7 results about "Branch and bound" patented technology

The application discloses a kind of incremental mapping method, equipment and storage medium, the incremental mapping method includes: if it is detected that mobile device is in priori map range outside, stop using priori map to locate, and locate based on the incremental map constructed;If it is detected that mobile device moves from priori map range outside to priori map range, enable priori map to locate, and the environment perception data of current time and priori map are matched based on branch and bound algorithm, and the matching score corresponding to the pose of matching success is obtained;Add monomial edge constraint to the pose corresponding to the environment perception data of matching score greater than preset score threshold;The accumulated error optimization is carried out to the pose calculated using monomial edge constraint, to make incremental map and priori map align. By adding monomial edge matched with priori map to constrain mapping pose, the alignment effect between incremental map and priori map can be improved, to avoid the appearance of map ghosting and fault.

The application discloses a ship deployment and refueling joint optimization method considering multiple uncertainties. The method firstly constructs a nonlinear mixed integer programming deterministic model with the minimum total cost as the target, then introduces random oil prices and berthing times, constructs a robust optimization model containing an uncertainty set, and integrates the two to form a two-stage robust optimization model. The robust optimal ship deployment scheme and fuel injection strategy are obtained by iteratively solving the model through a Benders decomposition algorithm embedded with a branch and bound algorithm. The application constructs an integrated joint decision framework, simultaneously optimizes the ship deployment and fuel management in a unified mathematical model, captures and utilizes the internal relationship between different decisions, avoids the potential huge cost caused by separate decisions, fully considers the influence of the uncertainty of oil prices and port berthing times, makes the decisions have good risk hedging characteristics, fundamentally improves the scientificity and economy of the decisions, and effectively reduces the comprehensive cost of operation.

This invention discloses a production scheduling method for mixed continuous and discrete processes, comprising: acquiring historical, static, and dynamic data of continuous and discrete production lines; determining standard time granules based on the acquired historical data, and calculating batch consumption times for continuous and discrete production lines based on the standard time granules; constructing scheduling constraints based on static data, dynamic data, and batch consumption times, and building a scheduling model with a scheduling objective function; solving the scheduling model based on an improved branch-and-bound algorithm and preset inventory verification constraints to obtain a production line scheduling plan for mixed continuous and discrete processes; discretizing the continuous production line through continuous feature analysis and extraction of the process route to solve the problem of inconsistent time dimensions between continuous and discrete production lines in data modeling; establishing a global constraint model for mixed continuous and discrete production lines to achieve global constraint optimization of scheduling; and using a branch-and-bound algorithm for batch continuous time occupancy processing to achieve high-efficiency solution.

The application discloses a kind of security constrained unit's mixed integer programming joint optimization method, system and equipment, it is related to power system unit combination optimization field, method includes: to the security constrained unit combination optimization model of pre-constructed model processing, obtain mixed integer linear programming model;According to prior rule, a plurality of variables in mixed integer linear programming model is fixed based on two-stage multivariate branch method;Artificial intelligence method is used to obtain solution information in the branch and bound solution process of the multivariate branch method, and the rule of the value of optimal solution and relevant parameter is learned, to predict the value of part of variables of optimal solution.The application is based on artificial intelligence method and large-scale integer programming solution interaction, obtain certain solution information in the branch and bound solution process, and learn the rule of the value of optimal solution and relevant parameter, to predict the value of part of variables of optimal solution, can reduce solution scale, and improve solution efficiency.

The embodiment of the invention provides a boiler heating surface crack detection radar side cloud collaborative task scheduling method and system. The method comprises the following steps: firstly, deconstructing a millimeter wave radar detection process into a plurality of calculation components with a data stream dependency relationship; further constructing a mixed integer nonlinear programming model with the goal of minimizing the total delay and the total energy consumption of the system, wherein model decision variables comprise task allocation positions, edges and cloud computing resources; solving by adopting an optimization algorithm integrating problem relaxation, Monte Carlo robust adjustment, branch and bound and genetic algorithms to obtain an optimal task scheduling and resource allocation strategy; and finally, dynamically deploying each component to an edge or a cloud node for execution according to the strategy. Unification of high precision, low delay and high energy efficiency of a detection task in a complex industrial environment is realized, and the system resource utilization rate and the environmental adaptability are improved.

This invention discloses a method, system, and storage medium for optimal differential and linear feature search in SPN-type cryptography. The invention constructs a lightweight Super S-box differential distribution table (DDTSS) and a linear approximation table (LATSS), respectively. Then, the DDTSS and LATSS are sorted by upper bounds of the differential and linear probabilities, respectively. Following an improved branch-and-bound algorithm, optimal differential and linear features are searched on the DDTSS and LATSS under given pruning conditions. This invention uses stricter pruning conditions to optimize the branch-and-bound search strategy, enabling the acquisition of precise security bounds against differential and linear analysis in SPN-type lightweight cryptography within a shorter time. This invention can be used for security evaluation of SPN-type lightweight cryptography and to assist in the design of lightweight cryptographic algorithms, possessing high practical value and application prospects.

This invention discloses a persistent routing planning method for low-Earth orbit (LEO) satellite communication networks based on mixed-integer linear programming (MILP), belonging to the technical field of LEO satellite network routing planning. The method includes the following steps: Step 1: Define the parameter representation of LEO satellite communication network attribute data, including network nodes, communication link contact windows, etc.; Step 2: Establish a persistent routing planning (MILP) model for LEO satellite communication networks based on communication link contact windows and feasible flow theory, characterizing the end-to-end persistent communication process of communication links through a network flow model; Step 3: Solve the model using a branch-and-bound algorithm to obtain the precise optimal link configuration that maximizes the end-to-end communication duration. This invention overcomes the limitation of heuristic algorithms in terms of solution quality evaluation, providing a reliable persistent routing planning tool for LEO satellite networks and improving the theoretical completeness and engineering applicability of persistent data routing strategies in the highly dynamic environment of low Earth orbit.