A smart traffic data processing method based on 5G wireless communication

Abstract

The present invention discloses a method for processing smart traffic data based on 5G wireless communication, including: collecting driver driving information, performing data preprocessing, and screening out driving information of drivers who have violated traffic regulations; using a deep learning algorithm to construct a data analysis model, and inputting the driving information of drivers who have violated traffic regulations into the data analysis model for training; obtaining driver driving status information and road information in real time based on 5G network slicing, fusing the two, and inputting them into the trained data analysis model for status prediction; performing two-way real-time early warning reminders based on the prediction results, and completing the data processing of smart traffic. The present invention combines deep learning algorithms and 5G network slicing to acquire and process smart traffic information data, which can improve the processing speed and accuracy, thereby being able to improve the smart traffic system and have better practical application effects.

Description

Technical Field

[0001] The present invention relates to the technical field of smart cities and 5G networks, and in particular to a smart traffic data processing method based on 5G wireless communication. Background Art

[0002] The urban road traffic system solution effectively applies advanced cloud computing, information fusion technology, data communication transmission technology, remote control technology, computer processing technology and other high-tech technologies to urban road traffic management. Travelers can choose real-time transportation methods and routes; traffic management departments can automatically conduct reasonable traffic diversion, control and accident handling; transportation departments can grasp the operation of vehicles at any time and make reasonable dispatches; thus, the traffic flow on the road network is in the best state, traffic congestion and blockage are improved, the road network's throughput capacity is maximized, and the mobility, safety and production efficiency of the entire road transportation system are improved, while also reducing the impact of road transportation on environmental pollution.

[0003] In the process of implementing the smart transportation system, in order to ensure the accuracy and timeliness of data acquisition and the effectiveness and accuracy of analysis, the 5G network will be used as a support. With the advent of 5G, the turning point of the era of smart transportation has also appeared. 5G will lead a new round of technology integration and innovation, comprehensively promote the development of technologies such as Internet of Vehicles, cloud computing, edge computing and autonomous driving, fully empower urban smart transportation construction, and bring profound changes to transportation information management and even the entire transportation industry. By analyzing the characteristics of 5G and combining the specific scenarios in transportation operation management, this paper discusses the application prospects of 5G in urban smart transportation construction, in order to realize the comprehensive empowerment of 5G for smart transportation. The strategic significance of 5G technology lies not in mastering huge amounts of data information, but in the rapid acquisition of these meaningful data, in improving the "ability to acquire" data, and realizing the "value-added" of data through "rapid acquisition of data".

[0004] In the process of connecting the existing network with the smart transportation system, the speed of obtaining information is slow and the information processing is not accurate enough, which makes it impossible to achieve fast and accurate analysis of data, and then it is difficult to improve the smart transportation system, resulting in poor actual use effect of the smart transportation system. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is that the existing technology is slow in acquiring and processing smart traffic information data, which leads to the imperfection of the smart traffic system and poor actual application effect.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: collecting driving information of drivers, and performing data preprocessing to screen out driving information of drivers who have violated traffic regulations; using a deep learning algorithm to build a data analysis model, and inputting the driving information of drivers who have violated traffic regulations into the data analysis model for training; based on 5G network slicing, real-time acquisition of driver driving status information and road information is carried out, the two are integrated, and the information is input into the trained data analysis model for status prediction; based on the prediction results, two-way real-time early warning reminders are carried out to complete the data processing of smart transportation.

[0009] As a preferred solution of the smart traffic data processing method based on 5G wireless communication described in the present invention, the data preprocessing includes data cleaning, data transformation and data set synthesis.

[0010] As a preferred solution of the intelligent traffic data processing method based on 5G wireless communication described in the present invention, wherein: the driver driving information includes driving status information and its corresponding road information.

[0011] As a preferred solution of the smart traffic data processing method based on 5G wireless communication described in the present invention, the data analysis model includes defining the objective function of the data analysis model as:

[0012]

[0013] in, represents the weight, t represents the driving time, m represents the number of violations, x i ,y i They respectively represent the traffic flow value and pedestrian flow value of the road section corresponding to the driving time period i, and k represents the road flow coefficient.

[0014] As a preferred solution of the smart transportation data processing method based on 5G wireless communication described in the present invention, the 5G network slice includes uRLL network slice: oriented to smart transportation and road traffic information acquisition demand-side response services.

[0015] As a preferred solution of the intelligent traffic data processing method based on 5G wireless communication described in the present invention, the process of fusing the driver's driving status information and road information includes establishing an information fusion model:

[0016]

[0017] Among them, Z i is the compatibility coefficient of the information data length threshold, is the adjustment function, y r is the flow channel coefficient, θ k is the information data length threshold, Δθ k It is the adjustment threshold of the message data length threshold.

[0018] As an optimal solution of the smart traffic data processing method based on 5G wireless communication described in the present invention, it also includes: fusing and adjusting the driver's driving status information and road information through the information fusion model to obtain fused information: calculating the compatibility coefficient of the driving status information data length threshold and the road information data length threshold through the information fusion model to obtain the compatibility coefficient of the driving status information data length threshold and the road information data length threshold; judging whether the compatibility coefficients of the driving status information data length threshold and the road information data length threshold both meet the compatibility condition, and if so, fusing the two; wherein the compatibility condition is: the compatibility coefficient is greater than 0 and less than 1.

[0019] As a preferred solution of the smart traffic data processing method based on 5G wireless communication described in the present invention, the two-way real-time early warning reminder based on the prediction results includes, when the value of the objective function of the data analysis model is between 0.4 and 0.9, issuing early warning information to the intelligent traffic management system and the driver at the same time.

[0020] Beneficial effects of the invention: The invention combines deep learning algorithms and 5G network slicing to acquire and process smart traffic information data, which can improve the processing speed and accuracy, thereby improving the smart traffic system and having better practical application effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0022] Figure 1 A basic flow chart of a method for processing intelligent traffic data based on 5G wireless communication is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0026] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0027] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0028] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] Example 1

[0030] Reference Figure 1, as an embodiment of the present invention, provides a method for processing intelligent traffic data based on 5G wireless communication, including:

[0031] S1: Collecting the driving information of drivers and preprocessing the data to filter out the driving information of drivers who have violated traffic regulations; it should be noted that

[0032] The driver's driving information includes driving status information and its corresponding road information, wherein the driving status information includes data such as the type of the car being driven, driving time, and personal identity information of the driver; the corresponding road information includes states such as congestion and non-congestion;

[0033] Data preprocessing includes data cleaning, data transformation and data set integration; specifically, data cleaning: cleaning of missing values, format content, logic errors and non-required data; data transformation: constructing features, classifying and quantifying data; data set integration: performing data statistics on the transformed data and merging the data into a unified data storage.

[0034] S2: Use deep learning algorithms to build a data analysis model, and input the driving information of drivers who have violated traffic rules into the data analysis model for training; it should be noted that

[0035] The data analysis model includes:

[0036] The objective function of the data analysis model is defined as:

[0037]

[0038] in, represents the weight, t represents the driving time, m represents the number of violations, x i ,y i They respectively represent the traffic flow value and pedestrian flow value of the road section corresponding to the driving time period i, and k represents the road flow coefficient.

[0039] S3: Based on 5G network slicing, the driver's driving status information and road information are obtained in real time, the two are integrated, and input into the trained data analysis model for status prediction; it should be noted that

[0040] 5G network slicing includes:

[0041] uRLL network slicing: oriented to smart transportation and road traffic information acquisition demand-side response services.

[0042] Furthermore, the process of integrating the driver's driving status information and the road information includes:

[0043] Establish information fusion model:

[0044]

[0045] Among them, Z i is the compatibility coefficient of the information data length threshold, is the adjustment function, y r is the flow channel coefficient, θ k is the information data length threshold, Δθ k It is the adjustment threshold of the message data length threshold.

[0046] Further:

[0047] The driver's driving status information and road information are fused and adjusted through the information fusion model to obtain the fused information:

[0048] The compatibility coefficient of the driving state information data length threshold and the road information data length threshold is calculated by the information fusion model to obtain the compatibility coefficient of the driving state information data length threshold and the road information data length threshold;

[0049] Determine whether the compatibility coefficients of the driving state information data length threshold and the road information data length threshold both meet the compatibility condition, and if so, merge the two;

[0050] Among them, the compatibility condition is: the compatibility coefficient is greater than 0 and less than 1.

[0051] S4: Provide two-way real-time warning and reminder based on the prediction results to complete the data processing of smart transportation; it should be noted that

[0052] Two-way real-time early warning reminders based on forecast results include:

[0053] When the value of the objective function of the data analysis model is between 0.4 and 0.9, warning information is sent to the intelligent traffic management system and the driver at the same time.

[0054] The present invention combines deep learning algorithms and 5G network slicing to acquire and process smart traffic information data, which can improve the processing speed and accuracy, thereby improving the smart traffic system and achieving better practical application effects.

[0055] Example 2

[0056] This embodiment is the second embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a verification test of a smart traffic data processing method based on 5G wireless communication, verifies and explains the technical effects used in this method, and this embodiment uses a traditional technical solution to conduct a comparative test with the method of the present invention, and compares the test results by means of scientific argumentation to verify the actual effect of this method.

[0057] Traditional technical solutions: have high latency and low accuracy of analysis and processing. To verify that this method has higher accuracy of analysis and processing and lower latency than traditional methods, this embodiment will use traditional intelligent traffic data processing methods and this method to conduct real-time measurement and comparison of the information acquisition speed and information data processing accuracy of simulated traffic scenes.

[0058] Test environment: Run the real-time traffic scene on the simulation platform to simulate vehicle driving and simulate the driving of pedestrians, vehicles and traffic lights in the actual traffic scene. Use the traditional method and the method of the present invention respectively, then turn on the automatic test equipment and use MATLB software programming to implement the simulation test of the two methods, and obtain simulation data according to the experimental results. Each method tests 20 groups of data, calculates the information acquisition speed and data analysis accuracy of each group of data, and compares the actual time and data input of the simulation to calculate the error. The results are shown in the following table.

[0059] Table 1: Comparison of experimental results.

[0060] Test samples Traditional methods Method of the present invention Latency ≥1ms 0.6ms Accuracy 88.62％ 97.48％

[0061] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for processing intelligent traffic data based on 5G wireless communication, characterized in that: include: Collect the driver's driving information, perform data preprocessing, and filter out the driving information of drivers who have violated traffic regulations; A data analysis model is constructed using a deep learning algorithm, and the driving information of the driver who has violated traffic regulations is input into the data analysis model for training; The objective function of the data analysis model is defined as: in, represents the weight, t represents the driving time, m represents the number of violations, x i ,y i They represent the traffic flow value and pedestrian flow value of the road section corresponding to the driving time period i, and k represents the road flow coefficient; Based on 5G network slicing, the driver's driving status information and road information are obtained in real time, the two are integrated and input into the trained data analysis model for status prediction; The 5G network slice includes: uRLL network slicing: for smart transportation and road traffic information acquisition demand-side response services; The process of fusing the driver's driving state information and road information includes: Establish information fusion model: Among them, Z i is the compatibility coefficient of the information data length threshold, is the adjustment function, y r is the flow channel coefficient, θ k is the information data length threshold, Δθ k is the adjustment threshold of the message data length threshold; The driver's driving state information and road information are fused and adjusted by the information fusion model to obtain fused information: Calculating the compatibility coefficient of the driving state information data length threshold and the road information data length threshold through the information fusion model to obtain the compatibility coefficient of the driving state information data length threshold and the road information data length threshold; Determine whether the compatibility coefficients of the driving state information data length threshold and the road information data length threshold both meet compatibility conditions, and if so, merge the two; Wherein, the compatibility condition is: the compatibility coefficient is greater than 0 and less than 1; Based on the prediction results, two-way real-time early warning reminders are carried out to complete the data processing of smart transportation.

2. The method for processing intelligent traffic data based on 5G wireless communication according to claim 1, characterized in that: The data preprocessing includes data cleaning, data transformation and data set synthesis.

3. The method for processing intelligent traffic data based on 5G wireless communication according to claim 2, characterized in that: The driver driving information includes driving state information and corresponding road information.

4. The method for processing intelligent traffic data based on 5G wireless communication according to claim 3, characterized in that: The two-way real-time early warning reminder according to the prediction result includes: When the value of the objective function of the data analysis model is between 0.4 and 0.9, a warning message is sent to the intelligent traffic management system and the driver at the same time.

Images