341 results about "Algorithm design" patented technology

The invention discloses a load prediction-based solid state disk power consumption optimization method and system, and particularly relates to the technical field of solid state disks, and the method comprises the following steps: S1, data acquisition; s2, constructing and training a load prediction model; s3, load prediction; s4, power consumption optimization: optimizing the power consumption of the solid state disk by adopting a multi-objective optimized dynamic power consumption adjustment algorithm according to a load prediction result; and S5, feedback adjustment: monitoring actual power consumption and performance parameters of the solid state disk after optimization in real time, and adjusting the load prediction model and the dynamic power consumption adjustment algorithm in combination with a reinforcement learning algorithm. According to the solid state disk power consumption optimization method and system based on load prediction, a set of complete and efficient solid state disk power consumption optimization scheme is formed from load prediction model construction, dynamic power consumption adjustment algorithm design and feedback adjustment mechanism optimization; and a new technical thought and a new practice direction are expected to be provided for reducing energy consumption and improving performance of the solid state disk.

The invention relates to the field of automatic algorithm design, in particular to an automatic algorithm design method for solving a vehicle path planning problem by utilizing a large language model, which comprises the following steps of: S1, constructing an AutoDH framework, and defining a decision form of an intelligent agent; s2, defining heuristics in an LLM pool, an improvement pool and a disturbance pool; s3, acquiring state information of the CVRPs, and intelligently selecting a heuristic mode through two-stage MDP; s4, designing a reward mechanism fusing solution quality improvement, heuristic time cost and LLMs API calling cost; and S5, training the intelligent agent to optimize the solution of the CVRPs. The method can intelligently select the most cost-effective heuristic algorithm according to the state information of the current CVRPs, and improves the flexibility and adaptability of algorithm design.

The invention relates to a double-layer double-objective optimization scheduling method based on a large language model, and the method comprises the steps: building a double-layer double-objective optimization model in a server, and building an upper-layer model and a lower-layer model for an AISTS problem based on a plurality of different variables, different constraint conditions and different objective functions; and prompting a dual-objective optimization model of adjustment and algorithm evolution to form a two-stage method based on LLM, the method can overcome the defects that a traditional algorithm needs to design the algorithm manually and adjust parameters manually, the calculation cost is high, the efficiency is low, complex problems are difficult to effectively decompose and coordinate double-layer optimization, a dynamic collaborative optimization framework is lacked, and the flexibility of algorithm combination is low.

The invention discloses a large language model enhanced reinforcement learning training method for key automatic driving scene stable decision, and the method comprises the steps: constructing an LLM-based vehicle decision intelligent agent, and providing a high-level guidance strategy through a chain thinking technology and a knowledge experience pool; then, jointly constructing a dynamic intervention mechanism based on the environmental risk sign and the decision boundary parameter, thereby determining an LLM guidance opportunity; finally, through expert guidance algorithm design, the prior knowledge of the LLM is effectively integrated into the deep reinforcement learning strategy network by adopting an adaptive exploration-imitation fusion loss function. The DRL model is better in performance in a vehicle driving decision-making task and shows higher generalization ability in various scenes, and the robustness, the sample efficiency and the overall generalization level of the DRL model can be remarkably improved.

The invention relates to the technical field of data processing, in particular to an intelligent storage space optimization method based on a digital twin technology. The method comprises the following steps: collecting original data, and preprocessing the original data to obtain preprocessed data; constructing a digital twinborn model based on the preprocessed data; based on a digital twin model, a self-adaptive probability space distribution optimization algorithm under multi-factor guidance is introduced, a storage space layout is designed, and the storage space layout is dynamically adjusted in combination with original cargo data. The technical problems that a traditional storage space layout mode is low in storage space utilization rate and unbalanced in resource allocation due to the fact that the accuracy of processing and analyzing storage environment, goods information and the like is low are solved.

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention relates to the technical field of digital twinning, particularly provides a digital twinning construction method based on multi-source point cloud data fusion, and solves the problems of low multi-source point cloud data fusion precision, low calculation efficiency and poor adaptability to complex scenes in the prior art. The method comprises the following steps: S1, preprocessing multi-source point cloud data: carrying out data denoising and coordinate unified processing on the point cloud data of each source; s2, designing a point cloud data fusion algorithm: performing feature extraction and description, feature matching and fusion decision and weighted fusion calculation on the preprocessed point cloud data to obtain fused point cloud data; s3, constructing and optimizing a digital twinborn model: performing triangular mesh generation, model optimization processing, texture mapping and attribute addition on the fused point cloud data, and constructing the digital twinborn model; the digital twinning construction method based on multi-source point cloud data fusion can effectively solve the problems that multi-source point cloud data fusion is low in precision, low in calculation efficiency and poor in adaptability to complex scenes.

According to the invention, a planning algorithm design fusing intention and target trajectory prediction is realized, and the state information of the vehicle under the Frenet coordinate system is obtained in real time through mutual conversion between the Cartesian coordinate system and the Frenet coordinate system; determining a driving reference line of the vehicle by using the global path, and predicting a driving track of the dynamic obstacle by fusing a driving intention and a target track prediction network model; planning a track and a speed by adopting a dynamic planning and quadratic planning algorithm; and integrating the tracks of the current frame and the planning frame through a polynomial splicing technology, and transmitting the integrated track to a track tracker.

The invention discloses an intelligent heuristic algorithm generation method for unmanned aerial vehicle routing inspection path planning, belongs to the technical field of unmanned aerial vehicle autonomous navigation and intelligent optimization, and aims to plan a routing inspection path with the total cost as low as possible on a building routing inspection three-dimensional point set for an unmanned aerial vehicle. Aiming at the problems of high path planning complexity and low efficiency of artificial design of a heuristic algorithm in a building coverage inspection task, the method comprises the following steps: firstly, constructing a constraint model of an unmanned aerial vehicle inspection scene, and converting multi-objective optimization requirements such as building coverage rate, total detection time and flight path length into a heuristic algorithm design problem; compared with a traditional manual design method, the method enables the heuristic algorithm to reach or exceed the design level of field experts on indexes such as building coverage integrity, path length and calculation efficiency, and provides an efficient path planning solution for intelligent inspection of the unmanned aerial vehicle.

The invention provides a seed screening method and system based on a laser radar technology, and relates to the technical field of agricultural automation, and the method comprises the steps: extracting geometric features and defect features from optimized point cloud data; extracting mildew and insect pest spectral features from the optimized spectral data; a multi-dimensional feature set is obtained; calculating seed plumpness, roundness and surface roughness based on the optimized point cloud data; calculating a sag index and a crack feature based on point cloud edge detection; fusing the geometric features, the defect features and the pest spectral features to generate a comprehensive score; a machine learning model is utilized to execute final grading, and a first probability predictor based on laser radar features and a second probability predictor based on pest spectral features are trained respectively; and inputting the probability outputs of the two predictors into a second-layer grading model of the meta-learning architecture, and outputting a final quality grade. According to the method, efficient and accurate seed screening is realized through automatic process and algorithm design.

The invention discloses an online programming system based on large language model assistance and a teaching recommendation method, the system comprises a base model acquisition module, a problem decomposition module, an abstract modeling module, an algorithm design module, a code analysis and assistance module and an evaluation module, and is used for helping students to finish a programming task of teaching design online. And performing problem decomposition, abstract modeling and algorithm design on actual problems by using a large language model, and solving the problems encountered by students during programming. Based on the system, the invention further designs a teaching recommendation method, multi-modal data in the information classroom teaching process is collected, a dynamic three-level teaching interaction diagram of the space-time environment, knowledge resources and cognitive behaviors is established through man-machine, life and teacher-student interaction behaviors, and accurate teaching recommendation and intervention strategies of the human-in-the-loop are provided. According to the invention, a three-dimensional comprehensive teaching field for learning environment intellectual computing is established in a programming scene, and a guarantee is provided for high-quality modern education development.

The invention relates to the technical field of power system court management, and particularly discloses a court identification and user binding method based on a special transformer acquisition terminal, which comprises the following steps: separating harmonic interference generated by start and stop of an electric welding machine through a kurtosis index and time-frequency energy entropy fusion detection method, and extracting a fundamental voltage and a pure power signal; constructing a dynamic time bending model containing phase consistency and gradient feature constraint based on the fundamental wave voltage, and calculating voltage track similarity to judge electrical coupling; for the users with electrical coupling, dynamic weight power cross-correlation analysis is fused to generate a comprehensive decision value, and a space-time diagram neural network is utilized to update a transformer area topology structure online in combination with a historical topology change event, so that the dynamic determination of the transformer area to which the users belong is realized; through multi-dimensional feature fusion and anti-interference algorithm design, the problems of phase deviation and waveform distortion caused by electric welding machine harmonic waves are reduced, and the transformer area identification accuracy and real-time performance are improved.

The invention discloses a random access preamble and detection algorithm design based on low earth orbit satellite communication. Specifically, firstly, a novel lead code with a plurality of subsequences is provided, in order to enlarge the number of available lead codes in a wave beam, a ZCM sequence is introduced to form the subsequences, and orthogonal frequency division multiplexing is adopted for modulation and is sent to a channel. Subsequently, a timing advance (TA) detection algorithm is proposed. The first sub-sequence is used for decimal time delay detection, and the other sub-sequences are used for integer time delay detection. In addition, the peak detection ingeniously utilizes the variance of the power delay spectrum, so that the detection performance is improved. Finally, a simulation result shows that the lead code provided by the invention is almost not influenced by frequency offset, does not need pre-compensation, and is suitable for being applied to low-orbit satellite communication with relatively large frequency offset.

The invention discloses a ChatGPT-python programming method, particularly relates to the field of artificial intelligence, and comprises three steps of problem definition, algorithm design and code implementation. According to the method, project situations are described in detail through question definition, and ChatGPT is guided to generate project schematic diagrams; through a design algorithm, a calculation problem is elaborated, and ChatGPT is required to provide an effective solution; by implementing codes, generating a feasible programming solution according to a calculation theory, providing intelligent suggestions in each process, ensuring theoretical accuracy and engineering feasibility of a result through manual examination, and comparing by taking three-dimensional slope stability analysis as a case and combining with a calculation result of commercial software, a calculation result of the three-dimensional slope stability analysis is obtained. It is verified that the Python algorithm obtained through ChatGPT programming has good calculation precision, an innovative auxiliary means is provided for engineering calculation, and the huge application prospect of artificial intelligence in the field of civil engineering is shown.

The invention belongs to the technical field of data processing, and discloses a book personalized recommendation method based on big data analysis. The recommendation method comprises the following specific steps: S1: data integration and cleaning; S2: data storage and index optimization; S3: personalized recommendation algorithm design; S4: keyword interaction mechanism; according to the invention, through deep cooperation of data storage optimization and recommendation algorithm design, double breakthrough of performance and precision is realized; a B + tree joint index of MySQL enables a collaborative filtering algorithm to obtain a user borrowing sequence at a millisecond level and support time decay weighted calculation, and a MongoDB composite index compresses response time of community theme extension recommendation, so that recommendation generation time consumption is reduced through cooperation of the B + tree joint index and the MongoDB composite index, and a concurrence scene of thousands of people can be easily coped with.

The invention discloses an intelligent verbal skill process configuration system and method based on a dynamic rule engine, and the system comprises a configuration analysis module, a process engine module, an execution engine module and a large model intention recognition module. The problems of rule solidification, high maintenance cost and low intention recognition accuracy in existing verbal skill process configuration are solved. The configuration analysis module analyzes a JSON format verbal skill rule configuration file to realize visual definition and dynamic update of the rule; the process engine module completes rule instantiation through variable replacement and drives dynamic routing based on an intention recognition result; the execution engine module instantiates a service class by using a reflection mechanism to realize decoupling of rules and service logic; the large model intention recognition module innovatively adopts a semantic similarity weighting algorithm, and intention recognition accuracy is remarkably improved by calculating semantic vector cosine similarity of user input and branch intention and combining service weight and context correlation degree.

The invention relates to the field of power dispatching systems, in particular to a user power consumption behavior prediction and intelligent dispatching system based on a deep learning model, which comprises a multi-source data acquisition and preprocessing module, a deep fusion prediction model and an intelligent dispatching optimization algorithm module, and integrates the modules or models into a unified system platform through a micro-service architecture. The system platform is in real-time butt joint with a power grid monitoring and dispatching center of an electric power company; and the intelligent scheduling optimization algorithm module establishes a multi-objective optimization model by taking load balance of the power system, maximization of power utilization satisfaction of users and minimization of energy consumption as comprehensive optimization objectives. The invention aims to provide a user power consumption behavior prediction and intelligent scheduling system based on a deep learning model, the user power consumption behavior is accurately predicted through innovative model construction and algorithm design, the optimized intelligent scheduling of power resources is realized on the basis, and the overall operation efficiency and the energy utilization efficiency of a power system are improved.

The invention relates to the technical field of computer science and industrial engineering, in particular to a tool workshop scheduling method, which comprises the following steps of: acquiring production characteristics of a current tool workshop, generating an optimal batch scheduling strategy according to the production characteristics of the current tool workshop based on a preset evolutionary strategy, and automatically designing an algorithm by utilizing a preset large model, and calculating the minimum completion time and the maximum completion time of the optimal batch scheduling strategy, and completing tool workshop scheduling according to the minimum completion time and the maximum completion time based on the optimal batch scheduling strategy. Therefore, the problems of high workshop scheduling complexity and the like caused by strong coupling of batch division and working procedure sorting, strict constraint between working procedures and high resource networking degree in workshop scheduling are solved, an automatic algorithm design framework based on a large model is introduced, the expert algorithm design time is shortened, and the workshop scheduling efficiency is improved. Therefore, the efficiency and feasibility of aircraft tooling production scheduling are improved.

The invention relates to the technical field of air temperature risk assessment, in particular to an AdaBoost-based urban air temperature inference and thermal exposure risk assessment method. The method comprises the following steps: acquiring original city form data and city meteorological data of a city; performing data preprocessing on the original city form and the city meteorological data of the city to generate processed original city form data and city meteorological data; integrating the processed original city form data and city meteorological data into a model training set and a model test set; and designing an artificial neural network architecture by adopting an AdaBoost algorithm, and performing model training on the model training set by utilizing the artificial neural network architecture to generate an urban air temperature inference pre-model. According to the method, by fusing multi-source data, applying an advanced machine learning algorithm and enhancing sensitivity analysis and Monte Carlo simulation, the accuracy and reliability of urban air temperature inference and thermal exposure risk assessment are improved.

The invention provides a vehicle control method and device, electronic equipment and a medium, and relates to the technical field of automatic driving, and the method comprises the steps: constructing a three-part loss function comprising a decision variable, a state variable and a control variable, and adding the outputs of the three parts in each time step to obtain a first sum value, the first sum values of the multiple time steps are accumulated to form a unified target sum value, a driving variable sequence for minimizing the target sum value is jointly solved under the constraint of a dynamics mathematical model of the first vehicle and a first constraint condition, and finally the vehicle is directly controlled through the optimal target driving variable sequence. Therefore, a coupling channel of discrete decision making (namely lane decision making) and continuous trajectory planning is effectively opened, and the lane decision making and trajectory generation are synchronized and coordinated under the same optimization target, so that the safety, the driving efficiency and the driving comfort of the automatic driving vehicle in a complex traffic scene are improved; the overall driving performance better than that of a traditional split algorithm design is achieved.

The invention relates to the technical field of intelligent traffic, and discloses an intelligent traffic jam real-time optimization method based on artificial intelligence. The method comprises the following steps: firstly, acquiring system operation state parameters and dynamic regulation and control parameters, defining action environment elements, and formulating core optimization parameters in combination with application scene features and existing bottleneck information; scheduling model training data and algorithm design data, determining a traffic flow regulation and control topology, and evaluating a collaborative adaptation effect to determine a parameter priority sequence; then querying a related algorithm to simulate and construct an optimization prototype, analyzing strategy regulation and control characteristics, calculating an efficiency prediction equivalent value to evaluate performance stability, determining regulation and control node requirements and operation condition constraints, and analyzing adaptation compatibility; and an optimal optimization algorithm is screened in combination with factors in multiple aspects, and an optimization implementation scheme is generated. The method can improve the accuracy and real-time performance of traffic congestion optimization, is suitable for different traffic scenes, and effectively alleviates congestion.

The invention relates to the field of deep learning, and discloses a multi-agent collaborative task state embedding method based on a multi-scale hypergraph and multi-dimensional aggregation. In order to solve the problems that an existing graph neural network is difficult to capture a high-order interaction relation, poor in dynamic environment adaptability and limited in communication, the method comprises the steps of constructing a state observation graph; calculating an adjacent matrix according to explicit states such as position and speed; generating a latent layer feature adjacency matrix through nonlinear conversion and similarity calculation of a graph convolutional network, and fusing the latent layer feature adjacency matrix with the Hadamard product of the interactive graph; constructing a multi-scale hypergraph (containing S scales) according to the latent layer matrix, and searching a high-density sub-matrix to form hyperedges; building a two-stage information aggregation model: integrating multi-dimensional features and calculating association degree and interaction types in a hyperedge aggregation stage, and updating node features by using a graph attention network GAT in a node aggregation stage; a multi-agent soft behavior-commentator algorithm MASAC is fused, a behavior and reward function is designed, and an MHGNN-MASAC model is formed; the method is applied to cooperative control task decision.

The invention provides a radio frequency device hyper-parameter automatic optimization algorithm library. The radio frequency device hyper-parameter automatic optimization algorithm library comprises an interface module, a directional optimization algorithm module and a multi-modal optimization engine module, according to the optimization algorithm library, the automation level and the performance optimization efficiency of radio frequency device design are remarkably improved by constructing a domain knowledge graph covering six core devices including a radio frequency filter, a power amplifier, a numerical control attenuator and a low-noise amplifier and combining a customized optimization algorithm and a cross-level cooperation strategy. On the algorithm design level, aiming at the specific characteristics of the device, different hybrid optimization strategies are adopted, the global search capability of a genetic algorithm and a particle swarm optimization algorithm and the local optimization advantage of a gradient descent method are combined, and dynamic weight adjustment is matched, so that the targeted radio frequency device optimization process design is realized. According to the optimization algorithm library, through an independent calling interface for packaging the automatic design algorithm, the use difficulty of the automatic design algorithm is simplified, and the optimization process is normalized.

The invention discloses a port field knowledge graph automatic construction method based on cooperation of multiple intelligent Agents, and the method comprises the steps: processing structured data, semi-structured data and non-structured data of a port field through intelligent Agents with an autonomous decision-making capability, and achieving the entity recognition, relation extraction and knowledge fusion; a plurality of intelligent Agents with the cooperation function are used for executing knowledge discovery, new knowledge verification, conflict detection, knowledge fusion and graph updating tasks respectively, cooperative communication among the intelligent Agents is achieved through a message queue, and dynamic evolution of a knowledge graph is completed; processing the knowledge graph by adopting a customized knowledge graph embedding method and a semantic fusion algorithm designed for professional terms and knowledge structures in the port field; and storing the processed knowledge graph data based on a distributed architecture, wherein the distributed architecture supports high concurrent processing and real-time response. According to the method, the port domain knowledge graph can be efficiently and automatically constructed.

The invention provides a CF / PEEK milling surface roughness modeling method based on an L1-LSSVR algorithm. The method comprises the steps that S1, static milling process parameters serve as experimental variables, an experimental scheme is designed, dynamic signals in the milling process are collected in the experimental process, and after the experiment is finished, the CF / PEEK three-dimensional surface roughness is measured; s2, time domain features of the dynamic signals are extracted, and the time domain features are used for L1-LSSVR model training after being combined with static milling process parameters; s3, analyzing the correlation between the features through a Pearson's correlation coefficient, and eliminating redundant features with similar influence rules on the roughness of the three-dimensional surface; and performing feature selection by using L1 norm regularization, and screening out features having high correlation with the three-dimensional surface roughness. S4, establishing a CF / PEEK milling surface roughness model by taking the features screened in the step S3 as the input of an LSSVR algorithm and the CF / PEEK milling three-dimensional surface roughness as the output; according to the algorithm designed by the invention, modeling of the milling three-dimensional surface roughness of the CF / PEEK material can be effectively realized.

The invention discloses a reinforcement learning-driven public building user energy consumption behavior simulation method, which comprises the following steps of: 1, acquiring basic data, and building a simulation environment model for simulating a mapping relationship between a user energy consumption behavior and building energy consumption as well as an environment comfort degree; 2, defining a state space and an action space of the intelligent agent, selecting a depth deterministic strategy gradient algorithm as a core learning algorithm of the intelligent agent, designing a multi-target comprehensive reward function of the core learning algorithm, and embedding a public building energy consumption control target; step 3, using an intelligent agent to interact with a simulation environment, simulating an energy consumption process of a user, and identifying different scenes according to a preset rule; 4, the intelligent agent iteratively updates network parameters in the core learning algorithm based on the number of samples collected in the interaction process, and after training convergence, a user energy consumption behavior optimization scheme is output.

The invention relates to a burn treatment path planning technology, and discloses a burn treatment optimal massage path planning method based on a graph theory and an SAC algorithm. The system comprises a data acquisition and initialization module, a path modeling module, a reinforcement learning module, an optimization and generation module and a closed-loop management module. The data acquisition and initialization module is used for generating accurate point cloud data through a binocular camera in combination with an IMU (Inertial Measurement Unit), and calculating burn average degree and uniformity to provide a basis for subsequent processing; the path modeling module adopts a graph theory method to model a burn area into nodes and edges, and multi-objective optimization is carried out by integrating treatment efficiency and patient comfort; the reinforcement learning module designs a state space, an action space and a reward function based on an SAC algorithm, and realizes optimization of a path and a strength strategy; the optimization and generation module generates a final treatment path through initial exploration and local optimization, and introduces an adaptive force adjustment function to prevent secondary injury; the closed-loop management module supports off-line learning and real-time feedback, and ensures high efficiency and individuation of a therapeutic scheme through dynamic parameter adjustment and report generation. According to the method, the accuracy and efficiency of burn treatment are effectively improved, and the method has wide clinical application potential.

The invention relates to a personnel identification and behavior detection method and device and a security robot, and the method achieves the whole-process intelligent processing from threat perception to precise intervention through a multi-module cooperative working mechanism and an innovative algorithm design. According to the method, the problem of balance between threat detection precision and real-time performance in the prior art is solved, and accurate three-dimensional positioning and tracking of a threat target are realized through effective integration of depth information. According to the method, through technologies of deep learning algorithm optimization, three-dimensional space positioning and the like, the threat detection capability, the response speed and the intervention accuracy of the security robot in a complex environment are comprehensively improved, powerful technical support is provided for high-risk scene safety prevention and control, and the method has remarkable practical value and popularization prospects.

The invention relates to the field of integrated circuit testing, provides an SRAM (Static Random Access Memory) fault testing structure, and particularly relates to an IEEE (Institute of Electrical and Electronic Engineers) 1687 standard and an SRAM self- According to the method, a common fault model of an SRAM (Static Random Access Memory) is analyzed, improvement is performed on the basis of a March C-algorithm, and a March C-pro algorithm capable of covering most common faults of the SRAM is derived; a modularized SRAM self-test structure is designed based on a March C-pro algorithm, so that the design cost caused by built-in self-test of a memory and possible additional interface requirements are avoided; a multi-layer network structure based on the IEEE 1687 standard is designed, access to an embedded instrument is achieved, an SRAM self-test module is connected, and an SRAM test in the SOC is achieved.

The invention belongs to the technical field of satellite navigation and positioning, and particularly relates to an integrated Beidou GNSS intelligent receiver with integrated design, which is suitable for high-precision positioning, real-time monitoring and stable operation in a complex environment. The implementation steps are as follows: step 1, hardware design and assembly; 2, designing an algorithm; 3, designing a working principle; 4, deploying a base station and a mobile station in an actual application scene; and step 5, performance test and optimization.