148 results about "Bicycle sharing" patented technology

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

The invention provides an implementation method of a shared bicycle real-time tracking and positioning system. The system comprises a controller, a plurality of tracking devices, a plurality of shared bicycles and a plurality of operation devices. And the operation equipment monitors and positions the shared bicycles in real time through the controller and the tracking equipment. Through the system, an operator can obtain accurate position information of the monitored shared bicycle in real time, and all-weather and high-precision bicycle tracking and positioning are realized. The system remarkably improves the operation and maintenance management efficiency of the shared bicycles, reduces the operation cost, and has a wide application prospect in the fields of smart transportation, urban shared travel and the like.

The invention relates to a shared bicycle brake pad abrasion monitoring system and a monitoring method thereof, and aims to solve the problems that in the prior art, shared bicycle brake pad abrasion monitoring is high in cost and low in precision, and abnormal abrasion cannot be detected, and accurate monitoring is achieved through a multi-parameter correction model of brake frequency statistics and pressure distribution perception. A microswitch is arranged on a brake pedal, three film pressure sensors are arranged on a brake pad steel backing, a brake pressure value, an average effective pressure Pavg and a pressure distribution difference delta P are synchronously collected, a wear estimation model is built by combining working condition data such as temperature and vehicle speed, and a material coefficient k, a vehicle speed correction coefficient f (v) and a temperature correction coefficient g (T) are adopted for multi-dimensional correction, so that the wear estimation precision is improved. And calculating the accumulated abrasion loss and predicting the residual thickness. Accurate recognition of abnormal wear such as eccentric wear and block falling is achieved by analyzing a delta P threshold value and Pavg mutation characteristics, parameter customization of brake pads made of different materials is supported, and a graded early warning function is achieved.

The invention relates to the technical field of shared bicycle scheduling methods, in particular to a shared bicycle multi-target dynamic scheduling method based on double-matrix variable neighborhood search. The system comprises four core components, namely a scheduling problem modeling module, a double-matrix coding module, an improved genetic algorithm module and a dynamic scheduling decision module. Constructing a multi-objective optimization model containing site topological data, operation constraint parameters and demand prediction data through a scheduling problem modeling module; the dual-matrix coding module adopts a dual coding structure of a path matrix and a scheduling quantity matrix, and respectively represents vehicle path planning and station loading and unloading quantity distribution; the improved genetic algorithm module implements a collaborative optimization strategy of hybrid crossover operation and variable neighborhood search; and the dynamic scheduling decision module finally outputs an optimal scheduling scheme meeting multi-target balance. According to the invention, through a double-matrix decoupling optimization architecture and an intelligent search mechanism, the problems of complex constraint and multi-target optimization in dynamic scheduling of shared bicycles are effectively solved.

The invention discloses a real-time collection and fusion scheduling method for Internet of Things data of a fusion communication platform, and relates to the technical field of data fusion and scheduling. The method comprises the steps of collecting multi-source traffic data, user travel demands and environmental factor data, and generating a travel route planning scheme through a multi-modal data fusion model. The model comprises a weather influence sub-model and an emergency response sub-model. Furthermore, according to the real-time position of the user and the travel emergency degree, the planning scheme weight is dynamically adjusted, and if a threshold value is exceeded, a transport capacity allocation or distribution instruction is triggered. Under the emergency conditions of rail transit delay and the like, an emergency scheduling mode is activated, delay duration, surrounding congestion and the number of affected passengers are comprehensively considered, a compensatory scheduling scheme is generated and comprises a connection bus scheduling strategy and a shared bicycle directional putting strategy, a connection route is optimized through a dynamic path planning method, and the evacuation efficiency is improved. According to the method, through multi-source data fusion and dynamic decision, the traffic scheduling accuracy and the emergency response capability are effectively improved.

The invention relates to the technical field of urban and rural traffic planning, in particular to a rail transit connection shared bicycle use influence analysis method based on built environment and interpretable machine learning, which comprises the following steps: S1, identifying traffic behaviors of taking shared bicycles as rail transit connection trips, obtaining shared bicycle order data in a target area, and determining the order data of the shared bicycles in the target area; constructing a shared bicycle connection use database; s2, determining a research area of a built environment around each subway station, and delimiting an annular buffer area around the subway station as a research range; s3, fusing multi-source built-up environment data, including objective built-up environment indexes and perceptual built-up environment indexes; s4, on the basis of the data in S1 and S3, establishing an influence factor data set of the environment on the use of the shared bicycle; the method comprises the following steps: identifying a traffic behavior of taking a shared bicycle as a rail traffic connection trip, obtaining shared bicycle order data in a target area, extracting riding OD point, distance and time information, and constructing a shared bicycle connection use database;

The invention discloses a shared bicycle delivery quantity prediction method and system, and relates to the technical field of public transportation. The method comprises the following steps: acquiring site data information and historical operation data of a shared bicycle putting site in a target area; the site data information comprises time data, weather data, user data and geographic data; screening out key information in the site data information based on the historical delivery quantity in the historical operation data; the key information is one or more of time data, weather data, user data and geographic data; taking the historical operation data and the key information as input data; and based on the input data, utilizing a pre-trained prediction model to obtain a prediction result of the delivery number of the shared bicycles. According to the prediction method provided by the invention, accurate prediction of the shared bicycle delivery quantity can be realized, and data support is provided for optimizing a shared bicycle delivery scheme.

The invention discloses a shared bicycle passenger flow prediction method based on a geographically weighted regression model and a related device. The method comprises the following steps: collecting spatial data and social economic data of a shared bicycle putting point of a target city; inputting the effective independent variable data set, the spatial autocorrelation of the effective independent variables, the multi-collinearity test result and the housing price factor data into a least square regression model and a geographical weighted regression model, and outputting a regression coefficient, a significance level and a goodness of fit; and according to the regression coefficient, the significance level, the goodness of fit, the delivery point category and the actual shared bicycle passenger flow data, calculating the prediction precision and the lifting amplitude at different delivery point categories. According to the method, the spatial heterogeneity relationship between the shared bicycle passenger flow volume and the influence factors is considered through the geographically weighted regression model, the regression coefficient is adjusted according to different geographic positions, the influence characteristics of a local area are reflected more accurately, and the prediction precision is remarkably improved.

The utility model discloses a full-automatic shared bicycle fixed-point returning electronic lock which comprises a base, two positioning frames are fixedly connected to the top of the base, limiting grooves are formed in the tops of the two positioning frames, recognition modules are fixedly connected to the bottoms in the limiting grooves in an embedded mode, a bicycle body is arranged between the two positioning frames, and the bicycle body is fixedly connected with the two positioning frames. A front wheel frame is arranged on one sides of the two positioning frames, and a bicycle locking assembly is jointly arranged on the sides, close to the front wheel frame, of the two positioning frames. The front wheel frame, the limiting groove and the electromagnetic lock are matched with one another, and the front wheel frame and the limiting groove are used for fixing the shared bicycle, so that a handlebar of the bicycle and a bicycle body including a front wheel are stably positioned and are not prone to toppling over; the electromagnetic lock at the top of the base attracts and locks the vehicle, the vehicle structure is not prone to being damaged when the vehicle is subjected to external acting force, and the service life of the vehicle is prolonged.

The application provides a shared bicycle demand prediction method, system, device and medium, and belongs to the field of computer application, and comprises the following steps: acquiring order data of shared bicycles at a traffic station in a target area; using sparrow search algorithm to optimize hyperparameters of a model based on long short-term memory neural network and random forest; combining long short-term memory neural network and random forest model by using least square weight combination to obtain a demand prediction model; inputting order data of shared bicycles in a region to be predicted into the demand prediction model to obtain demand prediction data of shared bicycles in a set time period. The model performs well on a data set with large demand and obvious change over time, is suitable for shared bicycle demand prediction at a subway station, and provides theoretical support for planning shared bicycle distribution and scheduling, and is complementary to subway service.

This invention discloses a graph neural network-based method for predicting the number of shared bikes entering and exiting a docked bike system. The method comprises the following steps: 1. Obtaining entry and exit data for all shared bike stations in a city; 2. Establishing a shared bike prediction model based on a graph neural network; 3. Constructing sample data to train the established adaptive shared bike number prediction model; and 4. Using the established model to perform predictions, the method aims to predict the number of vehicles entering and exiting all shared bike stations. The method constructs a local spatiotemporal information extraction module and a global spatiotemporal information extraction module to obtain global and local changes in the number of vehicles at shared bike stations in the shared bike system, fully exploring the potential correlation patterns between shared bike stations, thereby further improving the prediction accuracy for each station in the shared bike system.

The invention provides a shared bicycle multi-dimensional risk data early warning management method and system, and relates to the technical field of data processing, and the method comprises the steps: 1, obtaining riding data, bicycle positioning data and environment data, carrying out the preprocessing, and generating fusion data with geographic coordinates; step 2, based on the fusion data, constructing a geographic bounding box covering all vehicle positions as a virtual reference plane, dividing the virtual reference plane into four partitions according to distribution density characteristics of vehicles on the virtual reference plane, calculating a vehicle number change rate and a space-time stability index of each partition, and obtaining a partition adjustment coefficient; according to the invention, early warning and dynamic scheduling of the deposition and shortage risk of the shared bicycle are realized, and the operation management efficiency and the user bicycle use experience are improved.

The embodiment of the invention discloses a shared bicycle management system, method and device, electronic equipment and a storage medium, and relates to the technical field of augmented reality. The system comprises AR glasses, a management platform and a shared bicycle, the AR glasses send a vehicle searching request and the first position information to the management platform; the management platform determines a candidate shared bicycle, queries the real-time state of the candidate shared bicycle, determines a target shared bicycle, and sends second position information of the target shared bicycle to the AR glasses; the AR glasses generate virtual navigation elements according to the second position information, and display the virtual navigation elements in the real world in an overlapping manner based on the virtual screen; the AR glasses collect information of the target shared bicycle based on the camera and send unlocking information to the management platform; after the management platform verifies the user identity and the target shared bicycle, an unlocking instruction is sent to the target shared bicycle to unlock the target shared bicycle. According to the scheme, a user can be guided to quickly find and unlock the shared bicycle in a good state.

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 invention discloses an electric bicycle battery changing cabinet locating and sizing method and system based on bilevel programming. The method comprises the following steps: firstly, acquiring required parameters, constructing each individual of an initial population in an upper-layer optimization model based on the required parameters, and coding candidate site selection schemes and capacity configuration of the power conversion cabinet; then, the initial population is transmitted to a lower-layer solving module, the lower-layer solving module generates a plurality of feasible scheduling paths of a scheduling vehicle in a random scene through the randomization requirement of an electric bicycle station, Monte Carlo sampling and a variable neighborhood search algorithm, and the comprehensive index of the individual is calculated according to the feasible scheduling paths. And then, feeding back the comprehensive index to an upper layer optimization model for correcting the initial population, and obtaining a convergent optimal solution through an iterative evolution process of the upper layer model. Through the double-layer interaction mechanism, collaborative optimization of battery changing cabinet site selection, capacity configuration and scheduling vehicle path planning is realized, and the operation efficiency and robustness of the whole system are improved.

The invention discloses a shared bicycle supervision and identification method, system and device and a storage medium, and the method comprises the steps: obtaining a Bluetooth broadcast signal in a scanning coverage area through a shared bicycle supervision client, carrying out the anti-cheating identification, analysis verification and encryption processing of the obtained Bluetooth broadcast signal, and transmitting the Bluetooth broadcast signal to a cloud big data platform; the cloud big data platform decrypts the received data to obtain MAC addresses, then traverses a filing database based on the MAC addresses to determine associated filing two-dimensional codes, and counts the number of the MAC addresses associated with the associated filing two-dimensional codes; and when the number of the MAC addresses associated with the associated filing two-dimensional code exceeds the filing release limit, triggering a one-code multi-bicycle violation alarm, thereby effectively solving the problems of difficult shared bicycle supervision, low efficiency and high supervision cost in the prior art, and realizing automatic identification and early warning of the'one-code multi-bicycle 'violation behavior on the basis of not increasing the cost.

The present invention discloses a shared bicycle scheduling method based on site clustering, comprising the following steps: S1. Dividing the operating time period of a shared bicycle system into multiple planning periods; S2. Constructing a shared bicycle system model for the tth planning period; S3. Clustering the sites of the shared bicycle system using a clustering algorithm within the tth planning period; S4. Performing scheduling planning for shared bicycles within each cluster formed by clustering within the tth planning period; S5. Repeating steps S2 to S4 within different planning periods to complete the scheduling planning for shared bicycles within each planning period. The present invention utilizes a dynamic vehicle scheduling planning method that considers both bicycle shortages and surpluses, and a clustering algorithm that jointly considers geographic location and user traffic, thereby reducing losses caused by uneven resource distribution while improving overall computing efficiency.

The invention discloses a shared bicycle travel hot spot area positioning method and system based on a travel vector field, a medium and a terminal. The method comprises the following steps: obtaining geographic space information data of a to-be-identified area, and carrying out unit grid division; based on the travel data of the shared bicycle in the target research time period, the longitude and latitude information of the starting point and the ending point is matched to the corresponding grids; counting the travel volume of the shared bicycles in each grid to generate an accumulated travel vector of each grid; carrying out clustering analysis on all grids by taking the travel volume of the normalized grids and the modulus of the cumulative travel vector as features, and dividing unit grids into different categories; according to the accumulative travel vectors of the different types of unit grids, sub-travel vector fields are constructed respectively, and corresponding potential energy is calculated; and determining a corresponding potential energy weight according to the travel volume mean value of the grids of each category, and calculating the potential energy of the global travel vector field of the city. The problems that resident travel directivity features are not comprehensively considered, and the hot spot area of the shared bicycle is not accurately positioned are solved.

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 invention discloses a shared bicycle rebalancing method considering operator and user excitation. The method comprises the following steps: abstracting a shared bicycle initial distribution and multi-vehicle type rebalancing problem into a two-stage stochastic programming model and solving the two-stage stochastic programming model; describing a first-stage initial allocation optimization problem including a single vehicle total amount constraint and a budget constraint; after initial distribution is completed, in the rebalance problem of the second stage, user incentive constraints and truck dispatching related constraints are described; generating a random demand scene; an SAA algorithm is used for solving initial putting of the single vehicles, and the total expected cost is minimized; solving a user incentive task based on an upper-layer VNS algorithm; and further optimizing a shared bicycle redistribution strategy of the operator through the lower layer VNS, wherein the shared bicycle redistribution strategy comprises path planning of a scheduling motorcade and loading and unloading amount decision of each station. According to the invention, joint optimization of initial allocation and rebalance decision is realized, the resource utilization rate is improved, and the overall operation cost is effectively reduced.

The utility model relates to the technical field of shared bicycles, in particular to a shared bicycle saddle wiping structure which comprises a bicycle frame, a saddle tube is connected to the bicycle frame, a seat is connected to the saddle tube, a rotating shaft is fixedly connected to the bottom of the seat, a connecting block is connected to the top of the saddle tube, and the rotating shaft is rotationally connected to the connecting block. The bottom of the seat is slidably connected with a guide rod, the guide rod is inserted into the connecting block, and when the guide rod pulls or inserts the connecting block, the connecting block can be unlocked or locked on the rotating shaft; a storage roller is rotationally connected to the guide rod, wiping cloth is connected to the storage roller in a winding mode, and when the guide rod is pulled out of the connecting block, the wiping cloth is released from the wiping cloth; according to the structure, the seat can be overturned to be in a vertical state, so that water stains slide down, and meanwhile, the wiping cloth is arranged at the bottom of the seat, so that the wiping cloth can be conveniently used for wiping the seat stained with water.

The invention discloses a hybrid shared bicycle system environment impact assessment method and system based on digital twinning, relates to the technical field of shared bicycle system assessment, and realizes denoising and stable modeling of station occupation data by introducing a long-term baseline and a standardized data set, so that abnormal oscillation stations can be accurately identified. Through an oscillation intensity index (i.e., an accumulated variation value normalization result), normal periodic fluctuation and abnormal large fluctuation can be effectively distinguished. And a hotspot cluster set is formed by further combining density clustering, and the future short-term fluctuation direction is predicted based on a trend quantity, so that a potential abnormal region is locked in advance in double dimensions of space and time. The mechanism ensures that scheduling triggering does not depend on single-point data any more, but improves timeliness and accuracy of vehicle supplementing operation based on double judgment of stability and tendency.

The application provides a shared bicycle dispatching method based on data fusion and dynamic resource optimization, relates to the technical field of data processing, and fuses first position data of a bicycle itself and second position data collected by an environment to obtain fusion position data of each station; generates a dynamic capacity baseline of each station based on historical data, a bicycle movement line and environmental parameters; determines a redundancy value of the station according to the fusion position data of each station and the capacity baseline; and generates shared bicycle dispatching information based on the redundancy value, the position of the station and the attributes of a dispatching device, so that the supply of bicycles can be accurately and efficiently dispatched according to multi-dimensional factors corresponding to different regions, and problems such as uneven distribution of shared bicycles and low dispatching efficiency are solved.

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.