21 results about "Set constraint" patented technology

The invention relates to an excavator trajectory planning method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of engineering machinery trajectory planning. The method comprises the following steps: establishing a forward kinematics model through a D-H homogeneous coordinate transformation method; based on the forward kinematics model, establishing an inverse kinematics model through a geometric method; setting six path points in the track according to the mining track; based on an inverse kinematics model, converting the space coordinates of the six path points into space angles of joints; interpolation is carried out on the joint path points through a five-time B spline curve, and a track is generated; determining an optimization objective function according to actual application requirements, and setting constraint conditions according to a specific excavator type; and finally, the optimization problem is solved through an IADEMOPSO algorithm, an optimal Pareto solution is selected by using an average optimal evaluation formula, and an optimal track is generated. The track planning of the excavator can be realized.

This invention discloses a PIDNN-SO-based method for optimizing the layout of offshore wind farms, belonging to the field of wind farm optimization design technology. The method first inputs wind farm, turbine, and wind resource parameters, sets constraints on turbine spacing, boundaries, and wake overlap rate, and constructs a high-precision wake flow field physical model based on actuator disk theory, Navier-Stokes equations, and a weighted quadratic wake superposition model. Secondly, it builds a physical information dual neural network (PIDNN) with dual encoders, integrating physical equation loss and data loss to train the model, achieving rapid and accurate prediction of wake wind speed. Then, it uses the snake optimization algorithm (SO) to optimize the environmental evolution factor, completing an efficient search for turbine layout. Finally, it constructs a PIDNN-SO collaborative optimization framework, trains a PIDNN surrogate model through initial sampling, and outputs the optimal layout that maximizes annual power generation. This invention can effectively reduce wake loss and significantly improve the power generation efficiency of offshore wind farms.

The invention relates to the technical field of textile manufacturing, in particular to a scheduling method based on fusion of a large language model and an improved simulated annealing algorithm, and the method comprises the following steps: obtaining production data; performing problem description, setting constraint conditions, taking minimization of the maximum completion time as a target function, constructing a scheduling problem model, setting an iteration process, and repeatedly executing the iteration process; generating an initial scheme according to the production data and the problem description; in the iteration process, alternately calling the large language model and the improved simulated annealing algorithm for solving, and generating a to-be-verified scheduling scheme; the verification intelligent agent verifies the scheduling scheme to be verified, and after multiple iterations, an optimal solution is finally determined, and an optimal scheduling scheme is output. After the technical scheme is adopted, the maximum completion time of the generated scheduling scheme is relatively short, the generation speed of the scheduling scheme is relatively high, and the scheduling scheme can be ensured to be executable.

The invention discloses a power distribution network dynamic partitioning method based on a composite electrical distance. The method comprises the steps that the composite electrical distance between each pair of buses in a power distribution network is calculated; constructing an affinity matrix according to the composite electrical distance, and constructing an undirected weighted graph based on the affinity matrix; calculating a Laplacian matrix for the undirected weighted graph to obtain a preliminary partition of the power distribution network; a mixed integer linear programming method is adopted to optimize the preliminary partitioning result, it is ensured that each sub-region meets set constraints, and optimized partitions are output; topological change of a power distribution network and output fluctuation of a distributed power supply are monitored in real time, and when the change exceeds a preset threshold value, a dynamic adjustment mechanism is triggered, and a partition adjustment strategy is executed. According to the method, on the basis of comprehensively describing the electrical similarity of the bus, the engineering feasibility and the running state adaptability of the partitioning result can be realized by combining constraint optimization and a dynamic adjustment mechanism.

The invention relates to the technical field of spacecraft task planning, operational research and mixed integer planning, in particular to a multi-scene multi-stage task planning method and device for manned lunar surface activities, and the method comprises the steps: constructing a plurality of to-be-planned tasks for the manned lunar surface activities into a task pool, and setting constraint parameters corresponding to the task pool; preprocessing the task pool and the constraint parameters to construct a mixed integer programming model; and inputting the mixed integer programming model into a preset mathematical solver to find a final task execution scheme. Therefore, the problems that global optimization is difficult to complete at one time in multiple scenes such as normal planning, sudden adjustment and emergency return voyage in the prior art, modeling solving needs to be conducted again every time scene switching is conducted, and efficiency is low are solved.

The invention discloses a rudder parameter optimization method and system based on a simulated annealing algorithm and an MMG model, and the method comprises the following steps: determining a to-be-optimized parameter of a rudder and a value range of the to-be-optimized parameter, and setting a constraint condition; constructing a multi-objective optimization function; setting an initial temperature, a cooling rate, an iteration number threshold and a termination condition of a simulated annealing algorithm, and generating an initial solution; based on an initial solution, the value of a multi-objective optimization function is calculated through an MMG model, and the initial solution is the current optimal solution of the rudder; finely adjusting the value of each parameter of the current optimal solution to generate a neighborhood solution; calculating a value of a multi-objective optimization function by utilizing an MMG model based on the neighborhood solution; selecting a current optimal solution of the rudder from the two solutions according to a Metropolis criterion; whether the number of iterations at the current temperature reaches an iteration number threshold value or not is judged, if not, iteration continues, and if yes, cooling is conducted according to the cooling rate; and judging whether a termination condition is met or not, if not, continuing iteration, and otherwise, outputting the current optimal solution as the optimal solution of the rudder.

The invention relates to the technical field of power system safety, and provides a power distribution system safety operation optimization method and system based on low inertia. The invention discloses a power distribution system safe operation optimization method based on low inertia. The method comprises the following steps: constructing an evaluation model based on a minimum inertia demand by taking minimization of the total operation cost of a system as an optimization target; under a set constraint condition, solving the evaluation model based on the minimum inertia demand by using a grey wolf algorithm to obtain a minimum inertia demand value at each moment; in the process of searching the minimum inertia demand value at each moment, the grey wolf algorithm adopts a convergence factor to determine a search area; the convergence factor is represented as the fractional order power of the ratio of the difference value of the maximum number of iterations and the current number of iterations to the maximum number of iterations. Response reliability of the system to sudden disturbance can be improved.

The invention provides a new energy output curve generation method and system, and relates to the technical field of new energy power generation and power system planning. The method comprises the steps of constructing a new energy output characteristic evaluation system, based on historical new energy data, adopting a Markov chain to divide M time state intervals, adopting a Monte Carlo method to divide R longitudinal output intervals, and extracting data to generate an initial curve; and after correction in a wet season and a dry season, performing cyclic screening according to a set constraint condition, and outputting a curve conforming to a multi-time-dimension feature and an extreme scene. The system corresponds to the method. By the adoption of the method, the annual scale curve can be generated only through historical data without depending on meteorological data or massive historical samples, the problems that in the prior art, a scheduling method is not matched in time scale and insufficient in historical sample size are solved, and a reliable data boundary is provided for new energy installation planning and energy storage facility design.

The application provides a part pose determination method for ship small assembly part polishing equipment, comprising: acquiring contour information of a part through a visual sensor, grabbing the part, and moving to an initial position; inputting the contour information of the part into a trained deep learning model to predict the part position and generate a candidate pose; acquiring the candidate pose, comparing it with a set constraint condition, and selecting a candidate pose satisfying the constraint condition as a final pose; and acquiring the final pose, adjusting the pose of the part, and polishing it. The application acquires the contour information of the part through a visual sensor, determines the placement pose of the part using a deep learning algorithm after grabbing the part, and automatically polishes the free edge using a polishing tool, thereby solving the problems of low precision, low efficiency and complex constraint conditions in the prior art.

The invention provides a turbine transition section runner inverse design method based on a through-flow area, and the method comprises the following steps: S1, determining a first runner molded line, obtaining a distribution rule of the through-flow area along a flow direction, and determining constraints at two ends of a second runner molded line; s2, establishing an inverse problem model by setting constraint conditions; and S3, solving an inverse problem, solving the nonlinear equation set through a Newton iteration method, and obtaining coordinates of discrete points on the second flow channel molded line and the radius of an inscribed circle so as to obtain the second flow channel molded line. According to the inverse design method, the algorithm for inversely designing the turbine transition section flow channel based on the through-flow area is achieved, the design iteration efficiency of the turbine transition section flow channel is remarkably improved, the transition section flow channel is inversely designed through the through-flow area, and accurate control over the through-flow area can be achieved.

The invention discloses a tunnel multi-energy multi-layer optimization scheduling method and system, and the method comprises the steps: firstly obtaining an optimization input set corresponding to a future operation cycle of a tunnel, and then constructing a multi-layer mixed integer linear programming model through employing a lexicographical order structure; and finally, based on a set constraint condition, solving the multilayer mixed integer linear programming model according to the optimized input set to obtain a plan instruction in a future operation cycle. And obtaining an optimal value for zero off-supply of the primary load through a target function of a first layer of the multi-layer mixed integer linear programming model. Under the condition that the optimal value of the first layer is locked, the optimal value with the lowest operation cost can be obtained through the target function of the second layer. And on the premise that the optimal values of the first layer and the second layer are locked, the optimal values enabling the energy storage cycle number, the unit climbing number and the switching frequency to be minimum can be obtained through the target function of the third layer. Therefore, an energy scheduling strategy with the highest economical efficiency and the longest service life of equipment can be obtained on the premise of ensuring zero off-supply of the first-level load.

This invention provides a method, apparatus, and electronic device for optimizing the refrigerant mix ratio, relating to the field of liquefied natural gas (LNG) production technology. The method includes: obtaining the set condition parameter range and feed gas change conditions of the LNG preparation process; constructing a mapping model based on the set condition parameter range using a multivariate adaptive spline regression method; the mapping model characterizes the mapping relationship between the refrigerant mix ratio, feed gas conditions, and compressor specific power consumption in LNG production; and solving for the minimum compressor specific power consumption of the mapping model under feed gas change conditions and set constraints using a nonlinear optimization method to obtain the optimized refrigerant mix ratio. This invention eliminates the need to rebuild the process simulation model or reconstruct the mathematical model when feed gas conditions change. By utilizing the multivariate adaptive regression spline method and nonlinear optimization method, it achieves automatic optimization of the refrigerant mix ratio, reducing computational load and improving computational efficiency.

The present disclosure provides a task-based employee scheduling method and related device. A task-based employee scheduling method includes: obtaining task information and inter-task conflict information; obtaining employee information; obtaining shift information and shift coverage information for tasks; constructing an integer programming model by setting constraints and an objective function, the constraints including a first constraint and a second constraint, the first constraint requiring that an employee cannot perform tasks that conflict with each other, the second constraint requiring that an employee performing a task performs a shift that covers the task, the objective function being set based on multiple objectives; and solving the integer programming model using the obtained information to determine corresponding information of employees and tasks and corresponding information of employees and shifts, thereby scheduling employees.

The invention discloses a high-speed rail passenger and freight train working diagram optimization method, system and device, a medium and a product, and the method comprises the steps: firstly determining an optimization target, including minimizing the total travel time of a freight train, the operation deviation of the passenger train and the cargo delivery delay time; thirdly, constructing a mathematical model, and defining decision variables, an objective function and constraint conditions to form a high-speed railway passenger and freight train working diagram optimization model; on the basis of the model, a rolling time domain decomposition strategy is adopted, and a global scheduling problem is segmented into a local optimization problem of a plurality of time windows. And for each time window, obtaining a local optimal solution by using a linear programming solver so as to ensure that the dispatching of each train accords with the set constraint. Then, global search is carried out on the local solutions through a simulated annealing algorithm, the departure time and the operation plan of the train are optimized, and local optimum is avoided. And finally, a variable neighborhood local optimization method is applied, and the quality of the whole scheme is further improved by finely adjusting the solution.

Embodiments of the present application disclose a transfer orbit design method from GTO to the Sun-Earth collinear libration point, a device and a medium; the method comprises: calculating and obtaining an invariant manifold connected with the Halo orbit around the libration point according to the spacecraft dynamics equation and the potential function in the Sun-Earth barycenter coordinate system; taking the geosynchronous transfer orbit as the earth parking orbit to construct the constraint variable, taking the Halo orbit as the target orbit to construct the control variable, and correcting the control variable by using the least square differential correction method; based on the set constraint condition and performance index, the optimal HOI point corresponding to the optimal performance transfer orbit is selected from the transfer orbit meeting the constraint condition; according to the optimal HOI point, the relationship between the control variable and the parking orbit constraint variable is analyzed, and the differential correction initial value expression for determining the control variable initial value of the multi-constraint orbit design is constructed; the transfer orbit meeting multiple constraints is obtained by using the multi-stage differential correction method according to the differential correction initial value expression.

The present application relates to the technical field of textile manufacturing, and particularly relates to a scheduling method based on a large language model and an improved simulated annealing algorithm, comprising the following steps: obtaining production data; performing problem description, setting constraint conditions, taking the minimum maximum completion time as a target function, constructing a scheduling problem model, setting an iteration process and repeatedly executing the iteration process; generating an initial scheme according to the production data and the problem description; in the iteration process, alternately calling the large language model and the improved simulated annealing algorithm for solving, and generating a to-be-verified scheduling scheme; verifying the to-be-verified scheduling scheme by an intelligent agent; after multiple iterations, finally determining an optimal solution, and outputting an optimal scheduling scheme. After the above technical scheme is adopted, the maximum completion time of the generated scheduling scheme is relatively short, the scheduling scheme generation speed is relatively fast, and the scheduling scheme can be guaranteed to be executable.

The application relates to a power system multi-resource collaborative planning method based on attribution analysis, and belongs to the technical field of power system planning. The method comprises the following steps: constructing a target function based on the load demand of a power system and the output of each unit, taking the minimum mismatch degree between supply and demand as the target, constructing a planning model based on set constraints and the target function; solving the planning model based on multiple sets of boundary condition values of the planning model to obtain corresponding planning schemes; analyzing and obtaining quantitative representations of the influence degree of each boundary condition and each constraint on the planning scheme; adjusting the boundary condition values based on the quantitative representations and solving the planning model, and selecting the planning scheme with the lowest planning cost in the solving result as the optimal planning scheme. The application determines the key factors influencing the planning scheme through attribution analysis on power planning, and determines the optimal planning scheme by adjusting the key factors, so that the scientificity and economy of the planning scheme are further improved.

The invention discloses an intelligent purchase decision optimization method for alloy raw materials in the iron and steel industry based on multi-objective optimization, and relates to the technical field of purchase decision, and the method comprises the steps: determining a target cost component, employing multi-objective weighted summation, and constructing a mixed integer linear programming model with the total cost minimization as a target; setting constraint conditions of the mixed integer linear programming model; and obtaining input data of the mixed integer linear programming model, inputting the input data into the mixed integer linear programming model, solving the mixed integer linear programming model based on a mixed integer linear programming solver, and obtaining an optimal purchasing scheme. According to the method, the mixed integer linear programming model capable of simultaneously processing the continuous decision and the discrete decision is constructed, realistic constraints such as finished automobile transportation, minimum batch and material replacement are accurately reflected, and the fundamental problem that a traditional linear model cannot process discrete constraints is solved.

The application provides a recursive filtering method for a state-saturated system based on a high-speed network under the influence of a deception attack. The method specifically comprises the following steps: constructing a dynamic model of a state-saturated system to be studied, wherein the dynamic model comprises a system state and a measurement output model, and conditions that functions in the dynamic model satisfy are set; constructing a new measurement model based on a high-speed network and a random deception attack according to the measurement output model, and setting constraints that the deception attack satisfies; designing a state-saturated recursive filter according to the dynamic model and the new measurement model; obtaining a filtering error dynamic equation according to the system dynamic model and the filter model, and calculating a filtering error covariance upper bound; obtaining an optimal filter gain by minimizing the filtering error covariance upper bound; and verifying the effectiveness of the recursive filtering method through a numerical simulation example. The simulation experiment verifies the effectiveness of the proposed recursive filtering method, and the method is suitable for online calculation and exhibits good real-time performance.

The application belongs to the technical field of reservoir dispatching, and specifically discloses a reservoir intelligent dispatching method based on HHO and MPA double optimization driving, which comprises the following steps: establishing a target function with the maximization of total power generation of cascade reservoir groups as an optimization target; setting constraint conditions of the target function, including equation constraints and inequality constraints meeting reservoir operation; solving the target function based on an improved predation strategy optimization algorithm to obtain the operation water level of each reservoir in the cascade reservoir group at different time points within a dispatching period; wherein the improved predation strategy optimization algorithm is realized by introducing the following operation in the search strategy selection stage of the HHO algorithm: based on the escape energy, the global exploration of the MPA algorithm and the local development of the HHO algorithm are adaptively switched. The application effectively makes up for the imbalance problem of the exploration and development process of the HHO algorithm in the joint optimization and dispatching application of the cascade reservoir group, not only realizes the effective balance of global search and local development, but also has strong robustness.

The invention discloses a large model deployment method for enhancing a lama.cpp framework, and belongs to the technical field of large language model reasoning optimization. The method comprises the following steps: firstly, enhancing a lama.cpp framework to support operator-level tensor equipment distribution; secondly, constructing a 0-1 integer linear programming model with model throughput maximization as a target, defining three decision variables of operator allocation, whole layer allocation and operator cohesion by the model, and setting constraint conditions of single operator allocation, equipment resource capacity, allocation sequence and the like; and finally, solving the planning problem by adopting a branch and bound method to obtain an optimal operator-level resource allocation scheme, and deploying each operator of the model to a GPU or a CPU according to the scheme. According to the method, the video memory and the memory of the equipment can be finely managed, and operators which have great influence on the throughput are preferentially deployed in the GPU, so that the reasoning throughput and the video memory utilization rate of a large model are remarkably improved under the condition of limited resources.