CN117278328B - Data processing method and system based on Internet of vehicles - Google Patents
Data processing method and system based on Internet of vehicles Download PDFInfo
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- H—ELECTRICITY
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- H04L63/00—Network architectures or network communication protocols for network security
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Abstract
The invention relates to the technical field of data processing, and discloses a data processing method and system based on the Internet of vehicles, which are used for improving the accuracy of data processing based on the Internet of vehicles. Comprising the following steps: performing AES algorithm encryption on the vehicle state data to obtain encrypted state data, and performing vehicle identity information identification to obtain a corresponding identity tag; performing security verification on the identity tag, and performing edge calculation processing on the encrypted state data to obtain an edge data set of the encrypted state data when the identity tag passes the security verification; carrying out feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set; analyzing the vehicle environment information of the vehicle characteristic data to obtain vehicle environment data corresponding to the vehicle characteristic data; and predicting the vehicle space data of the vehicle environment data to obtain the vehicle space data, and constructing the vehicle control strategy of the vehicle space data to obtain the vehicle control strategy of the target vehicle.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method and system based on the Internet of vehicles.
Background
With the continuous development of the internet of vehicles, the traffic field is gradually moving into an intelligent and informationized age. The vehicle is taken as an important component of a traffic system, and the data of the vehicle has great potential and can provide support for realizing safer, efficient and intelligent traffic management and driving experience.
Currently, lack of unified standards in vehicle data processing and communication results in poor interoperability between vehicle systems of different vendors. Personal privacy protection for the driver and vehicle owner remains a challenge during the collection, transmission and processing of vehicle data, and is required to face real-time challenges to achieve lower latency and higher real-time.
Disclosure of Invention
The invention provides a data processing method and system based on the Internet of vehicles, which are used for improving the accuracy of data processing based on the Internet of vehicles.
The first aspect of the invention provides a data processing method based on the internet of vehicles, which comprises the following steps: acquiring vehicle data of a preset target vehicle through a plurality of sensors arranged on the target vehicle to obtain vehicle state data;
performing AES algorithm encryption on the vehicle state data to obtain encrypted state data, and simultaneously, performing vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag;
Performing security verification on the identity tag, and performing edge calculation processing on the encrypted state data to obtain an edge data set of the encrypted state data when the identity tag passes the security verification;
performing feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set;
analyzing the vehicle environment information of the vehicle characteristic data to obtain vehicle environment data corresponding to the vehicle characteristic data;
and predicting the vehicle space data of the vehicle environment data to obtain corresponding vehicle space data, constructing a vehicle control strategy of the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the acquiring, by using a plurality of sensors deployed on a preset target vehicle, vehicle data of the target vehicle, to obtain vehicle state data includes:
acquiring vehicle data of the target vehicle through a plurality of sensors to obtain vehicle state data;
And classifying the vehicle state data to obtain a vehicle movement data set, a vehicle direction information set and vehicle position information of the target vehicle, wherein the vehicle movement data set comprises vehicle speed data, vehicle acceleration data and vehicle angular speed data, and the vehicle direction information set comprises vehicle running direction and vehicle inclination angle data.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the encrypting the vehicle state data by AES algorithm to obtain encrypted state data, and at the same time, identifying vehicle identity information on the encrypted state data to obtain a corresponding identity tag, includes:
performing data block processing on the vehicle state data to obtain a plurality of vehicle state data blocks;
carrying out data block length analysis on each vehicle state data block to obtain data block length information corresponding to each vehicle state data block;
respectively carrying out threshold analysis on the length information of a plurality of data blocks to obtain a threshold analysis result;
performing data filling processing on each vehicle state data block according to the threshold analysis result to obtain a plurality of filling data blocks;
Performing AES algorithm encryption processing on each filling data block to obtain encryption state data;
and carrying out vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, performing security verification on the identity tag, and performing edge calculation processing on the encrypted state data to obtain an edge data set of the encrypted state data when the identity tag passes the security verification, where the method includes:
performing original data conversion on the identity tag to obtain original identity data corresponding to the identity tag;
carrying out security verification on the original identity data through a preset legal identity database to obtain a corresponding security verification result;
when the security verification result is that the identity tag passes the security verification, carrying out transmission protocol matching on the encrypted state data to obtain a target transmission protocol;
transmitting the encryption state data to a preset edge computing terminal for real-time event detection based on the target transmission protocol to obtain a real-time event set;
and carrying out data noise reduction processing on the real-time event set to obtain an edge data set of the encryption state data.
With reference to the first implementation manner of the first aspect, in a fourth implementation manner of the first aspect of the present invention, performing feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set includes:
extracting time domain features of the edge data set to obtain time domain features corresponding to the edge data set;
extracting frequency domain features of the edge data set to obtain frequency domain features corresponding to the edge data set;
extracting airspace characteristics of the edge data set to obtain airspace characteristics corresponding to the edge data set;
performing feature statistical analysis on the time domain features, the frequency domain features and the airspace features to obtain a statistical feature set;
performing time sequence analysis on the statistical feature set to obtain corresponding time sequence data;
and based on the time series data and the statistical feature set, carrying out data feature standardization processing on the edge data set to obtain vehicle feature data corresponding to the edge data set.
With reference to the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect of the present invention, the performing vehicle environment information analysis on the vehicle feature data to obtain vehicle environment data corresponding to the vehicle feature data includes:
Carrying out vehicle coordinate analysis on the target vehicle through the vehicle characteristic data and the vehicle position information to obtain vehicle coordinate data of the target vehicle;
based on the vehicle coordinate data, acquiring weather parameters of the target vehicle to obtain weather parameter data corresponding to the target vehicle;
based on the vehicle coordinate data, acquiring traffic flow data of the target vehicle to obtain traffic flow data corresponding to the target vehicle;
based on the traffic flow data, acquiring road environment of the target vehicle to obtain road environment data of the target vehicle;
and combining the weather parameter data, the traffic flow data and the road environment data into the vehicle environment data.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the predicting vehicle space data for the vehicle environment data to obtain corresponding vehicle space data, and meanwhile, constructing a vehicle control policy for the vehicle space data to obtain a vehicle control policy for the target vehicle, and generating vehicle control prompt information according to the vehicle control policy includes:
Constructing a virtual running space of the target vehicle based on the vehicle environment data;
performing vehicle space data analysis on the target vehicle in the virtual driving space to obtain the vehicle space data;
calculating the vehicle running influence factors of the vehicle space data to obtain an influence factor data set;
and constructing a vehicle control strategy for the target vehicle through the influence factor data set and the vehicle space data to obtain the vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy.
The second aspect of the present invention provides a data processing system based on the internet of vehicles, the data processing system based on the internet of vehicles comprising:
the acquisition module is used for acquiring vehicle data of a preset target vehicle through a plurality of sensors arranged on the target vehicle to obtain vehicle state data;
the encryption module is used for carrying out AES algorithm encryption on the vehicle state data to obtain encrypted state data, and simultaneously carrying out vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag;
the verification module is used for carrying out security verification on the identity tag, and carrying out edge calculation processing on the encryption state data when the identity tag passes the security verification to obtain an edge data set of the encryption state data;
The processing module is used for carrying out characteristic engineering processing on the edge data set to obtain vehicle characteristic data corresponding to the edge data set;
the analysis module is used for analyzing the vehicle environment information of the vehicle characteristic data to obtain the vehicle environment data corresponding to the vehicle characteristic data;
the prediction module is used for predicting the vehicle space data of the vehicle environment data to obtain corresponding vehicle space data, constructing a vehicle control strategy of the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy.
According to the technical scheme provided by the invention, the vehicle data acquisition is carried out on the target vehicle through a plurality of sensors deployed on the target vehicle, so as to obtain the vehicle state data; performing AES algorithm encryption on the vehicle state data to obtain encrypted state data, and performing vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag; performing security verification on the identity tag, and performing edge calculation processing on the encrypted state data to obtain an edge data set of the encrypted state data when the identity tag passes the security verification; carrying out feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set; analyzing the vehicle environment information of the vehicle characteristic data to obtain vehicle environment data corresponding to the vehicle characteristic data; and predicting the vehicle space data of the vehicle environment data to obtain corresponding vehicle space data, constructing a vehicle control strategy of the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy. In the scheme, the AES algorithm is adopted to encrypt the vehicle state data, so that the safety of the data in the transmission and storage processes is ensured, and unauthorized access is prevented. Meanwhile, through encryption and verification of the identity information, only legal users are ensured to be able to acquire the sensitive information of the vehicle, and the privacy of the vehicle owner is effectively protected. The real-time data acquisition, the edge calculation processing and the control strategy construction are carried out at the vehicle end, so that the time delay of data transmission to the cloud is reduced, and the real-time performance and the responsiveness of the system are improved. Through vehicle environment information analysis and spatial data prediction, the system can more comprehensively know the traffic environment of the vehicle. Based on the information, an intelligent vehicle control strategy is constructed, so that the vehicle can more intelligently cope with complex traffic situations, and the driving safety and efficiency are improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a data processing method based on Internet of vehicles according to an embodiment of the present invention;
FIG. 2 is a flowchart of encrypting the vehicle state data according to the AES algorithm according to the embodiment of the invention;
FIG. 3 is a flow chart of security verification of an identity tag in an embodiment of the present invention;
FIG. 4 is a flow chart of feature engineering processing on an edge data set in an embodiment of the invention;
FIG. 5 is a schematic diagram of an embodiment of a data processing system based on Internet of vehicles according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a data processing method and system based on the Internet of vehicles, which are used for improving the accuracy of data processing based on the Internet of vehicles.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and an embodiment of a data processing method based on internet of vehicles in the embodiment of the present invention includes:
s101, acquiring vehicle data of a target vehicle through a plurality of sensors deployed on a preset target vehicle to obtain vehicle state data;
it will be appreciated that the execution subject of the present invention may be a data processing system based on internet of vehicles, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, a plurality of sensors are deployed on a preset target vehicle, and these sensors cover various types, such as a speed sensor, an acceleration sensor, an angular velocity sensor, and the like, to ensure a comprehensive sense of the vehicle state. The comprehensive data acquisition method provides rich raw materials for subsequent data processing. And classifying the obtained vehicle state data, and extracting key information such as the movement, the direction and the position of the target vehicle from the mixed data by the server. For example, by classifying the speed data, the server obtains a set of speed data for the vehicle; by classifying the direction information, the server acquires the traveling direction and the inclination angle data of the vehicle. Specifically, the vehicle motion data set includes information such as vehicle speed, acceleration, and angular velocity. The speed data reflects the displacement change of the vehicle in a unit time, the acceleration data reveals the rate of change of the vehicle speed, and the angular speed data depicts the rate of rotation of the vehicle about its own axis. The three are combined to form a comprehensive description of the vehicle motion state. Meanwhile, the vehicle direction information set includes vehicle traveling direction and inclination angle data. The driving direction data can tell the server the current moving direction of the vehicle, and the inclination data reflects the inclination degree of the vehicle relative to the horizontal plane. This is critical for understanding the pose and direction of travel of the vehicle, especially in complex terrain or compact traffic situations. By this data processing method, the server obtains the vehicle position information of the target vehicle. This is derived by comprehensive analysis of the entire data set, including information on various aspects of the speed, direction, etc. of the vehicle. The vehicle location information provides a basis for subsequent environmental analysis and prediction.
S102, performing AES algorithm encryption on vehicle state data to obtain encrypted state data, and meanwhile, performing vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag;
the encrypted vehicle state data is subjected to block processing, so that a large amount of vehicle state data is better processed, and the vehicle state data is divided into a plurality of blocks, thereby facilitating subsequent data analysis and processing. Such a blocking process contributes to an improvement in data processing efficiency and a reduction in computational complexity. And carrying out data block length analysis to obtain the length information corresponding to each data block. The purpose is to know the size of each data block, providing the basis for subsequent threshold analysis. By analyzing the length of the data blocks, the server is better adapted to the data blocks with different sizes, and the processing flexibility is improved. And respectively carrying out threshold analysis on the length information of the plurality of data blocks to obtain a threshold analysis result. The method aims at determining the data filling requirement, and according to the analysis result, the server adjusts the data filling strategy to adapt to different conditions, so that the accuracy of data processing is ensured. And then carrying out data filling processing, namely carrying out proper filling on each data block according to the threshold analysis result so as to ensure that the length of the data block meets the requirements of a server. The data filling is to ensure consistency of the data blocks for subsequent encryption processing. And carrying out AES algorithm encryption processing on the filled data blocks. On the basis of ensuring the consistency of the data, the data is secondarily encrypted so as to enhance the safety of the data. Thus, even if interception occurs in the transmission process, the difficulty of maliciously acquiring data is further increased. And carrying out vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag. Ensuring that only authorized entities are able to decrypt and use the data. And verifying whether the receiver of the data has the right to access the decrypted vehicle state data through the identity tag by the server, thereby ensuring the safety and privacy of the data.
S103, carrying out security verification on the identity tag, and carrying out edge calculation processing on the encryption state data when the identity tag passes the security verification to obtain an edge data set of the encryption state data;
specifically, the original data conversion is carried out on the identity tag, so that the original identity data corresponding to the identity tag is obtained. Such raw identity data includes unique information related to the vehicle or driver, such as a vehicle ID or driver's identification number, etc. And carrying out security verification on the original identity data through a preset legal identity database. The database stores legal identity information, and the server obtains the security verification result by comparing the original identity data. This is to ensure that the receiving side of the data is legally authenticated, thereby preventing illegal access and data leakage. When the security verification result confirms that the identity tag passes the verification, namely, the identity of the receiver is legal, at the moment, transmission protocol matching is carried out on the encrypted state data, and the purpose is to determine the transmission protocol applicable to the current situation. The selection of the transmission protocol is based on factors such as network conditions, device compatibility of the receiver, etc., to ensure that the data can be transmitted safely and efficiently. And transmitting the encryption status data to a preset edge computing terminal based on the determined transmission protocol. Edge computing terminals are computing devices distributed across the edges of a network that have the ability to process and analyze data in real-time. At this stage, the server performs real-time event detection, and obtains a real-time event set by performing instant analysis on the transmitted data. In order to improve the accuracy and usability of the data, the server performs data noise reduction processing. The noise introduced in the transmission or collection process is eliminated, so that the finally obtained data set is ensured to be reliable and has practical significance. Through noise reduction processing, the server effectively improves the data quality, and provides a more reliable basis for subsequent analysis and application. Finally, the server obtains an edge data set of the encrypted state data. The data set contains the vehicle state information subjected to safety verification, and data subjected to transmission protocol matching, real-time event detection and data noise reduction processing provides a high-quality data base for subsequent vehicle feature analysis and environment data prediction.
S104, carrying out feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set;
the edge data set is subjected to time domain feature extraction. Time domain features typically include a series of characteristics that describe the change in the data in the time dimension, such as mean, standard deviation, maximum, minimum, etc. These time domain features provide an overall description of the vehicle state data reflecting its law of variation over time. And carrying out frequency domain feature extraction, and carrying out frequency domain analysis on the edge data set to obtain information of the data in a frequency domain. The frequency domain features include frequency spectrums, frequency components, etc., which help identify periodic variations in vehicle state or frequency components, such as periodic vibrations of the engine. Subsequently, spatial domain feature extraction is performed, which includes analysis of spatial distribution, revealing the spatial distribution law of the vehicle state. This involves consideration of the layout of the vehicle sensors, as well as the spatial relationship between the vehicle parts. Such airspace feature extraction helps to fully understand the overall structure of the vehicle state. And carrying out feature statistical analysis on the time domain features, the frequency domain features and the space domain features to obtain a statistical feature set. The set integrates statistical information on different feature dimensions to form a comprehensive and various feature description. Based on the statistical feature set, a time series analysis is performed. Revealing the time relation and dynamic change rule among the features is helpful for understanding the evolution process of the vehicle state more deeply. Finally, based on the time series data and the statistical feature set, carrying out data feature standardization processing on the edge data set. Normalization can ensure that the dimensions of the different features are consistent so that they can function in subsequent analysis. This step increases the comparability of the data, providing a more reliable basis for higher level model building and decision making.
S105, vehicle environment information analysis is carried out on the vehicle characteristic data, and vehicle environment data corresponding to the vehicle characteristic data are obtained;
specifically, vehicle coordinate analysis is performed on the target vehicle through the vehicle characteristic data and the vehicle position information, so as to obtain vehicle coordinate data of the target vehicle. Based on the vehicle position information, combining the vehicle characteristic data, accurately analyzing the position of the target vehicle on geographic coordinates. The acquisition of the vehicle coordinate data provides a spatial basis for subsequent analysis of the environmental information. After the vehicle coordinate data are acquired, the server acquires weather parameters. And calling a corresponding weather service or sensor by the server through geographic position information based on the vehicle coordinate data, and collecting weather parameter data of the position of the target vehicle. This includes information such as temperature, humidity, wind speed, etc., providing a weather background for the diversity of vehicle environmental data. Meanwhile, the server collects traffic flow data based on vehicle coordinate data. The server obtains traffic flow information of the position of the target vehicle, namely the number and the flow condition of the vehicles on the road. This helps in predicting traffic congestion, planning an optimal travel route, and the like. And collecting road environment based on the collected traffic flow data. The environment of the road where the target vehicle is located is analyzed in detail, including road type, road condition, intersection information, and the like. Such road environment data facilitates a more comprehensive understanding of the vehicle operating environment, thereby providing more accurate information for vehicle control and navigation. And finally, merging the acquired weather parameter data, traffic flow data and road environment data into vehicle environment data. The server integrates environmental information in multiple dimensions, and forms a comprehensive description of the surrounding environment of the target vehicle. The vehicle environment data is a key output based on the data processing of the internet of vehicles, and provides important input for subsequent applications such as intelligent traffic systems, driving auxiliary systems and the like.
S106, predicting vehicle space data of the vehicle environment data to obtain corresponding vehicle space data, constructing a vehicle control strategy of the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy.
The virtual travel space of the target vehicle is constructed based on the vehicle environment data. This step takes into account environmental characteristics of the target vehicle, including road conditions, traffic flow, weather, and the like. Through comprehensive analysis of the factors, the server establishes a virtual environment for future running of the target vehicle, and provides a basis for subsequent spatial data prediction. In the virtual travel space, vehicle space data analysis is performed on the target vehicle. The analysis process focuses on the space conditions around the vehicle, including information on the position, speed, travel track, etc. of other vehicles. Through in-depth analysis of the vehicle space data, the server obtains the specific position and motion state of the target vehicle in the virtual running space. Subsequently, vehicle travel influence factor calculation is performed on the vehicle space data, and various factors that influence the travel of the target vehicle, such as traffic conditions, road types, relative speeds, and the like, are determined. By calculating the factors, the server obtains an influence factor data set which reflects the influence degree of the surrounding environment of the vehicle on the running of the target vehicle. Then, the server performs construction of a vehicle control strategy based on the obtained influence factor data set and the vehicle space data. By comprehensively considering the influence factors, the optimal driving strategy is formulated for the target vehicle by adopting methods such as machine learning, a rule engine and the like. This strategy may include adjusting speed, changing lanes, taking emergency braking, etc. to accommodate changes in the current environment. Finally, based on the constructed vehicle control strategy, the server generates vehicle control prompt information. These prompts may be communicated directly to the driver or as inputs to the vehicle's autopilot system. In this way, the server not only provides a flexible control strategy for the vehicle, but also promotes the cooperative work between the driver and the automatic driving system through prompt information.
In the embodiment of the invention, the vehicle data acquisition is carried out on the target vehicle through a plurality of sensors deployed on the target vehicle to obtain the vehicle state data; performing AES algorithm encryption on the vehicle state data to obtain encrypted state data, and performing vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag; performing security verification on the identity tag, and performing edge calculation processing on the encrypted state data to obtain an edge data set of the encrypted state data when the identity tag passes the security verification; carrying out feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set; analyzing the vehicle environment information of the vehicle characteristic data to obtain vehicle environment data corresponding to the vehicle characteristic data; and predicting the vehicle space data of the vehicle environment data to obtain corresponding vehicle space data, constructing a vehicle control strategy of the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy. In the scheme, the AES algorithm is adopted to encrypt the vehicle state data, so that the safety of the data in the transmission and storage processes is ensured, and unauthorized access is prevented. Meanwhile, through encryption and verification of the identity information, only legal users are ensured to be able to acquire the sensitive information of the vehicle, and the privacy of the vehicle owner is effectively protected. The real-time data acquisition, the edge calculation processing and the control strategy construction are carried out at the vehicle end, so that the time delay of data transmission to the cloud is reduced, and the real-time performance and the responsiveness of the system are improved. Through vehicle environment information analysis and spatial data prediction, the system can more comprehensively know the traffic environment of the vehicle. Based on the information, an intelligent vehicle control strategy is constructed, so that the vehicle can more intelligently cope with complex traffic situations, and the driving safety and efficiency are improved.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Acquiring vehicle data of a target vehicle through a plurality of sensors to obtain vehicle state data;
(2) And classifying the vehicle state data to obtain a vehicle movement data set, a vehicle direction information set and vehicle position information of the target vehicle, wherein the vehicle movement data set comprises vehicle speed data, vehicle acceleration data and vehicle angular speed data, and the vehicle direction information set comprises vehicle running direction and vehicle inclination angle data.
Specifically, vehicle data acquisition is performed on the target vehicle by a plurality of sensors to obtain vehicle state data, and various types of sensors such as inertial sensors, visual sensors, radar, lidar, etc. may be used. These sensors can provide multi-dimensional information about vehicle motion, surrounding environment, and location. For example, inertial sensors may provide information about acceleration, angular velocity, etc. of the vehicle, visual sensors may capture images of the surrounding road and vehicle, and radar may be used to detect the distance and velocity of surrounding objects. By the cooperation of these sensors, a comprehensive vehicle data acquisition system can be established. Data classification is performed on the vehicle state data. Raw data acquired from the sensors is processed and parsed to distinguish between different types of information. For example, for acceleration and angular velocity data, the motion state information of the vehicle may be extracted by a signal processing method. Meanwhile, the image captured by the vision sensor can be subjected to target detection and tracking through a computer vision technology, so that the position information of the vehicle is acquired. The radar data may then be used to identify the location and motion status of surrounding obstacles. Thus, a vehicle motion data set, a vehicle direction information set, and vehicle position information are obtained. For example, through inertial sensors, the server obtains speed, acceleration, and angular velocity data of the vehicle. Cameras can capture images on roads for identifying lane lines, other vehicles, and traffic signs. Lidar can measure the distance and relative speed of surrounding obstacles. Thus, by the cooperation of the three sensors, rich vehicle state data can be obtained. The classification of vehicle state data is to better understand and utilize this information. The vehicle motion data set may include speed, acceleration, angular velocity, etc., which are important indicators of vehicle dynamics. The vehicle direction information set includes a traveling direction, an inclination angle, and the like, and provides key information of the vehicle orientation and posture. The vehicle position information covers the specific position of the vehicle in the geographic space, and is the basis of navigation, path planning and other applications. For example, the server knows the motion state of the vehicle by analyzing the speed, acceleration, and angular velocity data, and determines whether sudden braking or acceleration is required. By analyzing the vehicle direction information, it is possible to detect whether the vehicle is traveling on a correct lane, whether there is a lean or turn, or the like. And through analysis of the vehicle position information, the server determines the specific position of the vehicle on the map and provides accurate position information for the navigation system.
In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:
s201, performing data block processing on vehicle state data to obtain a plurality of vehicle state data blocks;
s202, carrying out data block length analysis on each vehicle state data block to obtain data block length information corresponding to each vehicle state data block;
s203, respectively carrying out threshold analysis on the length information of the plurality of data blocks to obtain a threshold analysis result;
s204, performing data filling processing on each vehicle state data block according to a threshold analysis result to obtain a plurality of filling data blocks;
s205, performing AES algorithm encryption processing on each filling data block to obtain encryption state data;
s206, identifying the vehicle identity information of the encrypted state data to obtain a corresponding identity tag.
The vehicle state data is subjected to data block processing. The continuous vehicle state data is divided into a plurality of smaller data blocks. Such data partitioning helps to improve processing efficiency while better accommodating different analysis and encryption processing requirements. For example, considering the motion state data of a vehicle over a period of time, it may be divided into a plurality of time windows, each window representing a block of data. A data block length analysis is performed for each vehicle state data block. The length of each data block is analyzed in detail to determine the amount of data and the time span within the data block. This analysis process may be determined based on the specific application requirements, for example, by performing a fixed length block analysis on a time basis. Then, the length information of the plurality of data blocks is subjected to threshold analysis to determine a proper threshold value for subsequent data filling processing. The threshold value is set based on the distribution of the data block length, and can be determined according to statistical indexes such as average length, standard deviation and the like. This threshold analysis is to efficiently process data blocks of different lengths to improve data consistency and stability. And (3) carrying out data filling processing on each vehicle state data block according to the result of the threshold analysis, so that the length of each data block reaches a relatively uniform level. For example, for a shorter length data block, the data may be padded by padding some dummy data or by interpolation to bring it to the length required for the threshold. And carrying out AES algorithm encryption processing on each filling data block. The AES algorithm is an advanced encryption standard commonly used to encrypt sensitive information. By AES encrypting the filler data blocks, the security of the vehicle state data during transmission and storage can be ensured. This is an important step in protecting the vehicle information from malicious access or tampering. And finally, carrying out vehicle identity information identification on the encrypted state data. The encrypted state data and the identity information of the vehicle are combined, and verification can be performed through a pre-established identity database. The identification of the identity information is to ensure that only authorized users can access and decrypt the vehicle state data, so that the privacy and the safety of the data are ensured.
In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:
s301, converting the original data of the identity tag to obtain the original identity data corresponding to the identity tag;
s302, carrying out security verification on original identity data through a preset legal identity database to obtain a corresponding security verification result;
s303, when the security verification result is that the identity tag passes the security verification, carrying out transmission protocol matching on the encrypted state data to obtain a target transmission protocol;
s304, transmitting the encryption state data to a preset edge computing terminal to detect real-time events based on a target transmission protocol, and obtaining a real-time event set;
s305, carrying out data noise reduction processing on the real-time event set to obtain an edge data set of the encryption state data.
It should be noted that, the original data conversion is performed on the identity tag. The identity tag is typically an encrypted string or code that needs to be converted into the original identity data for subsequent authentication. This process includes decryption, decoding, etc., to restore the identity tag to identifiable identity information. For example, if the identity tag is a Base64 encoded string, it needs to be decoded to obtain the original data. And carrying out security verification on the original identity data through a preset legal identity database, and ensuring the validity and effectiveness of the identity information. The server compares the original identity data with legitimate identity information previously stored in a database. If the identity information exists in the database and is valid, the verification result is passed; otherwise, the verification result is not passed. This security verification procedure ensures that only authorized users are able to perform subsequent data processing steps. When the security verification result is that the identity tag passes the security verification, transmission protocol matching is performed next, and how to safely transmit the encryption state data is determined. The server selects the data transmission mode most suitable for the current situation through a predefined transmission protocol. For example, the transmission of encrypted data using HTTPS protocol may be chosen to ensure that the data is not stolen or tampered with during transmission. And then, based on the determined transmission protocol, transmitting the encryption state data to a preset edge computing terminal for real-time event detection. An edge computing terminal is a distributed computing device, typically located near a data source, that can perform real-time event detection and processing locally. Therefore, the efficiency of data processing is improved, the time delay of data transmission is reduced, and privacy sensitive information is protected from being transmitted to a remote server. And after the real-time event set is obtained, carrying out data noise reduction processing on the events. The data noise reduction is to remove some irrelevant or redundant information, so that the finally obtained data is more refined and targeted. For example, sensor noise can be removed by a filtering algorithm, and truly meaningful events can be screened out. This step helps to improve the quality and usability of the data.
In a specific embodiment, as shown in fig. 4, the process of executing step S104 may specifically include the following steps:
s401, extracting time domain features of the edge data set to obtain time domain features corresponding to the edge data set;
s402, extracting frequency domain features of the edge data set to obtain frequency domain features corresponding to the edge data set;
s403, extracting airspace characteristics of the edge data set to obtain airspace characteristics corresponding to the edge data set;
s404, carrying out feature statistical analysis on the time domain features, the frequency domain features and the space domain features to obtain a statistical feature set;
s405, carrying out time sequence analysis on the statistical feature set to obtain corresponding time sequence data;
and S406, carrying out data feature standardization processing on the edge data set based on the time sequence data and the statistical feature set to obtain vehicle feature data corresponding to the edge data set.
And carrying out time domain feature extraction on the edge data set. Time domain features describe the law of change of data in time. By applying time domain analysis methods, such as mean, variance, standard deviation, root mean square, etc., the time domain features of the data can be extracted. For example, for vehicle motion state data, domain features such as average speed, standard deviation of acceleration, root mean square of steering angular velocity, and the like can be extracted. And carrying out frequency domain feature extraction on the edge data set. The frequency domain features describe the distribution and variation of data over frequency. Frequency domain features of the data, such as spectral density, dominant frequency, band energy, etc., may be acquired by applying fourier transforms or other frequency domain analysis methods. In a car networking application, the frequency domain features may be used to analyze the periodicity of the vehicle motion, detect abnormal vibrations or shocks, and the like. And carrying out airspace feature extraction on the edge data set. Spatial domain features concern the spatial distribution and relevance of data. For data such as images or sensor arrays, spatial features such as texture, edge information, spatial distribution, etc. can be extracted by spatial analysis methods. In the internet of vehicles, texture information of roads, positional relationships of vehicles, and the like can be acquired by performing airspace feature extraction on image data of surrounding environments. And carrying out feature statistical analysis on the time domain features, the frequency domain features and the airspace features. Various features are combined to form a comprehensive statistical feature set. The set of statistical features may include statistics of mean, variance, skewness, kurtosis, etc. of the individual features to more fully describe the characteristics of the data. Then, a time series analysis is carried out on the statistical feature set, and the evolution trend of the features along with time is understood. Periodicity, trending, and outliers in the data can be detected by time series analysis. This helps to understand aspects of vehicle motion status, environmental changes, etc. Based on the time series data and the statistical feature set, the data feature normalization process is performed on the edge data set. The data of the different features are normalized for better subsequent analysis and modeling. Normalization can be achieved by means of Z-score normalization, minMax normalization, etc., ensuring that the individual features have the same dimensions.
In a specific embodiment, the process of executing step S105 may specifically include the following steps:
(1) Carrying out vehicle coordinate analysis on the target vehicle through the vehicle characteristic data and the vehicle position information to obtain vehicle coordinate data of the target vehicle;
(2) Based on the vehicle coordinate data, acquiring weather parameters of the target vehicle to obtain weather parameter data corresponding to the target vehicle;
(3) Based on the vehicle coordinate data, traffic flow data acquisition is carried out on the target vehicle, and traffic flow data corresponding to the target vehicle is obtained;
(4) Based on traffic flow data, road environment acquisition is carried out on the target vehicle, and road environment data of the target vehicle are obtained;
(5) And combining the weather parameter data, the traffic flow data and the road environment data into vehicle environment data.
Specifically, vehicle coordinate analysis is performed on the target vehicle through the vehicle characteristic data and the vehicle position information, so as to obtain vehicle coordinate data of the target vehicle. The vehicle coordinate data is an accurate representation of the vehicle location information, and by analyzing these data, the exact location of the target vehicle in space can be obtained. For example, longitude and latitude information of the target vehicle is acquired by a Global Positioning System (GPS) or the like technique, thereby determining the accurate position thereof. And acquiring weather parameters based on the obtained vehicle coordinate data. By interacting with a weather site or other weather data source, weather parameters, such as temperature, humidity, wind speed, etc., of the location of the target vehicle may be obtained. These weather parameters help to understand the current situation of the vehicle and make preparations in advance to cope with severe weather. And meanwhile, collecting traffic flow data based on vehicle coordinate data. By interacting with the traffic management system or the sensor network, traffic flow information of the position of the target vehicle can be obtained. This includes data on the density of vehicles, speed of vehicles, etc. on nearby roads, which helps to understand the current traffic situation. Then, based on the traffic flow data, road environment collection is performed to acquire the environment characteristics of the road where the target vehicle is located, such as the road type, the number of lanes, the road condition and the like. Such information helps the vehicle system to better adapt to different driving environments. And combining the weather parameter data, the traffic flow data and the road environment data into vehicle environment data. By integrating the data, the server obtains comprehensive vehicle environment information which is provided for the vehicle control system and the decision algorithm. For example, if weather suddenly changes, traffic congestion is severe, and the server adjusts the driving mode of the vehicle or provides corresponding driving advice according to the combined vehicle environment data.
In a specific embodiment, the process of executing step S106 may specifically include the following steps:
(1) Constructing a virtual running space of the target vehicle based on the vehicle environment data;
(2) Performing vehicle space data analysis on the target vehicle in the virtual running space to obtain vehicle space data;
(3) Calculating a vehicle running influence factor on the vehicle space data to obtain an influence factor data set;
(4) And constructing a vehicle control strategy of the target vehicle through the influence factor data set and the vehicle space data to obtain the vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy.
Specifically, a virtual running space of the target vehicle is constructed based on the vehicle environment data. The virtual driving space is an analog and digital vehicle motion environment, and comprises information of road structures, traffic flow, obstacle positions and the like. By modeling and simulating the actual vehicle environment data, the server creates a virtual environment to better understand the scene in which the target vehicle is located. Vehicle space data analysis is performed on the target vehicle in the virtual travel space. And monitoring and analyzing the data such as the position, the speed, the direction and the like of the vehicle in the virtual environment in real time. The server captures the dynamic behavior of the target vehicle in the virtual environment by simulating the motion trail of the vehicle and the interaction with the surrounding environment. And calculating the vehicle running influence factor on the vehicle space data. The influence factors refer to various factors that influence the running behavior of the vehicle, such as traffic flow, road conditions, weather, etc. By analysis of the vehicle space data, a dataset of these impact factors can be calculated. For example, the degree of traffic congestion, the degree of road wetting, the speed of surrounding vehicles, etc. are all specific factors of influence. And constructing a vehicle control strategy for the target vehicle based on the obtained influence factor data set and the vehicle space data. And according to the environmental information acquired in real time, a reasonable driving strategy is formulated for the target vehicle through an advanced control algorithm. For example, at high traffic flows, the server selects a more cautious driving strategy to ensure traffic safety. And generating vehicle control prompt information according to the obtained vehicle control strategy. These hints may include suggested speed adjustments, lane change suggestions, predicted travel paths for intersections, and the like. These cues may be communicated to the driver via a vehicle interior display, voice prompts, or other interactive means, or applied directly to the autopilot system.
The data processing method based on the internet of vehicles in the embodiment of the present invention is described above, and the data processing system based on the internet of vehicles in the embodiment of the present invention is described below, referring to fig. 5, an embodiment of the data processing system based on the internet of vehicles in the embodiment of the present invention includes:
the acquisition module 501 is configured to acquire vehicle data of a preset target vehicle through a plurality of sensors deployed on the target vehicle, so as to obtain vehicle state data;
the encryption module 502 is configured to encrypt the vehicle state data by using an AES algorithm to obtain encrypted state data, and at the same time, identify vehicle identity information on the encrypted state data to obtain a corresponding identity tag;
the verification module 503 is configured to perform security verification on the identity tag, and perform edge calculation processing on the encrypted state data when the identity tag passes the security verification, so as to obtain an edge data set of the encrypted state data;
the processing module 504 is configured to perform feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set;
the analysis module 505 is configured to perform vehicle environment information analysis on the vehicle feature data to obtain vehicle environment data corresponding to the vehicle feature data;
The prediction module 506 is configured to predict the vehicle space data for the vehicle environment data to obtain corresponding vehicle space data, and simultaneously construct a vehicle control policy for the vehicle space data to obtain a vehicle control policy for the target vehicle, and generate vehicle control prompt information according to the vehicle control policy.
Through the cooperative cooperation of the components, the vehicle data acquisition is carried out on the target vehicle through a plurality of sensors deployed on the target vehicle, so as to obtain vehicle state data; performing AES algorithm encryption on the vehicle state data to obtain encrypted state data, and performing vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag; performing security verification on the identity tag, and performing edge calculation processing on the encrypted state data to obtain an edge data set of the encrypted state data when the identity tag passes the security verification; carrying out feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set; analyzing the vehicle environment information of the vehicle characteristic data to obtain vehicle environment data corresponding to the vehicle characteristic data; and predicting the vehicle space data of the vehicle environment data to obtain corresponding vehicle space data, constructing a vehicle control strategy of the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy. In the scheme, the AES algorithm is adopted to encrypt the vehicle state data, so that the safety of the data in the transmission and storage processes is ensured, and unauthorized access is prevented. Meanwhile, through encryption and verification of the identity information, only legal users are ensured to be able to acquire the sensitive information of the vehicle, and the privacy of the vehicle owner is effectively protected. The real-time data acquisition, the edge calculation processing and the control strategy construction are carried out at the vehicle end, so that the time delay of data transmission to the cloud is reduced, and the real-time performance and the responsiveness of the system are improved. Through vehicle environment information analysis and spatial data prediction, the system can more comprehensively know the traffic environment of the vehicle. Based on the information, an intelligent vehicle control strategy is constructed, so that the vehicle can more intelligently cope with complex traffic situations, and the driving safety and efficiency are improved.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or passed as separate products, may be stored in a computer readable storage medium. Based on the understanding that the technical solution of the present invention may be embodied in essence or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a storage medium, comprising instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (2)
1. The data processing method based on the Internet of vehicles is characterized by comprising the following steps of:
acquiring vehicle data of a preset target vehicle through a plurality of sensors arranged on the target vehicle to obtain vehicle state data; the method specifically comprises the following steps: acquiring vehicle data of the target vehicle through a plurality of sensors to obtain vehicle state data; data classification is carried out on the vehicle state data to obtain a vehicle movement data set, a vehicle direction information set and vehicle position information of the target vehicle, wherein the vehicle movement data set comprises vehicle speed data, vehicle acceleration data and vehicle angular speed data, and the vehicle direction information set comprises vehicle running direction and vehicle inclination angle data;
Performing AES algorithm encryption on the vehicle state data to obtain encrypted state data, and simultaneously, performing vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag; the method specifically comprises the following steps: performing data block processing on the vehicle state data to obtain a plurality of vehicle state data blocks; carrying out data block length analysis on each vehicle state data block to obtain data block length information corresponding to each vehicle state data block; respectively carrying out threshold analysis on the length information of a plurality of data blocks to obtain a threshold analysis result; performing data filling processing on each vehicle state data block according to the threshold analysis result to obtain a plurality of filling data blocks; performing AES algorithm encryption processing on each filling data block to obtain encryption state data; identifying the vehicle identity information of the encrypted state data to obtain a corresponding identity tag;
performing security verification on the identity tag, and performing edge calculation processing on the encrypted state data to obtain an edge data set of the encrypted state data when the identity tag passes the security verification; the method specifically comprises the following steps: performing original data conversion on the identity tag to obtain original identity data corresponding to the identity tag; carrying out security verification on the original identity data through a preset legal identity database to obtain a corresponding security verification result; when the security verification result is that the identity tag passes the security verification, carrying out transmission protocol matching on the encrypted state data to obtain a target transmission protocol; transmitting the encryption state data to a preset edge computing terminal for real-time event detection based on the target transmission protocol to obtain a real-time event set; carrying out data noise reduction processing on the real-time event set to obtain an edge data set of the encryption state data;
Performing feature engineering processing on the edge data set to obtain vehicle feature data corresponding to the edge data set; the method specifically comprises the following steps: extracting time domain features of the edge data set to obtain time domain features corresponding to the edge data set; extracting frequency domain features of the edge data set to obtain frequency domain features corresponding to the edge data set; extracting airspace characteristics of the edge data set to obtain airspace characteristics corresponding to the edge data set; performing feature statistical analysis on the time domain features, the frequency domain features and the airspace features to obtain a statistical feature set; performing time sequence analysis on the statistical feature set to obtain corresponding time sequence data; based on the time sequence data and the statistical feature set, carrying out data feature standardization processing on the edge data set to obtain vehicle feature data corresponding to the edge data set;
analyzing the vehicle environment information of the vehicle characteristic data to obtain vehicle environment data corresponding to the vehicle characteristic data; the method specifically comprises the following steps: carrying out vehicle coordinate analysis on the target vehicle through the vehicle characteristic data and the vehicle position information to obtain vehicle coordinate data of the target vehicle; based on the vehicle coordinate data, acquiring weather parameters of the target vehicle to obtain weather parameter data corresponding to the target vehicle; based on the vehicle coordinate data, acquiring traffic flow data of the target vehicle to obtain traffic flow data corresponding to the target vehicle; based on the traffic flow data, acquiring road environment of the target vehicle to obtain road environment data of the target vehicle; combining the weather parameter data, the traffic flow data and the road environment data into the vehicle environment data;
Carrying out vehicle space data prediction on the vehicle environment data to obtain corresponding vehicle space data, and simultaneously carrying out vehicle control strategy construction on the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy; the method specifically comprises the following steps: constructing a virtual running space of the target vehicle based on the vehicle environment data; performing vehicle space data analysis on the target vehicle in the virtual driving space to obtain the vehicle space data; calculating the vehicle running influence factors of the vehicle space data to obtain an influence factor data set; and constructing a vehicle control strategy for the target vehicle through the influence factor data set and the vehicle space data to obtain the vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy.
2. The data processing system based on the internet of vehicles is characterized in that the data processing system based on the internet of vehicles comprises:
the acquisition module is used for acquiring vehicle data of a preset target vehicle through a plurality of sensors arranged on the target vehicle to obtain vehicle state data; the method specifically comprises the following steps: acquiring vehicle data of the target vehicle through a plurality of sensors to obtain vehicle state data; data classification is carried out on the vehicle state data to obtain a vehicle movement data set, a vehicle direction information set and vehicle position information of the target vehicle, wherein the vehicle movement data set comprises vehicle speed data, vehicle acceleration data and vehicle angular speed data, and the vehicle direction information set comprises vehicle running direction and vehicle inclination angle data;
The encryption module is used for carrying out AES algorithm encryption on the vehicle state data to obtain encrypted state data, and simultaneously carrying out vehicle identity information identification on the encrypted state data to obtain a corresponding identity tag; the method specifically comprises the following steps: performing data block processing on the vehicle state data to obtain a plurality of vehicle state data blocks; carrying out data block length analysis on each vehicle state data block to obtain data block length information corresponding to each vehicle state data block; respectively carrying out threshold analysis on the length information of a plurality of data blocks to obtain a threshold analysis result; performing data filling processing on each vehicle state data block according to the threshold analysis result to obtain a plurality of filling data blocks; performing AES algorithm encryption processing on each filling data block to obtain encryption state data; identifying the vehicle identity information of the encrypted state data to obtain a corresponding identity tag;
the verification module is used for carrying out security verification on the identity tag, and carrying out edge calculation processing on the encryption state data when the identity tag passes the security verification to obtain an edge data set of the encryption state data; the method specifically comprises the following steps: performing original data conversion on the identity tag to obtain original identity data corresponding to the identity tag; carrying out security verification on the original identity data through a preset legal identity database to obtain a corresponding security verification result; when the security verification result is that the identity tag passes the security verification, carrying out transmission protocol matching on the encrypted state data to obtain a target transmission protocol; transmitting the encryption state data to a preset edge computing terminal for real-time event detection based on the target transmission protocol to obtain a real-time event set; carrying out data noise reduction processing on the real-time event set to obtain an edge data set of the encryption state data;
The processing module is used for carrying out characteristic engineering processing on the edge data set to obtain vehicle characteristic data corresponding to the edge data set; the method specifically comprises the following steps: extracting time domain features of the edge data set to obtain time domain features corresponding to the edge data set; extracting frequency domain features of the edge data set to obtain frequency domain features corresponding to the edge data set; extracting airspace characteristics of the edge data set to obtain airspace characteristics corresponding to the edge data set; performing feature statistical analysis on the time domain features, the frequency domain features and the airspace features to obtain a statistical feature set; performing time sequence analysis on the statistical feature set to obtain corresponding time sequence data; based on the time sequence data and the statistical feature set, carrying out data feature standardization processing on the edge data set to obtain vehicle feature data corresponding to the edge data set;
the analysis module is used for analyzing the vehicle environment information of the vehicle characteristic data to obtain the vehicle environment data corresponding to the vehicle characteristic data; the method specifically comprises the following steps: carrying out vehicle coordinate analysis on the target vehicle through the vehicle characteristic data and the vehicle position information to obtain vehicle coordinate data of the target vehicle; based on the vehicle coordinate data, acquiring weather parameters of the target vehicle to obtain weather parameter data corresponding to the target vehicle; based on the vehicle coordinate data, acquiring traffic flow data of the target vehicle to obtain traffic flow data corresponding to the target vehicle; based on the traffic flow data, acquiring road environment of the target vehicle to obtain road environment data of the target vehicle; combining the weather parameter data, the traffic flow data and the road environment data into the vehicle environment data;
The prediction module is used for predicting the vehicle space data of the vehicle environment data to obtain corresponding vehicle space data, constructing a vehicle control strategy of the vehicle space data to obtain a vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy; the method specifically comprises the following steps: constructing a virtual running space of the target vehicle based on the vehicle environment data; performing vehicle space data analysis on the target vehicle in the virtual driving space to obtain the vehicle space data; calculating the vehicle running influence factors of the vehicle space data to obtain an influence factor data set; and constructing a vehicle control strategy for the target vehicle through the influence factor data set and the vehicle space data to obtain the vehicle control strategy of the target vehicle, and generating vehicle control prompt information according to the vehicle control strategy.
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