68 results about "Set constraint" patented technology

The invention discloses a park integrated energy scheduling method and system based on a master-slave game model, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting basic operation parameter data in a park integrated energy system; constructing an upper-layer game model taking an operator as a dominant, setting constraints, and making an electric heating and cooling price strategy and a unit output plan by taking operator profit maximization as a target; constructing a lower-layer game model, and making a load energy consumption strategy by taking user energy consumption cost minimization as a target; and converting the upper-layer game model and the lower-layer game model into a mixed integer linear programming problem, and obtaining an electric heating cold price scheduling scheme and an output scheduling scheme of each unit by adopting an optimization solution algorithm. The building thermal inertia and demand response mechanism is introduced, the KKT condition, the linear relaxation technology and the strong dual theory are used for converting the complex problem into the MILP model easy to solve, optimization of operator and user benefits is achieved, and the calculation speed and the solving efficiency are remarkably improved.

The present invention discloses a method for dynamic energy dispatching of microgrids, microgrids and storage devices, including the following steps: S1, defining decision variables, decision variable types and setting constraints, establishing an objective function, and quantitatively simulating the optimal dispatching of microgrids with multiple constraints; S2, importing prediction variables, and using the characteristics of the source, network, load and storage of the next period of time obtained by the time series prediction algorithm based on the long short-term memory network of energy data; S3, running the precise algorithm of mixed integer programming / discrete optimization to obtain the feasible domain of all solutions that meet the constraints. The present invention can realize the flexible allocation of energy according to the dynamic changes of real-time power consumption, power generation and cost factors, and greatly reduce the unnecessary loss of energy; and by considering the equipment constraints in a refined manner, the service life of the equipment is greatly extended, and the frequency and cost of operation and maintenance are reduced; finally, relying on the optimization algorithm, the system operation efficiency and economic benefits are given equal weight, and the microgrid is promoted to operate stably, greenly and efficiently in all directions.

The invention relates to a multi-agent system distributed consistency optimization method considering an unbalanced directed communication graph, and belongs to the field of multi-agent system coordination control. The method comprises the following steps: S1, establishing a first-order dynamic model of a multi-agent system; s2, constructing an unbalanced directed communication topology network of the multi-agent system, and describing a communication relationship between agents through an adjacent matrix; s3, defining a global optimization target to minimize the sum of local cost functions of all agents; s4, constructing a distance-based adaptive precise penalty function, and converting a set constraint optimization problem into a set-free constraint optimization problem; s5, designing an adaptive coupling gain to solve a distributed optimization problem, and designing an auxiliary variable to estimate a left eigenvector corresponding to a zero eigenvalue of the Laplacian matrix; and then updating the state variable and the auxiliary variable of the intelligent agent according to the estimated value, and solving the problem of consistency optimization of the multi-intelligent-agent system of which the cost function cannot be differentiated.

The invention discloses a resource intelligent joint optimization method based on deep reinforcement learning, and the method comprises the following steps: 1, building a communication network topology, and detecting the data flow in the network topology; 2, randomly generating a task flow, and simulating analysis and sending of tasks in the network topology; 3, the switch receives the task and searches n simple paths of the task; 4, setting constraint conditions, and screening candidate paths from the n simple paths; step 5, taking the minimum total time delay as a target, selecting an optimal path from the candidate paths, and distributing optimal computer resources and communication resources for the optimal path; and step 6, issuing the communication resources allocated to each link of the optimal path to a switch to realize communication resource allocation. According to the method provided by the invention, more concurrent tasks can be effectively accommodated on the premise of ensuring the task delay requirement, so that the resource utilization rate is maximized.

The invention relates to the technical field of energy storage auxiliary peak regulation, and discloses an energy storage capacity optimal configuration method and system based on a multi-target particle swarm optimization algorithm, and the method comprises the steps: obtaining the performance parameters and state variables of various energy sources in a power system, and constructing various power generation and energy storage mathematical models; setting constraint conditions of the power system according to the mathematical model, and constructing an energy storage multi-target optimization scheduling model including operation cost, environmental protection cost and peak regulation effectiveness based on the constraint conditions; and solving an optimal solution of the energy storage multi-objective optimization scheduling model by using a multi-objective particle swarm optimization algorithm, and obtaining an energy storage peak regulation configuration strategy according to the optimal solution. According to the method, the three targets of operation cost, environmental protection cost and peak regulation effectiveness are comprehensively considered for optimal scheduling, and on the basis of the stability of energy storage auxiliary peak regulation, economic benefits and environmental protection benefits are improved, the stability and reliability of a power system are enhanced, and the quality of power supply is ensured.

The invention discloses a knowledge graph-driven line storage space joint optimization scheduling model construction method. The method comprises the following steps: determining an optimal scheduling target and an overall target function of row storage space joint flood control scheduling, and setting constraint conditions; under a knowledge graph theoretical framework, constructing a response relationship between single-class line storage space scheduling and a river flood discharge state; selecting an excess peak water level exceeding a river embankment control water level as a coordination variable, decomposing the overall objective function into system association constraint equations of a river embankment, a reservoir, a flood storage and detention area and a flood drainage pump station, and reducing a multi-class line storage space joint scheduling problem into a problem of optimization scheduling of each single-class line storage space scheduling; and respectively solving by adopting an improved particle swarm algorithm to obtain an optimal scheduling process of each single-class line storage space. The method not only solves the problem that global optimum is difficult to ensure by directly applying a traditional optimization algorithm due to many row storage space optimization targets and a competitive relationship, but also reduces the calculation time of the model.

A transverse obstacle avoidance planning method based on expansion coefficient dual transformation comprises the following steps that S1, a vehicle polygon convex set and an obstacle polygon convex set are constructed based on surrounding obstacle polygon envelope; s2, combining the motion model of the vehicle and the polygonal convex set constraint of the obstacle to construct an optimal control problem; and S3, solving an optimal state sequence and a control sequence according to the optimal control problem in the step S2, and calculating a real-time path under the Cartesian coordinate system. According to the invention, through dual transformation constraint of the collision coefficient, a constraint expression form capable of being used for accurate obstacle avoidance is effectively provided, and an optimal control problem is established to solve an optimal solution.

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.

The invention provides a multi-AGV flexible job shop active scheduling method based on an improved VNS-INSGA-II algorithm and a related device, and belongs to the technical field of shop active scheduling. According to the method, a multi-objective optimization model for active scheduling of the multi-AGV flexible job shop is constructed, and constraint conditions of the multi-objective optimization model are set; the VNS-INSGA-II algorithm is improved by adopting a multi-layer coding mode, a hybrid population initialization mode, a time-varying coefficient-based dual-strategy binary tournament selection strategy and a variable neighborhood search algorithm, and the improved VNS-INSGA-II algorithm is obtained; based on a set constraint condition of the multi-objective optimization model, an improved VNS-INSGA-II algorithm is adopted to solve decision variables in an objective function of the established multi-objective optimization model for active scheduling of the multi-AGV flexible job shop, and a Pareto frontier is obtained; and selecting a group of solutions from the Pareto frontier to obtain an active scheduling result, thereby carrying out active scheduling on the multi-AGV flexible job shop. According to the invention, the problems of low stability and low scheduling precision of the scheduling system are solved.

The invention discloses a BVT format high-speed motion instruction generation method based on time expression and a related device, a tool nose displacement instruction is firstly defined, the tool nose displacement instruction is a track point sequence sampled in an equal period, and each sampling point is expressed by sampling time and an accumulated displacement value; then segmenting a tool nose displacement instruction, dividing a continuous track into a plurality of sub-segments by analyzing the geometric curvature of a time-displacement curve, and ensuring that the curvature change of each sub-segment is gentle and each sub-segment contains a preset number of sampling points; then selecting a decatic polynomial curve as a fitting model, fitting sampling points by using a least square method under set constraints, and ensuring that the curve is smooth and third-order derivatives are continuous; introducing a Lagrange multiplier to construct a function, converting a constrained curve fitting problem into an unconstrained extreme value problem, and solving a decatic polynomial coefficient; and finally, based on the fitting polynomial curve, generating a high-speed motion instruction which covers a straight line, an arc and an NURBS interpolation instruction, and each instruction comprises parameters such as polynomial times, fitting points and the like. The invention aims to solve the problems of low interpolation precision, poor track smoothness and slow response of the traditional motion instruction format in high-speed and high-precision motion control.

The invention is suitable for the field of power distribution network post-disaster reconstruction, and discloses a power distribution network post-disaster rapid reconstruction method, system and device based on radial constraint and a medium, and the method comprises the steps: obtaining post-disaster power distribution network data, and constructing a post-disaster reconstruction model; based on the post-disaster reconstruction model, establishing an optimization objective function and various constraint conditions; establishing a mixed integer linear programming model based on the optimization objective function and a plurality of constraint conditions, judging whether the set constraint conditions are met by combining a particle swarm optimization algorithm, and solving an optimization problem of the mixed integer linear programming model to obtain a final decision result; and obtaining a switch operation sequence based on the decision result, calculating a recovery result of each power supply unit, and obtaining a reconstruction decision scheme of the post-disaster power distribution network, thereby improving reconstruction calculation efficiency and engineering applicability, and realizing rapid recovery of the key load and stable operation of the post-disaster power distribution network.

The invention discloses a volume fractured horizontal well segmentation clustering method based on mechanical specific energy and a genetic algorithm, and relates to the field of well drilling and completion engineering in petroleum and natural gas. A traditional geometric division method is difficult to meet the refining requirement on a segmentation scheme in actual engineering; drilling dynamic data are collected, a drilling dynamic data set table under the equal well depth interval is formed according to the well depth step length, and drill torque and bit pressure are calculated; mechanical specific energy of the horizontal well section is calculated according to the drilling dynamic data set table, and a mechanical specific energy value set along a borehole is obtained; generating an initial population based on a set constraint condition, and obtaining the fitness of each individual in the initial population; randomly extracting a set number of individuals from the initial population to form a candidate set, and performing fitness calculation, selection, crossover and mutation operations until the maximum number of iterations is reached to complete iterative optimization; and outputting an optimal segmentation scheme and perforation cluster distribution, and performing drilling construction according to the optimal segmentation scheme and the perforation cluster distribution.

The invention discloses a unit commitment optimization method and system containing transient voltage security constraints, and belongs to the field of transient security evaluation and control of a power system. The method comprises the following steps: constructing a unit commitment model according to transient voltage safety constraint conditions; simplifying an embedded differential-algebraic equation set constraint in the unit commitment model through a trajectory sensitivity algorithm to obtain an optimization model under transient voltage under multiple time scales; decomposing the optimization model according to a generalized Benders decomposition algorithm to obtain a mixed integer linear programming problem which does not contain alternating current power flow calculation and a nonlinear programming problem which meets transient voltage constraint after iteration; and respectively solving the mixed integer linear programming problem and the nonlinear programming problem through a solver to obtain an optimal solution of the unit commitment model. Therefore, by simplifying the constraint of the embedded differential-algebraic equation set, the model is decomposed into the main problem and the sub-problem with different emphasis, and the main problem and the sub-problem are solved respectively, so that the efficiency of solving the original problem is greatly improved.

The invention provides a four-way shuttle vehicle warehouse-in and warehouse-out composite operation scheduling method in a cross-layer operation mode and a related device, and belongs to the technical field of warehouse scheduling. According to the method, a random initial population mode, a double-layer coding mode, a discrete integer decoding mode, a nonlinear convergence factor and a variable neighborhood search mechanism are adopted to improve a whale optimization algorithm, and a multi-strategy whale optimization algorithm is obtained; based on the set constraint conditions and decision variables of the four-way shuttle vehicle in-out warehouse compound operation scheduling optimization model, a multi-strategy whale optimization algorithm is adopted to solve the established target function of the four-way shuttle vehicle in-out warehouse compound operation scheduling optimization model, and an approximate optimal solution is obtained; and a scheduling result is obtained based on the approximate optimal solution, so that the four-way shuttle vehicle warehouse-in and warehouse-out composite operation scheduling is completed. The problem that the working efficiency of equipment is not high is solved.

The application discloses a kind of edge cloud computing environment under Internet of Vehicles based on blockchain data sharing method and system, the method includes: collecting the user information and data demand of data sharing party and receiver;According to user information, the utility function of receiver is calculated, the utility function of sharing party and the overall utility function of proxy node;Set constraint, determine target equation, establish optimization equation under the condition of constraint;Solve the extreme value of optimization equation by Lagrange multiplier method;Design the iterative two-way auction algorithm based on game theory to transform the problem;According to the bid price of user, the direction and result of data sharing are determined.The application can improve the enthusiasm of user sharing data, ensure that user shares data safely and efficiently, protect the private information of user, and can realize the maximization of overall utility function.

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 relates to a power system multi-resource collaborative planning method based on attribution analysis, and belongs to the technical field of power system planning. Comprising the steps of constructing a target function by taking the minimum supply and demand mismatching degree as a target based on the load demand of a power system and the output of each unit, and constructing a planning model based on set constraints and the target function; solving the planning model based on the multiple groups of boundary condition values of the planning model to obtain corresponding planning schemes; analyzing to obtain quantitative expressions of the influence degrees of the boundary conditions and the constraints on the planning scheme; and corresponding boundary condition values are adjusted based on quantitative representation, the planning models are solved, and the planning scheme with the lowest planning cost in solving results is selected as the optimal planning scheme. According to the method, the key factors influencing the planning scheme are determined by performing attribution analysis on the electric power planning, and the optimal planning scheme is determined by adjusting the key factors, so that the scientificity and the economical efficiency of the planning scheme are further improved.

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 application relates to the field of power systems and discloses a power grid outage scheduling system based on multi-agent deep reinforcement learning, which comprises a scheduling problem conversion module and a decision solving module; the scheduling problem conversion module is used for converting a power grid outage scheduling problem into a multi-agent Markov decision process model; and the decision solving module is used for training and solving the multi-agent Markov decision process model by using a multi-agent deep deterministic policy gradient algorithm under the set constraints of the power grid outage scheduling, so as to obtain a power grid outage scheduling strategy. The scheme is inspired by the single-agent reinforcement learning idea, converts the power grid outage scheduling problem into a multi-agent Markov decision process, regards power equipment as a single agent, trains the equipment with intelligent outage scheduling by using the multi-agent deep deterministic policy gradient algorithm, and learns the optimal outage scheduling strategy, so as to guarantee the safe operation of the power grid and the equipment.

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 embodiment of the invention discloses a distributed model prediction control method and device based on state estimation. The method comprises the following steps: acquiring a discretization model of an identified agent; receiving reference state information in a neighborhood range of the intelligent agent; performing state prediction on the state of the neighbor agent according to the past time state to obtain a virtual estimation state; based on the reference state information and the virtual estimation state, constructing a cost optimal problem for solving; based on set constraint conditions, obtaining an optimal control input sequence and a virtual reference state; the method does not depend on centralized control, and can better deal with the problems of local information loss or communication failure and the like; each agent only performs local optimization calculation, so that the calculation burden is reduced, and the control efficiency is improved; the optimization result of the previous time is considered, and the robustness is good.

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 invention relates to a distributed generator system scheduling optimization method and system based on dual decomposition, and the method comprises the steps: obtaining the operation data of a power distribution system, including distributed generators and public utilities; the distributed generator is used as a sub-agent, and the public utility is used as a main agent; constructing a scheduling objective function by taking power generation cost minimization as an objective, setting constraint conditions based on operation data, and constructing a scheduling optimization problem; converting the scheduling target problem into a corresponding dual problem by using a Lagrange dual function, and proving that the optimal solutions of the scheduling target problem and the corresponding dual problem are the same based on a Slater condition; the decomposed dual problem comprises a sub-agent optimization problem and a main agent optimization problem; solving a main agent optimization problem by using a subgradient descent method, and carrying out convergence analysis on an optimal solution; iteratively updating the step length and the gradient until a termination criterion is met, and obtaining an optimal scheduling vector; the method has expandability, is suitable for distributed generator deployment, and makes full use of generator capacity.

The present invention proposes a hybrid powertrain parameter optimization method based on time-frequency analysis, comprising: step 1, establishing a dynamic model of the hybrid powertrain; step 2, establishing a total transient torque model T of the engine. e ; Step 3, establish the transmission torque model T of the torsional vibration damper d ; Step 4, calculate the total moment of inertia J3 of the gears on the input shaft of the gearbox and the total moment of inertia J4 of the gears on the output shaft; Step 5, calculate the moment of inertia J of the vehicle body body , the output torque T of the drive motor MG2 mg2 , resistance torque T1; Step 6: Establish a system of differential equations of motion for hybrid powertrain mode; Step 7: Perform time-frequency analysis based on the system of differential equations of motion; Step 8: Select several key parameters as optimization variables; Step 9: Set constraints and objective function; Step 10: Execute a genetic algorithm to obtain the optimal solution. This invention establishes a hybrid powertrain model and combines time-frequency analysis with a multi-objective genetic algorithm to achieve optimal design of the powertrain.

The application discloses a kind of group optimization processing system and method based on big data, belong to big data technical field.The decision variable is defined, and the coding sequence structure is planned;Set constraint condition, filter individual solution;Establish objective function, calculate the objective function value of each individual solution in initialization coding sequence population;Individual solution is assigned non-dominated layer, and the crowding degree of individual solution is calculated based on non-dominated layer;Based on the non-dominant level and crowding degree of individual solution in non-dominated layer, the fitness of individual solution is obtained;Based on fitness, judge whether individual solution meets optimal solution of scheduling, if it meets optimal solution of scheduling, then output individual solution;If not, carry out crossover operation and mutation operation, generate new individual solution;Based on the individual solution output in algorithm iteration module, generate scheduling table;Further, the optimal solution is obtained, and the scheduling efficiency and quality are improved.

The invention provides a task-based employee scheduling method and a related device. The task-based employee scheduling method comprises the following steps: acquiring task information and inter-task conflict information; employee information is acquired; acquiring class system information and coverage information of a class system on a task; constructing an integer programming model by setting constraints and a target function, the constraints including a first constraint and a second constraint, the first constraint requiring employees not to execute mutually conflicting tasks, the second constraint requiring employees executing the tasks to execute a shift system covering the tasks, the target function being based on a plurality of target settings; and solving an integer programming model by using the obtained information to determine corresponding information of the employees and the tasks and corresponding information of the employees and the class system, thereby arranging the class for the employees.

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.