62 results about "Taxis" patented technology

The invention discloses an intelligent driving vehicle dynamic network redundancy configuration and switching method in a complex information source scene, and relates to the technical field of intelligent traffic and communication. According to the method, communication quality data of a plurality of communication links at different positions of the unmanned taxi in the driving process are detected, the data are broadcasted to other unmanned taxies around through a vehicle-mounted communication system, and communication quality data broadcasted by other unmanned taxies are received; the method does not depend on a centralized server or a cloud platform, and a signal map is fused and generated in a decentralized manner only based on self-detected and received data; dynamically configuring a network redundancy strategy according to the signal map and a preset task priority; after the strategy is executed, scores are recorded and broadcasted, scores of other vehicles are received, and the strategy is optimized and adjusted in advance. According to the method, the problem of unstable communication in a complex information source scene is solved, and the communication reliability and efficiency of the intelligent driving vehicle are improved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent scheduling method and device for unmanned taxis, and the method comprises the steps: constructing a spatial topological structure for connecting a target unmanned taxi, passengers and a charging station based on the received vehicle data, passenger data and environment data; capturing a spatial dependency relationship among the target unmanned taxi node, the passenger node and the charging station node to generate a spatial coding feature and a time coding feature, and combining the spatial coding feature and the time coding feature with corresponding weights to generate a spatial-temporal feature of the target unmanned taxi; and generating a passenger matching result and / or a path plan and / or a charging decision of the target unmanned taxi based on the spatial-temporal characteristics. Therefore, the problems that a traditional unmanned taxi scheduling method does not fully consider complex factors in an actual driving scene, scheduling decisions are still mostly single-target guiding, dynamic adaptability is lacked, and the increasing travel requirements of passengers cannot be met are solved.

The invention relates to the technical field of taxi transport capacity scheduling, in particular to a cruising taxi dynamic transport capacity scheduling method and system based on a space-time network, and the method comprises the steps: obtaining a road map layer and dividing a grid region, building a position and time combination point to recognize a cruising state, and extracting a waving track amplification to form a request trend region; and screening the empty vehicles, calculating the arrival path distance and sorting, extracting the get-off gathering points to generate guide grid points, and correcting the path end point to form a dynamic scheduling result set. The method comprises the following steps of: judging a vehicle state by constructing a position and time combination point; extracting a waving track amplification to identify an active area; screening empty vehicles by combining road connectivity and path distance; strengthening a connection relationship between resources and a target area, extracting a guide grid point based on get-off point aggregation intensity and distance convergence, and correcting a path end point to point to a passenger flow area. And the resource delivery collaboration and the scheduling rhythm matching degree are improved.

The invention discloses an airport taxi passenger distribution method and system, and relates to the technical field of taxi intelligent decision making, and the method comprises the steps: estimating the queuing waiting time of each taxi according to the number of passengers in an airport and the number of taxies, and carrying out the queuing waiting time of the taxies of which the queuing waiting time is smaller than a set threshold value; according to the airport queuing passenger-carrying income and the empty vehicle returning passenger-carrying income, a recommendation scheme whether to continue to queue in the airport is given; aiming at taxis which continue to queue, an optimization function with the purpose of minimizing the average passenger getting-on time is constructed according to the time for the taxis to run from the vehicle storage pool to the getting-on points and the time for the passengers to start to get on the vehicles, and the optimization function is solved according to the number constraint of the getting-on points and the number constraint of the taxis at each getting-on point; and the optimal number of the taxi getting-on points and the optimal number of the taxies at each taxi getting-on point are obtained, so that the taxi getting-on points of the continuously queued taxies are distributed, and the airport operation efficiency is improved.

The invention provides an automatic driving taxi charging and discharging scheduling method based on POI area perception and multi-target reinforcement learning. The method comprises the following steps of 1, obtaining urban historical renting order data, and modeling orders, automatic driving electric taxis and charging stations; 2, acquiring data of an urban POI buffer area, and performing area modeling; step 3, establishing an electric taxi order receiving-charging environment model; 4, establishing a charging station-electric taxi matching model; 5, constructing a state space and an action space structure in combination with POI region features; step 6, constructing a deep reinforcement learning network based on an IPPO algorithm; 7, performing real-time charging scheduling based on a deep reinforcement learning network; according to the intelligent scheduling method for the automatic driving electric taxi team, urban spatial feature recognition, energy adjustment optimization and deep strategy learning are integrated, intelligent regulation and control of the urban electric taxi team by the scheduling platform can be realized, and the method has high timeliness and practical value.

The invention discloses a vehicle-road collaborative optimization scheduling method based on a demand thermodynamic diagram and real-time monitoring data, and relates to the field of intelligent traffic. Comprising the steps that an unmanned taxi periodically broadcasts the geographic position, the no-load state and the number of taxi taking requests received in a current neighborhood; the unmanned taxies receive broadcast information of other unmanned taxies in the neighborhood and generate local travel demand thermodynamic diagrams according to the broadcast information; the unmanned taxi broadcasts the local travel demand thermodynamic diagram again after generating the local travel demand thermodynamic diagram; the unmanned taxies continuously receive local travel demand thermodynamic diagrams broadcasted by other unmanned taxies in a neighborhood and a multi-hop neighborhood in a certain range, and multiple local travel demand thermodynamic diagrams are subjected to weighted fusion to generate an extended travel demand thermodynamic diagram; and the unmanned taxi calculates an allocation priority score based on the extended travel demand thermodynamic diagram, selects an allocation target grid and executes allocation. According to the invention, the problems of dynamic change of urban traffic demands and low unmanned taxi scheduling efficiency are solved.

The application is suitable for the field of spatio-temporal data mining technology, and provides a taxi supply and demand imbalance road section detection method, which comprises the following steps: extracting a GPS data entry sequence corresponding to each license plate from a taxi GPS original data set in chronological order; determining a taxi supply point and a taxi demand point according to all GPS data entry sequences; determining a correction point of the taxi supply point and the taxi demand point in a city road network; finely dividing the city road network to obtain a final city road network; taking a road section with a taxi demand correction point in the final city road network as a seed road section, and expanding the seed road section along the final city road network through an iterative optimization method to obtain a plurality of candidate taxi supply and demand imbalance road sections; performing significance test by using a Monte Carlo simulation method, and taking a candidate taxi supply and demand imbalance road section that passes the significance test as a taxi supply and demand imbalance road section. The application can improve the accuracy of taxi supply and demand imbalance road section detection.

The invention discloses a transportation hub transport capacity scheduling system based on big data, and particularly relates to the technical field of traffic scheduling, which comprises a data aggregation and sensing module, an early warning and triggering module, a scheduling scheme simulation and decision module and an instruction distribution and execution monitoring module. The system collects subway, bus, taxi, online car-hailing and passenger flow data in real time through a multi-source interface and a sensor, and a unified transport capacity diagram is generated after cleaning and anomaly detection; carrying out transport capacity gap identification and early warning based on a multi-index coupling risk analysis model; a plurality of scheduling schemes are generated and evaluated through a microscopic simulation model integrated with passenger behavior prediction, and an optimal scheme is recommended; finally, the scheme is converted into a specific instruction to be distributed and executed, and real-time monitoring and feedback are performed to form closed-loop management. According to the invention, the transformation from passive response to active intervention and from experience decision to scientific scheduling is realized, and the transport capacity cooperation efficiency and passenger evacuation capability of the transportation hub are significantly improved.

The invention provides a high-speed rail passenger transport hub connection transport capacity configuration optimization method, and belongs to the field of traffic management. The method comprises the following steps: proposing a simplified calculation formula of waiting time for connecting a subway, a bus, a taxi and an online hailed taxi in a high-speed rail passenger transport hub; a high-speed rail passenger transport hub connection transport capacity configuration optimization model based on double-layer programming is constructed, an upper layer model is a departure interval decision-making model based on multi-objective optimization, and a lower layer model is a traveler travel mode selection model based on random user balance; and a hybrid intelligent optimization algorithm is provided for solving the bilayer planning. According to the method provided by the invention, optimization of connection transport capacity configuration of the high-speed rail passenger transport hub can be realized, and a basis is provided for formulating a passenger flow evacuation scheme of the high-speed rail passenger transport hub.

The invention discloses an intelligent management system applied to cruising taxis to obtain short-distance reloading opportunities, belongs to the technical field of intelligent traffic, and aims to solve the problem of service quality caused by receiving short-distance orders by taxis in transportation junctions. The system automatically identifies and verifies short-distance orders received at a hub station through data acquisition, order identification, point management, reloading opportunity granting and other modules, and grants points to a driver according to multi-dimensional factors such as waiting time and mileage. And the driver can use the obtained credits to exchange the authority of queuing-free access to the reloading channel. According to the invention, multi-source data such as vehicle snapshot, a GPS and a taximeter are fused to carry out order authenticity verification, and an excitation strategy can be dynamically optimized based on operation data. According to the scheme, traditional passive punishment is converted into active excitation, the driver and passenger contradiction is effectively solved, and the taxi operation efficiency and the travel experience of passengers are remarkably improved.

According to the present invention, taxis are allocated in consideration of advertising revenue.　A vehicle allocation management server 10, which is a vehicle allocation determination device, comprises: an acquisition unit 11 that acquires, for each of taxis that are a plurality of vehicle allocation candidates, information indicating a route from the locations of the taxis to the location of a vehicle allocation target and the time required to travel the route; a calculation unit 12 that calculates, for each of the taxis, the revenue from an advertisement, displayed on the outside of the taxi, according to the route indicated by the information acquired by the acquisition unit 11, and at the same time calculates, for each of the taxis, a penalty corresponding to the required time indicated by the information acquired by the acquisition unit 11; and a determination unit 13 that determines a taxi to be allocated from among the plurality of taxis on the basis of the revenue and the penalty calculated by the calculation unit 12.

The invention relates to the technical field of public transportation, in particular to an airport taxi real-time scheduling system based on deep learning, which is characterized in that heat vector diagrams of passengers, taxis and private cars are generated by comprehensively acquiring and fusing passenger, vehicle and environment data of an airport area; and demand prediction and transport capacity suggestion of each region of the airport in a future time period are realized by combining external factors. According to the system, a private car popularity vector diagram is introduced to correct passenger and taxi popularity vector diagrams, and multi-source data and a neural network model are fused to carry out intelligent decision making, so that the initiative and accuracy of airport taxi scheduling are improved.

The application discloses a dynamic carpooling scheduling method for unmanned electric taxis, which comprises the following steps: obtaining data and performing cleaning; mapping the data in each hexagonal grid; establishing a travel demand feature matrix and a spatial dependence matrix; constructing a spatio-temporal graph convolutional neural network based on an attention mechanism to obtain a travel demand prediction result; setting a constraint condition for single-car matching in a dynamic carpooling scenario; constructing a single-car matching decision model based on distributed deep reinforcement learning based on the travel demand prediction result and the single-car matching constraint condition; designing a Double DQN solving algorithm; performing real-time order matching and idle vehicle scheduling based on the result; notifying the matched vehicles to complete the corresponding orders; and notifying the idle vehicles that have not been matched to perform regional scheduling or charging energy supplementing. In the dynamic carpooling of multiple passengers and a single car, the electric vehicle endurance journey constraint and the vehicle charging energy supplementing demand are considered, so that the quality is improved and the actual operation requirements are met.

The invention discloses a detection and processing method for two-segment trajectory cheating, which identifies and repairs jumping, rollback and overlapping phenomena through a vector included angle cosine value and an ellipse method, thereby improving the integrity of trajectory data and ensuring the accurate calculation of mileage. In combination with trajectory clustering and a Markov chain model, the system can realize more accurate short-term and long-term trajectory prediction, and platform scheduling is optimized; by means of similarity analysis and unsupervised learning, the system can efficiently detect abnormal track behaviors, and automatic recognition can be achieved even under the condition that no annotated data exists; besides, through a transfer learning framework and in combination with track data of taxis and buses, the system can effectively detect illegal online taxi-hailing behaviors, and the problem of insufficient annotation data is solved; finally, the system accurately calculates the driving mileage of the vehicle through the repaired track, and the fairness and accuracy of cost calculation are ensured.

A drive unit for air vehicle, which allows building the vertical take-off and landing vehicles, intended for use, for instance, in the production of flying taxis, as well as in the model-making branch and in the toy industry.The drive unit is composed of the air channel, in the form of a straight segment of a tube with circular section, which has fans with engines fixed on its both ends. The vertical draft force outlet-inlet nozzle opening is located between fixed fans of the drive unit.

An integrated intelligent safety and surveillance system for vehicles combining multiple cameras (internal fisheye, front, rear), radar collision sensor, high-precision GPS unit, environmental sensors (temperature, humidity, air quality), and ambient LED lighting within a single embedded unit with internal battery and optional solar power. Features protocol-agnostic communication architecture (GSM / 3G / 4G / 5G, Wi-Fi, Bluetooth, satellite, CAN bus) and supports multiple platforms (Android, Arduino, ESP, Raspberry Pi, STM32). Provides collision warning, 360-degree monitoring, real-time GPS tracking, geofencing, stolen vehicle recovery, driving behavior analysis, and risk heat maps. Targets commercial vehicles, taxis, ride-sharing, and fleet management. Core innovation: integrating all systems into one flexible platform with comprehensive spatial-temporal analytics.

1. Name of the product in this design: Display screen. 2. Purpose of this design: To display vehicle status and advertising information on the roof of taxis. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key features: the front view.

The invention provides a vehicle operation maintenance method, a vehicle operation maintenance device and electronic equipment, which are used for carrying out operation maintenance on an unmanned taxi by cooperating a driver so as to reduce the employment cost of a safety officer and improve the operation maintenance efficiency. The method comprises the steps of determining a maintenance type and a vehicle position of a to-be-maintained vehicle in response to an operation maintenance instruction of an unmanned taxi; and if the maintenance type is a specified type in which the driver has the processing capability, determining candidate drivers arriving at the vehicle position in the first time period based on the vehicle operation information of each driver in the platform. Based on the total number of vehicles to be maintained in the platform, a driver order receiving income mean value in the current time period and the relative distance between the candidate driver and the vehicle position, a maintenance reward value of the candidate driver and a parking order loss value generated by executing vehicle maintenance are determined; and issuing a maintenance task of the to-be-maintained vehicle to the target driver to indicate the target driver to obtain a corresponding maintenance reward value after maintaining the vehicle.

This utility model discloses a car rubber floor mat with a drainage structure, comprising a rubber floor mat body, an internal drainage mechanism, and limiting plates on the front and rear sides of the top left side of the rubber floor mat body. The drainage mechanism includes absorbent cotton, a rubber sheet, several drainage grooves, and limiting grooves. The bottom of the absorbent cotton is fixedly connected to the top of the rubber sheet. The drainage grooves are located on the top of the rubber floor mat body, and the limiting grooves are located on the left side of the top of the rubber floor mat body. This invention solves the problem that some existing car rubber floor mats lack a drainage structure, which, especially in rainy seasons and in vehicles such as taxis and ride-hailing vehicles where passengers frequently get in and out, can easily bring rainwater into the surface of the car rubber floor mat, causing water to accumulate on the surface and become slippery, affecting riding comfort and safety when getting in and out of the vehicle.

The invention relates to the field of traffic communication transmission control, in particular to a vehicle-to-vehicle communication data transmission control method and device for a taxi. According to the method, the distribution of distances between vehicles in each taxi local area and the difference of vehicle speeds are analyzed to obtain the crowding degree of each taxi at the current moment, and the crowding degree of each taxi at the current moment is calculated according to the change of the position point of each vehicle in each taxi local area at each moment in a preset time period before the current moment. The method comprises the following steps: acquiring a trajectory disorder index of each vehicle in a local area at the current moment, acquiring a communication transmission weight of each taxi at the current moment in combination with the crowding degree of each taxi at the current moment, and performing communication transmission with different priorities on each taxi based on the communication transmission weight. According to the invention, the taxi with congestion or high collision risk can transmit communication data in time, and the timeliness of taxi communication is ensured.

A method for detecting and analyzing the airport taxi capacity flow based on security monitoring videos includes the following steps: Step 1: Divide the prediction period; Step 2: Identify passengers based on airport security monitoring videos; Step 3: Identify taxis based on airport security monitoring videos; Step 4: Predict the passenger flow arrival rate; Step 5: Predict the taxi capacity flow arrival rate; Step 6: Predict the passenger queuing time; Step 7: Predict the taxi queuing time. Thus, the present invention provides a better and more appropriate real-time perception of passenger flow demand and capacity supply, and can better accurately predict the queuing times of passengers and taxi drivers according to the supply-demand matching analysis, effectively improving the airport landside traffic service level and being highly innovative.

The image shown in the image diagram is for displaying the results of analysis of vehicle operation data such as taxis and other commercial vehicles. When a target area is clicked on the map located on the left side of the image, data such as sales amount, average unit price, or number of sales for a certain period of time, day of the week, etc. for the clicked target area is displayed in a table located on the right side of the map. A list button for selecting an employee is displayed in a vertical rectangular frame located on the right side of the image.

The invention belongs to the technical field of intelligent traffic and low carbon, and particularly discloses a hub taxi intelligent scheduling method and system based on dynamic zoning, and the method comprises the steps: dividing passengers into three levels: short distance, midway and long distance; state detection and code management are carried out on the taxis entering the hub area, and a vehicle attribute set is constructed; the method comprises the following steps of: dividing lanes in a hub passenger waiting area into three types of short-distance special lanes, long-distance special lanes and flexible adjustment lanes, and periodically adjusting the number of the lanes of each type based on passenger type distribution data and taxi queuing data which are acquired in real time; and based on the passenger level, the identification number and the vehicle attribute set, realizing optimal matching of the passenger and the taxi through a multi-factor weighted matching algorithm, and planning an optimal driving path of the order. According to the invention, the problems that the prior art lacks a systematic scheme of demand perception-dynamic adjustment-precise matching-effect feedback and cannot meet the demands of drivers, passengers and environments at the same time are solved.

This invention belongs to the field of intelligent transportation and low-carbon technology, specifically disclosing a hub taxi intelligent dispatching method and system based on dynamic zoning. The method includes: classifying passengers into three levels: short-distance, medium-distance, and long-distance; performing status detection and coding management on taxis entering the hub area to construct a vehicle attribute set; dividing the lanes in the hub waiting area into three types: dedicated short-distance lanes, dedicated long-distance lanes, and flexible adjustment lanes, and periodically adjusting the number of each type of lane based on real-time collected passenger type distribution data and taxi queuing data; and achieving optimal matching between passengers and taxis through a multi-factor weighted matching algorithm based on passenger level, identification number, and vehicle attribute set, and planning the optimal driving route for the order. This invention solves the problem that existing technologies lack a systematic solution for demand perception, dynamic adjustment, precise matching, and effect feedback, and cannot simultaneously meet the needs of drivers, passengers, and the environment.

The application relates to the field of traffic communication transmission control, in particular to a car-to-car communication data transmission control method and equipment for taxis. The method obtains the congestion degree of each taxi at the current moment by analyzing the distance distribution between each vehicle in the local area of each taxi and the speed difference, obtains the trajectory disorder index of each vehicle in the local area at the current moment according to the position point change of each vehicle in the local area at each moment in a preset time period before the current moment of each taxi, and obtains the communication transmission weight of each taxi at the current moment in combination with the congestion degree of each taxi at the current moment. The communication transmission of each taxi is carried out with different priorities based on the communication transmission weight. The application can make the taxis with greater congestion or collision risk transmit communication data in time, and ensure the timeliness of taxi communication.

The invention provides a method, system and device for predicting a passenger-carrying hotspot, a medium and a program product. The method comprises the following steps: acquiring historical data of a taxi passenger-carrying point; performing data clustering on historical data based on a density clustering algorithm of a greedy strategy to obtain a passenger-carrying hotspot clustering result; the passenger-carrying hotspot clustering result comprises a plurality of passenger-carrying hotspots for taxi drivers to select. According to the method, data mining is performed on the sampled historical data through the density clustering algorithm of the greedy strategy, so that the problems that a traditional clustering algorithm cannot well process large-scale data and the clustering effect is poor due to insufficient priori knowledge can be avoided; meanwhile, the adaptability of a search radius and a density threshold value during clustering of a density data set with uneven density is improved, the clustering efficiency of large-scale data is improved, a passenger-carrying hotspot clustering result serves as a decision-making basis for reasonable distribution and scheduling of taxies, the scheduling efficiency and the passenger-carrying success rate are improved, and the service life of the taxies is prolonged. And the operation efficiency of the taxi can be improved.

This invention discloses a charging guidance method and system for two-shift electric taxis based on Q-learning. The method first constructs an electric taxi operation process model to simulate the vehicle's route selection and state of charge changes under different states. Second, it establishes a charging behavior model based on the Q-learning algorithm. By defining discretized time, region, and state of charge spaces, it sets passenger-finding and charging actions and designs a sum-of-benefits penalty function that includes state of charge threshold constraints at shift change times, training to obtain the optimal charging decision strategy. Finally, the guidance effect is evaluated from three dimensions: charging cost, satisfaction of residents' travel needs, and grid power supply balance. This invention effectively reduces unnecessary charging times through intelligent guidance, smooths the charging load during shift change times, lowers operating costs, and simultaneously improves grid stability and residents' travel security.

A taxis analysis method for performing taxis analysis of nematodes using a container in which a reference point is provided in the container or a culture medium in the container, the taxis analysis method including steps of: imaging a distribution mode of the nematodes in the container after the nematodes and a specimen of a subject are dropped into the container; detecting a position of an object of the reference point corresponding to the reference point included in the image obtained by imaging; determining an attraction region and a avoidance region on the basis of the position; and executing taxis analysis using objects of the nematodes in the determined attraction region and avoidance region.

This invention relates to the field of vehicle management technology, specifically to a management system for managing unauthorized substitute drivers. The system includes a management settings module, a non-driving fare meter unlocking / locking module, an information data management module, an anti-fraud electronic service supervision module, and a hardware interaction module. The management settings module manages the activation and deactivation of non-driving detection, automatically locking the fare meter upon detection failure, and sets the detection interval, detection frequency, and facial recognition matching threshold. The non-driving fare meter unlocking / locking module is used for locking and unlocking the fare meter. The information data management and query module stores information on officially hired drivers. The anti-fraud electronic service supervision module obtains driver information. The hardware interaction module enables two-way data communication between the dynamic monitoring platform and the taxi. This system controls driver qualifications from the source, manages the entire process, completely eradicates industry management loopholes related to unauthorized substitute drivers in taxis, and reduces operational risks for businesses.

The present invention discloses a method for dynamic network redundancy configuration and switching of intelligent driving vehicles in complex signal source scenarios, which relates to the fields of intelligent transportation and communication technologies. The method of the present invention detects the communication quality data of multiple communication links at different positions during the driving of driverless taxis, broadcasts the data to other surrounding driverless taxis through the vehicle-mounted communication system, and receives the communication quality data broadcast by other driverless taxis; without relying on a centralized server or a cloud platform, it decentralizes and fuses to generate a signal map only based on its own detected and received data; according to the signal map and the preset task priorities, it dynamically configures the network redundancy strategy; after executing the strategy, it records the score and broadcasts it, receives the scores of other vehicles, and optimizes and adjusts the strategy in advance. This method solves the problem of unstable communication in complex signal source scenarios and improves the communication reliability and efficiency of intelligent driving vehicles.