4 results about "Re optimization" patented technology

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 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.