35 results about "Particle swarm intelligence" patented technology

The invention discloses an intelligent power distribution network fault recovery method based on a multi-target discrete particle swarm. The method comprises the following steps: 1), initializing parameters; 2), initializing a position and a speed of a discrete particle swarm optimization algorithm, and according to intelligent power distribution network island dividing and load recovery algorithms, calculating each fitness value fitness k relative to a multi-target function for each particle; 3), based on a fitness control concept, performing classification of a control population and a non-control population; 4), according to a corresponding updating rule, updating a particle position and a particle speed in the control population; 5), performing dynamic exchange between the control population and the non-control population; 6), detecting whether a maximum iteration frequency is reached, and if the maximum iteration frequency is reached, skipping to step (4), and otherwise, entering step 7); and 7), outputting a final optimization result.

The invention discloses a charging scheduling method for an electric automobile battery swapping station. The charging scheduling method comprises the following steps that: S1, a scheduling department sends a next day load forecast to a scheduling system at the end of each day, and a management system calculates the upper limits and lower limits of total capacity and charging power of a battery within 24 time intervals in next day and submits the limits to the scheduling system; S2, a charging scheduling system of the electric automobile battery swapping station transmits received data to a charging scheduling model building module 2; S3, a charging power instruction calculation module calculates charging power Pij of the jth time interval of the ith battery swapping station in next day by a particle swarm intelligent optimization algorithm, and a charging power instruction output module transmits the charging power Pij of the Jth time interval of the ith battery swapping station in next day to the ith battery swapping station management system; and S4, an electric automobile power transformer management system executes charging operation according to a transmitted charging power instruction according to the battery condition in the station. By the charging scheduling method, the charging demand of the battery swapping station can be met, and the peak-load shifting effect on the power grid is realized; and the requirements of power grid running and electric automobile power transformation are coordinated.

The invention discloses a collaborative scheduling method of a power distribution network for the output uncertainty of a distributed power supply. The method comprises the following steps of (1) carrying out tracking analysis on a power flow direction of the distributed power supply in a control region of the power distribution network through a power flow tracking method and building interactive connection among the distributed power supply, the power distribution network and an electrical load in the power distribution network; (2) building a collaborative optimization scheduling model of a source network load for the output uncertainty of the distributed power supply under the given confidence level through building chance-constrained programming, and carrying out optimization employing the economic efficiency as the goal; and (3) solving an optimization model by combining a monte-carlo simulation method and a particle swarm intelligence algorithm and completing collaborative optimization. According to the collaborative scheduling method, uncertain factors, caused by the uncertainty of output fluctuation of the distributed power supply and power generation and load prediction errors, for optimization operation of the power distribution network are considered, and optimization operation and risk reduction are achieved through introducing a chance-constrained programming method of the uncertain factors.

The invention discloses a back analysis method of slope displacement based on safety monitoring. In the method, displacement monitoring data is fully utilized to perform optimized inversion and determine the rock mechanical parameter, the minimum error function between the displacement value of numerical calculation and the monitored displacement value is used as a target function, the modified particle swarm intelligent algorithm is utilized to optimize the target function, the numerical calculation is combined with the modified particle swarm intelligent optimization inversion method, and repeated iteration is carried out to fast converge the target function at a globally optimal solution, therefore, the optimal value of the specific parameter is determined, and a corresponding relationship between the displacement monitoring value and the rock mechanical parameter is established. In a practical project, the displacement value predicted by using the mechanical parameter of inversion perfectly matches with the actually measured displacement value and has little error, which indicates that the inversion method of the invention has relatively good practical application value in the back analysis of rock displacement.

The invention discloses an intelligent planning method for a three-dimensional global flight path of an unmanned aerial vehicle in an enemy threat uncertainty environment. The intelligent planning method comprises the steps: firstly building a three-dimensional environment model of an unmanned aerial vehicle according to a priori map; determining three objective functions for evaluating the advantages and disadvantages of the flight path, and establishing a three-objective optimization model of three-dimensional global flight path planning of the unmanned aerial vehicle in an enemy threat uncertain environment; secondly, performing particle swarm intelligent planning on the global path of the unmanned aerial vehicle by adopting an improved multi-target backbone particle swarm optimizationalgorithm; and finally, smoothing all paths in the obtained optimal path set by adopting a linear interpolation method, and displaying a plurality of solved feasible paths on a simulation map, so thata decision maker can select a final path according to actual conditions. The path selected by the intelligent planning method not only can avoid obstacles, but also can avoid threats of enemies, andthe length of the path is short, and a decision maker can select and obtain the optimal path according to actual requirements.

The invention discloses a dynamic parameter setting method for a superheater mechanism model by combining with field data. The method comprises the following steps that: according to a basic physicallaw, from the internal working process of a system, establishing a system mechanism simulation model; taking a superheater outlet parameter as a feature parameter, and carrying out modeling on the superheater in five sections in order to improve model accuracy; in addition, adding the dynamic parameter [Alpha] of the model; and according to practical operation features, carrying out optimal regulation on the simulation model. By use of the method, the superheater mechanism model is combined with a transfer function distinguishing model based on field data, a particle swarm intelligent algorithm is used to automatically set dynamic parameters in the mechanism model according to an error function on the basis of the time constant of the transfer function model distinguished by the field data, simplification and deficiency for the dynamic features of the practical process when the mechanism model is established can be effectively improved, and dynamic feature simulation accuracy and the development efficiency of a simulation system are improved.

The invention discloses an optimal dispatching method for a multi-energy complementary power generation system. According to the method, when a wind and light power generation system and a water suction energy storage power station are switched in a large power grid for operation, a coordinated dispatching method is applied, and the coordination problem between the power grid loss generated when the wind and light power generation system and the water suction energy storage power station are switched in the large power grid and the economic optimal object of a complementary system is solved. Moreover, the absorptive problem of wind and light power generation is further considered, a coordinated dispatching strategy is provided, and the method is used for carrying out system optimization onthe overall system containing the wind and light power generation system and the water suction energy storage power station. Therefore, a flexible, safe and reliable technology can be provided amongthe interior of the wind and light power generation system, the wind and light power generation system and the water suction energy storage power station and between the complementary system and the large power grid by using a particle swarm intelligence algorithm, flexible economic dispatching is realized, and the stability of the complementary system is guaranteed.

The invention provides a content center network caching method based on a software defined network. Under a fused architecture of the software defined network and a content center network, concentrated control and overall cache optimization are performed on caching nodes and content via perception of a controller on overall network topology and cached information. The controller periodically makes statistics on the cached information, and then performs cache optimization according to a data layer cache decision-making request. According to the method provided by the invention, an importance degree and an edge degree of the nodes and the popularity of content are brought into a cache decision-making strategy, mathematical modeling is performed according to the above information, and a particle swarm intelligent algorithm is used for optimizing the mathematical model. The method provided by the invention fully utilizes advantages of the controller on overall control and logic centralization, and thus the cache can perform decision optimization under multi-node coordination.

The invention discloses an intelligent optimization method for realizing multi-antenna position optimization configuration of a mobile terminal. System channel capacity ( ) is used as a system performance optimizing index, the antenna position of the mobile terminal is optimized through a particle swarm intelligent optimization algorithm, an optimal solution is obtained, and the obtained optimal solution is the optimum distribution position of mobile terminal antennas. According to the intelligent optimization method, the intelligent algorithm is applied to the DMIMO transmit-receive system mobile terminal, the antenna position of the mobile terminal is optimized, the performance of a current communication system can be well improved, the capacity performance of the current system can be well improved through the obtained mobile terminal optimal antenna distribution, requirements of communication services are met, and a reference is provided for the design of antenna distribution of the mobile terminal in the future.

The invention discloses a control method of a power station house cold source system based on machine learning and particle swarm optimization. The control method comprises the following steps that S1, modeling is carried out on the power station house cold source system according to an air conditioning refrigeration process mechanism, a refrigerator model, the actual refrigerating capacity of a refrigerator, the chilled water outlet temperature of the refrigerator, the wet bulb temperature and the cooling water supply and return temperature difference are input, and the cooling water inlet temperature of the single refrigerator is output; S2, modeling prediction is carried out on energy consumption of the power station house cold source system based on historical data; and S3, for predicted load demand data, the control parameters of the air conditioner cold source system are optimized in combination with the particle swarm optimization. According to the control method of the power station house cold source system based on machine learning and the particle swarm optimization, a mechanism model and a data driving model of the cold source system are combined, and the PSO intelligentcontrol algorithm is applied to optimize the air conditioner cold source system, so that the total energy consumption of the cold source system is reduced, and therefore the COP index is improved.

The invention relates to a steam turbine exhaust enthalpy predicating method based on a PSO-SVR soft measurement model. The method comprises the following steps of acquiring a sample data set; introducing a particle swarm intelligent algorithm, constructing a fusion type regression model based on a support vector machine for predicating the exhaust enthalpy, namely a PSO-SVR exhaust enthalpy softmeasurement model; training the PSO-SVR exhaust enthalpy soft measurement model based on the sample data set, performing solving for obtaining a best predication model, and establishing a corresponding exhaust enthalpy regression function; and performing steam turbine exhaust enthalpy predication based on the exhaust enthalpy regression function. Compared with the prior art, the steam turbine exhaust enthalpy predicating method has advantages of high predication capability, high predication precision, etc.

The invention belongs to the field of power system operation and control, and especially relates to an active safety verification method for a power transmission network line based on an extreme learning machine. The method provided by the invention gives consideration to a problem that a conventional heuristic-type active safety correction method based on sensitivity cannot be good in quickness and precision of strategy formulating adjustment at the same time, and the method provided by the invention balances the quickness and precision. The method comprises the steps: carrying out the analysis and learning of operation data of a power grid in different states based on the extreme learning machine, and obtaining the sensitivity of injection power of each node to a power transmission line; building an active safety correction model for the sensitivity of the power transmission line based on the injection power of the nodes; finally solving the active safety correction model through a particle swarm intelligent algorithm, and obtaining a generator and a load adjustment scheme.

The invention discloses a non-Gaussian fluctuating wind speed prediction method based on a hybrid intelligent algorithm. The method comprises the following steps: generating a large-span spherical roof structure spatial point non-Gaussian fluctuating wind speed sample through JT transformation and AR model simulation, and dividing the non-Gaussian fluctuating wind speed of each spatial point into a training set and a test set, and performing normalization processing before each simulation test; respectively deducing kernel parameter combinations consisting of kernel parameters and penalty factors searched based on cuckoo and particle swarm intelligent algorithms; by using a CS+PSO-LSSVM learning machine, transforming a non-Gaussian fluctuating wind speed training set sample into a kernel function matrix, mapping the kernel function matrix to a high-latitude feature space, then inputting the sample data and mapping it to a high-dimensional feature space through a nonlinear function; testing multiple linear algorithms on the kernel function matrix optimized by a hybrid intelligent algorithm, and acquiring a non-linear model of the non-Gaussian fluctuating wind speed training sample; and predicting the non-Gaussian fluctuating wind speed test set sample by using the non-linear model.

The invention relates to a BD-WPT system power coordination control method based on PI controller optimization, and the method specifically comprises the following steps: 1) controlling a primary sideinverter circuit to generate a constant voltage, and keeping a phase angle and a phase shift angle of the primary side inverter circuit unchanged; 2) taking the output power of the secondary side asan output variable, comparing the output power with a corresponding reference value, providing a comparison error for the optimized PI controller, and obtaining PI controller parameters in combinationwith a classical ZN setting method and a particle swarm intelligence algorithm; 3) generating a phase shift amount which should be generated by the secondary side output voltage of the system throughan amplitude limiting link, a gain link and the like, and adjusting the phase shift amount of the secondary side inverter output voltage of the system to be equal to the value; 4) comparing the output power of the system with the reference value again, and adjusting the actual phase shift amount to be equal to the value; and (5) repeating the steps (3) and (4) on the premise of ensuring that thephase difference between the output voltages of the inverters on the secondary side and the primary side of the system is fixed to be + / -90 degrees, and after a certain response time, ensuring that the system output tends to be stable.

The invention, which relates to the technical field of the monitoring system of the boiler explosion, discloses a monitoring system for a steam explosion in a furnace. The system comprises a real-time data acquisition module in a furnace. According to a boiler explosion early-warning system included by the real-time data acquisition module, a pressure sensor and a gas sensor are arranged in a boiler and collect relevant data in the furnace; and the relevant data are operated by using a relevant model in the software design. The system has the following beneficial effects: at a hardware design stage, the pressure sensor and the gas sensor are used for collecting and controlling data; and at a software design stage, a temperature element is added. With a particle swarm intelligent mining searching algorithm, a possible dangerous situation in a furnace is detected in artificial intelligence; and the explosion early-warning accuracy rate of the system is high on the condition of different boiler laying situations.

The invention discloses an optimum design method of a distributed antenna system suitable for highway and high-speed railway environments. N fan-shaped or linear areas are arranged in round communities or linear communities, and under the condition that particles are randomly distributed in the round communities or the linear communities, optimal positions of ports of base station antennas in different communities are obtained through a particle swarm intelligent optimization algorithm. The particle swarm intelligent optimization algorithm comprises the steps that a particle swarm is initialized, and the initial adaptive degrees of M particles are calculated; the position information and the speed information of the particles are updated, and whether the particles are still in a search area is judged; the individual optimal solution pbesti of the ith particle and the global optimal solution Gbest of the whole group are updated; the two earlier steps are executed until the convergence criterion of the algorithm is met, and the global optimal solution Gbest is output. According to the method, the layout positions of the antenna ports in the communities are subjected to theoretical optimization, the optimal coverage of the port antennas can be achieved, energy conservation and environment friendliness of the distributed antenna system can be conveniently achieved, and cost of network distribution is reduced.

The invention discloses a near-field underwater fixed multi-element linear array three-dimensional correction method, which comprises the following steps of: arranging a single signal correction signal source system at a plurality of positions and a plurality of depths around a seabed fixed multi-element underwater linear array in a time-sharing placement mode, transmitting single-frequency high-power correction signals, and recording azimuth information and time information of the signal source; recording depth information of each position of the array by using a depth sensor; carrying out synchronous time service on signal data sent by a fixed multi-element linear array receiving signal source by utilizing a navigation satellite signal, and carrying out time marking and conditioning on the data; after signals are received, constructing a near-field signal model containing amplitude-phase errors and three-dimensional position errors, then constructing a target function by taking a spatial spectrum estimation method as a theoretical basis, setting related parameters of a particle swarm intelligent algorithm, optimizing a target, and obtaining estimated values of the amplitude-phase errors and the three-dimensional position errors of an array; and substituting the error estimation value into an original array manifold, completing near-field array error correction, and obtaining absolute latitude and longitude coordinates of each array element of the whole array at the same time.

The invention relates to the technical field of power grid disaster prediction, and discloses a power grid rainstorm disaster prediction correction method based on a probability matching series-parallel connection coupling multi-model, so as to improve the accuracy of power grid rainstorm disaster prediction. The method comprises the following steps: selecting a forecast area, and obtaining actually measured precipitation and power grid rainstorm disaster information of the area in a historical period so as to establish at least two power grid rainstorm forecast models for forecasting the precipitation of the area; correspondingly correcting the precipitation of each power grid rainstorm forecasting model by using a probability matching correction method; predicting an error sequence et ofeach power grid rainstorm prediction model at the moment t by adopting an error autoregression model, and obtaining a precipitation sequence through series correction of each model; s4, carrying outparallel correction on the plurality of models in the step S4 by utilizing a least square method; and respectively solving parameters of series correction and parallel correction by adopting a particle swarm intelligent optimization algorithm, establishing an integrated coupling power grid rainstorm forecast correction model, and calculating a corrected result.

A method for optimizing the matching of flow component of reverse osmosis seawater desalination high-pressure pump features that the geometrical parameters of impeller, guide vane and anti-guide vaneare optimized. The method includes the following steps: first, the Latin square test design is used to obtain the multi-scheme design of the high-pressure seawater desalination pump, and then the sensitivity analysis of the pump multi-parameters is carried out, and the rule that the optimization objective is affected by the variation of these factors is explained by changing the numerical value ofthe relevant design variables one by one. At the same time, the response surface test design method is used to design several groups of test plans, and the function of the quadratic term between theoptimization objective and the design variables is established, and the quadratic regression analysis is carried out, so as to obtain the design variables which significantly affect the pump performance and remove the design parameters which have little influence on the pump performance. Thirdly, a PSO intelligent optimization algorithm is used to realize the automatic optimization of the performance. After many iterative calculations, the optimal parameter combination of the over-current components can be obtained in the global scope, and the performance matching design of the over-current components can be realized.

The invention relates to a micro-gird community distributed energy distribution method based on a demand side response. The method includes following steps: (1) establishing an Operator optimization model; (2) providing a distributed optimization algorithm based on the demand side response; and (3) introducing a peak-to-average ratio PAR to adjust a load side to satisfy the requirement of a largepower grid. According to the demand side response technology, a distributed control policy is employed, micro-grids perform parallel operation, the leakage of privacy information is effectively avoided, the problem of single-point fault is avoided, the load requirement of the whole micro-grid community can be controlled in a coordinated manner through the setting of the PAR, and through a particleswarm intelligent optimization algorithm, the optimal operation cost of the micro-grid is guaranteed, and the power consumption cost of users is reduced.

The invention discloses a super-large shield section frozen soil body temperature characteristic optimization method based on Gaussian process machine learning, and the method comprises the following steps: S1, carrying out the fitting of a temperature function of a reinforced soil body through the Gaussian process machine learning, and obtaining a function rule of the temperature; and S2, performing particle swarm intelligent optimization on the obtained implicit function, and obtaining the frozen surface of the frozen body. The invention also discloses a super-large shield section frozen soil body temperature characteristic optimization device based on Gaussian process machine learning, and the device comprises a temperature function fitting module which carries out the fitting of a temperature function of a reinforced soil body through the Gaussian process machine learning, and obtains a function rule of the temperature; and a frozen body function fitting module which is used for carrying out particle swarm intelligent optimization on the obtained implicit function and obtaining a frozen surface of the frozen body. The method can be widely applied to the technical field of tunnels and underground engineering by effectively controlling the time length of positive freezing, intelligently optimizing freezing, reducing resource waste and enhancing environmental protection.

The invention belongs to the technical field of aviation electromechanics, and discloses an intelligent parameter optimization method for a thermal management type combined power device, which comprises the following steps of: determining input and output parameters of each component of the thermal management type combined power device, performing mathematical modeling on the input and output parameters, establishing a system common working equation according to each input and output parameter, and optimizing the parameters of each component; establishing an equation set and a simulation model according to a system balance relation, and solving to obtain an output parameter result of each component; determining a function target, a performance target, an input and output parameter, an optimization parameter and a system constraint condition of the device according to the system requirement of the current working mode; and optimizing the simulation model by applying a particle swarm intelligent algorithm, and finally obtaining system output parameters with optimal energy efficiency. According to the device, an optimization evaluation system with energy consumption as an evaluation index and system requirements as constraint conditions is established, and optimal input parameters meeting the optimization evaluation system are obtained through parameter optimization, so that the energy consumption is reduced, and the system efficiency is improved.

The invention relates to a modeling method of an internal combustion engine-lithium bromide combined cooling heating and power system. The modeling method comprises: determining model input and outputvariables, determining a system model structure, determining a model parameter identification variable,; and substituting the system operation data into the improved particle swarm intelligence algorithm by adopting a method of combining a mechanism and data-driven modeling to identify unknown parameters of the system model so as to obtain a final model. Compared with the prior art, the technicalproblems that the internal combustion engine-lithium bromide combined cooling heating and power system is difficult to model, optimize and control are effectively solved, and a premise is provided for operation optimization and control strategy research of the combined cooling heating and power system.

The invention discloses a dynamic parameter setting method of a superheater mechanism model combined with field data. The method starts from the internal working process of the system according to the basic physical laws, establishes a system mechanism simulation model, and uses the superheater outlet parameters as characteristic parameters. In order to improve Model accuracy, the superheater is divided into five sections for modeling, and the model dynamic parameter α is added to optimize and adjust the simulation model according to the actual operating characteristics. This method combines the superheater mechanism model and the transfer function identification model based on field data, using particle swarm intelligence Algorithm, in the case of closed-loop operation of thermal power units, according to the time constant of the transfer function model identified from the field data, the dynamic parameter size in the mechanism model is automatically adjusted according to the error function, which can effectively improve the dynamic characteristics of the actual process when the mechanism model is established. Simplify and improve the simulation accuracy of dynamic characteristics and the development efficiency of the simulation system.

The invention discloses a method for establishing a high-frequency SPICE model of a multi-resonance-point capacitor, which is applied to the field of electronic component modeling, and aims to solve the problem that the modeling of the high-frequency model of the multi-resonance-point capacitor cannot be well solved in the prior art. The method comprises the following steps: measuring an impedancecurve of a capacitor through an impedance analyzer; obtaining impedance data of the capacitor; determining a high-frequency equivalent circuit model according to the impedance curve in the S1; obtaining an amplitude-frequency expression of impedance according to the high-frequency equivalent circuit model; further, according to the impedance data of the capacitor and the amplitude-frequency expression, using a least square method and a particle swarm intelligent optimization algorithm for solving, and obtaining optimal equivalent circuit parameters; and finally, writing the equivalent circuitparameters into a corresponding SPICE model according to an equivalent circuit.

The invention discloses an injection upper limit optimization method and system during a distributed power supply communication fault, and the method comprises the steps: S1, building a correspondingmodel of a communication fault scene and a link effectiveness state according to a fault mode consequence analysis method, and obtaining a communication fault scene and link effectiveness state table;S2, based on the communication fault scene and link validity state table, establishing a double-layer multi-objective optimization model of the differential injection upper limit of the distributed power supply; and S3, performing loop iteration on the double-layer multi-objective optimization model by adopting an interval power flow and particle swarm intelligent optimization algorithm to realize optimization of the upper limit of the differential injection of the lost DG.

The present invention proposes a software-defined network-based content-centric network caching method. Under the fusion architecture of software-defined network and content-centric network, through the controller's perception of global network topology and cache information, centralized control and monitoring of cache nodes and content Overall cache optimization. The controller periodically collects statistics on the cache information, and then performs cache optimization according to the cache decision request of the data layer. The invention incorporates the importance degree and edge degree of the node and the popularity degree of the content into the cache decision-making strategy, carries out mathematical modeling according to the above information, and optimizes the mathematical model by using the particle swarm intelligent algorithm. The invention makes full use of the controller's advantages of global control and logic centralization, so that the cache can perform decision-making optimization under multi-node cooperation.

The invention relates to an intelligent optimization method for fans used in auxiliary converter cabinets, comprising the steps of: S1, establishing an initial geometric and physical model of the original fan and the cabinet body of the converter cabinet; S2, selecting an optimization scheme according to the initial geometric and physical model; S3 , according to the selected optimization scheme, perform parametric modeling; S4, conduct orthogonal experiments on the parameters in the modeling; S5, establish a comprehensive evaluation model of fan vibration and noise performance; S6, construct a neural network, analyze parameters and vibration and noise The corresponding relationship of performance; S7, introduce particle swarm intelligence algorithm, determine the optimal optimization scheme in the neural network; S8, conduct simulation verification on the optimal optimization scheme. The present invention can not only obtain the optimal optimization scheme of auxiliary converter cabinet fan, reduce the aerodynamic noise of cooling fan to the greatest extent, but also analyze the internal relationship between various geometric parameters, flow field, sound field and even product performance in various optimization schemes , which is conducive to the initial design of products and other forms of technological transformation.

The invention discloses a method and system for optimizing the upper limit of injection when a distributed power supply fails in communication. The method for optimizing the upper limit of injection includes: Step S1: according to the failure mode consequence analysis method, establish a correspondence model between communication failure scenarios and link validity states , to obtain the communication failure scenario and the link availability status table; Step S2: Based on the communication failure scenario and the link availability status table, establish a two-layer multi-objective optimization model of the upper limit of the distributed power differential injection; Step S3: Use the interval power flow And the particle swarm intelligent optimization algorithm performs loop iteration on the double-layer multi-objective optimization model to realize the optimization of the upper limit of the differential injection of the disconnected DG.

The invention provides a design method for hinge points of a pullshovel working device of a monobucket hydraulic excavator and belongs to the technical field of monobucket hydraulic excavators. The design method for a hinge point (B) of a movable arm oil cylinder and a movable arm and a hinge point (F) of the movable arm and a bucket rod is characterized by comprising the following steps of: 1, selecting the size and the pose of the working device of an original excavator; 2, establishing a mechanism model; 3, enabling the movable arm oil cylinder to be equivalent to an equal-diameter rod during full shrinkage; 4, establishing a mechanism elastokinetics model; 5, establishing a system quality matrix and a stiffness matrix; 6, establishing constraints; 7, performing optimum design on the hinge point B; and 8, performing optimum design on the hinge point F. According to the method provided by the invention, the hinge points are designed through calculating the maximal hinge point position at natural frequency of the pullshovel working device by utilizing a particle swarm optimization intelligent algorithm, so that the method provided by the invention has the advantages of short design period, high programming degree, systematically improved dynamic characteristic of the pullshovel working device, and the like.