33 results about "Algorithmic skeleton" patented technology

The invention discloses an unmanned ship control method based on deep reinforcement learning in a multi-task scene. The method comprises the following steps: constructing dynamics and kinematics models of an unmanned ship; the method comprises the following steps: constructing a high-fidelity simulation environment based on Isaac Sim and a parallel training framework thereof, and respectively designing a state space and an action space for various unmanned ship tasks; reward functions are respectively designed; constructing an unmanned ship control strategy, and designing an algorithm framework of a centralized importance sampling and shearing strategy optimization mechanism based on an end-to-end deep reinforcement learning algorithm; and for different task scenes, multiple times of training-verification are performed on the strategy network through a PPO algorithm, and the trained strategy network is used for realizing multiple control tasks of the unmanned ship. According to the method, a high-fidelity simulation environment is constructed through Isaac Sim and a parallel training framework thereof, parallelization support which is crucial to deep reinforcement learning training is achieved, and various actual control tasks of the unmanned ship can be achieved.

The invention discloses a task allocation method and system based on an improved whale optimization algorithm framework, relates to the technical field, and is used for optimizing order type adaptive cross-domain traffic control network task allocation and improving the key task response capability of a time-sensitive traffic system. ICWOA initializes a population through Chebyshev mapping, and introduces Levy flight disturbance to enhance the optimization ability; dynamically balancing local and global search by means of adaptive parameters; relieving population diversity attenuation through randomness retention, diversity maintenance and boundary constraint; dimension type pinhole imaging reverse learning is fused to reduce high-dimensional optimization dimension interference. According to the algorithm, the convergence speed and the solving precision are better, sub-second calculation time is kept under different task scales, the task distribution efficiency is improved, and the time-sensitive scene task success rate is remarkably improved. The method solves the problems that an existing algorithm is insufficient in adaptive order type'order application-order sending 'structure and prone to falling into local optimum, population diversity attenuation and calculation speed.

The invention discloses a civil aviation element configuration optimization method based on mixed integer dynamic programming. The method comprises the following steps: S1, constructing a Chinese civil aviation core element and carbon emission spatial-temporal characteristic database; s2, establishing a mixed integer dynamic programming model with the purpose of minimizing the total cost of the system in the programming period; and S3, establishing decision variables, constraint conditions and a fusion solution algorithm framework required by the mixed integer dynamic programming model. And S4, based on the fusion solution algorithm framework, solving the mixed integer dynamic programming model by using a mathematical programming solver integrated with a robust optimization module so as to obtain a civil aviation core element dynamic configuration scheme which is optimal in total system cost in multiple periods in the future and meets the uncertainty scene. According to the method provided by the invention, a multi-source data fusion algorithm is optimized, a carbon emission measuring and calculating method is deepened, and solver parameters are adjusted, so that the collaborative requirements of improving the operation efficiency and reducing the carbon emission from the perspective of carbon cost are met.

The invention relates to a multi-target logistics distribution path optimization method based on AI, and the method comprises the following steps: obtaining order data, road network data and vehicle data of logistics distribution of a customer point, extracting basic information from the three types of data, and constructing the multi-dimensional features of the customer point; inputting the multi-dimensional features of the customer points into a pre-trained graph attention network for feature embedding, generating a distribution network feature graph fusing customer demands and a road network relationship, inputting a pre-trained reinforcement learning agent, and generating an initial path population with quality and diversity balance; performing iterative optimization on the initial path population based on a Memetic algorithm framework; and calculating the fitness of each individual on a plurality of preset optimization objectives, maintaining a Pareto solution set by adopting a multi-objective evolutionary algorithm with reference points, and recommending a final path scheme from the Pareto solution set based on user preferences. The method has the effect of remarkably improving the quality, efficiency and dynamic adaptability of path optimization.

The invention discloses an AUV trajectory tracking deep reinforcement learning method based on an improved curiosity mechanism. On the basis of an internal curiosity mechanism, a self-adaptive internal reward coefficient mechanism is provided, and an improved internal curiosity module IICM is constructed, so that the AUV can dynamically adjust the exploration capability of the AUV according to the actual tracking effect. Meanwhile, the IICM is combined on the SAC algorithm framework, an SAC + IICM algorithm is provided, the exploration behavior of the AUV is stimulated through a self-adaptive internal reward mechanism, and the understanding depth of the AUV on the environment is improved. Besides, in order to improve the tracking effect and the training efficiency, a composite reward function fusing factors such as path errors, speed changes and yaw angle errors is designed, and a state and action space highly matched with a tracking task is constructed. The method has the advantages of being high in autonomy, good in adaptability, high in convergence speed, high in control precision, high in robustness and the like, and is suitable for an AUV autonomous operation scene in a complex marine environment.

The invention discloses a multi-agent-based micro-grid energy management method and device containing flexible resources, and a storage medium. The method comprises the following steps: step 1, determining each main body of a micro-grid; 2, constructing a state space, an action space and a reward function of each main body in the three main bodies, thereby constructing a sequential decision model of the micro-grid intelligent body under a GPRO algorithm framework; 3, determining an objective function and constraint conditions; step 4, generating a training sample, and performing multiple rounds of training on the sequential decision model of the micro-grid intelligent agent under a GPRO algorithm framework through the training sample to obtain a micro-grid energy management model; and 5, inputting the current state of each main body in the micro-grid to be managed into the micro-grid energy management model to realize micro-grid energy management. The device and the storage medium are used for implementing the method. According to the method, flexible resources on the load side are fully considered, memory occupation is reduced, and training resources are remarkably reduced.

The application provides a mechanical state monitoring method based on an interpretable sparse optimization unfolding network, comprising the following steps: S100, performing mechanical state data monitoring test to obtain mechanical state signals; S200, constructing a sparse optimization model, and using sparse feature representation for the extracted mechanical state features; S300, using an alternating multiplier method to derive an iterative optimization solving algorithm of the sparse optimization model; S400, introducing learnable parameters to replace the parameters in the iterative optimization solving algorithm; S500, using an unfolding algorithm framework to construct an interpretable sparse optimization unfolding network; S600, using the interpretable sparse optimization unfolding network to identify the health state of a test machine; the application embeds a Q-adjusted wavelet transform into a sparse optimization model, and uses the multi-scale characteristics and wavelet dictionary structure for sparse feature representation, so that accurate capture of key state information can be realized in the feature extraction stage.

The invention discloses a robot path planning method based on an adaptive fuzzy and hybrid strategy, and belongs to the technical field of robot motion planning, and the method comprises the steps: carrying out environment perception, and obtaining environment information; the method comprises the following steps: on the basis of an RRT-Connect algorithm framework, fusing a fuzzy control theory, a hierarchical hybrid expansion mechanism and a dynamic path optimization thought to obtain an improved RRT-Connect algorithm; and an improved RRT-Connect algorithm is utilized, and robot path planning is realized based on environment information. According to the method, the fuzzy control theory is utilized to endow the path planning process with a macroscopic intelligent decision-making capability, and a microscopic accurate execution and fault-tolerant capability is provided through a hierarchical mixed strategy, so that the method is an efficient and robust universal solution; the success rate, efficiency and path quality of path planning of the robot in a complex high-dimensional space are remarkably improved, and more efficient and better collision-free path generation can be achieved.

The application provides an IRS transmission power optimization method based on a quasi-affine transformation evolution algorithm, and comprises the following steps: step 1, system model construction and parameter initialization; step 2, channel modeling; step 3, constructing a target for minimizing transmission power while meeting the constraint condition of the signal-to-noise ratio of all users; step 4, searching by using a quasi-affine evolution-based meta-heuristic algorithm; and step 5, outputting an IRS phase shift vector and an AP beamforming vector matrix. The application has the beneficial effect of improving the quasi-affine transformation evolution algorithm framework, aiming to overcome the problems of the traditional optimization method, such as the dramatic increase in the calculation complexity under the condition of a large number of IRS unit numbers N and user numbers K, and the defects of the existing heuristic algorithm (such as the particle swarm optimization) such as being prone to local optimization and low search efficiency.

The invention relates to the field of human resource management, in particular to a human resource management method based on multi-modal data fusion and adaptive learning, and adopts the technical scheme that the method comprises the following steps: constructing a time sequence data portrait of a target object through static attribute data, dynamic behavior data and external environment data; and inputting the multi-modal data into a preset feature extraction model for feature extraction to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a pre-trained human resource analysis model to obtain an analysis result, and finally executing a corresponding human resource management operation according to the analysis result. The personnel and post matching degree score, the demission risk probability and the recommended development path are analyzed, intelligent resume screening and personnel and post matching optimization can be carried out, employee demission risk prediction and intervention suggestion can be carried out, personalized employee development path planning can be carried out, and the employee development path planning efficiency is improved. And deep learning and dynamic optimization capabilities are improved through an algorithm framework of multi-modal data fusion and dynamic adaptive learning.

The invention discloses a risk electric appliance identification online continuous updating method based on an improved aTLAS algorithm, and the method comprises the steps: 1) carrying out risk electric appliance characteristic analysis and task vector initialization, constructing a risk electric appliance characteristic library, and generating an initial task vector library; 2) constructing a general learning algorithm framework, generating a combined model by the initial task vector library through electric power fingerprint-oriented anisotropic scaling, and outputting a final prediction result through a decoupling head prediction structure; 3) realizing online continuous updating of the identification model, and dynamically updating the task vector library through a new sample detection and anomaly detection module; and 4) edge deployment and real-time reasoning are carried out, and when a new risk electric appliance is detected, the task vector library is updated, and dynamic iteration of the model is realized. According to the method, high-precision identification and dynamic self-adaptive evolution of risk electric appliances are realized, memory occupation and evolution time are remarkably reduced, and edge equipment deployment requirements are met.

The application belongs to the technical field of neural network simulation and analog, and particularly relates to a large-scale cortex pulse neural network asynchronous parallel simulation algorithm based on GPU. The application utilizes the advantages of multi-thread and texture memory of a computing graphics card, combines the general form of a biological brain receiving external stimulation and the general connection mode between neurons in the cortex, designs an asynchronous parallel algorithm framework of GPU and CPU, GPU is responsible for parallel evolution of neuron dynamics equations and block parallel calculation of isotropic connection in a local network, CPU is responsible for processing anisotropic long-range connection, and different neuron dynamics equations and plasticity learning rules can be compatible. Compared with the prior art, the application can effectively improve simulation speed, provides a tool for simulating a biological brain cortex in a single computing node, and is suitable for a single node multi-graphics card and a multi-node multi-graphics card distributed operation model.

The invention discloses a target detection algorithm based on stable learning, and relates to the technical field of computer vision, and the algorithm comprises the following steps: S1, constructing an algorithm framework; s2, data preprocessing; s3, feature extraction; s4, stable learning; and S5, target prediction. According to the target detection algorithm based on stable learning, the weight of the training sample is dynamically adjusted through the stable learning module, the dependence of the model on irrelevant features is reduced, and the influence of data noise and abnormal values on model training is reduced, so that the model is more stable in the training process, and the convergence speed is higher; the algorithm of the invention can effectively identify and utilize key features really related to labels, improves the adaptability of the model to different data distributions, and can still maintain high detection precision and significantly enhance generalization ability under the condition that the distribution of training data and the distribution of test data are different.

The invention discloses a multi-unmanned aerial vehicle cooperative task unloading and trajectory optimization method, and relates to the field of vehicle networking and unmanned aerial vehicle cooperative computing. According to the invention, the space-time attention mechanism of Transform is combined with a multi-agent depth deterministic strategy algorithm framework, and intelligent collaboration and dynamic optimization of an unmanned aerial vehicle group are realized from three aspects of system architecture, feature modeling and decision strategy. The method is composed of four main stages: system architecture construction, feature modeling and state characterization, strategy generation and decision execution, and model training and parameter updating. A closed loop is formed among the four stages, and the whole-process collaboration from environment perception to intelligent decision-making to strategy optimization is realized step by step.

This invention provides an automatic solution method and system for job scheduling problems based on a multi-agent framework. The method includes: acquiring a natural language description of the job scheduling problem and loading functions from a preset heuristic algorithm framework; calling a manager agent to parse the natural language description to obtain specific constraints and objectives, and identifying the target function to be modified in the heuristic algorithm framework based on the specific constraints and objectives; calling a code generation agent to parse the functional description of the target function and the specific constraints and objectives to generate reconstructed function code; calling a code detection agent to verify the reconstructed function code; if the verification fails, calling a code modification agent to modify the reconstructed function code, and calling the code detection agent again to verify the modified reconstructed function code, until the reconstructed function code passes verification; if the verification passes, reconstructing the heuristic algorithm framework based on the reconstructed function code, and executing each function in the reconstructed heuristic algorithm framework to output a job scheduling scheme.

This invention discloses a method for processing error data of smart energy meters based on edge computing, belonging to the technical field of error data processing methods. The invention includes: S1, calculating the mean and standard deviation of the total energy increment sequence within a sliding window, defining a first-order autoregressive hysteresis correction term, setting an adaptive threshold, defining the state machine system state, and finding the computer increment sequence during inactive periods by constructing an inactive period criterion; S2, storing the filtered effective data increments, calculating the energy increment of each sub-meter, defining a continuous zero increment detection function, collecting effective rearranged data points by setting the meter failure detection window and parameters, and constructing an observation matrix X and an observation vector Y; S3, using an improved genetic optimization algorithm framework, calculating eigenvalues ​​and condition numbers, and iteratively obtaining optimal and distinct individuals; S4, determining the optimal regularization parameters using the L-curve method, and calculating the error coefficients of each energy meter using improved Tikhonov regularization.

The invention discloses an MO-KTO law model enhancement method, device and equipment and a storage medium, and the method comprises the steps: obtaining a plurality of law-related optimization targets, and generating a multi-bit binary signal tag according to the performance of an original law text sample in each optimization target; inputting the law big language model, the multi-bit binary signal data labeled according to the multi-bit binary signal label and each optimization target into an MO-KTO algorithm framework to obtain training process data and an optimized target law big language model; performing multi-dimensional capability evaluation on the target law big language model by using a preset law test set to obtain performance indexes under each optimization target, and judging whether the target law big language model is successfully enhanced or not; performance reduction caused by target conflicts in traditional multi-target reinforcement learning can be effectively avoided; it is ensured that key law dimensions are remarkably improved in a balanced mode, and therefore it is accurately verified that enhancement success of the target law large language model is achieved.

The invention discloses an offline reinforcement learning data enhancement method and device based on uncertainty guide diffusion, and belongs to the technical field of reinforcement learning. The method comprises the following steps: acquiring an offline data set; training a diffusion model to learn off-line data distribution; constructing a guide buffer area in a strategy training process, and storing a high-uncertainty state-action pair; the training classifier distinguishes the line data and the guide buffer area data; a classifier gradient is introduced in the diffusion generation process to serve as a guide item, and synthetic data is generated; and mixing the synthetic data with the original data for strategy training. The device comprises an offline data storage module, a diffusion model generation module, a strategy training module, an uncertainty estimation and guide buffer module, a classifier training module and a data mixing module. According to the method, through an uncertainty guide diffusion mechanism, on the premise that an original algorithm framework is not changed, the problem of Q value over-estimation caused by distribution offset is remarkably relieved, and the generalization performance and the training stability of the strategy are improved.

The invention discloses a porosity prediction method and device for a clastic rock reservoir, equipment and a medium, and relates to the technical field of oil and gas reservoir prediction, data cleaning, abnormal value detection and processing and feature engineering processing are performed on logging effective sample data of the clastic rock reservoir, and a sample data set is obtained; constructing a porosity prediction model based on a deep learning algorithm and an integrated learning algorithm; dividing the sample data set by using a weighted integrated multi-algorithm framework, and performing data preprocessing on the divided data set to obtain a target data set; performing model training on the porosity prediction model based on a programming language framework, and performing model testing and performance evaluation on the trained porosity prediction model; taking the porosity prediction model passing the performance evaluation as a target porosity prediction model; and inputting the target well section data into the target porosity prediction model, and outputting a porosity prediction value, thereby improving the precision and stability of porosity prediction, and solving the problem that a complex nonlinear relationship cannot be effectively processed.

The application discloses a kind of SLAM algorithm based on probability depth estimation and geometric feature optimization.The method uses an improved SLAM algorithm EMDE-LVISAM, through the basis of LVI-SAM algorithm framework, introduces PCA principal component analysis technology in laser radar front end, and the feature of point cloud is reduced dimension and screened, and the plane and edge features more with geometric representation are extracted by eliminating redundant noise;In the visual front end, the depth of visual feature point is estimated by using Gaussian mixture model and expectation maximization algorithm, and the hard matching error of traditional method in depth correlation is solved.The experimental results show that the algorithm has optimal performance in the key accuracy indicators such as the sum of squares of absolute pose error, the root mean square error of absolute pose error and the standard deviation of absolute pose error.

The invention provides an IRS transmitting power optimization method based on a quasi-affine transformation evolutionary algorithm. The IRS transmitting power optimization method comprises the following steps: step 1, constructing a system model and initializing parameters; step 2, channel modeling; step 3, a construction target is minimum transmitting power, and constraint conditions of signal-to-noise ratios of all users are met at the same time; step 4, searching by using a meta-heuristic algorithm based on quasi-affine evolution; and step 5, outputting an IRS phase shift vector and an AP beam forming vector matrix. The method has the advantages that improvement is carried out based on a quasi-affine transformation evolution algorithm framework, and the method aims at solving the problems that in a traditional optimization method, under the condition that the high-dimensional IRS unit number N and the user number K are large, the calculation complexity is sharply increased, and an existing heuristic algorithm (such as particle swarm optimization) is prone to falling into local optimum and low in search efficiency.

The application discloses a kind of collaborative filtering methods based on logic box embedding inference, comprising the following steps: S1, original data is collected and data set is made;S2, original data is cleaned and handled, and user interaction sequence and score sequence are generated;S3, user interaction sequence and score sequence generate first-order logic query, and then generate logic calculation graph;S4, user interaction sequence generates multiple item box embedding, and score sequence generates multiple relationship vector embedding according to user interaction behavior and score in logic calculation graph;S5, item box embedding and relationship vector embedding are calculated according to logic calculation graph to obtain a target item box embedding;S6, the distance between target item box embedding and the nearest item box embedding is calculated, and the nearest n items are recommended to the current user as the most suitable project.This method can effectively integrate the recommendation algorithm framework of collaborative filtering and logical reasoning, effectively improve the performance of recommendation, and provide a new idea for subsequent engineering application.

This invention discloses a quantum circuit segmentation method and system based on a two-layer algorithm. The method employs a two-layer optimization architecture: the outer layer constructs an algorithm framework based on a genetic algorithm, using the qubit sequence as chromosomes, dividing it into fixed segments according to hardware capacity constraints, generating an initial population, and performing genetic operations; the inner layer constructs an algorithm framework based on a greedy algorithm, simulating the quantum circuit execution process through a dual-priority decision rule, calculating the minimum number of quantum teleportations and the fitness value required for the segmentation scheme. The inner and outer layers iterate collaboratively until the termination condition is met, outputting the optimal quantum circuit segmentation and quantum teleportation scheme. This invention achieves global search through the outer layer genetic algorithm and precise evaluation through the inner layer greedy algorithm, effectively avoiding local optima defects and significantly reducing the number of quantum teleportations while satisfying hardware capacity constraints.

The invention relates to an automatic code generation method, which comprises the following steps of: receiving a demand document, and analyzing and obtaining a functional demand; decomposing the functional demand to obtain a business process; analyzing the business process to obtain a key scene; analyzing the key scene to obtain a plurality of basic functions, setting the basic functions as nodes in a graph database mode, and setting data interaction among the basic functions as edges; based on the graph database, constructing a pseudo code, and describing an algorithm framework and a data IO dependency relationship; displaying a pseudo code constructed based on the graph database in a visual mode, annotating each node and edge, and marking the nodes and edges as feasible algorithm logic and doubt algorithm logic; and the suspicious algorithm logic is manually corrected. According to the method, the codes needed by the user are generated in a staged and self-adaptive mode in a multi-module combination mode, and after the codes are automatically generated, whether the codes can be executed or not is determined in combination with virtual environment simulation running.

The application discloses a neural network topology structure mapping method for a many-core architecture, uses a four-step algorithm framework based on scale reduction, preliminary segmentation, scale expansion and mapping scheme construction, saves the topology structure to a file system, and applies a graph partition algorithm and a force guiding algorithm, so that memory occupation during compilation of the topology structure of a large-scale neural network is greatly reduced, and the range of the neural network that can be deployed to a neural computing chip is expanded. Meanwhile, by using a heuristic algorithm specific to a mapping problem, the number of iterations and running time are greatly reduced, the compilation efficiency is improved, and the quality of the compilation result is ensured.

The invention relates to the technical field of intelligent power grid communication, in particular to an edge computing power grid time delay optimization method based on reinforcement learning, which comprises the following steps: data acquisition and preprocessing: multi-target time delay optimization modeling: establishing a data transmission model, a time delay model and constraint conditions; the joint optimization of the task unloading proportion and the transmitting power is converted into a quantifiable optimization problem; multi-objective optimization solution based on deep reinforcement learning: adopting a deep deterministic strategy gradient (DDPG) algorithm framework, and learning an optimal task allocation and power allocation strategy through interaction with the environment; and outputting and executing an optimization decision. The method has the advantages that the task unloading proportion and the transmitting power of each link can be jointly optimized under a unified model, and compared with a scheme which only aims at a single index or adopts a simple weighted summation mode, the method is beneficial to reducing the total time delay of the system on the premise of meeting constraint conditions, gives consideration to power consumption, and improves the reliability of the system. Therefore, the comprehensive service quality in the edge computing power grid scene is improved.

The application discloses a momentum-based data evaluation method and system and a storage medium. Streaming data commonly used in machine learning is input; the contribution of data samples to a classification model is determined according to the change in momentum, wherein the momentum adopts a heavy ball momentum algorithm, and the change value of the momentum is calculated according to two adjacent iterations; the original data samples are dynamically adjusted, the data samples are evaluated according to the change value of the momentum, a threshold value is set, the current iteration input data samples and previous data samples are weighted to form a new data sample set; the model determines whether the classification is correct according to the label and outputs the classification result. The application is more suitable for an algorithm framework in a deep learning background, can effectively evaluate the redundancy and imbalance of data, can effectively evaluate text, video, voice and other training sample data under the condition of limited resources, and the performance of the momentum algorithm can still surpass SGD under the condition that the data set is unbalanced.