63 results about "Branch and bound" patented technology

Lowest cost usage scheduling of an energy system during a time interval of interest is achieved by utilizing two components of learning and optimization. First, a number of learning approaches including Linear Least Square Regression (LLSR), Auto Regressive Integrated Moving Average (ARIMA), and Multi-Layer Perceptron Deep Learning (MLPDL) are used to forecast energy production and storage of the energy producing components of a given group of energy components as a function of historical production data and weather data collected from weather and geo reports. Then, an optimization problem is formulated to create the optimal schedule of energy usage during time intervals of interests for the given group of energy components subject to scheduling and equipment constraints. Two alternative solutions, namely Simplex Branch and Bound (SBB) and Interior Point Branch and Bound (IPBB) are presented to solve the optimization problem. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the cost of energy consumption for the given group of energy components within time intervals of interest.

The invention discloses an ARX password anti-differential and linear analysis security assessment method and system, and relates to the password technology, and the method comprises the steps: respectively constructing a differential distribution table CDDT of dependent carry of modular addition and a linear approximation table CLAT of dependent carry; respectively sorting the CDDT and the CLAT according to the differential probability and the linear probability; after sorting, searching the optimal differential feature and the linear feature of the ARX password based on a branch and bound algorithm; and after all plaintext differences and masks are traversed, obtaining the maximum difference and linear feature probability as the maximum difference and linear feature probability of the ARX password of the corresponding round number. The invention provides an ARX password anti-differential and linear analysis security assessment method based on a carry-dependent differential distribution table and a linear approximation table, and solves the problem of anti-differential and linear analysis security assessment in an ARX password design process.

The invention relates to the technical field of enterprise management, in particular to an enterprise process custom configuration method and system based on a code-free technology, and the method comprises the steps: constructing a multi-layer directed hypergraph containing data, control and condition dependence, calculating an edge weight, and converting the dependence into a first-order logic constraint set; performing incremental satisfiability detection, and outputting a conflict core set containing conflict types and severity in combination with minimum unsatisfiability subset mining and strong connected component analysis; mapping the conflicts into modifiable elements, and generating parameterized candidate repair schemes based on a repair operation library and historical cases; converting the candidate scheme into a weighted constraint optimization problem, and solving an optimal scheme by adopting a branch and bound algorithm; influence propagation verification is completed in the copy, a differential patch and a rollback identifier are generated, transactional submission is carried out after passing, and the weight and the case library are updated. According to the method, automatic diagnosis and low-risk repair of implicit dependency conflicts are realized.

The invention provides a book box with variable partition plates and a boxing method. The method comprises the steps that a book list and an initial box body space are obtained; based on a branch and bound-greedy hybrid optimization algorithm, carrying out space state circulation processing to obtain an optimal space allocation scheme; based on the optimal space distribution scheme, re-planning the space layout, updating the optimal position of the partition plate, and obtaining the sizes of the four divided spaces; and on the basis of the sizes of the four divided spaces, book placement information is obtained, and books are placed in sequence. According to the method, the optimal space distribution scheme of the four partition spaces is obtained through the branch and limit-greedy hybrid optimization algorithm, so that the book placement direction and position are selected, and the residual space is minimum.

The invention belongs to the technical field of new energy power generation, and particularly relates to a multi-energy complementary system capacity double-layer optimization configuration method, device and equipment and a medium. The objective of the invention is to solve the problem that a single-level optimization framework is difficult to coordinate conflicts between a global economy target and a local operation target in the prior art. According to the method, a double-layer optimization model is constructed, and the upper layer optimizes and configures wind power, photovoltaic, energy storage, heat storage and hydrogen storage capacities by taking the maximum net income of the system as a target; and the lower layer takes the minimum system power supply gap as a target, considers the power generation cleanliness and the multi-energy complementary operation constraint, and optimizes the system operation state. In order to efficiently solve the double-layer mixed integer nonlinear programming problem, the invention provides a solving algorithm based on a value function and branch and bound nesting, and efficient and rapid solving of the model is realized by converting a double-layer model into a single-layer mixed integer programming problem and combining a branch and bound strategy.

The invention discloses a three-stage AC optimal transmission switching method based on a relaxation model, and the method comprises the following steps: 1, screening a breakable branch through solving an optimal solution of the relaxation model in a first stage, and reducing the scale of a system; 2, in the second stage, sorting is carried out by solving the optimal solution of the alternating-current optimal power flow of disconnection of a single branch; and step 3, in the third stage, solving by adopting a branch and bound method according to the sorting sequence of the second stage, and finally determining an optimal solution of alternating current optimal transmission switching and a branch on-off scheme. According to the method, the dimensionality of the optimal transmission switching problem is effectively reduced, and the calculation efficiency and speed are improved; an alternating current optimal transmission switching relaxation problem is solved by adopting a method based on a quotient gradient system and IPOPT, so that the calculation convergence and speed are guaranteed, and the algorithm robustness is improved; branch and bound calculation is carried out according to the alternating current optimal power flow solution sorting result sequence of the cut-off single branch, and it is guaranteed that a high-quality line cut-off scheme is obtained through solving; in the calculation process, island branch processing is considered, the island phenomenon in the topology change process is prevented, and power grid splitting and unnecessary load outage are effectively avoided.

The invention provides an intelligent metasurface optimal position deployment method and system in an electric power scene, and the method is characterized in that the method comprises the following steps: constructing a single-input single-output system model for the inspection of a multi-intelligent metasurface (RIS)-assisted unmanned aerial vehicle, the system comprises a base station, an unmanned aerial vehicle and a plurality of RISs, the RIS is deployed in a candidate site set # imgabs0 # between a base station and an inspection area, the height # imgabs1 # azimuth angle theta i and the number Ti of daughter boards of each RIS are adjustable, and the inspection area is divided into N grids; establishing a channel model, and calculating average power gains of direct connection and cascade channels; an optimization problem with the goal of minimizing the total deployment cost is constructed, the total deployment cost comprises RIS site fixed cost and daughter board hardware cost, and the constraint condition is that the system coverage rate is not lower than a preset threshold value eta min; and converting the optimization problem into an integer linear programming problem, introducing a binary variable to represent RIS deployment configuration, and solving through a branch definition algorithm to obtain the optimal RIS position, height, azimuth angle and daughter board number.

A ship deployment and refueling joint optimization method considering multiple uncertainties comprises the steps that firstly, a nonlinear mixed integer programming deterministic model with the total cost minimization as the target is constructed, then random oil price and berthing time are introduced, a robust optimization model containing an uncertainty set is constructed, and the nonlinear mixed integer programming deterministic model and the robust optimization model are integrated to form a two-stage robust optimization model; iterative solution is carried out through a Benders decomposition algorithm with embedded branches and delimits, so that a robust optimal ship deployment scheme and a fuel oil filling strategy are obtained; according to the method, an integrated joint decision framework is constructed, ship deployment and fuel oil management are optimized in a unified mathematical model at the same time to capture and utilize the internal relation between different decisions, potential huge cost brought by separated decision is avoided, the uncertainty influence of oil price and port berthing time is fully considered, and the method has the advantages of being high in reliability and high in reliability. Therefore, the decision has a good hedging risk characteristic, the scientificity and economy of the decision are fundamentally improved, and the comprehensive cost of operation is effectively reduced.

The invention provides a rotational symmetry point cloud registration method and device based on fuzzy clustering and a storage medium, and the method comprises the steps: extracting inner points through employing an algorithm combining the minimum median square and random sampling consistency, and estimating a rotating main axis through principal component analysis; realizing point cloud global coarse registration based on a rotating main shaft; fuzzy clustering is carried out on the fixed point cloud to construct local geometric description, a progressive constraint mechanism and a directional search strategy based on a main shaft are introduced, and fine registration is completed in combination with branch and bound and gradient descent algorithms. According to the embodiment of the invention, the registration precision and efficiency of point clouds with rotational symmetry, such as a computer-aided design (CAD) model, can be effectively improved.

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.

The invention provides an off-grid ReP2A integrated system topology capacity collaborative planning method, and relates to the technical field of renewable energy source hydrogen production and synthesis ammonia, and the method comprises the steps: constructing a mixed integer second-order cone programming model covering a power network, energy storage, hydrogen production, hydrogen storage, hydrogen transmission and synthesis ammonia full-chain coupling constraint; wherein the mixed integer second-order cone programming model takes the minimum annual average comprehensive cost of the system as a target to collaboratively optimize equipment capacity configuration, power transmission and hydrogen transmission network topology and a system operation strategy; and solving the mixed integer second-order cone programming model by adopting a branch and bound Benders decomposition algorithm, and outputting a programming construction scheme. The scheme can solve the problems of high comprehensive cost and low operation efficiency of the system.

The invention provides a scheduling method for dual-objective optimization of a multi-agent flow shop under time-of-use electricity price, and relates to the technical field of intelligent manufacturing and production scheduling. According to the method, unified modeling is carried out on the total power cost TEC and the total customer completion time TCTA, and a dual-target mixed integer programming model is constructed; a double-node decision tree structure is constructed, and synchronous processing of task sorting and processing time interval distribution is achieved; analyzing the structural characteristics of the multi-agent flow shop under the constraint of time-of-use electricity price, designing a series of inequality constraints and multiple pruning rules, and compressing a search space on the premise of ensuring the integrity of a feasible solution; the method comprises the following steps of: performing dual-objective cooperative solution, embedding a branch and bound algorithm into an improved epsilon-constraint framework to perform iterative solution, accurately obtaining a Pareto frontier, and providing multiple optimal scheduling schemes balanced between cost and service quality for decision makers. According to the method, dual decisions of task sorting and cycle allocation can be realized, and the problems of decision model splitting, low solving algorithm efficiency and the like are solved.

The invention discloses a power distribution network new energy bearing capacity determination method and device, a medium and electronic equipment, and relates to the technical field of new energy access, and the method comprises the steps: obtaining a plurality of transformer area topology networks of a target power distribution network and operation parameters of multilevel new energy bearing capacity evaluation indexes corresponding to the transformer area topology networks; performing constraint condition construction by adopting the operation parameters corresponding to the topological networks to obtain constraint conditions corresponding to the topological networks; based on each constraint condition and a target function taking maximization of the photovoltaic access capacity of the target power distribution network as a target, a mixed integer second-order cone programming model is adopted to carry out model construction, and an optimization model of the target power distribution network is obtained; and solving the optimization model by adopting a branch and bound algorithm to obtain the new energy bearing capacity of the target power distribution network. The new energy bearing capacity of the power distribution network determined by the method is more accurate.

The present invention discloses a low-cost deployment method for hint chains in a zero-trust access network, including: introducing a verification cost balance VCB factor to reduce the verification times of zero trust ZT; proposing a sub-hint chain branch and bound SCB algorithm based on the verification cost balance VCB factor, and gradually selecting the optimal PES nodes to deploy hint chains, so as to achieve the minimum service cost; the present invention not only solves the conflict problem between the deployment cost and verification cost of hint chains in the ZT network, but also provides an economical, efficient and adaptable deployment method, thereby enhancing the service quality and resource utilization efficiency of generative artificial intelligence applications in the ZT network.

A method and system for configuring a product is disclosed. The method comprises obtaining, by a processor (102) of the PDM system (100), a plurality of options and one or more constraints defined for the product from a product database (116), wherein each of the options has one or more variants. Further, ordered logical groups are generated from the plurality of options obtained, based on a weight associated with each of the options. Furthermore, a branch and bound algorithm is used to determine one or more buildable product configurations based on the ordered logical groups and one or more of the constraints applicable to the options, wherein applicability of the one or more constraints is determined using a Satisfiability Modulo Theory-based solver. The one or more buildable product configurations are further outputted on an output device (110).

The invention discloses a cut plane selection method and system in mixed integer programming, and the method comprises the steps: carrying out the coding to obtain the global structure information of a whole branch and bound tree, inputting the global structure information into a multi-layer graph neural network, and enabling the graph neural network to carry out the local and global connection of a node neighborhood, dynamically calculating the feature representation of each variable node, constraint node and cut plane node, and outputting a feature vector representing the global state of the whole branch and bound tree; and carrying out joint modeling and inference on the candidate cutting plane set based on the feature vector, determining an optimal cutting plane selection strategy by adopting a reinforcement learning mode, and carrying out cutting plane selection based on the optimal cutting plane selection strategy. According to the method, the accumulation and cooperative influence of the cut plane among different nodes in the whole solving process can be accurately captured, the overall collaboration of cut plane allocation is remarkably improved, and the computing resources and time overhead required by high-dimensional information coding and model reasoning are reduced.

The application provides a kind of optimization method for frame out of warehouse in forklift dense storage system, belongs to the technical field of warehouse management system, comprising: collecting the basic information of stacking area, obtaining the retrieval order of frame and initializing the stacking state of stacking area;Using heuristic method to determine the upper bound of the number of times of turning over and the turning over scheme;Using the lower bound estimation method of the number of times of turning over to determine the lower bound of the number of times of turning over and the turning over scheme;If the upper bound of the number of times of turning over is equal to the lower bound of the number of times of turning over, the turning over scheme determined by heuristic method is the optimal turning over scheme, otherwise, the upper bound of the number of times of turning over is the initial optimal upper bound, the lower bound of the number of times of turning over is the initial optimal lower bound, and the branch and bound algorithm is used for deepening iterative search to obtain the optimal turning over scheme.The application provides an optimization method for frame out of warehouse in forklift dense storage system, which can optimize the out of warehouse turning over scheme and improve the out of warehouse efficiency of dense storage system.

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 subway train timetable and passenger flow OD collaborative optimization method, comprising: obtaining the expected arrival time period and origin-destination point of all passengers, obtain the minimum train interval, train capacity, line station and length;Construct subway train timetable and the collaborative optimization model of passenger flow OD, this is a mixed integer programming model, constraint conditions include departure interval constraint, delay passenger quantity constraint and passenger service constraint;Design adaptive branch and bound algorithm, calculate the above collaborative optimization model, output train departure scheme, the successful boarding time period and origin-destination point of all passengers.The application can reduce congestion and passenger delay time effectively while optimizing cost, improve the travel efficiency and experience of passengers.On this basis, based on the customization improvement of branch and bound algorithm on the basis of the theoretical characteristics of collaborative optimization model, can provide certain reference for solving large-scale subway network calculation efficiency problem.

The present invention discloses a method for optimizing the path of an unmanned aerial vehicle based on an improved branch and bound algorithm. First, the task points are divided into different priority levels, and a dynamic weight mechanism is designed to drive high-priority tasks to be preferentially responded to. Secondly, the global MTSP problem is transformed into the clustering of a planar point set and the local TSP problem, and collaborative solution is achieved by fusing the K-means clustering and the branch and bound algorithm. Aiming at the challenge of computational complexity in large-scale task scenarios, an approximate optimization strategy based on space partitioning is proposed, and a multi-level optimization architecture is adopted to balance the solution accuracy and the computational efficiency. Compared with the traditional heuristic algorithm and the branch and bound algorithm, the method of the present invention can obtain a better solution in small-scale TSP instances. For large-scale task scenarios, the computational efficiency of the algorithm is greatly improved. And through the dynamic weight mechanism, the access timing of high-priority task nodes is optimized and advanced, so that both the system response timeliness and the system comprehensive QoS are improved.

The present application relates to a kind of micro energy network planning optimization method based on economic-environmental comprehensive evaluation, comprising the following steps: establishing micro energy network system optimization configuration model, the objective function of micro energy network system optimization configuration model is comprehensive evaluation index objective function, wherein, the comprehensive evaluation index objective function is obtained by the weighted processing of economic cost objective function and environmental protection cost objective function based on random weighting method, and economic cost objective function is determined by annual average fixed cost, annual operating cost, annual maintenance cost and annual average carbon trading income;Establish optimization scheduling constraint condition;Micro energy network system optimization configuration model is solved based on linear / nonlinear-branch and bound algorithm optimization, and the optimal configuration scheme is obtained.Compared with prior art, the present application introduces carbon trading income index, has the advantages that the conflict of economic cost and environmental cost is considered, the planning is more reasonable, etc.

The application discloses a GPU-based ARX type block cipher optimal differential characteristic search method, which first selects a high-probability differential transition to split the whole search process into forward search and reverse search; differential transition construction and probability calculation are taken as kernel functions, a GPU-based parallel scheme is designed, and the differential transition probability of different states in each round is calculated in parallel; the GPU parallel calculation result and the set probability threshold are taken as the determination condition of pruning in the search process, the optimal differential characteristic is searched round by round according to the branch and bound strategy, the forward and reverse searches are completed, and the optimal differential characteristics of the two parts are obtained; finally, the optimal differential characteristics of the two parts are spliced to obtain the optimal differential characteristics of the complete rounds. The search method can effectively reduce the recursion depth, improve the search efficiency through parallel calculation, shorten the calculation time and improve the search speed of the ARX type block cipher optimal differential characteristic.

A method for managing order processing includes obtaining a set of time series datasets associated with the order processing for an order processing system, generating a state space for the set of time series datasets using bi-partite graph representations for a mixed integer program (MIP), applying a transition function on the state space to obtain state-to-state transition function, performing a graph embedding on the state-to-state transition function to obtain historical transition data, applying a transformer multi-head attention function on the historical transition data to obtain positional encodings of observation history and feature representation, and perform an agent deployment on an order processing system based on finalized state-to-state transition functions, wherein the finalized state-to-state transition functions are based on the positional encodings.

The invention discloses an elastic power distribution network planning method and digital system considering wind and rain microtopography coupling, and relates to the technical field of power system disaster prevention and planning, and the method comprises the steps: constructing a typhoon wind field model considering microtopography correction, recognizing ridge features based on a ridge recognition algorithm, and outputting a wind speed acceleration ratio; simulating spatial and temporal distribution of rainfall in combination with a corrected wind field and a terrain uplift effect, constructing a two-dimensional hydrodynamic rainfall flood waterlogging model, and outputting a ponding evolution process of each grid region; a combined failure model and a node failure probability function are constructed for an overhead line, an underground cable and power distribution node equipment, a typical disaster scene is generated and reduced after fusion, and a two-stage mixed integer stochastic programming model is constructed; and solving the two-stage mixed integer stochastic programming model by adopting an improved dual decomposition algorithm, and solving dual gap global optimum by combining branch and bound processing. According to the method, the expected power failure loss under the extreme event is reduced, and the toughness support under the extreme environment is improved.

A hybrid integer optimal control optimization problem is solved by using a branch and bound (B&B) optimization that searches for a global optimal solution within a search space to control a system. The B&B optimization iteratively partitions the search space into a region-nested tree and prunes at least one region from the region-nested tree before finding a local optimal solution for each region when a dual objective value of a projection of a suboptimal dual solution estimate for each region in a dual feasible space is greater than an upper bound of a global optimal solution maintained by the B&B optimization or less than a lower bound of the global optimal solution.

The invention belongs to the technical field of circuit fault diagnosis, and particularly relates to a solving method and device based on multiple observation aggregation fault diagnosis and a medium. In order to solve the problems of one-sided single observation information, redundant diagnosis results and the like, multiple times of fault observation are performed on a circuit system, and multiple independent diagnosis results are generated and integrated into a diagnosis set; establishing a mapping relation between a diagnosis set and a unique keyword by using a map function of the STL, and converting the component combination into an integer identifier to simplify operation; calling a BWSS algorithm to perform collision calculation on the mapped keywords to obtain a candidate solution set covering all observation scenes; after the keyword solution set is converted to an original diagnosis form, deleting a superset according to a redundancy rule to form a simplified intermediate diagnosis solution set; and finally, through a branch and bound minimization algorithm, according to potential classification and ascending order arrangement of the diagnosis sets, high-order supersets are deleted layer by layer by adopting a heuristic strategy, and finally, a minimum diagnosis solution set of all faults is output.

The invention provides an optimal design method and system for a material preparation formula and a medium. The optimization design method of the material preparation formula comprises the following steps: acquiring experimental data including different formula combinations and corresponding performance indexes thereof; performing formula variable space design on the basis of the category and value rule of formula variables in the experimental data and formula constraint conditions to determine a formula search boundary comprising discrete variables and continuous variables; based on a target acquisition function, performing search optimization in the formula search boundary by adopting particle swarm optimization in combination with a branch and bound method so as to determine a plurality of candidate formula combinations, the target acquisition function being defined based on performance prediction values and prediction uncertainty of the candidate formula combinations determined by a support vector regression model; and in response to a termination condition meeting search optimization, determining the candidate formula combination with the optimal target acquisition function value as a formula design scheme.

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 invention relates to the technical field of power systems, and discloses a branch cutting method and device for accelerating power system security constraint unit commitment solution. In the branch and bound process, if the relaxation problem of a target node has no solution, simple single-point pruning is not carried out any more, but a network constraint minimum illegal model is constructed, and if no solution is caused by network constraint, global feasible cut is generated through a Lagrange multiplier at an optimal solution, so that the network constraint minimum illegal model is constructed. The global feasible cut is a constraint condition of a specific unit commitment mode which causes a relaxation problem to have no solution, and the global feasible cut is added to a branch and bound cut pool, so that a subsequent infeasible solution similar to the specific unit commitment mode can be cut off, a large amount of repeated invalid exploration in a branch and bound process is avoided, solution is accelerated, and the problem is solved. The safety and the economical efficiency of power grid dispatching are ensured; the global feasible cut is strictly based on the strong duality theory, it is ensured that the feasible solution or the optimal solution cannot be cut by mistake, and the feasibility and optimality of the security constraint unit commitment solving result are fundamentally guaranteed.