A fee deduction method, system, device and medium for intelligent transportation system

Abstract

The present invention provides a fee deduction method, system, device and medium for an intelligent transportation system, belonging to the field of intelligent transportation technology. The method comprises: collecting vehicle images, vehicle driving information and traffic flow information at a preset position of the transportation system; the vehicle image comprises a license plate image of the vehicle; using a deep learning algorithm to identify and analyze the vehicle image, and obtain the license plate number information and vehicle model information; based on the historical driving data of the vehicle, predicting the driving path and travel time of the vehicle; dynamically calculating the deduction amount according to the driving path, travel time and real-time traffic flow information of the vehicle; sending the deduction amount to the user's mobile terminal, and providing payment services. The present invention realizes accurate and reasonable traffic and parking management by constructing an intelligent recognition system, acquiring vehicle information in real time, and dynamically deducting fees in combination with multiple factors.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and more specifically relates to a fee deduction method, system, device and medium for an intelligent transportation system. Background Art

[0002] In the context of rapid urbanization, traffic congestion and parking resource shortage have become important bottlenecks restricting urban development. Traditional traffic fee deduction systems mostly rely on manual operation or traditional sensor equipment to record and calculate fees, which is not only inefficient but also costly. In addition, due to technical limitations, these systems often cannot provide high-precision fee deduction services and have slow response speeds, making it difficult to meet the needs of modern urban traffic management.

[0003] Traditional parking lot and road toll collection systems often have fixed charging standards that lack sufficient flexibility and real-time performance. This rigid charging model cannot be dynamically adjusted according to changes in traffic flow, time periods, and traffic pressure in specific areas, resulting in a waste of resources and low traffic efficiency.

[0004] More importantly, the existing traffic fee deduction system generally lacks the ability to intelligently analyze vehicle behavior and paths. This means that traffic management departments cannot accurately grasp the driving trajectory, parking habits and real-time location of vehicles, making it difficult to effectively predict and manage traffic flow. This lack of information not only limits the improvement of traffic management level, but also affects the smooth and efficient operation of urban traffic. Summary of the invention

[0005] In response to the above problems, the purpose of the present invention is to provide a charging method, system, device and medium for an intelligent transportation system. By building an intelligent recognition system, vehicle information can be obtained in real time, and dynamic charging is performed based on multiple factors to achieve accurate and reasonable traffic and parking management.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a fee deduction method for an intelligent transportation system, comprising:

[0008] Collecting vehicle images, vehicle driving information and traffic flow information at a preset location in the traffic system; the vehicle images include vehicle license plate images;

[0009] Use deep learning algorithms to identify and analyze vehicle images to obtain license plate number and vehicle model information;

[0010] Predict the vehicle's travel path and travel time based on the vehicle's historical travel data;

[0011] Dynamically calculate the deduction amount based on the vehicle's driving path, travel time and real-time traffic flow information;

[0012] The deducted amount is sent to the user's mobile terminal and payment services are provided.

[0013] In an optional embodiment, the collecting of a vehicle image, a vehicle position and time information at a preset position of a traffic system; wherein the vehicle image includes a license plate image of the vehicle, comprises:

[0014] The cameras installed at traffic arteries, parking lot entrances and exits, and toll booths collect vehicle images and automatically adjust camera parameters according to real-time lighting conditions;

[0015] Traffic flow information of the current road section is collected through sensor modules installed at traffic arteries, parking lot entrances and exits, and toll booths;

[0016] The entry time of vehicles is recorded through RFID readers installed at traffic arteries, parking lot entrances and exits, and toll booths;

[0017] The collected vehicle images, the traffic flow information of the current road section and the vehicle's entry time are sent to the central processor through the network.

[0018] In an optional implementation, the method of using a deep learning algorithm to identify and analyze a vehicle image to obtain license plate number information and vehicle model information includes:

[0019] Use convolutional neural network (CNN) to detect the vehicle position in the vehicle image and determine the vehicle area;

[0020] Use the pre-trained OCR model to perform OCR processing on the vehicle area and identify the license plate number;

[0021] Based on the vehicle area, the deep learning algorithm is used to analyze the vehicle characteristics and determine the vehicle model information based on the analysis results;

[0022] Record the vehicle's entry time and corresponding entrance location;

[0023] The license plate number, vehicle model information and entrance location are encrypted and stored in the database.

[0024] In an optional implementation, the predicting of the vehicle's travel path and travel time based on the vehicle's historical travel data includes:

[0025] Extract the vehicle's historical route information and stop location information from the traffic system based on the license plate number as historical driving data;

[0026] Clustering algorithms are used to group historical route information, identify vehicle driving patterns, and build vehicle driving models;

[0027] Extract key features from the vehicle's historical route information, including driving speed, dwell time, and frequently traveled sections; based on the key features, use a classification algorithm to analyze the vehicle's driving patterns, and use a deep learning algorithm to predict the vehicle's future driving path;

[0028] Based on the future driving path of the vehicle, the historical traffic data and real-time traffic data of the relevant road sections are extracted from the traffic system; an LSTM neural network model is constructed, and the historical traffic data and real-time traffic data are input to predict the traffic flow information of the relevant road sections in the future period and generate the prediction results;

[0029] The vehicle's travel time is predicted based on the vehicle's future travel path and traffic flow in the future period.

[0030] In an optional implementation, the dynamically calculating the deduction amount based on the vehicle's travel path, travel time and real-time traffic flow information includes:

[0031] Obtain the entry and exit time of vehicles in a specific road section or parking area in real time and calculate the vehicle's stay time;

[0032] Construct a dynamic fee calculation model for the vehicle, the dynamic fee calculation model comprising:

[0033] Y = B × L × C × D × T;

[0034] Among them, Y is the predicted deduction amount of the vehicle, B is the basic rate, L is the road section coefficient, C is the vehicle type coefficient, D is the time period coefficient, and T is the vehicle's stay time;

[0035] The road section coefficient of the vehicle is determined according to the traffic flow information of the future time period; the vehicle model coefficient is determined according to the vehicle model information; the peak traffic time included in the travel time is identified according to the travel time to determine the vehicle's time period coefficient; and the toll amount is calculated using the vehicle's dynamic toll calculation model.

[0036] In an optional embodiment, the dynamic fee deduction calculation model of the vehicle is optimized by a loss function;

[0037] The loss function includes:

[0038]

[0039] in, is the actual deduction amount for vehicle a, It is the predicted deduction amount calculated by the dynamic deduction calculation model, and n is the number of samples.

[0040] In an optional implementation, the sending of the deducted amount to the user's mobile terminal and providing payment services include:

[0041] Sending deduction information to the user's mobile terminal; the deduction information includes the deduction amount, as well as the deduction willingness, deduction section information and deduction time generated according to the vehicle's driving path, travel time and real-time traffic flow information;

[0042] Connect the user's mobile terminal to the payment platform, automatically upload the deduction amount, and generate a deduction operation interface. After the user confirms, the deduction is completed.

[0043] In a second aspect, the embodiment of the present application further provides a fee deduction system for an intelligent transportation system, including:

[0044] A data acquisition module, used to collect vehicle images, vehicle driving information and traffic flow information at a preset position in the traffic system; the vehicle image includes a license plate image of the vehicle;

[0045] The data processing module is used to identify and analyze vehicle images using deep learning algorithms to obtain license plate number information and vehicle model information;

[0046] A prediction module is used to predict the vehicle's travel path and travel time based on the vehicle's historical travel data;

[0047] Dynamic fee deduction module, used to dynamically calculate the fee deduction amount based on the vehicle's driving path, travel time and real-time traffic flow information;

[0048] The payment module is used to send the deducted amount to the user's mobile terminal and provide payment services.

[0049] In a third aspect, an embodiment of the present application further provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the toll deduction method of the intelligent transportation system as described in any one of the above items are implemented.

[0050] In a fourth aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of deducting fees in the intelligent transportation system as described in any of the above items are implemented.

[0051] It can be seen from the above technical solutions that the present invention has the following advantages:

[0052] This application uses deep learning algorithms to efficiently and accurately identify and analyze vehicle images, and can quickly extract license plate numbers and vehicle model information. At the same time, by collecting multi-dimensional data such as vehicle location, time, and traffic flow in real time, it lays a solid data foundation for subsequent fee calculations and ensures the accuracy and completeness of data analysis.

[0053] Based on the historical driving data of vehicles, this application uses advanced clustering and deep learning algorithms to accurately predict the driving path and travel time of vehicles. Combined with real-time traffic flow information, this application can dynamically calculate the deduction amount and realize real-time and flexible adjustment of the deduction standard, which not only reflects fairness but also ensures the rationality of the charges.

[0054] This application constructs a dynamic vehicle fee calculation model and introduces a loss function for continuous optimization, which significantly improves the precision and accuracy of the fee calculation. The model comprehensively considers multiple factors such as the basic rate, road section coefficient, vehicle model coefficient, time period coefficient, and stay time, ensuring the comprehensiveness and rationality of the fee standard and further improving user satisfaction.

[0055] This application greatly facilitates users to understand the deduction situation by sending deduction information to the user's mobile terminal, including detailed information such as the deduction amount, deduction reason, deduction route and time. At the same time, the user's mobile terminal is seamlessly connected to the payment platform to achieve automatic uploading of the deduction amount and generation of the payment interface, simplifying the payment process and improving the convenience and intelligence level of user experience.

[0056] This application not only improves the level of intelligence in traffic charges, but also provides traffic management departments with rich traffic data and insights. By collecting and analyzing traffic data in real time, management departments can promptly discover and solve problems such as traffic congestion, optimize traffic planning and management strategies, improve traffic operation efficiency and service levels, and promote the intelligent development of the transportation field. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0058] Figure 1 A flowchart of the fee deduction method for the intelligent transportation system provided in this application.

[0059] Figure 2 A schematic diagram of the structure of the toll collection system of the intelligent transportation system provided in this application.

[0060] Figure 3A schematic diagram of the structure of the electronic device provided in this application. DETAILED DESCRIPTION

[0061] In the specific steps of the fee deduction method of the intelligent transportation system described in detail below, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0062] Hereinafter, the terms "include" or "may include" used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or a combination of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or a combination of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or a combination of the foregoing.

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] See also Figure 1 The figure is a flowchart of a method for charging a fee in an intelligent transportation system in a specific embodiment, the method comprising:

[0065] S1: Collecting vehicle images, vehicle driving information and traffic flow information at a preset location in a traffic system; the vehicle image includes a license plate image of the vehicle.

[0066] It should be noted that in the transportation system, high-definition cameras, sensors, RFID readers and other equipment are deployed at major traffic arteries, parking lot entrances / exits, toll stations and other locations.

[0067] In this step, vehicle images are collected by cameras installed at major traffic routes, parking entrances and exits, and toll booths, and the camera parameters are automatically adjusted according to the real-time lighting conditions. Among them, high-definition cameras are used to capture vehicle images. The vehicle license plate is photographed by the camera and the vehicle image is transmitted to the central processing system. At the same time, the image quality is automatically checked to ensure that the image is still clear in low light or bad weather conditions. For blurred images, the image quality is improved by automatically adjusting the camera parameters or adding fill lights.

[0068] Traffic flow information on the current road section is collected through sensor modules installed at major traffic arteries, parking lot entrances and exits, and toll booths.

[0069] The vehicle's entry time is recorded by RFID readers installed at major traffic arteries, parking lot entrances and exits, and toll booths.

[0070] The collected vehicle images, traffic flow information of the current road section and vehicle entry time are sent to the central processor through the network, waiting for subsequent processing.

[0071] S2: Use deep learning algorithms to identify and analyze vehicle images to obtain license plate number information and vehicle model information.

[0072] The purpose of this step is to identify and analyze the collected image data and convert the raw data into useful traffic information.

[0073] S3: Based on the historical driving data of the vehicle, predict the vehicle's driving path and travel time.

[0074] The purpose of this step is to learn the historical driving data of the vehicle and predict its possible driving path and travel time to support dynamic charging strategies.

[0075] S4: Dynamically calculate the deduction amount based on the vehicle's driving path, travel time and real-time traffic flow information.

[0076] The purpose of this step is to dynamically calculate the deduction amount based on the vehicle's route, model, dwell time, and real-time traffic conditions.

[0077] S5: Send the deducted amount to the user's mobile terminal and provide payment services.

[0078] For example, the deduction information is sent to the user's mobile phone APP, SMS or email. The user can view the detailed deduction record on the APP. The deduction information includes the deduction amount, deduction reason, deduction section and time, etc.

[0079] Among them, the user's mobile phone is connected to the payment platform (such as bank card, e-wallet), providing a variety of payment methods. The user can choose the bound payment method to complete the deduction, and the system will automatically deduct the money. In addition, if the user has any questions about the deduction, he can submit feedback through the APP, and the system will automatically review the deduction record and provide a detailed explanation to the user.

[0080] In this embodiment, by collecting and analyzing vehicle information, combining historical driving data and real-time traffic flow, dynamically calculating the deduction amount, and conveniently notifying the user to pay, the accuracy and efficiency of charging are improved and the user experience is optimized.

[0081] In an embodiment of the present invention, based on step S2, a possible embodiment is given below to illustrate its specific implementation scheme in a non-limiting manner.

[0082] This embodiment discloses a vehicle image recognition and analysis method, which specifically includes the following steps:

[0083] S201: Detect the vehicle position in the vehicle image using a convolutional neural network (CNN) to determine the vehicle area.

[0084] As an example, using a deep learning algorithm (convolutional neural network CNN), the vehicle position is first detected in the vehicle image to ensure that only the vehicle area is processed subsequently.

[0085] S202: Perform OCR processing on the vehicle area using the pre-trained OCR model to identify the license plate number.

[0086] As an example, the detected vehicle area is subjected to OCR processing to identify the license plate number. This step uses a trained OCR model to accurately recognize the license plate text and store the license plate number information in a database.

[0087] S203: Based on the vehicle area, the vehicle characteristics are analyzed using a deep learning algorithm, and the vehicle model information is determined according to the analysis results.

[0088] As an example, the vehicle features (size, color, shape) are analyzed and the vehicle model is determined using a deep learning algorithm. The vehicle model information can be used for subsequent differentiated charging strategies.

[0089] S204: Record the vehicle's entry time and corresponding entry location.

[0090] As an example, the entry time, entrance location and other information of each vehicle are recorded to calculate its driving path and stay time.

[0091] S205: Encrypt the license plate number, vehicle model information and entrance location, and store them in a database.

[0092] For example, sensitive data such as the identified license plate number, vehicle model and location are encrypted and stored in the database to protect user privacy.

[0093] In an embodiment of the present invention, based on step S3, a possible embodiment is given below to illustrate its specific implementation scheme in a non-limiting manner.

[0094] This embodiment discloses a method for predicting a vehicle's travel path and travel time, which specifically includes the following steps:

[0095] S301: Extracting the vehicle's historical travel route information and stop location information from the traffic system according to the license plate number as historical travel data.

[0096] S302: Use a clustering algorithm to group historical travel route information, identify vehicle driving patterns, and construct a vehicle driving model.

[0097] S303: Extract key features from the vehicle's historical route information, including driving speed, dwell time, and frequently passed sections; based on the key features, use a classification algorithm to analyze the vehicle's driving patterns, and use a deep learning algorithm to predict the vehicle's future driving path.

[0098] This step uses machine learning algorithms to analyze vehicle driving patterns and predict future driving paths. For example, traffic flow data can be used to predict peak-hour routes or to identify abnormal driving behaviors (such as repeatedly entering and exiting a specific road section).

[0099] As an example, key features such as driving speed, dwell time, frequently passed sections, etc. are extracted from the vehicle's driving data. Then, classification algorithms (such as decision trees, random forests) or regression algorithms (such as linear regression, support vector machines) are used to analyze driving patterns. Finally, reinforcement learning or deep learning algorithms (such as LSTM, GRU) are used to predict the vehicle's future driving path.

[0100] In order to predict the driving path of the vehicle, a Markov chain is used to model it. Assuming that the vehicle has different transition probabilities between nodes (such as intersections and toll booths), the transition probability matrix of each node is:

[0101]

[0102] Among them, P ij Represents the probability of a vehicle transferring from node i to node j. Through historical driving data, the transition probability matrix P can be trained to predict the driving path of vehicles between different road sections to optimize traffic flow and allocate resources.

[0103] S304: Based on the future driving path of the vehicle, extract the historical traffic data and real-time traffic data of the relevant road section in the traffic system; build an LSTM neural network model, input the historical traffic data and real-time traffic data, predict the traffic flow information of the relevant road section in the future period, and generate a prediction result.

[0104] In this step, based on historical traffic flow data and real-time traffic information, the system uses machine learning models (such as time series analysis or LSTM networks) to predict traffic flow conditions in the next few hours, providing a basis for charging decisions.

[0105] First, use time series models such as ARIMA and Prophet to analyze historical traffic data. Then, build an LSTM neural network model, input historical traffic data and real-time traffic data, and predict future traffic. Among them, historical data and real-time data can be integrated to improve the accuracy of prediction.

[0106] S305: Predicting the vehicle's travel time based on the vehicle's future travel path and traffic flow in the future period.

[0107] In this step, the vehicle travel time can be predicted through a variety of algorithm models, including time series method, Kalman filter method, neural network method, support vector machine (SVM), hybrid model prediction algorithm and prediction model based on graph convolution network (GCN).

[0108] In an embodiment of the present invention, based on step S4, a possible embodiment is given below to illustrate its specific implementation scheme in a non-limiting manner.

[0109] First, the entry and exit times of vehicles on a specific road section or parking area are obtained in real time to calculate the vehicle's residence time. Specifically, the timestamps of the vehicle entering and leaving the specific road section or parking area are recorded. The difference between the two timestamps is calculated to obtain the vehicle's residence time. The residence time data is stored in the database for subsequent analysis.

[0110] Then, a dynamic fee deduction calculation model for the vehicle is constructed, and the dynamic fee deduction calculation model includes:

[0111] Y = B × L × C × D × T;

[0112] Among them, Y is the predicted deduction amount of the vehicle, B is the basic rate, L is the road section coefficient, C is the vehicle model coefficient, D is the time period coefficient, and T is the vehicle's stay time.

[0113] Finally, the road section coefficient of the vehicle is determined according to the traffic flow information of the future time period; the vehicle model coefficient is determined according to the vehicle model information; the peak traffic time included in the travel time is identified according to the travel time to determine the vehicle's time period coefficient; and the vehicle's dynamic deduction calculation model is used to calculate the deduction amount.

[0114] Among them, the dynamic deduction calculation model is continuously optimized through machine learning methods, and the loss function used is the mean square error (MSE), which is defined as:

[0115]

[0116] in, is the actual deduction amount for vehicle a, It is the predicted deduction amount calculated by the dynamic deduction calculation model, and n is the number of samples.

[0117] By minimizing the loss function, the predicted deduction amount is made closer to the actual deduction amount, thereby optimizing the dynamic pricing strategy.

[0118] In a specific implementation, the method can also optimize the charging model through user feedback and continuous updating of charging data to ensure that the system adapts to changes in different traffic environments. The main implementation steps include:

[0119] 1. Feedback data collection: record user feedback on deduction information, including payment success rate, deduction accuracy, user satisfaction, etc.

[0120] 2. Model optimization: Use machine learning algorithms to analyze feedback data and improve the deduction algorithm by optimizing dynamic deduction calculation model parameters (such as pricing coefficients).

[0121] 3. Traffic regulation feedback: Use information such as traffic flow and congestion as an important basis for model optimization, and dynamically adjust pricing strategies to make them more reasonable and adaptable.

[0122] 4. Self-learning: By gradually accumulating historical deduction data, automatically learning and adjusting parameters, the deduction strategy can be adapted to different roads and time periods, thereby improving the flexibility and accuracy of the deduction system.

[0123] like Figure 2 As shown, the following is an embodiment of the toll collection system of the intelligent transportation system provided by the embodiment of the present disclosure. The system and the toll collection method of the intelligent transportation system of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the toll collection system of the intelligent transportation system, please refer to the embodiment of the toll collection method of the above-mentioned intelligent transportation system.

[0124] A fee deduction system for an intelligent transportation system comprises: a data collection module 1, a data processing module 2, a prediction module 3, a dynamic fee deduction module 4 and a payment module 5.

[0125] The data acquisition module 1 is used to collect vehicle images, vehicle driving information and traffic flow information at a preset position in the traffic system; the vehicle image includes a license plate image of the vehicle.

[0126] The data processing module 2 is used to identify and analyze vehicle images using a deep learning algorithm to obtain license plate number information and vehicle model information.

[0127] The prediction module 3 is used to predict the vehicle's travel path and travel time based on the vehicle's historical travel data.

[0128] The dynamic deduction module 4 is used to dynamically calculate the deduction amount based on the vehicle's driving path, travel time and real-time traffic flow information.

[0129] The payment module 5 is used to send the deducted amount to the user's mobile terminal and provide payment services.

[0130] The toll deduction system of the intelligent transportation system provided in this embodiment realizes intelligent and dynamic management of vehicle tolls by integrating functional modules such as data collection, deep learning processing, driving prediction, dynamic toll deduction and mobile payment. It not only improves the efficiency and accuracy of toll deduction, but also provides users with a more convenient and transparent payment experience, effectively promoting the intelligent upgrade of traffic management.

[0131] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0132] The charging method of the intelligent transportation system provided in the embodiment of the present application can be applied to electronic devices. It can be understood by those skilled in the art that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or less components than shown, or combine certain components, or arrange different components. In an embodiment of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0133] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0134] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0135] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0136] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0137] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement data storage functions. For example, files such as music and videos can be saved in the external memory card.

[0138] The internal memory can be used to store computer executable program codes, which include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0139] The wireless communication function of an electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor.

[0140] The wireless communication module can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0141] Electronic devices can implement audio functions, etc. through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0142] Electronic devices can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0143] Electronic devices can achieve display functions through GPU, display screen and application processor.

[0144] The GPU is a microprocessor for image processing that connects the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0145] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0146] The above-mentioned electronic device realizes the charging method of the intelligent transportation system of the present application by constructing an intelligent recognition system, obtaining vehicle information in real time, and dynamically charging based on multiple factors, thereby achieving accurate and reasonable traffic and parking management.

[0147] The storage medium provided in the present application stores a program product that can implement the fee deduction method of the intelligent transportation system.

[0148] The charging method of the intelligent transportation system includes: collecting vehicle images, vehicle driving information and traffic flow information at a preset location of the transportation system; the vehicle image includes the vehicle license plate image; using a deep learning algorithm to identify and analyze the vehicle image to obtain the license plate number information and vehicle model information; based on the vehicle's historical driving data, predicting the vehicle's driving path and travel time; dynamically calculating the deduction amount based on the vehicle's driving path, travel time and real-time traffic flow information; sending the deduction amount to the user's mobile terminal and providing payment services.

[0149] In some possible implementations, the fee deduction method of the intelligent transportation system disclosed in the present invention can be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above "Exemplary Method" section of this specification according to various exemplary implementations of the present disclosure.

[0150] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0151] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fee deduction method for an intelligent transportation system, characterized in that: include: Collect vehicle images, vehicle driving information and traffic flow information at preset locations in the traffic system; The vehicle image includes a license plate image of the vehicle; Use deep learning algorithms to identify and analyze vehicle images to obtain license plate number and vehicle model information; Predict the vehicle's travel path and travel time based on the vehicle's historical travel data; Dynamically calculate the deduction amount based on the vehicle's driving path, travel time and real-time traffic flow information; Send the deducted amount to the user's mobile terminal and provide payment services; The deep learning algorithm is used to identify and analyze the vehicle image to obtain the license plate number information and vehicle model information, including: Use convolutional neural network (CNN) to detect the vehicle position in the vehicle image and determine the vehicle area; Use the pre-trained OCR model to perform OCR processing on the vehicle area and identify the license plate number; Based on the vehicle area, the deep learning algorithm is used to analyze the vehicle characteristics and determine the vehicle model information based on the analysis results; Record the vehicle's entry time and corresponding entrance location; The license plate number, vehicle model information and entrance location are encrypted and stored in the database; The method of predicting the vehicle's travel path and travel time based on the vehicle's historical travel data includes: Extract the vehicle's historical route information and stop location information from the traffic system based on the license plate number as historical driving data; Clustering algorithms are used to group historical route information, identify vehicle driving patterns, and build vehicle driving models; Extract key features from the vehicle's historical route information, including driving speed, dwell time, and frequently traveled sections; based on the key features, use a classification algorithm to analyze the vehicle's driving patterns, and use a deep learning algorithm to predict the vehicle's future driving path; Based on the future driving path of the vehicle, the historical traffic data and real-time traffic data of the relevant road sections are extracted from the traffic system; an LSTM neural network model is constructed, and the historical traffic data and real-time traffic data are input to predict the traffic flow information of the relevant road sections in the future period and generate the prediction results; Predict vehicle travel time based on the vehicle's future travel path and traffic flow in future time periods; The dynamically calculating the deduction amount according to the vehicle's travel path, travel time and real-time traffic flow information includes: Obtain the entry and exit time of vehicles in a specific road section or parking area in real time and calculate the vehicle's stay time; Construct a dynamic fee calculation model for the vehicle, the dynamic fee calculation model comprising: Y = B × L × C × D × T; Among them, Y is the predicted deduction amount of the vehicle, B is the basic rate, L is the road section coefficient, C is the vehicle type coefficient, D is the time period coefficient, and T is the vehicle's stay time; Determine the road section coefficient of the vehicle according to the traffic flow information of the future period; determine the vehicle model coefficient of the vehicle according to the vehicle model information; identify the peak traffic time included in the traffic time according to the traffic time to determine the vehicle time period coefficient; calculate the deduction amount using the dynamic deduction calculation model of the vehicle; The dynamic fee deduction calculation model of the vehicle is optimized through a loss function; The loss function includes: in, is the actual deduction amount for vehicle a, It is the predicted deduction amount calculated by the dynamic deduction calculation model, and n is the number of samples.

2. The fee deduction method of the intelligent transportation system according to claim 1, characterized in that: The collecting of vehicle images, vehicle location and time information at a preset location of the traffic system; wherein the vehicle images include vehicle license plate images, includes: The cameras installed at traffic arteries, parking lot entrances and exits, and toll booths collect vehicle images and automatically adjust camera parameters according to real-time lighting conditions; The traffic flow information of the current road section is collected through sensor modules installed at traffic arteries, parking lot entrances and exits, and toll booths; The entry time of vehicles is recorded through RFID readers installed at traffic arteries, parking lot entrances and exits, and toll booths; The collected vehicle images, the traffic flow information of the current road section and the vehicle's entry time are sent to the central processor through the network.

3. The fee deduction method of the intelligent transportation system according to claim 1, characterized in that: The deduction amount is sent to the user's mobile terminal and payment services are provided, including: Sending deduction information to the user's mobile terminal; the deduction information includes the deduction amount, as well as the deduction willingness, deduction section information and deduction time generated according to the vehicle's driving path, travel time and real-time traffic flow information; Connect the user's mobile terminal to the payment platform, automatically upload the deduction amount, and generate a deduction operation interface. After the user confirms, the deduction is completed.

4. A fee deduction system for an intelligent transportation system, characterized in that: The system adopts the fee deduction method of the intelligent transportation system as claimed in any one of claims 1 to 3; The system comprises: A data acquisition module, used to collect vehicle images, vehicle driving information and traffic flow information at a preset position in the traffic system; the vehicle image includes a license plate image of the vehicle; The data processing module is used to identify and analyze vehicle images using deep learning algorithms to obtain license plate number information and vehicle model information; A prediction module is used to predict the vehicle's travel path and travel time based on the vehicle's historical travel data; Dynamic fee deduction module, used to dynamically calculate the fee deduction amount based on the vehicle's driving path, travel time and real-time traffic flow information; The payment module is used to send the deducted amount to the user's mobile terminal and provide payment services.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the fee deduction method of the intelligent transportation system as described in any one of claims 1 to 3 are implemented.

6. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the fee deduction method of the intelligent transportation system as claimed in any one of claims 1 to 3 are implemented.

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