23 results about "Bicycle sharing" patented technology

This invention discloses a method and system for optimizing community bus service zones and sub-zone routes based on connection resistance, comprising the following steps: S1, constructing a main model for community shuttle bus service zones based on finite center-subordination rules to obtain the service sub-zone allocation scheme for community shuttle bus stops; S2, for each service sub-zone, constructing a sub-model for optimizing community shuttle bus service routes based on bus service impedance index to obtain shuttle bus service route schemes for each sub-zone connecting to public transportation hubs, determining whether the minimum service standard for shuttle buses is met by checking the connection resistance; for all sub-zones that fail the connection resistance test, adding the corresponding sub-zone partitioning constraint set to the main problem, and returning to S1 to resolve the main model until all shuttle bus route schemes for each sub-zone pass the test. This invention, through connection resistance testing, makes the designed community shuttle bus connection routes more competitive in service compared to shared bicycle travel.

The embodiment of the present specification provides a smart city shared bicycle delivery and operation area planning method and an Internet of Things system. The method is executed based on a management platform of the smart city shared bicycle delivery and operation area planning Internet of Things system. The method comprises the following steps: obtaining reference information of human flow of at least one target area, determining predicted human flow of the at least one target area at a target time based on the reference information of human flow, and determining a shared bicycle demand based on the predicted human flow; obtaining shared bicycle historical data of a plurality of reference areas, predicting a shared bicycle change amount of the at least one target area at the target time based on the shared bicycle historical data; and determining a shared bicycle delivery amount of the at least one target area at the target time based on the shared bicycle demand and the shared bicycle change amount.

The present application relates to the field of intelligent transportation system and big data analysis technology, and more particularly to a shared bicycle rail connection analysis method considering travel stage built environment, comprising: obtaining shared bicycle order trajectory data in a target area, identifying travel behavior, performing OD mapping of the identified connection travel starting point-destination, and generating shared bicycle travel OD samples; performing shared bicycle trajectory data corresponding to the OD samples for riding path reconstruction and path restoration; for different travel stages of shared bicycle connection travel, corresponding built environment features are extracted respectively; based on the built environment features of different travel stages, prediction models for predicting shared bicycle use probability of rail transit inbound connection and outbound connection are constructed respectively; the prediction models are explained and analyzed, and key influencing factors and their action relationships are output. The present application can more comprehensively and truly reflect the formation mechanism of shared bicycle and rail transit integrated travel.

The application provides a multi-constraint-based spatial element dynamic aggregation method and system, and belongs to the technical field of spatial data processing. The method comprises the following steps: acquiring shared bicycle parking point spatial element data set and multi-dimensional constraint conditions; performing spatial profiling on a target research area based on spatial division constraints, generating a plurality of initial aggregation service range units, and mapping elements in the spatial element data set into the initial aggregation service range units, and calculating statistical indexes of the initial aggregation service range units; constructing a graph structure with the initial aggregation service range units as nodes, and the spatial adjacency relationship and semantic similarity between the units as edges; constructing a graph structure aggregation model, including a target function with the minimum variance of the statistical indexes between the initial aggregation service range units as the target, and a constraint condition based on the integrity of the spatial division constraints; and solving the graph structure aggregation model by using a heuristic search algorithm or a graph partitioning algorithm to generate a dynamic aggregation service range result satisfying the statistical balance constraint.

The application relates to the technical field of urban bus line network and timetable integration optimization, and particularly discloses a bus line network and timetable integration optimization method and device based on graph reinforcement learning. The method is used for generating a bus line network structure and a timetable in a multi-mode public transport network containing BRT, subway and shared bicycles. Firstly, a multi-mode public transport road network is constructed, and passenger flow demand data is obtained; a bus line network design and a synchronous timetable compilation process are modeled as a Markov decision process; according to a current bus line network state, the road network is reconstructed into a heterogeneous graph containing time sequence evolution arcs and spatial topology arcs, and a double attention graph neural network is used to extract candidate node features; according to the node features, an action is selected through an action network, and the bus line network state is updated; the node feature extraction, the action selection are repeated until a termination condition is met, and the bus line network structure and the line timetable are output. The application can generate a bus line network and timetable which are more coordinated with other public transport modes, and reduce the operation cost of bus enterprises and the travel and transfer time of passengers.

The application discloses a shared bicycle demand prediction method based on a geographical distance enhanced space-time graph neural network, belongs to the technical field of bicycle demand prediction, and inputs bicycle data cleaned and aggregated into a double-branch framework integrating local and global space-time information, which is composed of a local information branch and a global information branch. In the local branch, the distance information between stations is introduced, a multi-scale Gaussian kernel is designed for processing, and is added to the calculation of attention scores to solve the deficiencies of existing models in global and local information integration and real geographical distance information extraction. In the global information branch, global spatial patterns and long-term time trends are extracted through the combination of GAT and TCN, and information is further fused through shared GNN and shared MLP to comprehensively integrate global space-time information, better grasp the overall change rule of bicycle demand in time and space, and improve the prediction accuracy and stability.

The application provides a locking structure for a shared bicycle, and relates to the technical field of bicycle locks. The locking structure comprises a main shaft and a hub wheel arranged on the main shaft. A locking piece is arranged between the hub wheel and the main shaft, and the locking piece is located in the interior of the hub wheel. The locking piece comprises a lock disc which is mounted in the hub wheel through bolts. A plurality of insertion holes are formed in the lock disc. A fixed plate is fixedly mounted on the main shaft. A mounting seat is mounted on one side of the fixed plate close to the lock disc through bolts. A push-pull electromagnetic lock is mounted on the mounting seat. A lock rod of the push-pull electromagnetic lock is inserted into one of the insertion holes. The locking piece is arranged in the hub wheel, and the locking piece is protected by the hub wheel. The direct damage of external environmental factors to the locking piece can be effectively reduced. The failure rate of the locking piece caused by environmental factors is reduced. The service life and reliability of the shared bicycle are improved.

The application provides a shared bicycle travel network community mining method based on deep learning. The method comprises the following steps: analyzing and counting the road network data information, the shared bicycle data information and the travel data of users in a specified area, and constructing a traffic travel network; quantitatively describing the travel characteristics of users in the shared bicycle travel network, taking the user travel quantitative indicators as network measurement indicators of a graph neural network model, and constructing the graph neural network model; performing community clustering analysis based on the graph neural network model by using a ClusterNet algorithm, and mining community structures. According to the method, a dynamic travel network is constructed according to different time periods, and the spatiotemporal distribution of travel demands in different stages is analyzed. By using a spatial statistics and a complex network method, indicators are constructed to quantitatively describe the travel characteristics, so that the changes of the travel modes of users in different periods can be clearly understood, and the community structures in the travel network can be dynamically mined.

This invention provides a vertical shared bicycle transfer device, belonging to the technical field of shared bicycle transfer. It includes a balancing transfer device and a vertical shared bicycle frame. The balancing transfer device comprises a lifting base, a balancing bicycle body, and a lifting base including a lifting platform, a support base, and a transmission mechanism disposed between the two. The transmission mechanism includes two hydraulic cylinders and two pairs of lifting links. The lifting links consist of a first lifting link and a second lifting link. Three vertical shared bicycle frames are arranged in an array on a shared bicycle frame fixing plate, each including a vertical retainer, a rear wheel support, a front wheel clamp, a first locking mechanism, a constant force spring, a second locking mechanism, and auxiliary wheels. The vertical retainer consists of a slide rail, a slider, and a mounting frame. The mounting frame is bolted to the shared bicycle frame fixing plate, the slide rail is bolted to the mounting frame, and the slider is slidably connected to the slide rail to achieve the up-and-down movement of the slider. The two sides of the mounting frame are connected to the auxiliary wheels via a transmission system.

This invention provides an integrated parking and returning device for shared bicycles, comprising: a fixing member for fixing to the bicycle stand; a trigger handle, the front end of which is hinged to the fixing member via a pivot, and a magnet is fixed to the front end for triggering the returning operation upon foot contact; a spring, the two ends of which are respectively connected to the fixing member and the trigger handle; and a trigger assembly, including a fixed housing mounted on the fixing member and a Hall sensor mounted inside the fixed housing, wherein the Hall sensor is used to detect changes in the position of the magnet. The device of this invention allows for simultaneous parking and returning of the bicycle through structural linkage and the Hall element when the trigger handle is pressed, eliminating the need for manual operation; foot-triggered returning eliminates hygiene concerns; and the device can be directly installed on the bicycle stand without modifying existing bicycle stands.

This invention relates to the fields of deep learning and public transportation technology, and discloses a method for differentiated and precise deployment of shared bicycles at various stations based on Extreme Learning Machine (ELM). First, an ELM prediction model is constructed. Then, data on the number of shared bicycles at each station in the target area within a specified time interval is collected, and the actual usage of shared bicycles at each station within that time interval is calculated to form a historical dataset. The historical dataset from day t (N+1 days prior) is normalized and formatted into a three-dimensional array {N+1,7,1}, which is then input into the ELM prediction model for training to predict the demand for shared bicycles at each station on day t+1 within the corresponding time interval. When the total predicted demand exceeds the threshold of the total number of schedulable bicycles in the target area, the total number to be reduced is calculated, resulting in the actual number of shared bicycles deployed at each station. This invention proposes using a deep learning model to accurately predict the demand for shared bicycles at each station from a time perspective, thereby scientifically guiding vehicle deployment.

The application provides a shared bicycle fault vehicle marking method and system based on multidimensional data, relates to the technical field of data processing, and the server obtains decomposition information by decomposing multidimensional data of a bicycle end, a device end and a service end corresponding to a target bicycle, and obtains first classified information and second classified information by classifying the decomposition information; a management end generates a judgment specification tree of the classified information based on a standard specification tree and image interaction; if independent first classified information or combined second classified information reaches a judgment condition of the judgment specification tree, a fault label is added to the corresponding bicycle end and a first moment is extracted; multidimensional data of the target bicycle from a first moment to a second moment in a normal state is obtained, and the fault label and / or the standard specification tree are updated, so that the fault vehicle can be identified and marked in a timely and accurate manner.

This application discloses a shared bicycle seat protection device, which automatically protects the shared bicycle seat by setting up a protective cover and a delayed flip-cover device assembly. Compared with the prior art, this application sets up a power spring in the delayed flip-cover device and relies on its tightening energy storage characteristics and the delayed energy release to automatically flip the protective cover located behind the seat to the top of the seat in a short time, achieving the effect of automatically flipping the protective cover.

The application discloses a kind of public transport line network optimization methods based on multi-mode travel data, including steps S1: collecting multi-mode traffic data information;Step S2: extracting single trip start location in traffic data information, trip end location, trip start and end time information;Step S3: grid city space, city space is divided into multiple regions according to uniform rule;Step S4: the extracted traffic data information is mapped to the grid of divided city space, and the data is classified according to demand;Step S5: determine the public transport line optimization scheme according to the demand classification result, and execute optimization according to scheme.The city traffic informationization foundation is used, and the passenger flow of bus, taxi, online car, private car, shared bicycle and other multiple traffic modes is included in passenger flow demand category, and effective potential public transport passenger flow demand is identified, and on this basis, multi-objective demand is carried out Line network optimization decision, provide a more accurate optimization method.

The application discloses a method and system for identifying the professional type of a shared bicycle commuting user, relates to the technical field of traffic management and urban planning, and comprises the following steps: receiving POI data, urban road network data and shared bicycle data, and processing the shared bicycle data to obtain effective shared bicycle data; calculating the normal parameter of the user using the shared bicycle in the morning peak of a weekday, performing primary screening on the effective shared bicycle data, and extracting primary effective shared bicycle data; performing secondary screening on the primary effective shared bicycle data in the time dimension to obtain secondary effective shared bicycle data; performing tertiary screening on the secondary effective shared bicycle data in the space dimension to obtain tertiary effective shared bicycle data; determining the decision area of the professional type of the user according to the maximum walking radius and in combination with the urban road network data, determining the corresponding relationship between the POI data and the professional type, and identifying the professional type of the user by using the POI data, the tertiary effective shared bicycle data and in combination with the decision area.

A real-time dispatching method for shared bicycles based on average dwell time is disclosed. This invention relates to the fields of urban traffic management and shared mobility technology, specifically to real-time dispatching of shared bicycles based on the average dwell time index. The purpose of this invention is to solve the problems of existing dispatching strategies failing to fully consider the supply and demand relationship of shared bicycles in different areas and at different times, resulting in serious waste of shared bicycle resources, low efficiency, and poor real-time performance. The process is as follows: Step S1: Obtain continuous trip data of shared bicycles within the urban area; set a time threshold; decompose the continuous trip data of shared bicycles into riding trips and dwell status according to the time threshold; Step S2: Divide the urban area into street zoning units; calculate the average dwell time of each street zoning unit; Step S3: Determine the supply and demand balance threshold, and generate a shared bicycle dispatching scheme based on the supply and demand balance threshold and the average dwell time of each street zoning unit.

This invention relates to the field of urban shared bicycle demand forecasting technology, specifically disclosing a method and apparatus for short-term demand forecasting of shared bicycles. The method is used to predict the demand for shared bicycles at urban parking spots for each time period (one hour per time period). The main components include: initializing a historical data sample set of shared bicycle usage, where each data sample contains spatiotemporal trajectory data of a user using one shared bicycle and weather information; selecting a basic feature set based on the data samples from three dimensions: time, space, and weather of shared bicycle travel; since the time-segmented demand sequence of shared bicycles on the same day this week is highly similar to that of the same day last week, the demand sequence of the same day last week is used as a supplementary similarity feature, and this similarity feature, together with the basic feature, constitutes a similarity-enhanced feature set; establishing a CNN-LSTM parallel prediction model based on similarity enhancement, and predicting the demand for shared bicycles at each time period based on the similarity-enhanced feature set. This invention can simultaneously capture temporal similarity and spatiotemporal correlation, accurately predicting the demand for shared bicycles at each station, providing reliable predictive support for shared bicycle operators in carrying out shared bicycle deployment and dynamic scheduling.

This invention proposes a shared bicycle dispatching method, device, electronic device, and storage medium, relating to the field of vehicle dispatching technology. The method generates the expected vehicle demand for each bicycle station in each time period by fusing privacy-protected operator base station signaling data and privacy-protected shared bicycle historical order data. This enables in-depth analysis of users' potential riding intentions, effectively avoiding distortion of demand perception. By constructing a comprehensive utility function based on enterprise utility and user utility and solving it to obtain the pre-planned optimal initial vehicle deployment scheme and optimal dispatching scheme, it effectively balances user experience and enterprise economics. Through real-time monitoring of dispatched vehicle load and calculation of immediate delivery value, it determines whether to immediately execute delivery and issues clear, structured dispatching instructions, linking the instruction execution results to dispatcher performance, effectively avoiding the problem of performance degradation caused by goal-oriented game theory.

This invention relates to the field of bicycle parking technology, and discloses a shared bicycle parking mechanism based on smart parking and its usage method, including a stabilizing mechanism, a parking mechanism, a control box, and a hydraulic door. In this invention, the control box selects the corresponding stabilizing mechanism and suspension mechanism for parking bicycles. The bicycle on the stabilizing mechanism enters the middle of the support structure, and is then suspended on both sides of the handlebars and seat, achieving the effect of staggered placement of bicycles. This allows more bicycles to be parked in a certain space, maximizing the utilization of the parking space and avoiding excessive occupation of public space by bicycles. After the clamping structure clamps and supports the bicycle wheels, the gear meshes and drives the flat plate to move horizontally along the inner side of the base plate. The flat plate horizontally sends the bicycle into the corresponding suspension mechanism for suspension and parking, allowing the bicycle to be parked in a sealed environment inside the isolation compartment, preventing the bicycle from being exposed to the outdoors and easily aging due to environmental factors, and preventing the bicycle from being easily damaged.

This utility model discloses a front fender for shared bicycles, belonging to the field of shared bicycle technology. The front fender includes a corrugated leading edge, a plate, and a wing section. The leading edge is fixedly connected to the plate, and the wing section is fixedly connected to the end of the plate away from the leading edge. When riding a shared bicycle, the airflow passes sequentially through the leading edge, plate, and wing section. The leading edge divides the concentrated airflow into multiple small vortices, and the wing section reduces the intensity of the wingtip vortex. This utility model divides the concentrated water flow into multiple small vortices, breaking the formation of large-scale vortices and delaying the airflow separation point, reducing the wake turbulent kinetic energy by 50%, suppressing the generation of Karman vortex streets, and simultaneously suppressing separation, blocking the path of high-frequency howling.