698 results about "Bus transit" patented technology

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The invention provides a fixed-point area path optimization method and system for response type public transportation service, and relates to the technical field of intelligent transportation, and the method comprises the steps: constructing a response type public transportation service system model, and determining a mathematical expression of a fixed-point area path optimization problem; designing a path optimization solver by using an improved ant colony algorithm, executing a local search operation, and performing local optimization on the evaluated path to obtain an optimized path set; on the basis of the optimized path set, global pheromone distribution is updated; constructing a multi-objective optimization framework, taking the updated global pheromone distribution as prior information, and solving a Pareto optimal solution set by using an improved NSGA-II algorithm under the multi-objective optimization framework; generating a candidate path set according to the short-term passenger demand, the Pareto optimal solution set and the updated global pheromone distribution; and selecting an optimal path from the candidate path set by using a deep reinforcement learning algorithm, and carrying out vehicle distribution and scheduling.

The invention discloses an intelligent decision analysis system and method based on multi-source data, and belongs to the technical field of artificial intelligence. According to the invention, multi-source data related to bus operation is acquired, real-time acquisition and uploading of bus operation data are realized, data preprocessing is carried out, and the multi-source data are integrated; feature engineering is carried out, and a passenger flow prediction model is established; based on a passenger flow prediction model, obtaining predicted passenger flows in different time periods of the day, and predicting a driver state; establishing a bus scheduling model by using a deep learning model, and inputting predicted passenger flow, predicted driver state, historical data of bus operation and urban traffic condition data to obtain a bus scheduling scheme on that day; when the dispatched buses in a certain period of time of the bus route are not enough, a route from a bus station closest to a starting station of the bus route to the starting station is obtained, and idle buses are dispatched; and if no idle bus exists in the bus station, the buses of the rescue bus route allowed to be dispatched are dispatched.

The invention discloses a public transport passenger flow monitoring method and system based on human body detection, and the method comprises the steps: synchronously collecting skeleton key points, heat distribution and contour features of passengers through a vehicle-mounted multi-view camera and an infrared sensor, and generating a passenger space-time feature matrix; outputting a passenger movement track set based on the passenger space-time characteristic matrix; generating a dynamic congestion degree index according to the staying duration and the spatial distribution density of the passengers in the passenger movement track set; calculating an abnormal behavior probability based on the dynamic congestion degree index, and outputting an abnormal event early warning signal; and fusing the dynamic congestion degree index, the abnormal event early warning signal and the real-time passenger flow data, and generating a bus passenger flow monitoring result through space-time correlation analysis. According to the embodiment of the invention, high-precision, low-delay and intelligent passenger flow state monitoring and abnormity early warning can be realized.

The invention discloses a platform vehicle abnormal behavior intelligent analysis method based on deep learning. Accurate early warning is realized through multi-dimensional data acquisition and deep learning model construction. Firstly, platform passenger flow, road conditions, video monitoring and vehicle operation information are integrated, and a comprehensive data basis is provided for anomaly analysis. After the data is preprocessed, a graph neural network and an attention mechanism are utilized to perform preliminary fusion, and an AGCN adaptive graph convolutional network and a DTW dynamic time warping algorithm are introduced to optimize a fusion result, so that time sequence data are effectively aligned, and the feature extraction accuracy is improved. And then, a deep learning model is constructed to automatically identify abnormal behavior modes of the platform vehicles, such as illegal parking and passenger congestion. Early warning information is issued in real time, potential risks can be timely handled, and safe and efficient operation of a public transportation system is guaranteed. According to the scheme, the identification and early warning capability of the abnormal behavior of the station vehicle is enhanced, and powerful support is provided for bus safety management.

The invention discloses a dynamic public transport scheduling optimization system based on artificial intelligence, and the system comprises a mobile application program which is used for collecting the feedback of passengers, and displaying the real-time bus information, predicted arrival time, route suggestions and ticket price information to the passengers; the background management module is used for monitoring the state of the optimization system, adjusting a scheduling strategy and managing for feedback, and regularly maintaining and updating the optimization system; the data collection module is used for collecting vehicle GPS data, traffic flow monitoring data, weather conditions, special event information and historical and real-time data fed back in real time on social media; and the multi-modal traffic data fusion processing module is used for fusing the historical and real-time data collected by the data collection module and preprocessing the historical and real-time data. The invention also discloses a dynamic public transport scheduling optimization method based on artificial intelligence. According to the invention, the operation efficiency and the passenger satisfaction degree of public transportation are improved.

The invention relates to the technical field of intelligent scheduling of bus real-time data, in particular to an intelligent scheduling optimization system based on bus real-time data and a control method thereof, comprising a data acquisition module, a regional scheduling module, a charging strategy module, an intelligent optimization module, a communication module and a display and control module; the data acquisition module is used for acquiring operation data of the electric bus in real time, including position, speed, battery remaining capacity, current passenger number and environment information; according to the system, the running route and the charging plan of the buses can be dynamically adjusted, the operation efficiency of the electric bus fleet is improved, the charging strategy module combines the battery electric quantity, the endurance mileage prediction and the charging station load, the charging process is optimized, the charging efficiency is improved, and the charging efficiency of the electric bus fleet is improved. And the intelligent optimization module considers a battery life optimization strategy, and the service life of the battery is prolonged by reasonably scheduling the charging time and power.

The invention discloses an electric bus charging and discharging combined scheduling method, and belongs to the field of intelligent bus scheduling. The method comprises the following steps: firstly, collecting an operation plan and time-of-use electricity price data of an electric bus, and then constructing an electric bus participation vehicle network integration charging and discharging scheduling model which comprehensively considers cost benefits; the model can decide the optimal charging and discharging strategy of each electric bus according to the operation plan and the residual electric quantity of the electric bus and the real-time electricity price of a power grid, so that the bus operation requirement is met, the charging at the electricity price valley period and the discharging at the electricity price peak period can be fully realized, and the charging and discharging efficiency is improved. Therefore, the charging cost is reduced and the electricity selling income is maximized. Through the method, the electric bus not only can meet the requirements of public transportation, but also can participate in peak load shifting of a power grid, efficient utilization of energy and maximization of economic benefits are achieved, and powerful technical support is provided for sustainable development of the electric bus.

The invention discloses a trunk line intelligent network connection bus speed and signal timing collaborative optimization method, which comprises the following steps: acquiring basic information of bus lines, such as departure time interval, line length, station distribution and intersection signal phase; an intelligent networked bus speed control model is constructed, and dynamic speed guidance is implemented for different deviation types by calculating the deviation of the time headways of adjacent vehicles in real time; based on a vehicle speed control result, intersection signal lamp timing is optimized, and the negative influence of traffic signals on the driving speed of the public transport vehicle is reduced. By implementing accurate speed and signal lamp cooperative control at each bus station and intersection, the method can improve the overall operation efficiency of the trunk bus system to the maximum extent and reduce intersection delay while ensuring that the buses arrive on time, and compared with a traditional whole-line adjustment strategy, the method has the advantages that the overall operation efficiency of the trunk bus system is improved to the maximum extent, and the cost is reduced. According to the control strategy, the problem that the time headway deviation of a local road section is large can be solved more accurately.

The invention discloses a public traffic composite traffic network key node identification method based on an improved K-shell algorithm, and the method comprises the steps: firstly extracting the line, geographic position, vehicle arrival interval and rated passenger capacity information of a subway and a bus station from multi-source data, and constructing a weighted composite traffic network considering the intra-layer transportation capacity and inter-layer transfer relation; node stripping is carried out based on a K-shell algorithm, and a shell fine position KP value is calculated in combination with a node removal sequence; further introducing a neighborhood structure overlap ratio, and constructing a node local propagation influence index; meanwhile, a comprehensive correction coefficient is constructed by considering the difference between intra-layer node strength and transfer attributes and the transportation capacity difference between subway and bus layers; and finally, a node comprehensive importance index IKA is formed and is used for sorting and identifying network key nodes. Finally, the recognition effect is verified through network efficiency, connectivity and SIR (susceptibility-infection-recovery) propagation simulation, and the result shows that the method is superior to a traditional centrality algorithm in the aspects of recognition precision and propagation influence measurement.

The invention discloses a bus intelligent travel service dynamic recommendation transfer method, and relates to the technical field of bus services, and the method comprises the steps: S1, building a bus route set; s2, matching the overlapped stations with adjacent stations; s3, acquiring a starting point and an ending point of a user and generating candidate paths; s4, calculating transfer efficiency density, and performing path pruning; s5, calculating a departure time rhythm staggering coefficient between the transfer nodes; s6, calculating a transfer chain type risk accumulation function; s7, estimating total consumed time and waiting time; and S8, constructing a path comprehensive score and a recommendation sequence. According to the bus smart travel service dynamic recommendation transfer method provided by the invention, based on the public traffic operation path and station information, in combination with adjacent station distance calculation and departure rhythm feature construction, the structural association between transfer nodes can be identified more accurately, and path screening is realized through the rhythm interleaving factor, so that the transfer efficiency is improved. The recommendation frequency of the high-risk transfer path is effectively reduced, and the stability of the transfer process is improved.

The invention provides an automatic driving and control system of an intelligent public transport vehicle, which belongs to the technical field of intelligent public transport vehicles and comprises a cloud subsystem and a vehicle terminal subsystem, and the vehicle terminal subsystem comprises a cooperative control module, an in-vehicle monitoring module and an automatic driving control module. Wherein the cloud subsystem can determine a reference comfort threshold value of the intelligent public transport vehicle under multiple preset working conditions based on vehicle parameters and road condition information, and the cooperative control module can determine whether the intelligent public transport vehicle enters any target working condition in the multiple preset working conditions or not according to passenger image data collected by the in-vehicle monitoring module under the condition that the intelligent public transport vehicle enters any target working condition in the multiple preset working conditions. And the reference comfort threshold value under the target working condition is corrected to obtain a target comfort threshold value under the target working condition, so that the automatic driving control module can control the intelligent bus to run based on the target comfort threshold value, the comfort and safety of passengers taking the intelligent bus are fully ensured, and the taking experience of the passengers is effectively improved.

The present disclosure provides a coordinated and optimized dispatching method for electric buses and belongs to the technical field of smart buses. The present disclosure allows for comprehensive optimization of an electric bus dispatching strategy in time and space dimensions, establishment of a bi-level programming model for bus dispatching with consideration of a bus capacity, a transfer problem, and characteristics of electric buses, and solving of the model by a genetic algorithm. The present disclosure enables generation of a dispatching strategy for electric buses encompassing time and space aspects. The dispatching strategy is closer to an actual passenger flow situation and has better actual benefits.

A sensor bar or other array of sensors are mounted to the bus beneath or adjacent to a doorway of the bus. Ultrasonic sensors are located within the sensor bar and detect the presence of foreign objects such as an entrapped passenger close to the doorway. A safety output is generated by the system when an unsafe condition is detected. Examples of safety outputs can include warning sounds, warning lights, automatic door opening, using the brake transmission shift interlock (BTSI) to prevent transmission shifting for vehicle movement, and other safety outputs.

The bus shelter comprises a supporting assembly and a ceiling, a groove is formed in the bottom of the ceiling, a rack is fixedly arranged in the groove, and a first sliding groove and a second sliding groove are formed in the two sides of the groove respectively; a motor is fixedly installed in the first sliding groove, a lead screw is fixedly arranged on an output shaft of the motor and is in threaded connection with a second sliding block, and a rotating shaft is rotationally arranged on one side of the second sliding block. A sliding rod is fixedly arranged in the second sliding groove and slidably sleeved with a first sliding block, and the axis of the sliding rod is parallel to the axis of the lead screw. A gear and a baffle are fixedly connected to the rotating shaft. The gear is meshed with the rack. The rotating shaft is rotationally connected with the first sliding block. In the process that the motor drives the baffle to slide to the end of the ceiling, the gear makes contact with the rack, and therefore the baffle is driven to rotate. The baffle rotates to the inclined state so that the rain shielding area of the ceiling can be increased, meanwhile, rainwater can be prevented from being blown to the position below the ceiling, and passengers below the ceiling are prevented from being drenched by the rain.

The invention relates to the technical field of epoxy primer, and discloses a water-based bi-component epoxy primer for public buses, which comprises the following raw materials in parts by mass: a component A: 38-43 parts of water-based epoxy resin; 15-20 parts of an anti-rust and anti-corrosion pigment; 10 to 15 parts of a pigment filling agent; 10 to 15 parts of body filler; 2.5 to 3.0 parts of a cosolvent; 2.5 to 3.0 parts of a dispersant; 0.3 to 0.5 part of a defoaming agent; 0.5 to 1 part of a wetting leveling agent; 0.5 to 1 part of a flash rust inhibitor; 0.2 to 0.5 part of a rheological additive; 10 to 15 parts of deionized water; a component B: 15-30 parts of an epoxy curing agent; and 10 to 20 parts of deionized water. The water-borne epoxy resin is a mixed solution of two emulsions with different particle sizes, and the anti-rust pigment is a mixed pigment of aluminum tripolyphosphate and composite zinc phosphate. The water-based bi-component epoxy primer for the public bus is high in adhesive force, good in flexibility, excellent in impact resistance and excellent in salt mist resistance and chemical resistance.

The invention discloses an unmanned bus distribution scheduling method and system based on demand response, and the method comprises the steps: firstly, sensing the riding demands of all bus stops in real time through a bus stop sensing module, obtaining the riding demand data, and outputting the riding demand data to a rear-end scheduling module; then, the back-end scheduling module is used for calculating and obtaining an optimal scheduling scheme by using an unmanned bus flexible formation method according to the riding demand, and outputting a scheduling instruction to a bus-end operation module of the unmanned bus; according to the method, the function of dynamically adjusting the bus formation and scheduling by adopting the real-time sensing technology and the demand prediction model is realized, the waiting time of passengers is reduced, the vehicle passenger carrying rate is optimized, the formation recombination cost is reduced, the flexibility and adaptability of bus operation are enhanced, the public traffic service quality is improved, and the system is suitable for popularization and application. And sustainable traffic development is promoted, and the method is suitable for being widely popularized and used.

The invention discloses a bus short-time passenger flow prediction method and related equipment, a multi-source feature data set is constructed by collecting bus multi-source data and comprehensively considering the influence of multiple factors such as time, space and external factors on bus passenger flow, and a comprehensive and rich data basis is provided for establishing a high-precision prediction model. And inputting the processed bus multi-source data into the bus passenger flow prediction model to obtain a bus short-time passenger flow prediction result. The public transport passenger flow prediction model provided by the invention is based on a CNN-LSTM deep learning combination model, and the combination model not only can effectively extract the spatial features of the passenger flow data of the public transport station, but also can accurately capture the dynamic change of the passenger flow data on the time sequence, thereby realizing the accurate prediction of the short-time passenger flow of the public transport station. The public transport short-time passenger flow prediction method provided by the invention enriches a method system for passenger flow prediction of a public transport station, and effectively solves the problems that the existing research does not comprehensively consider influence factors on passenger flow prediction and the model is not widely applicable.

The invention relates to the field of urban public transport deployment, and provides a public transport network matching method and system based on an urban space structure, which divides urban areas by constructing a traffic model, identifies areas with dense demands on the basis of power consumption density, and provides a public transport network matching method and system based on the urban space structure. And the coverage condition of the existing public transport network on the areas with dense demands is evaluated, and uncovered areas are identified. For the uncovered areas, the information of stores and companies in the map data is used to subdivide the uncovered areas into a plurality of types of areas. A bus rapid transit system is additionally arranged in a commercial district to improve transportation efficiency, a peak period special line is additionally arranged in an office building area to meet commuting requirements, a customized bus special line is provided in an industrial district to meet requirements of specific periods and routes, and a conventional bus route is additionally arranged in a residential district to improve daily travel. Accurate matching between the urban space structure and the public transport demand is realized, and the coverage rate and efficiency of public transport service are improved.

The invention provides a public transport line passenger flow prediction method for optimizing a deep neural network based on a CPO algorithm, and belongs to the technical field of public transport. Comprising the following steps: data collection and arrangement; obtaining multi-source data and preprocessing the data; mining features in the public transportation system; features in the public transportation system are integrated into the passenger flow prediction model; the training set is used to train the obtained CNN-LSTM-Attention model; calculating a fitness function of a CPO optimization layer through the verification set, outputting a performance evaluation and passenger flow prediction result, and obtaining a verified CNN-LSTM-Attention model; and the verified CNN-LSTM-Attention model is used to predict the passenger flow of the bus route. According to the method, multi-factor comprehensive consideration is effectively realized, and various factors influencing the public transport passenger flow are effectively fused. And through the strong feature learning ability of the deep neural network, potential relationships between the factors and the passenger flow are mined, so that an internal driving mechanism of passenger flow change is comprehensively and meticulously reflected, and a prediction result is more suitable for an actual operation scene.

The invention discloses a bus passenger getting-on station estimation method based on multi-source data fusion, and relates to the field of public traffic operation planning and management, and the method comprises the steps: obtaining multi-source sample data, and carrying out the integration processing, and obtaining database storage information; processing and extracting by using the public transport data preprocessing terminal to obtain a public transport operation time parameter; segmenting and constructing a fixed threshold time window; matching the intelligent card data to obtain an intelligent card data set of the candidate site; performing first data screening, constructing a candidate site group, and calculating by using a maximum likelihood estimation model to obtain a first data set of a determined site; performing second data screening and division to obtain a time sequence subset, and performing prediction by using a prediction neighbor information algorithm to obtain site information of the second data; and carrying out unified site numbering to obtain a site matching data set, and carrying out verification by using a multi-dimensional data terminal to obtain an evaluation result of the multi-dimensional data. According to the method, key field dependence is broken through, and data adaptability is improved.

An energy conversion apparatus and a vehicle are provided. The energy conversion apparatus includes a motor coil of a motor (101), a bridge arm converter (102), a bus capacitor (103) connected to the bridge arm converter (102) in parallel, and a controller (104) connected to the bridge arm converter (102). When the energy conversion apparatus is connected to an external power supply, according to to-be-driven power of the motor and to-be-charged power of an external battery (105), the controller (104) controls the bridge arm converter (102) to cause electrical energy of the external power supply to flow to a drive-charging circuit, and adjusts a current of the drive-charging circuit, to cause the external power supply to drive the motor to output drive power and charge the external battery (105) at the same time.

The invention provides a conventional bus resource optimal configuration method and system considering subway toughness improvement, and belongs to the technical field of urban rail transit emergency connection. According to the method, actual factors such as a rail transit and bus network structure, conventional bus transportation requirements, metro emergency connection requirements, limited bus transportation capacity and bus system extension cost are comprehensively considered, and conventional bus service planning and metro interruption emergency response are collaboratively optimized; according to the method, stable operation of conventional buses can be guaranteed, meanwhile, passengers detained in the metro can be evacuated to the maximum extent, the service quality of a rail transit and bus connection mode can be improved, and the public transport network toughness can also be enhanced to the great extent. Meanwhile, in the collaborative optimization process of the conventional bus service and the bus connection service, a Pareto optimal method is introduced in view of the complexity of a dual-objective planning problem, so that efficient solving of an actual scale case is realized, and the method is closer to an actual problem.

A circuit for interconnecting a first controller area network (CAN) bus with a second CAN bus includes a first CAN transceiver, a second CAN transceiver, and a pair of transistors and six resistors electrically interconnecting the first and second CAN transceivers. The first CAN bus is included in a first device, such a bed to support a person or occupant, and the second CAN bus is included in a second device, such as a mattress supported by the bed. The circuit for interconnecting the first and second CAN busses is situated in an interior region of the mattress.

The invention discloses an intelligent public transportation transfer recommendation system based on multi-source data fusion, and relates to the technical field of intelligent transportation systems, and the system comprises a transfer recommendation management center which is in communication connection with the following modules: a data acquisition module which is used for collecting and preprocessing traffic data from a plurality of traffic data sources, and transmitting the traffic data to a data processing module; various traffic modes of metros and buses are covered; and the multi-source data fusion analysis module is used for carrying out fusion processing on the collected multi-source heterogeneous data. By fusing multi-source traffic data and combining simulation analysis and real-time traffic states, the transfer path is dynamically optimized, the transfer waiting time and total consumed time of the user are remarkably shortened, the user can obtain optimal path recommendation based on real-time traffic information, long-time waiting caused by sudden delay is avoided, the travel efficiency and experience are improved, and meanwhile, the user experience is improved. Double constraints of walking distance and transfer waiting time are supported, it is ensured that path planning meets actual requirements of users, and travel experience is further optimized.

The invention relates to the field of intelligent traffic systems, in particular to a multi-source data fusion-based vehicle-road collaborative bus priority system, which comprises the following modules: a data acquisition module, which comprises an electronic police, a bayonet device, a monitoring device, a thunder-vision all-in-one machine and a vehicle-road system device and is used for acquiring traffic flow data at an intersection and related road sections thereof; the data processing module comprises a data cleaning unit, a data classification unit and a data analysis unit and is used for cleaning, classifying and analyzing the collected data and extracting traffic flow features, and the data fusion module is used for fusing the traffic flow features through three-level fusion (vehicle data fusion, traffic flow feature fusion and vehicle state fusion). Accurate vehicle position and state information and real-time traffic conditions of the intersection and the vicinity of the intersection are obtained, and the traffic prediction module can accurately predict the traffic flow condition of the whole intersection within a short period of time by combining real-time data and historical data.

The invention discloses an urban circle traffic demand prediction method and system, and belongs to the technical field of traffic data processing, and the method comprises the steps: taking a four-stage theory as a basis, efficiently obtaining a travel demand OD matrix of urban circle global traffic middle area granularity in the future, and mastering the overall traffic demand pattern of an urban circle from a macroscopic level; on the basis of a trip chain theory, a city traffic cell granularity trip demand OD matrix with higher time-space precision in the future year is accurately obtained, and the method can adapt to more application scenes and reflect traffic prediction and evaluation analysis of traffic demand details; through a traffic distribution algorithm of motor vehicles and public traffic, traffic flow indexes borne by urban circle traffic supply facilities are obtained, and continuity and consistency of spatial and temporal distribution of the current situation and indexes such as traffic supply facilities, travel demands, road flow and bus passenger volume in the future are achieved. And therefore, the traffic demand prediction and evaluation index result of the urban circle in the future year can scientifically meet the needs of users.

The invention provides a bus automatic driving method and system, and relates to the technical field of intelligent public transportation, passenger flow prediction is carried out on a target area, route planning is carried out according to a passenger flow prediction result, and a bus operation baseline is constructed; collecting travel reservation requests of users, performing spatial-temporal clustering analysis, and constructing a travel demand cluster; and performing scheduling decision analysis on the travel demand cluster and the candidate vehicle information, establishing a bus scheduling strategy, issuing and executing the strategy, receiving a real-time travel request of a user, performing matching analysis on the real-time travel request and the initial running path, and performing dynamic insertion updating on the initial running path according to an analysis result. The technical problems of low scheduling efficiency and resource waste caused by the fact that personalized and decentralized travel requirements cannot be responded in time due to fixed lines and scheduling response lag in the prior art are solved, and the technical effects of improving the bus travel service matching rate and enhancing the operation flexibility and the resource configuration efficiency are achieved.

The invention belongs to the technical field of urban traffic management, and particularly relates to an electric bus cooperative scheduling method in a charging facility time-sharing sharing mode. The objective of the invention is to solve the problem that the existing method faces a single electric bus route, neglects the fluctuation of the charging demand of private cars in each time period, and is easy to cause low utilization rate caused by excessive charging facility distribution number in individual time periods, or long charging queuing time of private cars caused by insufficient charging facility distribution number. The method comprises the following steps: constructing a collaborative optimization model of a bus scheduling scheme and a charging facility sharing scheme based on the daily charging cost of an electric bus line and an expression of an estimated value of earnings obtained by sharing a charging pile to an electric car every day by a bus enterprise; solving the collaborative optimization model to obtain an optimal feasible solution and a corresponding objective function value; the optimal feasible solution comprises an electric bus scheduling scheme and a charging facility sharing scheme.

This application provides a motor controller for an electric vehicle. The motor controller includes a control circuit and an inverter circuit. The control circuit is configured to: in response to that the drive motor rotates with a pair of wheels of the electric vehicle, control a switch module to connect a center tap of the multiphase winding to the other end of the power battery, and control the inverter circuit to adjust a voltage at both ends of the bus capacitor. The bridge arms of the inverter circuit of the motor controller provided in this application and the winding of the drive motor form a voltage conversion circuit to adjust the voltage at both ends of the bus capacitor, to improve power performance and vehicle endurance of the electric vehicle.