85results about How to "Fast solution" patented technology

The invention provides a parallel iteration solving method and system for an electromagnetic finite element equation set, and relates to the technical field of electromagnetic finite elements, and the method comprises the steps: constructing a to-be-solved electromagnetic finite element equation set # imgabs0 #; dividing the electromagnetic finite element equation set into a plurality of calculation sub-tasks, and distributing a sub-matrix block # imgabs 1 # and a sub-right-end item vector # imgabs 2 # of the sub-tasks to each process in the super computer; by means of the initialized Hoseholder-GMRES algorithm, the processes are subjected to subtask calculation in parallel, and the local optimal solution # imgabs3 # of each process is obtained; summarizing the local optimal solution # imgabs4 # of each process to obtain an optimal solution # imgabs5 # of the electromagnetic finite element equation set; according to the method, the electromagnetic finite element, the Householder-GMRES algorithm and parallel computing are combined, and the solving efficiency of the linear equation set of the electromagnetic finite element is improved by utilizing a parallel optimization technology.

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

The invention discloses a time sequence production model deduction method and apparatus, a terminal device and a storage medium. The method comprises the steps of obtaining a first time sequence production model of a power system; four different thinking chains are constructed; wherein the thinking chain is solved by using time decomposition, space decomposition or parallel calculation; the first time sequence production model is solved according to the four thinking chains, and a fourth solving result is obtained; the result with the minimum Euclidean distance sum with all the solving results is calculated and serves as a target result; and planning and scheduling the power system according to the numerical value of the decision variable in the target result. According to the method, the simulation precision is remarkably improved through time decomposition and space decomposition. Through parallel calculation, the solving speed is improved, important details are reserved, and errors caused by simplification are reduced. In addition, the thinking chain integration effectively integrates the results of a plurality of reasoning paths, and the representativeness and accuracy of the simulation result are improved.

The invention discloses a dynamic vehicle path optimization method based on online deep reinforcement learning. The method mainly solves the problems that an existing dynamic vehicle path optimization method is poor in dynamic change adaptive capacity and poor in algorithm real-time performance. The implementation scheme is as follows: 1) generating a data set; 2) constructing a decision model; 3) constructing and training a strategy network model and a value function estimation network model; and 4) solving a dynamic vehicle path problem with a time window by using the trained strategy network model. According to the method, a partially observable Markov decision model is established according to a time sequence of delivery clients of a motorcade, and a corresponding strategy network and a value function estimation network are designed to optimize a vehicle path. The strategy model obtained through training can quickly give the dynamic vehicle optimal path, and can adjust the planned path on line according to the dynamic change of related conditions, so that the solution quality is ensured, the solution speed is higher, and the real-time performance of the algorithm in processing the dynamic vehicle path optimization problem is improved.

The invention discloses a robot task allocation method based on an improved multi-target genetic algorithm, and relates to the technical field of robot task allocation, and the method comprises the following steps: building a multi-target robot task allocation problem model, and initializing model parameters; and optimizing the multi-target robot task allocation problem model by adopting an improved multi-target genetic algorithm to obtain a task allocation result. According to the method, the population is divided into a plurality of sub-populations, improved crossover mutation operation, population knowledge sharing interaction operation and fusion selection operation are introduced, an improved multi-target genetic algorithm is formed and applied to optimization of a multi-target robot task allocation problem model, and the problems that an existing robot task allocation technology is poor in solving quality and high in efficiency can be effectively solved. The solving speed is slow, and the diversity of solution sets is poor.

The invention relates to the technical field of power systems, in particular to a control method and device for a tracking-networking fusion phase angle of a converter, and the method comprises the steps: respectively obtaining initial phase angles of a tracking control link and a networking control link in the tracking-networking fusion converter; determining an initial fusion coefficient according to the SCR and the system inertia of the power system accessed by the tracking-networking fusion converter; adjusting the initial fusion coefficient by using the direct current side current and the grid side current to obtain a final fusion coefficient; and calculating a fusion phase angle of the tracking-networking fusion converter according to the final fusion coefficient, the initial tracking phase angle and the initial networking phase angle. Therefore, the problems that in the related technology, only the influence of SCR is considered, the influence of the inertia of the connected system and the power amplitude limiting of the converter itself is not considered, the fusion coefficient cannot be adaptively adjusted under multiple working condition power grids, consequently, the supporting capacity of the power grids and the stability of the converter are poor, solving is complex, the response time is long, and the stability of the converter is poor are solved. And actual conditions such as different faults and current limiting cannot be adapted.

The invention discloses a mold for core making equipment and a curing method and application of the mold, and belongs to the technical field of core making machine molds. The curing method comprises the following steps that sand shooting forming is conducted, specifically, in the mold closing state, molding sand is shot into a mold cavity from a sand shooting nozzle in a mold body to be used for forming a sand core; and heating and curing are conducted, specifically, a curing medium heated to be in a gaseous state is injected into the cavity through a plurality of air inlet channels, the curing medium makes contact with the peripheral side of the sand core and then is diffused and permeated, the sand core is cured from the periphery of the sand core, and the air inlet ends of the air inlet channels are separated from the sand shooting nozzles. According to the invention, molding sand injected into the mold can be quickly and uniformly attached to the surface of the loose core, and each part of the sand core can be quickly and uniformly cured in a short time, so that the curing speed of large pieces and high pieces is greatly increased, the curing effect and efficiency are improved, the compactness of the sand core can be improved through vibration, and the production efficiency is improved. And the product quality is further improved.

The invention discloses an electric energy scheduling method, system and equipment based on wind power uncertainty, and particularly relates to the technical field of electric energy scheduling, and the technical key points are as follows: calculating an error value of a mixed skewed distribution fitting curve and wind power output sample data, and fitting the wind power output sample data and the error through a normal cloud model, a fitting function used for representing wind power short-term output prediction error distribution is obtained; performing data sampling on the fitting function and the load prediction error distribution curve to respectively obtain a wind power prediction error value and a load prediction error value, and establishing an uncertainty constraint condition by using the wind power prediction error value and the load prediction error value; based on the uncertainty constraint condition and a pre-constructed first deterministic constraint condition, taking the minimum total operation cost of the main network system in the scheduling period as a target function, and constructing an electric energy scheduling model; and solving the electric energy scheduling model through a CPLEX solver to obtain an electric energy scheduling scheme of the electric power system.

The invention discloses a wind-light storage configuration method considering transient voltage constraint and inertia constraint, and the method comprises the steps: obtaining a wind-light typical output scene and a direct-current power transmission plan power through employing a k-means clustering method based on wind-light output and receiving end load data, employing a bilevel programming model for wind-light-storage capacity configuration, and obtaining a wind-light storage capacity configuration model; the upper-layer model takes the minimization of the total investment cost as an optimization target, and constructs a transient overvoltage constraint, an inertia support constraint and a capacity constraint based on the short-circuit capacity; and the lower layer model takes the operation cost of the thermal power generating unit and the new energy power abandoning cost as targets, considers the power balance constraint, the operation constraint and the like of the wind-light-storage channel direct current power transmission delivery system, introduces the power transmission confidence probability constraint, and realizes the measurement of the delivery reliability of the wind-light storage channel. An improved grey wolf optimization algorithm is adopted, a Logistic-Tent mapping opposite learning mechanism is added, a mutation operator, a crossover operator and a selection operator are introduced in the differential mutation process of the algorithm, the population number and category richness in the algorithm are improved, and the solving speed is increased.

The invention provides an intelligent scattered wave deception jamming method for three-channel SAR-GMTI, and belongs to the technical field of radar jamming and artificial intelligence. The method comprises the following steps: firstly, acquiring interference scene parameters from intelligence support and electronic reconnaissance equipment; then, interference parameters are calculated based on the scene parameters, a distance-direction imaging position error objective function, an azimuth-direction imaging position error objective function and a Doppler slope error objective function are designed, a synthetic objective function is constructed, and an iterative optimization method is designed to solve the interference parameters including the carrier speed of interference equipment and the irradiation position of interference beams; and finally, adjusting the speed of an interference equipment aerial carrier and the irradiation position of an interference beam, and intercepting and forwarding a radar signal. According to the invention, by dynamically adjusting the motion trail of the interference airborne machine and the irradiation position of the interference beam, a vivid false motion target can be generated, the interference precision and efficiency are effectively improved, and the defects of slow speed and low precision of a traditional scattered wave deception interference method are overcome.

The invention discloses a data-driven enhanced optimization solving method for a security constraint unit commitment problem. The method comprises the following steps: constructing a security constraint unit commitment problem model of a power system; historical operation load data of a power system is collected, a confidence data set is constructed by using a Turing statistical method, and the confidence data set is divided into a training set and a verification set according to a proportion; constructing a neural network model, and inputting the training set and the verification set for network training; new load data of a power system is used as input, a multi-layer neural network is used as a classifier, and a logic strategy of unit operation is predicted online; by eliminating integer variables, an SCUC problem is converted into a continuous convex optimization problem, and by sorting a plurality of candidate integer solutions, feasibility of the model is simplified and optimized, and rapid online solution of the method is realized. According to the method, a high-quality approximate optimal solution can be provided, the solving speed is high, and the expandability is high. Not only can efficient power system scheduling optimization be realized, but also the peak regulation capability and the emergency response capability of the power grid can be enhanced.

The present application relates to the field of visual recognition technology, and discloses a visual recognition obstacle detection method, device, equipment and storage medium, the method comprising: performing time alignment and spatial registration on image data collected by a visual camera in a walking robot and point cloud data collected by a laser radar to obtain a multimodal data stream; inputting the multimodal data stream into a recursive gated deep convolutional neural network for feature extraction to obtain temporal multimodal fusion features; calculating an inter-modal correlation matrix, and constructing a three-dimensional voxel grid to obtain a three-dimensional environment representation; performing two-stage detection on the three-dimensional environment representation to obtain a comprehensive obstacle representation; performing environmental complexity assessment based on the comprehensive obstacle representation, and generating an adaptive real-time obstacle avoidance control strategy; converting the adaptive real-time obstacle avoidance control strategy into an optimal control problem, and performing rolling time domain optimization to obtain an optimal obstacle avoidance control sequence, thereby improving the accuracy of obstacle detection.

The invention aims to provide a compressor disc flow thermal coupling temperature calculation method. The compressor disc flow thermal coupling temperature calculation method comprises the following steps: acquiring a flow analysis result of fluid in a compressor disc cavity; the compressor disc cavity is divided into a first cavity body and a second cavity body according to the analysis result; simplifying the temperature in the first cavity into a cavity temperature node, and obtaining the temperature of the cavity temperature node and the temperature of a wall surface node of the first cavity; and the temperature in the second cavity is simplified into a heat flow line which exchanges heat with the wall face of the compressor disc cavity, a heat balance equation of the heat flow line is constructed, and the temperature of a node of the wall face of the second cavity is obtained according to the heat balance equation. The method can improve the heat analysis precision of the compressor disc.

The invention relates to the field of power grid material quality management and control, in particular to the technical field of quality evaluation, and more particularly relates to a power grid material sampling inspection strategy making method and system based on multiple dimensions, aiming at a special application scene of continuous small-batch arrival acceptance inspection of power grid materials by multiple suppliers, and aiming at the target of'quality-quality few inspection and quality-quality multiple inspection '. By introducing the AQL theory and integrating multiple factors such as the historical supply quality level of suppliers and the bearing capacity of detection resources, a total cost minimization model under the condition of maximum supplier sampling inspection strength and quality level evaluation sorting consistency is constructed, so that the optimal sampling inspection strategy of different power grid materials and different suppliers can be intelligently obtained, and the sampling inspection efficiency is improved. And a support is provided for an electric power company to make a sampling inspection scheme more accurately and realize reasonable allocation and utilization of resources.

The invention discloses a power flow optimization method and system containing a new energy power distribution network, and relates to the technical field of power flow optimization of a power distribution system. The method comprises the following steps: firstly, establishing a model for representing uncertain wind power and photovoltaic output, establishing a steady-state power flow model containing a new energy distribution network system, and realizing power flow calculation of the new energy distribution network; then, equality constraints and inequality constraints are determined, a multi-target optimal power flow model is established with the minimum economic cost and the minimum network loss as targets, and a Pareto set evaluation model is established; and finally, adopting a fast non-dominated sorting genetic (NSGA-II) algorithm with an elitist strategy to carry out power flow optimization solution on the power distribution system containing the new energy. The result shows that the method can effectively balance a plurality of targets, the optimized power flow distribution remarkably improves the operation stability and economy of the system, and a feasible optimization scheme can be provided for a power distribution system containing high-proportion renewable energy sources in the future.

The invention discloses an intelligent passenger car obstacle avoidance path planning method based on a genetic algorithm and extended RRT, and belongs to the technical field of intelligent car path planning. Firstly, a sampling flexible boundary is set according to a global path, an RRT algorithm sampling space is reduced, and a feasible path is guided to be close to the global path through a heuristic extension node; designing a metric function for search tree nodes to select father nodes, establishing a passenger car simplified model to detect collision, then constructing a path optimization area, solving an optimal obstacle avoidance path by using a genetic algorithm, and finally smoothing the path by using a Bezier interpolation method; according to the method, the obstacle avoidance local path can be quickly planned in consideration of the path quality and safety, the safety of the vehicle during driving is remarkably improved, and the accident rate of the vehicle is effectively reduced.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

The invention provides a rapid maximum efficiency tracking control method and system for a wireless charging system, and solves the problems of high time complexity, low solving speed and low maximum tracking speed of an existing maximum efficiency tracking control method. According to the method adopted by the invention, firstly, a current solution is compared with a previous solution and a latter solution, and if the current solution is superior to the previous solution and the latter solution at the same time, an interval in which an optimal solution is located can be determined; secondly, maximum efficiency tracking is carried out through a golden section search algorithm; the time complexity of the golden section search algorithm is generally O (log n), and n is the dimension of a solution space. The solution space dimension is generally greater than 1, and O (log n) is less than O (n) at the moment, so that the maximum efficiency tracking speed can be improved to a certain extent by adopting the perturbation observation-golden section method.

The present invention discloses a flexible job shop scheduling method based on an improved Seagull optimization algorithm, which belongs to the field of shop scheduling. The method includes: 1) solving the problem of small search space by improving the initialization population; 2) solving the problem that the Seagull optimization algorithm is prone to fall into the local optimum by redesigning the local search model; 3) enabling the offline trained model to quickly adapt to the new problem environment by integrating reinforcement learning. Through the present invention, the problem that the population diversity of the Seagull optimization algorithm decreases in the later iteration period and the algorithm is prone to fall into the local optimum can be solved; the convergence speed of the algorithm is accelerated and the accuracy is improved, so that the processing efficiency of the flexible job shop can be effectively improved, and the economic benefits of the factory can be improved. Finally, the method provided by the present invention is verified by examples and compared with other algorithms, which reflects the superiority of the method proposed by the present invention.

The present invention provides a human-machine collaborative manufacturing decision-making method and device considering heterogeneous operation resources, including: establishing and analyzing a mixed integer programming model for the human-machine collaborative manufacturing decision-making problem considering heterogeneous operation resources, decomposing the decision-making problem into four sub-problems: a task allocation sub-problem, a worker allocation sub-problem, a robot allocation sub-problem, and a workstation resource scheduling sub-problem; randomly creating a task operation sequence list, a worker allocation list, and a robot allocation list, performing task allocation, generating an initial task allocation list and a task operation mode list, respectively as initial solutions to the task allocation sub-problem and the workstation resource scheduling sub-problem, and using the randomly created worker allocation list and robot allocation list as initial solutions to the worker allocation sub-problem and the robot allocation sub-problem; based on an improved simulated annealing algorithm framework, iteratively optimizing the initial solutions of the four sub-problems to obtain the optimal solution. The present invention can achieve efficient problem solving.

The invention discloses a dual-channel multi-pulse signal synthesis method, which belongs to the technical field of multi-pulse signal synthesis and comprises the following steps: acquiring a plurality of pulse signals and parameters thereof; the pulse signals are stored in a random type signal pool and a periodic type signal pool according to time domain characteristics in the parameters; synthesizing the pulse signals in the periodic signal pool into a periodic pulse sequence; synthesizing the pulse signals in the random signal pool into a random pulse sequence; synthesizing the periodic pulse sequence and the random pulse sequence into a dual-channel multi-pulse signal; according to the method, hardware resources used by the two channels are small, the two channels process signal sets with different properties, priorities do not need to be set for pulse signals to be synthesized, and when pulse overlapping occurs, the probability that each pulse can be reserved is not affected by the priorities; according to time domain feature classification, pulse synthesis is performed by taking a single pulse as a unit, so that the condition of large-scale pulse loss is avoided.

The present invention relates to the technical field of harmonic source modeling, and specifically to a harmonic source principal component probability modeling method based on a typical power interval. Based on the harmonic coupling admittance matrix model, the power interval is divided according to the rated active power of the monitoring point in the substation area, and the probability of occurrence of each power interval within the monitoring time range is calculated, and the initial matrix of each power interval is solved, and a sub-probability density optimization method is proposed. Then, based on the improved moth fire optimized particle swarm algorithm, the parameters of the mixed probability distribution function are identified; the models of different power intervals are compared for similarity, and the probability distributions of elements at the same position are compared at the element level; the obtained similar intervals are clustered at the matrix level to obtain the optimal number of models based on the typical power interval. The model parameters are thresholded through the probability distribution of the diagonal elements of the typical power interval, and the dominant elements under the typical power interval are screened out to achieve effective characterization of the node harmonic emission level.

The invention provides a natural gas industry chain simulation method and system, a computer readable storage medium and electronic equipment, and relates to the technical field of oil and gas simulation tests.The method comprises the steps that a natural gas industry chain simulation model is constructed, and the natural gas industry chain simulation model comprises initial values of all preset indexes; determining a time sequence-based production constraint condition of the natural gas industry chain simulation model; and inputting a to-be-predicted production condition into the natural gas industry chain simulation model for operation, and obtaining a production operation condition after a preset time period. A set of production operation simulation model which can cover each link of the natural gas industry chain and is based on a time sequence is established by adopting a graph theory and system dynamics, and the production operation condition of the future natural gas industry chain under subsequent different time period requirements can be deduced by taking a certain time as a period according to different production technical schemes; decision support is provided for natural gas industry chain production and operation, the simulation effect is accurate, and the solving efficiency is high.

The invention provides a time-varying network-considered large-scale travel order connection plan automatic generation method and device and a readable storage medium, and the method comprises the steps: carrying out the parameter configuration through reading order and vehicle information; carrying out space-time clustering and local path planning on the orders, and merging on-the-way orders; and assigning the orders by using a genetic algorithm, and generating a connection plan of each vehicle / driver. According to the method, the calculation amount is effectively reduced, the solving efficiency is improved, the workload of workers is reduced, and the utilization rate of the connection vehicles is improved.

The invention relates to an engineering method suitable for rapid attack task planning of multiple types of targets, and belongs to the field of task planning of multiple unmanned aerial vehicles. Comprising the steps of constructing a multi-type target model; calculating the flight path length from each aircraft to each target according to the aircraft position and the target position, and determining the aircraft executing the attack task on the target according to the flight path length and the own capability of the aircraft; the ability of the aircraft comprises a residual voyage and a residual strike ability; establishing a strike task teaching model, screening a target with the minimum evaluation index as a currently selected optimal strike target according to a greedy algorithm, and performing an unmanned aerial vehicle sequence on the target according to the number of unmanned aerial vehicles required for performing strike on the target; and planning an air route for the unmanned aerial vehicle which is allocated to execute the attack task, and searching an optimal attack angle in a circular traversal mode by taking the shortest air range as a target. According to the method, the multi-unmanned aerial vehicle collaborative strike task flight path planning solving time is effectively shortened, and the engineering application capability of the algorithm is improved.

This invention discloses a method for testing the absolute efficiency of a hydroelectric turbine based on the surge method in a surge chamber. Applied to hydroelectric power stations equipped with surge chambers, the method measures key parameters during normal operation and after load shedding when surges occur in the surge chamber. These key parameters are then substituted into the turbine flow equation corresponding to the surge chamber type to obtain the turbine flow rate during normal operation. The initial load of the turbine during normal operation is changed, and the above steps are repeated. The volute flow coefficient is calibrated based on the turbine flow rate and the differential pressure in the corresponding key parameters during normal operation. The turbine flow rate under the current operating condition is measured using the differential pressure flow measurement method. The turbine working head is calculated based on the current flow rate, and the turbine output formula is used to back-calculate the turbine absolute efficiency. This invention can conveniently and accurately obtain the turbine flow rate under various operating conditions, thereby achieving accurate calculation of the turbine's absolute efficiency.

This invention discloses a SKU shelving method and storage medium for a mobile robot warehousing system. The method includes: acquiring data information from the mobile robot warehousing system; constructing an SKU shelving decision model based on the acquired data information; constructing a calculation method for an SKU shelving strategy based on the data information and the SKU shelving decision model; calculating the SKU shelving result of the mobile robot warehousing system according to the calculation method, and implementing the method. The SKU shelving method strategy for a mobile robot warehousing system provided by this invention proposes an SKU shelving method from a globally optimal perspective. Compared with existing technologies, it can more rationally allocate shelving locations for SKUs, reduce the number of shelves required to complete all orders, and improve picking efficiency. This invention has good practicality and operability, and the calculation method is simple, providing a new and efficient strategy for solving the SKU shelving problem in mobile robot warehousing systems.

The application discloses a kind of based on uncertainty complement's multi-wind farm aggregated day-ahead output decision and deviation control method, comprising: step 1, generating wind power data, and carrying out uncertainty fitting;Step 2, the aggregated day-ahead output collaborative optimization model of multi-wind farm grouping is established;Step 3, the aggregated day-ahead output collaborative optimization model established in step 2 is solved and generates in-group settlement and responsibility signal.The application utilizes the uncertainty complement between wind farm under the premise of not depending on external flexibility resource, and is combined with the integrated collaborative optimization of conditional risk value and mixed integer programming, realizes the closed-loop process of "grouping-aggregated day-ahead output decision-deviation calculation and apportionment-settlement".

This invention relates to the field of satellite telemetry, tracking, and command (TT&C) technology, and provides a method, system, device, and medium for satellite IoT TT&C mission planning. The method includes: S1: obtaining the set of observable arc segments for each ground station; S2: dividing the mission planning time period into m time segments; S3: first solving the mission planning result for the first time segment; S4: removing observable arc segments in the current time segment that intersect with the previous time segment, completing the mission planning for the current time segment; S5: taking the current time segment as the previous time segment and the next time segment as the current time segment, repeating step S4 until the mission planning for the second to the mth time segments is completed; S6: combining the mission plans of the m time segments to form the satellite IoT TT&C mission plan. This scheme can plan all actions within the planning time period at once, designing a reasonable planning scheme and reducing the complexity of the mission planning algorithm.

The application discloses an auxiliary power output control method and system for an electric bicycle assist vehicle mode, and the method comprises the following steps: measuring the pitch angle, acceleration and speed of the electric bicycle during driving; during riding, when the rider stops pedaling, updating a second-order system model according to the pitch angle, acceleration, speed and direct-current brushless motor torque of the electric bicycle during driving; when the rider pedals the electric bicycle, calculating the torque output by the expected direct-current brushless motor according to the second-order system model, the pitch angle, acceleration, speed of the electric bicycle and the torque applied to the electric bicycle pedal by the rider, and driving the direct-current brushless motor to rotate according to the calculation result of the torque output by the expected direct-current brushless motor to output auxiliary torque. The application greatly reduces the manufacturing cost of the electric bicycle and improves the riding experience of the rider.