218 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 discloses an urban traffic distribution analysis method and system based on multi-source big data, and the method can comprehensively describe the travel general situation of urban residents through integrating the big data of multiple traffic modes of buses, subways, taxis and shared bikes in a city, avoids the deviation caused by a single data source, and improves the accuracy of urban traffic distribution analysis. According to the data preprocessing, original data are converted into a unified spatial point data format, accurate analysis is ensured, OD extraction not only records a travel starting point and a travel ending point, but also calculates travel duration, a key basis is provided for travel behavior analysis, spatial and temporal distribution feature analysis comprehensively considers time and spatial scales, and a spatial interaction network of various traffic modes is revealed finely. In the step of interaction matrix construction, OD is matched to space units, an OD interaction matrix between stations or grids is constructed, travel behaviors are quantified, travel characteristics are compared to reveal travel differences in different time periods, traffic bottlenecks and hot spots are recognized, and in the step of travel mode recognition, high-frequency OD pairs and interaction areas are found, and scientific guidance and suggestions are provided for urban traffic planning and management.

The invention discloses a shared bicycle dangerous behavior identification method based on GPS trajectory data tracking, and belongs to the technical field of traffic safety. Comprising the steps of abnormal point elimination, point supplementation, coordinate system unification and normalization so as to improve data accuracy, and clustering division is carried out on data according to data features; in combination with a road network and POI data, hotspot and non-hotspot areas are determined through track intensity analysis; six dangerous behaviors are defined, historical data are analyzed, a threshold value is set, a predefined method is used for recognizing dangerous driving behaviors, and finally a recognition model is established to achieve accurate recognition of the six dangerous behaviors. Through mathematical statistics, the riding characteristics and dangerous behavior characteristics of the shared bicycle under different conditions are compared and researched; according to the dangerous behavior identification method based on the shared bicycle GPS track data, the utilization rate of the shared bicycle GPS data can be effectively improved, dangerous behaviors can be accurately identified, and the safety of riders and other traffic participants is guaranteed.

The invention discloses a shared bicycle borrowing and returning demand prediction method based on multi-source data, a terminal and a medium, and the method comprises the steps: obtaining shared bicycle riding order data, PO I data and historical weather data, and carrying out the statistics of the borrowing and returning amount of each parking site per hour for each parking site of a shared bicycle; time dimension features, space dimension features and weather dimension features of bicycle borrowing and returning demands of the shared bicycle stations are mined respectively, and the relation between the three dimension features and the bicycle borrowing and returning demands of the stations is determined; constructing interactive station passenger flow characteristics and similar station passenger flow characteristics; and constructing a bicycle borrowing and returning demand prediction model based on the interactive station passenger flow characteristics and the similar station passenger flow characteristics, and training the bicycle borrowing and returning demand model by using historical passenger flow data. According to the method, the relationship between various influence factors and the bicycle borrowing and returning demands of the shared bicycle parking station is deeply explored, bicycle borrowing and returning quantity prediction is realized, a reasonable bicycle allocation quantity is provided for shared bicycle scheduling, and the scheduling cost of a shared bicycle enterprise is reduced.

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 belongs to the technical field of shared bicycles, and particularly relates to a station type shared bicycle system dynamic management and control method and system considering platform game, and the method comprises the steps: obtaining the related data of a target operation range, and initializing a simulation module; defining the state, action, income and state transition probability of the intelligent agent of the platform in a Markov state transition equation according to the operation target and management and control means of each platform, and initializing a neural network used for evaluating the action quality of the intelligent agent in a specific state to realize initialization of an optimization control module; supply and demand deduction and strategy optimization are carried out in the simulation module; judging whether the income of each platform in the simulation module tends to be stable or not; according to the shared bicycle-electric bicycle dynamic management and control method, a traditional shared bicycle static management and control normal form is broken through, and dynamic management and control of a shared bicycle-electric bicycle system are achieved in a high-dimensional random scene through the Markov decision process model and the deep reinforcement learning algorithm.

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 provides a shared bicycle track feature extraction method and system based on Bluetooth sniffing. The method comprises the following steps that a Bluetooth sniffing device obtains a Bluetooth signal of a shared bicycle; extracting feature information of the shared bicycle from the Bluetooth signal; positioning the shared bicycle according to the Bluetooth signal, and obtaining position coordinates of the shared bicycle at different time points; obtaining the travel time of the shared bicycle based on the appearance time and the disappearance time of the Bluetooth signal; obtaining the residence time of the shared bicycle in a certain area based on the duration of the Bluetooth signal; and obtaining the speed change and the total distance in the whole riding process based on the position coordinates of the shared bicycle at different time points. The data quality is improved by adopting filtering and error correction algorithms.

The invention discloses a shared bicycle and user access rule analysis method and system, and relates to the field of traffic data. According to the method, the access rules of the shared bicycles and the specific places of the shared bicycle users or the access rules of the activity areas in different time periods can be obtained by analyzing the past shared bicycle data, and the method can be widely applied to shared bicycle flow rule analysis of different cities; the space-time distribution rule of the shared bicycles and the inflow and outflow of the users of the shared bicycles at various places or active areas in a city can be obtained; the relationship between the input and the output of the shared bicycles and the users of the shared bicycles in different places or activity areas and different factors can be obtained; the rule of the number and frequency of access places of a single shared bicycle or a single user can be obtained. According to the method and the system, the access rules of the shared bicycles and the users in different areas and time can be effectively analyzed.

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 belongs to the technical field of traffic system prediction and intelligent scheduling, and discloses a shared bicycle dynamic scheduling method based on station demand prediction and a user reward mechanism. The method comprises the following steps: based on travel data of shared bicycles, clustering shared bicycle stations in combination with a DBSCAN clustering algorithm and a K-Means clustering algorithm, and predicting the future demand quantity of each station by adopting a CNN-LSTM neural network; building a shared bicycle dynamic scheduling model based on demand prediction and a user reward mechanism by comprehensively considering the fixed cost and the transportation cost of the scheduled bicycle, the enterprise rebate cost under the user reward mechanism and the punishment cost deviating from the expected inventory; and solving the model through a greedy algorithm, dynamically updating the bicycle data of the shared bicycle station and predicting the future demand of the station, optimizing a dynamic path in real time, and solving an optimal scheme of the minimum total scheduling cost. Therefore, the scheduling path planning problem of the shared bicycles can be effectively solved, and the scheduling efficiency and the overall performance of the system are improved.

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 invention provides a real-time intelligent scheduling method and scheduling system for shared bicycles. Generally, the demand quantity of the shared bicycles is obtained based on historical data, bicycle parking key and hot spot areas are monitored in real time in a shared bicycle operation area, and the demand quantity of the shared bicycles is calculated according to the maximum threshold value (the maximum load can be borne) set in the area and the real-time parking quantity condition of the parked bicycles. And overall scheduling is performed in combination with the current number and the required number of the shared bicycles, so that dynamic intelligent scheduling is realized, and the defect that scheduling resources are redundant and uneven due to the fact that current shared bicycle scheduling needs manual experience scheduling is overcome.

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 invention provides a shared bicycle fault vehicle marking method and system based on multi-dimensional data, and relates to the technical field of data processing, a server decomposes the multi-dimensional data of a bicycle end, an equipment end and a server end corresponding to a target bicycle to obtain decomposition information, and classifies the decomposition information to obtain first classification information and second classification information; the management end generates a judgment specification tree of classification information based on the standard specification tree and image interaction; if the independent first classification information or the combined second classification information reaches a judgment condition of the judgment specification tree, adding a fault tag to the corresponding bicycle end and extracting a first moment; the multi-dimensional data of the target bicycle from the first moment to the second moment in the normal state is acquired, and the fault label and / or the standard specification tree are / is updated, so that the fault bicycle can be timely and accurately identified and marked.

The invention discloses a health index monitoring device and system in a motion state based on a shared bicycle, and relates to the technical field of crowd health monitoring, the health index monitoring device comprises a box body, a fixing band, a signal processor arranged in the box body, a GPS module, a controller, a display, a GPRS communication unit, a Bluetooth wireless module and a power supply, and a sensor arranged on the fixing band. One side of the fixing belt half wraps the box body, and the other side of the fixing belt is wound on the handle of the shared bicycle, so that the whole health index monitoring device is fixed on the handle of the shared bicycle, and the health indexes of urban people in the motion state are monitored by means of the shared bicycle. According to the invention, the health index monitoring device is arranged on the urban shared bicycle, and the health index data of urban people in the riding state can be monitored and collected in a large-scale distributed manner by means of the characteristics of multi-point distribution, large riding crowd scale and all types of crowds of the urban shared bicycle; the purpose of continuously monitoring the health indexes of the crowd in the motion state in a large-scale and real-time manner is achieved.

The invention discloses a method and a system for identifying the multivariate relationship between a shared bicycle and a public transport system, which are used in the field of intelligent transportation, and the method comprises the following steps: defining the multivariate relationship between the shared bicycle and the public transport; defining the type of the subway station; a regression model is utilized to analyze a regression relationship between subway passenger flow and the number of shared bicycle users in different connection buffer areas in different time periods, and threshold values of the connection buffer areas in different time periods of different subway stations are obtained; and according to the threshold values of the connection buffer areas at different time periods of different subway stations, the multivariate relationship between the shared bicycles and the public transport is judged. According to the method, the relationship between the subway passenger flow and the number and distribution of the shared bicycle users is deeply analyzed, the connection characteristics of the subway passenger flow and the shared bicycle users in different time periods are discussed, a reasonable connection buffer area range threshold value is determined, the overall efficiency of an urban traffic system is improved, and the service life of the urban traffic system is prolonged. And important reference can be provided for urban traffic planning and formulation of a shared bicycle putting strategy.

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 relates to an urban riding traffic flow prediction method and system, and the method employs a limit gradient lifting model to predict the traffic flow of shared bicycles in a stable epidemic situation period, and comprises the steps: carrying out the data preprocessing of the station information of shared bicycle stations in a target urban space, and obtaining a data preprocessing result; constructing a Thiessen polygon buffer area with a preset size based on the preprocessed information; meteorological factors, epidemic factors and urban space factors in the Thiessen polygon buffer area are collected and processed to obtain meteorological indexes, epidemic variables and urban space factor indexes; a data set is constructed based on meteorological indexes, epidemic variables and urban space factor indexes, a limit gradient lifting model is input, shared bicycle travel traffic flow prediction is output, and the system is used for implementing the method. Compared with the prior art, the method has the advantages of high prediction speed, high prediction precision, accurate fitting of a nonlinear relation, reflection of spatial heterogeneity and the like.

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.