cells having the missing data or the corrupted data. The imputed data approximates the missing data or approximates an expected value of the corrupted data before the corrupted data was corrupted.

This invention discloses a routing method and electronic device for heterogeneous low-Earth orbit satellite networks. The method includes: constructing a domain-specific network architecture and establishing a hierarchical architecture comprising a data plane, a control plane, and a meta-learning layer; establishing an end-to-end latency model and an energy consumption model; defining a multi-objective optimization function based on KL divergence; and executing meta-learning routing decisions, which includes a meta-optimization stage and an agent optimization stage. In the meta-optimization stage, an adaptive learning objective is generated through the meta-network; in the agent optimization stage, the routing method is updated according to the learning objective, and routing actions are executed. This invention solves the problems of poor adaptability of routing methods and difficulties in multi-objective trade-offs in heterogeneous networks, achieving adaptive generation and efficient collaboration of routing methods, and improving the overall performance of the network.

The invention discloses a meta-optimization adversarial sample generation algorithm combined with frequency domain feature fusion. The meta-optimization adversarial sample generation algorithm comprises the following steps: S1, frequency domain feature decomposition; s2, low-frequency reconstruction: reconstructing a low-frequency reconstructed image of the input image by adopting inverse discrete wavelet transform, and calculating a corresponding high-frequency image according to an original image and a low-frequency reconstructed difference value; s3, feature storage: respectively inputting the low-frequency image and the high-frequency image of the clean sample into a white box model, and extracting and storing features corresponding to each layer; s4, performing feature fusion; s5, inputting the fusion features obtained in the step S4 into a subsequent layer of the white box model to obtain model output; and S6, executing a meta-optimization strategy, calculating and combining internal and external gradient information to update the adversarial sample, and outputting a finally generated adversarial sample. According to the method, the image is decomposed and reconstructed in the frequency domain, and the multi-frequency features and the meta-optimization updating strategy are extracted and fused respectively, so that conflicts among different frequency domain features are effectively relieved, and the attack mobility of the generated confrontation sample to a conventional model and a defense model is improved.

A method for driving a quantum computational network for determining an extremal value of a cost function for solutions of a quadratic unconstrained binary optimization problem includes initializing qubits, sequentially applying layers of quantum gates to the qubits, determining an output state of the quantum computational network for obtaining a solution associated with the set of variational parameters {right arrow over (θ)}, and determining an output state for shifted variational parameters {right arrow over (θ)}* to evaluate a partial derivative with respect to the subset of the variational parameters {right arrow over (θ)} for determining a gradient of the cost function based on the output state for the shifted variational parameters {right arrow over (θ)}*, and by updating the variational parameters {right arrow over (θ)} based on an update function of a moving average over the gradient of the cost function and of a moving average over the squared gradient of the cost function.

This invention discloses a hierarchical federated learning method and system based on split meta-learning. The method specifically includes: a cloud server initializing a global model and distributing it to various edge servers; the edge server layer distributing the global model to its respective clients; each client updating the feature extraction module of the global model locally based on local data, while simultaneously freezing the classifier module of the global model; the edge server layer aggregating the updated parameters of the feature extraction modules from multiple clients and performing meta-optimization on the classifier module using a local validation dataset to generate an edge local model; and the cloud server layer periodically collecting model parameters from multiple edge servers and updating the global model using a gradient-sensitive momentum aggregation strategy. This invention, through a three-layer "vehicle-edge-cloud" architecture and a two-level collaborative optimization mechanism, achieves coordinated optimization between privacy protection, communication efficiency, and model adaptability in a vehicle-to-everything (V2X) environment.

The invention relates to the technical field of petroleum and natural gas engineering drilling, and discloses a track profile dynamic optimization method and system for drilling, and the system comprises a track model library, an initial value generation unit, an optimization solving unit, a constraint evaluation unit and a result output unit. The orbit model library constructs three-section and five-section orbit section models based on a constant tool face angle, and the optimization solving unit fuses a quasi-Newton method and an artificial bee colony algorithm to realize orbit parameter optimization under multiple variables and multiple constraints. According to the method, an adaptive track profile type is selected through real-time drilling data, and optimized parameters are output after initial value solving, algorithm optimization and constraint evaluation. According to the method, the feedback of the drilling process can be dynamically responded, the target window drilling rate is remarkably improved, the track complexity and the dragging pressure risk of a long horizontal section are reduced, the drilling requirements of directional wells and horizontal wells under complex stratums are met, and technical support is provided for efficient and safe development of drilling operation.

This invention belongs to the field of wind power equipment diagnosis, specifically relating to a semi-supervised acoustic signature diagnosis method for wind turbine blades based on meta-optimization. The method includes the following steps: acquisition and preprocessing of wind turbine blade acoustic signature signals; construction of labeled and unlabeled datasets; initialization and baseline training of the semi-supervised diagnostic model; pseudo-label correction and construction of a quality scoring network; virtual model update based on corrected pseudo-labels; meta-loss calculation based on labeled data; determination of the training value of sample-level pseudo-labels; training of the pseudo-label quality scoring network and sample selection; iterative training of the semi-supervised model and diagnostic output. This invention achieves dynamic evaluation and adaptive correction of pseudo-label quality by explicitly modeling the impact of unlabeled sample pseudo-labels on the model optimization objective. This improves the accuracy and robustness of wind turbine blade fault diagnosis under limited labeled data conditions, enhances stability during training convergence, and improves the accuracy of the final model.

The present disclosure relates to a non-expert data-assisted meta-reinforcement learning method for frequency-hopping interference resource allocation, comprising: constructing a frequency-hopping interference resource allocation problem based on a communication adversarial scenario; modeling the frequency-hopping interference resource allocation problem as a Markov decision process; randomly initializing a meta-strategy to obtain an initial policy network; and meta-training the initial policy network based on the MAML algorithm. The meta-training includes: in a task-specific adaptation phase, optimizing the initial policy network using a reinforcement learning loss function and a behavioral cloning loss function based on non-expert data to obtain a task-specific policy and calculate the reinforcement learning loss for each interference task; in a meta-optimization phase, minimizing the reinforcement learning loss for all interference tasks to obtain an optimized allocation policy; and fine-tuning the optimized allocation policy based on the new interference task. The present disclosure combines reinforcement learning and behavioral cloning to extract useful information from non-expert data, enabling efficient strategy exploration.

The invention discloses a high-dimensional feature screening method and device based on quantum annealing, relates to the technical field of quantum computing, and realizes accurate and efficient feature screening in a high-dimensional financial data scene. The method comprises the following steps: acquiring data of a target financial variable and data of a to-be-preliminarily screened characteristic factor; constructing an initial total prediction linear term based on a linear term of the secondary unconstrained binary optimization model, and obtaining a selected to-be-primarily-screened characteristic factor as a to-be-finely-screened characteristic factor; based on a linear term and a quadratic term of the quadratic unconstrained binary optimization model, constructing a total prediction linear term and a redundant quadratic term, and obtaining a selected to-be-finely screened characteristic factor as a to-be-optimized characteristic factor; the permutation importance of each to-be-optimized feature factor is calculated, a target feature factor is screened from the to-be-optimized feature factors according to the permutation importance, and the target feature factor is used for predicting a target financial variable.

The present application relates to the technical field of data processing, and especially relates to a metal open-pit mine production scheduling optimization system based on multi-source data fusion, comprising: a data processing module, comprising a data acquisition unit for acquiring metal open-pit mine data of a plurality of data sources; a model training module, comprising a model generation unit for model training according to the feature extraction unit to generate a genetic algorithm model; a production scheduling module connected with the model training module, comprising an analysis unit connected with the model generation unit for analyzing the metal open-pit mine data through the genetic algorithm model to output an analysis result; and an optimization module connected with the data processing module, the model training module and the production scheduling module respectively, for determining time alignment tolerance of different data sources according to a loss rate of the metal open-pit mine data. The present application improves the optimization stability of metal open-pit mine production scheduling.

The invention relates to a heterogeneous road side unit optimized layout method, in particular to a heterogeneous road side unit optimized layout method oriented to risk perception and prevention and control. The invention aims to solve the problem that the existing RSU layout scheme research is lack of consideration on risk perception, prevention and control and heterogeneous road side units. The method comprises the following steps: 1, discretizing a road area into space-time units; mapping the accident record into a corresponding space-time unit, and counting the accident frequency in each space-time unit; standardizing the accident frequency in each time-space unit to obtain the risk intensity of each time-space unit; 2, setting an effective risk prevention and control interval on a space unit # imgabs0 #; 3, acquiring a communication range and a sensing range of a # imgabs 1 # type road side unit RSU; 4, constructing a target function; and 5, solving the objective function by adopting a genetic algorithm to obtain an optimization result. The method is applied to the field of heterogeneous road side unit layout.

Decomposing problems such as quadratic unconstrained binary optimization problems is disclosed. A problem is decomposed into subproblems and the subproblems are classified into a first set of subproblems for classical annealers and a second set of subproblems for quantum annealers. The sets are processed to move subproblems from the second set to the first set. This is achieved by exchanging qubits among the subproblems and then reclassifying the modified subproblems. Moving subproblems from the second set to the first set reduces the need for quantum annealers. The subproblems in the two sets are executed and a solution to the original problem is determined from the solutions to the subproblems.

The application provides a network intrusion model construction method based on meta learning, comprising the following steps: a global server constructs a plurality of client nodes based on a federated learning framework, and constructs a node detection model in each client node; the client nodes collect local multi-element heterogeneous data at a fixed time interval, generate simulated attack samples using a GAN network, and then form training data by combining the simulated attack samples and normal samples; the node detection model is iteratively trained using the training data; the node detection model after training is meta-optimized using an irrelevant meta algorithm; the global server dynamically allocates an aggregation weight to each client node, aggregates the models by a weighted average method, and generates a global detection model. The application solves the problem of class imbalance of model training samples in the prior art, which leads to insufficient detection capability of the detection model for minority class attacks and difficulty in quickly adapting to new network attacks.