1124results about How to "Fast solution" patented technology

A method and apparatus for interfacing the motion of an object with a digital processing system includes a sensor which has a sensing resolution and detects movement of the object along a degree of freedom. An amount of play less than the sesning resolution exists between the sensor and the object. A passive actuator is coupled to the mechanism to transmit a resistive force to the object along the degree of freedom. A play mechanism is coupled to the actuator to provide a desired amount of play between the actuator and the object along the degree of freedom. The desired amount of play is greater than the sensing resolution of the sensor so that the sensor can detect the desired play when the user moves the object, even when the actuator has locked the object into place. Such desired play can be torsion flex (compliance) or rotary backlash. The actuator and the sensor provide an electromechanical interface between the object and the digital processing system. A gimbal mechanism or slotted yoke mechanism can be coupled between the actuator and the object. The interface is well suited for simulations of medical procedures and simulations in which an object such as a stylus or a joystick is moved and manipulated by the user.

A computer controlled hydraulic resistance device for apparatus such as a prosthetic knee for above knee amputees, includes a two stage pilot operated solenoid valve connected to control the flow of hydraulic fluid to and from a hydraulic actuator which applies resistance to the prosthetic knee or other apparatus through a coupling. Hydraulic pressure is sensed on the high and low side of the actuator by a spring biased magnet and a magnetically actuated electronic sensor and is used by a micro-controller in a closed-loop manner to compensate automatically for variations in the device and in the hydraulic fluid viscosity. The device also has magnetically actuated electronic sensors which sense positions of the apparatus and feed back to the micro-controller for applying a predetermined resistance profile to the apparatus.

The invention discloses a combination forecast modeling method of wind farm power by using gray correlation analysis, belonging to the technical field of wind power generation modeling. In particular, the invention is related to a weighted combination forecast method of wind power based on a least square support vector machine and an error back propagation neural network. The forecast method comprises that forecasted values of wind speed and wind direction are acquired in advance from meteorological departments while real-time output power is acquired from a wind farm data acquiring system; that the forecasted values of wind speed and wind direction and the real-time output power are inputted into a data processing module for data analyzing extraction and data normalization, and then normalized data is loaded to a database server; processed data in the database server is extracted by a combination forecast algorithm server to carry out model training and power forecast, and the wind farm sends running data to the data processing module in real time to realize rolling forecasting. The method of the invention achieves the goal of combination forecast of wind farm output in a short time. The method not only maximally utilizes advantages of two algorithms but also increases forecast efficiency by saving computing resources and shortening computing time.

The invention discloses a D2D user resource allocation method based on a deep reinforcement learning DDPG algorithm. According to the invention, cellular users and D2D user related information are utilized; an optimal D2D user channel allocation and transmitting power combined optimization strategy is obtained by utilizing a deep reinforcement learning method; D2D users select appropriate transmitting power and distribution channels to reduce interference to cellular users and maximize own information rate, efficient resource distribution is achieved under the condition that QoS of the cellular users is not affected, the throughput of a cellular network is improved, and the requirement for green communication is met. The DDPG algorithm effectively solves the joint optimization problem of D2D user channel distribution and power control; t the method is stable in performance in optimization of a series of continuous action spaces, the time step required for solving the optimal solution is far less than that required by DQN, and compared with a DRL method based on a value function, the deep strategy gradient method based on the AC framework is higher in strategy optimization efficiency and higher in solving speed.

The invention discloses a hierarchical target allocation method for multiple unmanned aerial vehicle formations. The method is characterized by comprising the following steps of 1, clustering a plurality of ground targets to form a plurality of target clusters according to the number of the unmanned aerial vehicle formations; 2, regulating the obtained target clusters to keep the number of the ground targets in each target cluster consistent; 3, allocating the target clusters to the unmanned aerial vehicle formations in a one-to-one correspondence way by using an inter-unmanned aerial vehicle formation target allocation model and an inter-unmanned aerial vehicle formation target allocation algorithm; 4, allocating the ground targets to each unmanned aerial vehicle in the unmanned aerial vehicle formations by using an intra-unmanned aerial vehicle formation target allocation model and an intra-unmanned aerial vehicle formation target allocation algorithm. According to the method, the target allocation efficiency can be improved, and the problem of excessively long calculation time during large-scale target allocation for the multiple unmanned aerial vehicle formations can be effectively solved, so that the requirements of an application scenario such as a battlefield with a higher requirement on real-time performance are met.

The invention discloses a water unmanned ship local hierarchical path planning method based on the navigation error constraint. The method mainly includes the steps of local static path planning based on the navigation error constraint and combined with a genetic algorithm; local dynamic path planning fused with the maritime affair rule. The hierarchical thinking is adopted, local path planning is divided into two levels of static path planning and dynamic path planning, the static barrier avoiding problem is solved, and the dynamic barrier avoiding problem is also solved. According to static path planning based on the navigation error constraint and combined with the genetic algorithm, adverse influences of navigation errors on path selection are reduced, and the safety of path planning is improved; according to dynamic path planning based on the marine affair rule, the dynamics constraint of a water unmanned ship is considered, and the feasibility of path planning is improved.

The invention discloses a method for quick solution of six-degree-of-freedom humanoid dexterous arm inverse kinematics. The method includes: firstly, setting up a six-degree-of-freedom human arm simulated joint structural model, solving an elbow joint angle and two joint angles of the shoulder joint according to a planar relation formed by the shoulder center, the elbow center, the wrist center and the neck center of the dexterous arm and the tail end position of the dexterous arm, and further solving three joint angles of the wrist joint by the aid of the dexterous arm tail-end posture relation; and calculating position error of a dexterous arm target link to prove effectiveness of the solution of the inverse kinematics by means of a positive kinematic model. Compared with a numerical solving method for robot inverse kinematics, the method for quick solution of six-degree-of-freedom humanoid dexterous arm inverse kinematics is high in precision and quick in speed, and can be used for humanoid dexterous arm operational tasks having high requirements on precision and instantaneity.

The invention discloses an improved multi-time scale coordinated control method for independent microgrid containing hybrid energy storage, this method is based on the day-ahead planning decision model, The intra-day rolling optimization model based on model predictive control, the quasi-real-time coordinated control model based on comprehensive criteria and the real-time coordinated control modelare constructed. Firstly, the day-ahead planning decision-making model takes the operation economy as the optimization objective, the operation restriction of distributed generation and the safety and stability of the system as the constraint conditions. Then the controllable unit start-stop plan deviation and load switching plan deviation are introduced into the rolling optimization model as soft constraints, which allow to revise the unit start-stop plan and load switching plan before the day. Secondly, a comprehensive criterion is introduced into the quasi-real-time coordinated control todecide the priority of distributed generation. Finally, in the real-time coordinated control, the hybrid energy storage system adopts the improved first-order low-pass filter algorithm to compensate the second-order unbalanced power adaptively.

The invention discloses a rice blast resistance gene Pi9 gene specificity molecular marker Pi9SNP as well as preparation and application thereof. The molecular marker Pi9SNP is prepared by amplifying from total DNA (Deoxyribonucleic Acid) of a rice blast resistant variety with rice blast resistance gene Pi9 by using a primer pair F1 and R1 so as to obtain a nucleotide segment I, and carrying out digestion on the nucleotide segment I by using restriction enzyme Hind III, so as to obtain a nucleotide segment II which is in specificity banding pattern with the obtained rice blast resistance gene Pi9. The molecular marker is the first one which is developed for marking Pi9 gene specificity SNP (Signal Nucleotide Polymorphism) for an internal sequence of the Pig gene, and has the following advantages that the specificity is high, the cost is low and the flux is high in practical application, and the molecular marker is widely applied to colonies of different genetic backgrounds. By utilizing the molecular marker, the efficiency in auxiliary molecular marker selective breeding, gene pyramiding breeding and transgenosis breeding is improved.

The invention relates to the field of inverse kinematics of mechanical arms, in particular to a quick and simple method for solving inverse kinematics of a six-degree-of-freedom mechanical arm. A coordinate of an intersection of three rear joint shafts is determined according to the position and a posture matrix of the tail end of the mechanical arm; angles which three front joint shafts rotate by are solved in a geometrical method; the angles which the three rear joint shafts rotate by are solved according to an Euler angle transformation matrix. The concept put forward in the method can be applied to six-degree-of-freedom mechanical arms which have different geometrical structures but belong to the same type. According to the method, the solving process of the inverse kinematics of the mechanical arm is greatly simplified, the solving speed of the inverse kinematics is improved, and requirements for quickness and accuracy in the real-time control of the industrial mechanism arm can be met.

The invention discloses a distributed robust coordinated optimization scheduling model for an electrothermal integrated system with the consideration to wind power uncertainty. The electrothermal integrated system which takes a combined heat and power generation unit and an electric boiler as coupling units is constructed, and wind power output uncertainty is considered. The two-stage distributedrobust coordinated optimization scheduling model is constructed on the basis of the available wind power historical data of the scheduling system. In the model, the start / stop state of a conventionalunit and the related variables of a heat storage device and an electricity storage device are designed to be first-stage robustness variables, and other variables are designed to be second-stage variables. The model is decomposed into a main question and a subquention through a decomposition method for repeated iteration solving, thereby speeding up the solving. The equivalent conversion of an absolute value constrained condition is performed so as to convert a solving problem into a mixed integer programming problem, and a conventional solving toolkit CPLEX is used for effective solving.

The invention relates to a reference detecting device of an automatic drilling and riveting system of airplane components and a detecting method thereof. The detecting method comprises the following steps of: acquiring information of a reference profile by a 2D laser displacement sensor and a distance sensor, establishing a parameter space of a reference hole center coordinate by applying a least square method, then realizing transformation from a reference hole boundary space domain to the reference hole center coordinate by applying generalized Hough transformation, fitting and calculating a reference center position, and realizing detection of an actual position of the reference, thus realizing determination of the actual position for the processing reference of the automatic drilling and riveting system so as to determine the deviation between the theoretical position and the actual position of the reference. The positions of points to be preprocessed on a product are compensated and corrected, so that the manufacture and installation errors are eliminated, and the drilling and riveting accuracy is increased; and the detecting method is simple, the solving speed is high and the online detection of the reference can be realized.

The invention provides a microbial sand-fixing dust depressor and a preparation method thereof. The microbial sand-fixing dust depressor comprises the following components in percentage by mass: 0.8-2.5% of microbial powder, 3.0-5.0% of urea, 8.1-10.1% of calcium chloride and the balance of deionized water, wherein the microorganism in the microbial sand-fixing dust depressor is bacillus pasteurii powder. The preparation method comprises the following steps: proportionally mixing the calcium chloride with the deionized water, stirring till the calcium chloride is completed dissolved, adding the urea, and after the urea is dissolved, adding the bacillus pasteurii powder till the bacillus pasteurii powder is completely dissolved, thereby obtaining a mixed solution, namely the microbial sand-fixing dust depressor. Compared with the traditional inorganic sand-fixing agent, the microbial sand-fixing dust depressor is high in permeability; compared with the traditional organic sand-fixing agent, the microbial sand-fixing dust depressor has the characteristics of high ageing resistance, simplicity in preparation, convenience in construction and the like. Proved by desert sample tests, the sand-fixing dust depressor is high in ageing resistance and wind erosion resistance and has unconfined compressive strength higher than 1MPa. Proved by raise dust sample tests, the sand-fixing dust depressor has a good dust fixing effect.

The invention relates to an active distribution network energy-storage system dynamic planning method which studies active distribution networks which contain distributive photovoltaic and energy-storage systems. The method herein optimizes an output strategy of the energy-storage system on the basis of a bilevel programming model in combination with the shortest path theory so as to determine the installed capacity of the distributive photovoltaic and energy-storage system under the optimal output strategy of the energy-storage system. For the bilevel programming model, upper level programming determines the installed capacity of the distributive photovoltaic and energy-storage system with the minimum annual general fee of a power distribution network as the goal and generates a planning scheme, and the lower level planning optimizes the output strategy of the energy-storage system in one operation cycle and in one day with the minimum typical daily operation fee and load peak-valley difference of the power distribution network as the goal. Compared with prior art, according to the invention, the method herein can effectively improve operation characteristic of the active distribution network and optimization configuration of the distributive power supply and energy-storage system.

The present invention relates to a route planning method oriented to goods delivery. A method of grouping firstly and then arranging a route is adopted, and a large-scale logistics delivery vehicle path problem is divided into multiple small-scale logistics delivery vehicle path problems, so that a calculation amount can be greatly reduced, and a solution speed is improved. Moreover, for a constraint to a load weight of a freight vehicle, relatively high uniformity of the number of delivery points in each delivery region obtained by division is ensured by adding a limitation to a capacity constraint for each cluster set in a clustering method, so that a total weight of goods in the delivery region obtained by division will not exceed a maximum load weight of the freight vehicle, and region division becomes more reasonable; and furthermore, by integrally considering an optimization principle of a freight vehicle route and a regional principle of a delivery point geographical location, a same freight vehicle is arranged for the delivery points of which the geographical locations are close to each other, and therefore, the delivery efficiency is improved.

The invention discloses a three-dimensional virtual resource scheduling method of cloud computing energy consumption key. The method comprises the following steps: step 1, performing virtual resource allocation; step 2, performing overload host detection, and detecting each physical host of a cloud data center by adopting a multi-dimensional host overload detection method to judge whether the physical host is overload; step 3, performing migration virtual machine selection, and selecting a to-be-migrated virtual machine by adopting a virtual machine selection method; step 4, selecting a target host, and selecting a new physical host mode for each migrated virtual machine by adopting a target node selection method; and step 5, performing virtual resource optimization to further reduce the energy consumption. According to the three-dimensional virtual resource scheduling method disclosed by the invention, how to effectively reduce the energy consumption of the data center is fully considered from the three dimensions of CPU, memory and network bandwidth, so that the virtual resources of the cloud computing data center are efficiently scheduled and managed, the resource utilization rate is improved, and the energy consumption of the data center is effectively reduced.

The invention discloses an electromagnetic scattering analyzing method for superspeed flight targets. For non-uniform characteristics of plasmas around the superspeed flight targets, a part of equivalent relative dielectric constant, approximate to one, of the plasmas is used as the air to be processed by analyzing through a volume-surface integer equation, and areas with larger equivalent relative dielectric constant are encrypted by the adaptive network so as to achieve required solving accuracy. By adopting conventional method of uniform grid subdivision plasma casing, the electromagnetic scattering analyzing method can greatly save computing resources, green function adopted in the volume-surface integer equation is green function in vacuum, multilayer speedy multi-step ion technology is used for accelerating solving, so that fewer internal memory and shorter computing time are required for solving the problem of scattering of the superspeed flight targets.

The invention relates to a microgrid optimization method based on an improved binary system particle swarm optimization algorithm. For a microgrid containing various micro power sources, taking start-stop strategies of controllable micro power sources into account, a microgrid optimization operation mathematical model considering economic costs and environmental benefits is established. An improved binary system particle swarm optimization algorithm based on a catfish effect is provided, output of the micro power sources in the microgrid in each period of time is solved under different control strategies. A specific microgrid including wind, light, a micro gas turbine, a fuel cell and a diesel generator is used as an embodiment, and embodiment analysis is performed.

The invention discloses a mechanical arm kinematics parameter calibration method based on measuring of a laser tracker, and belongs to the field of industrial mechanical arm kinematics, and relates to the mechanical arm kinematics parameter calibration method based on measuring of the laser tracker. The method is based on the mechanical arm joint construction. An improved DH parameter method is adopted for building a mechanical arm kinematics model, a kinematics modeling result is used for building a mechanical arm kinematics parameter error calibration model, in consideration with the mechanical arm three-dimensional space work range, the laser tracker is adopted for measuring N absolute position coordinates of the tail end of the mechanical arm, on the basis of the kinematics parameter error calibration model, through repeated iteration, the mechanical arm kinematics parameter error is solved, a corrected kinematics parameter is input into a mechanical arm controller, and offline calibration of the mechanical arm is achieved; the mechanical arm tail end position measuring method is high in robustness, high in speed, and high in process automation degree, the adopted error solving method is high in speed, high in precision and simple in process, and the high-precision rapid calibration of joint type mechanical arm kinematics parameters can be achieved.

A stratospheric airship plane path tracking control method based on model predictive control comprises a step of desired tracking value giving; a step of guidance error calculation in which the distance error and angle error between a desired position and an actual position are calculated; a step of vertical and horizontal dynamic equation decomposition in which only the horizontal state quantity is adopted in controller design; a step of discrete system equation solving in which a stratospheric airship lateral continuous system obtained by the steps above is linearly processed, error derivatives are linearly processed, then, the airship lateral state quantity and error are taken as an extended quantity, and an extended continuous state space equation is discretized; a step of system future prediction in which the state or output quantity of a future period of time is predicted according to the current state quantity obtained by a combined inertial navigation sensor and the like; and a step of model predictive control objective function construction in which an objective function is constructed with a predicted state quantity, and the function is solved by a standard QP algorithm to obtain the system input quantity.

The invention provides a typical scene recognition based dynamic reconstruction method of a power distribution network. The dynamic reconstruction method comprises the steps of determining a weight of an optimal target under each typical scene; performing time division on a daily load curve of the power distribution network to obtain a reconstruction daily load forecast curve; identifying a type of the typical scene to which each time belongs on the reconstruction daily load forecast curve; building a multi-target reconstruction model of each time on the reconstruction daily load forecast curve to obtain an optical switch state of each time on the reconstruction daily load forecast curve; and adjusting the optimal switch state to obtain an optimal dynamic reconstruction scheme of the power distribution network. The reconstruction method proposed by the invention is high in reliability and fast in solution speed, and load change can be dealt with to acquire a targeted reconstruction scheme according to different typical scenes; the reconstruction scheme is optimal and is more reasonable; and the reconstruction stability and reliability of the power distribution network is ensured, the power distribution network after dynamic adjustment conforms more to the demand of actual running of the power distribution network, and the running stability of the power distribution network is improved.

The invention provides a large-scale flexible work workshop scheduling optimization method. A large-scale production task is reorganized to be downscaled and then solving and optimization are performed by using the adaptive improved genetic algorithm. The method comprises the following concrete steps that (1) the workpieces of which the processing technology is similar, the workpiece dimension iswithin the same range and the blank material is the same are clustered and grouped for batching so as to reduce the problem solving scale; and (2) the initial parameter of the algorithm is set, the three-layer gene coding technology, the OBX crossover mode and the certain variation strategy are adopted, the crossover length is selected through combination of the simulation experiment and optimization and solving are performed by using the adaptive improved genetic algorithm. According to the method, the problem solving scale can be reduced and the solving speed can be improved; and the workpiece completion time and delay time can be reduced.

The invention relates to a wind power optimal planning modeling method considering the adaptability and economy of a power system. The method comprises following steps: acquiring basic information of the power system; establishing a calculation model; setting the installed capacity, installed areas, power transmission line load rate upper limits of a wind power plant; selecting wind curtailment cost coefficients according to the deflection degree of an object; and working out the model. The power generation cost, power transmission investment and wind curtailment cost of the power system are considered in an objective function, and factors such as system peak regulation and power transmission capacity are also taken into account, so that wind power integration consumption and system investment operation economy of the planning model can reach the equilibrium. Through the solution of the model, the economy and adaptability of a wind power planning scheme can be multi-dimensionally evaluated, and a reasonable wind power construction area, a reasonable wind power construction scale, and an optimal construction scheme and an optimal time sequence for power transmission lines can be obtained, so that decision bases can be provided for wind power planners.

The invention discloses a neural network optimizing method and a device to solve the problems in the prior art of neural network technology that the calculation speed is low, and that the timeliness is poor. The method comprises: performing binarization to the convolution layer inputted data along the channel direction and packaging the bits to obtain the zipped data; performing binarization to the convolution kernels of the various convolution layers and packaging the bits to obtain the corresponding zipped convolution kernels; dividing the zipped input data into data blocks of the same sizes as the convolution kernels according to the calculation order in a succession manner with the input data contained in one convolution calculation forming a data block; performing convolution calculations to each data block of the zipped input data successively with the zipped convolution kernels to obtain the convolution result; and according to the convolution result, obtaining a plurality of output data for the convolution layers. The technical schemes of the invention are able to make the neural network calculation more speedy and more timely.

The present invention is chemical reduction process of preparing nanometer copper powder in water solution, and relates to electrochemical process for preparing powdered material. The preparation process adopts the proper combination of main salt, reductant and additives, the main salt is copper sulfate or copper chloride, the reductant is sodium hypophosphorate, sodium borohydride and formaldehyde, and the additives include silver nitrate or palladium chloride, gelatin or SDBS, potassium sodium tartrate or triethanolamine and sodium hydroxide or potassium hydroxide. During the preparation, outer ultrasonic field is applied. The preparation process is simple and high in preparation speed, and has no coagulation of copper powder to ensure the medium powder size smaller than 100 nm for ideal use effect of the nanometer copper powder.

The invention discloses a method for image fusion based on representation learning, which comprises the steps of acquiring a multi-source image, learning features of the multi-source image through a learning framework of a deep neural network formed by a sparse adaptive encoder, a deep confidence network formed by a Boltzmann machine and a deep convolutional neural network, completing fusion of the multi-source image by using the automatically learned features, and establishing an image fusion model; studying a convex optimization problem of the image fusion model, and carrying out initialization on the networks by using unsupervised pre-training in deep learning, thereby enabling the networks to find an optimal solution quickly in the training process; and establishing a deep learning network for cooperative training according to the features of the multi-source image through two or more deep learning networks, thereby realizing an image fusion technology of representation learning. The method disclosed by the invention studies feature-level fusion of the image by using artificial intelligence and a deep learning based feature representation method. Compared with a traditional pixel-level fusion method, the method disclosed by the invention can better understand image information, and thus further improves the quality of image fusion.

The invention relates to a method for exerting an ant colony algorithm on an optimal covering problem of a wireless sensor network node, the emphasis of the problem is emphasized on a random covering problem in wireless sensor network node covering when the ant colony algorithm is used as an optimal tool to solve the problem, and a problem model is divided to a 0 / 1 programming problem, and the ant colony algorithm is used to solve. The method comprises putting pheromones on a sensor network node in an algorithm which is proposed, endowing two pheromones to each node, and respectively indicating the selected and unselected information quantity of the node. And meanwhile, effective heuristic information is effectively arranged in the algorithm to guide the searching behaviors of ants, and the ants can decide which nodes to be used to cover according to the pheromones and the heuristic information. And finally, the efficiency and high efficiency of the algorithm which is invented are checked through a simulation test.

The invention discloses an initiative power distribution network scheduling optimizing method based on virtual power generation. The initiative power distribution network scheduling optimizing method includes the following steps: 1, building a scheduling optimization objective function; 2, obtaining constraint conditions of the scheduling optimization objective function, wherein the constraint conditions include system load balance constraint, system spinning reserving constraint, conventional-unit and demand-side resource technology contributing constraint, conventional unit climbing constraint, conventional-unit minimum running time and minimum stopping time constraint, demand-side maximum continuous controlled time constraint and electricity use mode satisfaction constraint; 3, according to the scheduling optimization objective function and the constraint conditions, determining the starting-stopping states of all the units one period by one period with the disperse particle swarm optimization based on a heuristic rule; 4, after the starting-stopping states of all the units in all the periods are calculated, on the basis of the known starting-stopping states, economically distributing the units according to the constraint conditions. In this way, initiative power distribution network scheduling optimization is achieved.

The invention relates to the technical field of artificial intelligence algorithms, and provides a method for optimizing a disaster emergency decision system path. The method comprises the steps that S11, an objective function and a fitness function are defined; S12, chromosome coding is carried out; S13, operators are selected according to the fitness function; S14, the operators are crossed; S15, the operators are mutated according to the fitness function and pheromone updating guiding mutation rules; S16, a plurality of sets of optimal solutions are generated, and the best solution is output through the fitness function. The quality and efficiency of the solutions are effectively improved through the pheromone guiding mutation rules of an ant colony algorithm. A large amount of useless redundant iteration of a genetic algorithm is avoided through the positive feedback mechanism and the parallelism of the ant colony algorithm, and the solution speed of the genetic algorithm is further improved. The decision model with a single saving point and multiple disaster points based on continuous supply losses is more suitable for emergency situations, and practical significance is achieved.

The invention discloses an aircraft crew scheduling method, which comprises the following steps: (1) acquiring original data meeting requirements of a user from a user interface and a database, and analyzing, converting and arranging the original data to acquire all essential data meeting required forms; (2) carrying constraint control on the essential data according to a scheduling rule, generating all probably scheduled task combinations for each aircraft crew, namely generating a relative task string LoW for each aircraft crew, and evaluating and correcting the task strings LoW; (3) converting all corrected task strings into a task column and establishing a constraint matrix; (4) solving the constraint matrix by utilizing an algorithm of disassembling a large-scale problem formed by integer programming problems into sub problems; and (5) outputting calculation results. The invention further discloses a system for realizing the aircraft crew scheduling method, wherein the system comprises a data input module, a model generation module, a solving module, a control module, a basic module and the like. According to the system for realizing the aircraft crew scheduling method, disclosed by the invention, requirements on equitable distribution of flight number plane, aircraft crew scheduling and flight task are met through cooperation of all modules.