9 results about "Re optimization" patented technology

The invention discloses a machine room group control multi-objective optimization digital twin platform and an optimization method, and relates to the technical field of machine room group control. Constructing a multi-objective optimization model, wherein the multi-objective optimization model comprises a three-dimensional objective function and constraint conditions; the three-dimensional objective functions comprise an energy consumption minimization function, a temperature and humidity control precision maximization function and an equipment life loss minimization function. Compared with a traditional static twinborn model, the method has the advantages that the deviation is greatly reduced, and a precise virtual-real mapping basis is provided for optimization decision making. According to the whale optimization algorithm, the global search capability and the NSGA-IIPareto solution screening capability are fused, the convergence speed of the algorithm is improved through fuzzy membership degree fitness calculation and crowding degree screening, when deviation exceeds a threshold value, optimization can be shortened to 15 seconds, compared with an existing algorithm, the response speed is greatly improved, and the real-time control requirement under the dynamic load of a machine room is met.

This invention discloses a database query optimization method, terminal, and storage medium. The method includes: acquiring table data and a query statement to be executed from a database; inputting features from the table data and query statement into a training model to predict K nodes that need re-optimization; dividing the K nodes according to a partitioning strategy to obtain K+1 execution stages; and sequentially operating on the K+1 execution stages according to a preset execution logic to complete the query optimization. This invention combines a machine learning model with traditional query optimization methods, primarily employing a multi-stage, step-by-step execution of a single SQL statement. It mainly uses Join nodes predicted by the machine learning model to segment the query plan. Then, it further optimizes the plan at different stages using real-time cardinality. This method solves the problem of inaccurate estimation in traditional cardinality estimation methods under multi-table joins, while avoiding the drawbacks of a single machine learning model.

The invention discloses an existing operation station shield receiving construction step sequence optimization method and system based on digital twinning, and the method comprises the steps: collecting the associated data of an existing operation station, constructing a digital twinning basic model, achieving the full-factor digital mapping of entity engineering, and carrying out the parameterization and lightweight optimization. And establishing dynamic association between the model component and the original data. Dynamic monitoring and construction process data are collected in real time and are uniformly accessed through an integrated platform, and a real-time synchronization mechanism of the data and a model is established after preprocessing and screening. Based on the model and integrated data, setting a multi-dimensional optimization constraint, embedding a dynamic optimization algorithm, simulating, deducing and optimizing a construction step sequence by taking safety, high efficiency and low risk as targets, and generating an initial scheme; and when a preset trigger event occurs, re-optimization is carried out, and an adaptive scheme is output. And the scheme is pushed to a construction execution end, an execution state is tracked, actual and preset data deviations are compared, and an early warning mechanism is established. And smooth construction of the existing operation station is ensured.

The application discloses a kind of based on digital twinning existing operation station shield receiving construction step sequence optimization method and system, method includes: collection existing operation station associated data, constructs digital twinning basic model, realizes entity engineering full factor digital mapping, by parameterization and light weight optimization, the dynamic association of model component and original data is established.Model and real-time synchronization mechanism of data are established by real-time acquisition dynamic monitoring and construction process data, unified access through integrated platform, after pre-processing screening.Model and real-time synchronization mechanism of data are established by real-time acquisition dynamic monitoring and construction process data, unified access through integrated platform, after pre-processing screening.The initial scheme is generated by setting multidimensional optimization constraint based on model and integrated data, embedding dynamic optimization algorithm, simulating and optimizing construction step sequence with safety, efficiency, low risk as target;When preset trigger event occurs, then re-optimization, output adaptive scheme.Scheme is pushed to construction execution end, and the execution state is tracked, and the deviation between actual and preset data is compared, and early warning mechanism is established.The existing operation station construction is smoothly promoted.

The invention discloses a dynamic path correction method for an artificial intelligence robot, which comprises the following steps of: 1, generating an initial sequence: receiving initial environment observation O by a correction system, and quickly generating a preliminary complete action sequence A0 = [a10, a20,..., aT0] through an initial predictor; and step 2, iterative optimization circulation: the system does not directly execute the action sequence A0, but sends the action sequence A0 to an iterative optimization module for N rounds of optimization, and N is greater than 1. And step 3, sequence execution: after N rounds of optimization, outputting a final optimization sequence AN, and starting to execute actions in the sequence AN by the robot. According to the method, through a global attention mechanism, an action sequence which is highly consistent and smooth in time can be generated, and motion jitter caused by local decision is fundamentally avoided; the defect that autoregression model sequence generation is irreversible is overcome, and the unexecuted part of the sequence can be re-optimized, so that the unexecuted part can flexibly cope with environmental changes and uncertainty.

This invention discloses a lightweight deep processing method based on BIM, belonging to the field of BIM lightweight technology. It involves constructing the complexity of a target model from the structural data obtained through identification. If the obtained complexity exceeds expectations, a lightweight strategy is provided for the target model based on several model features using a model optimization knowledge graph. The target model is simplified, and the accuracy of the simplified model is analyzed. If the obtained model accuracy exceeds expectations, the data within the target model is compressed. After performance testing of the target model, a performance coefficient is constructed from the test data set. If the obtained performance coefficient is not lower than its previous value, the optimization degree of the target model is constructed. If the optimization degree exceeds expectations, a re-optimization instruction is issued. This completes the lightweight processing of the model, reducing its bloat, maintaining model performance, improving model operating efficiency, and enhancing the efficiency of subsequent work.

The invention provides a laser cladding process dynamic optimization method and system based on NSGA-II and PSO-BPNN cooperation, and belongs to the field of laser additive manufacturing process optimization. The method is characterized in that key process parameters are preliminarily screened through an orthogonal experiment; a BP neural network (PSO-BPNN) optimized by a PSO algorithm is utilized to establish a high-precision nonlinear mapping model between the process parameters and cladding layer quality indexes (aspect ratio, dilution rate and hardness); adopting an NSGA-II algorithm to carry out multi-objective optimization in an initial parameter range; a dynamic range adjustment mechanism is innovatively introduced, when an optimal solution does not meet requirements, the most sensitive technological parameter range of substandard indexes is directionally adjusted based on the range analysis result of an orthogonal experiment, and optimization is carried out again until a satisfactory solution is obtained. According to the method, the problem of repeated experiments caused by improper setting of the initial parameter range in a traditional method is solved, rapid and accurate optimization under a small number of experiments is achieved, and the method is particularly suitable for on-site rapid remanufacturing of large equipment.

The application discloses an autonomous intelligent agent-oriented candidate action safety release and closed-loop updating method and system and a storage medium, and belongs to the technical field of intelligent control and intelligent agent execution management. The method acquires task context data, current state data and risk constraint data, and constructs a current feature vector. According to the similarity with a historical feature library, a candidate action instruction is generated among historical action direct reuse, local optimization and re-optimization solution. The target state after execution is predicted in a boundary checking sandbox, and the minimum distance of the target state to a safety boundary is calculated. When the minimum distance does not meet the safety margin requirement, a correction action instruction is generated according to the action variable sensitivity and checked again. When the checking again still does not meet the release condition, a conservative action instruction is generated. The actual execution result, the state after execution, the candidate action generation path, the boundary checking result and the final release conclusion are written back to the historical feature library and the historical decision cache, so as to update the credibility score, the reuse priority, the cache level and the path selection threshold parameter, and the updated result is used for subsequent candidate action generation path judgment and action release decision. The scheme is helpful for reducing the dependence on artificial review, improving the automatic release efficiency and closed-loop learning ability.

The invention discloses a legal provision intelligent reasoning and comparison method based on a knowledge graph. The method comprises the steps that the legal knowledge graph with legal provisions and concepts as nodes and provision logic relations as edges is constructed; extracting a dynamic logic rule set by using an LLM-DA algorithm; searching a path through an ant colony optimization algorithm by taking the preliminary rule as heuristic information; re-optimizing the dynamic logic rule set according to the path set; searching the path again by utilizing the updating rule set and updating the pheromone value; judging the difference degree of the path set and carrying out iterative optimization; and outputting legal provisions according to the optimized path and rule to intelligently reasone and compare conclusions. According to the invention, accurate dynamic reasoning and comparison of legal provisions are realized, and the accuracy and interpretability of intelligent reasoning are improved.