A vehicle-road cooperation traffic big data processing method, system, device and medium

Abstract

The application provides a vehicle-road cooperation traffic big data processing method, system, equipment and medium, and belongs to the technical field of data processing.The method comprises the following steps: collecting vehicle information data on a road, marking the vehicle information data and transmitting the vehicle information data to a cloud server; identifying a vehicle state after preprocessing the vehicle information data on the cloud server, and correcting a position offset according to the vehicle state; and storing the vehicle information data after completing the position offset correction on the cloud server.The application improves the vehicle positioning accuracy in the vehicle-road cooperation traffic big data scene, reduces the positioning error and improves the reliability of positioning; and the adaptive algorithm can automatically adjust parameters according to different environmental conditions, thereby improving the adaptability of the system.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to a method, system, equipment and medium for processing vehicle-road collaborative traffic big data. Background Art

[0002] Vehicle-road collaboration is the intelligent management of the transportation system through information exchange and interaction between vehicles and road infrastructure. Vehicle-road collaboration uses advanced communication technology and information processing technology to enable vehicles to obtain and process road information, traffic information and other vehicle status in real time, so as to make intelligent driving decisions. Vehicle-road collaboration technology has become a core component of intelligent transportation. By realizing real-time information exchange between vehicles, roadside infrastructure and traffic management systems, it has greatly improved the intelligence level and operational efficiency of the transportation system. The development of vehicle-road collaboration technology has also brought about an explosive growth in the amount of traffic big data. This data covers multi-dimensional information such as vehicle speed, traffic flow, road conditions, etc., providing traffic management departments with rich decision-making basis, but at the same time, how to efficiently and accurately collect, process and analyze these massive amounts of data has become an urgent problem to be solved.

[0003] Existing traffic data collection systems rely heavily on traditional sensors and data collection methods, resulting in low data collection accuracy, poor real-time performance, and insufficient data processing capabilities. These issues are particularly pronounced when dealing with the complex and ever-changing traffic data generated by vehicle-road collaboration. Furthermore, as data volumes increase, redundant data and noise also increase, severely impacting the accuracy and efficiency of data analysis.

[0004] This is a shortcoming of the existing technology. Therefore, in order to address the above-mentioned defects in the existing technology, it is very necessary to provide a method, system, equipment and medium for processing vehicle-road collaborative traffic big data. Summary of the Invention

[0005] Existing traffic big data collection systems rely heavily on traditional sensors and data collection methods, resulting in problems such as low data collection accuracy, poor real-time performance, and insufficient data processing capabilities. These issues are particularly pronounced when dealing with the complex and ever-changing traffic data generated by vehicle-road collaboration. Furthermore, as data volume increases, redundant data and noise also increase, severely impacting the accuracy and efficiency of data analysis. The present invention provides a method, system, device, and medium for processing vehicle-road collaborative traffic big data to address these technical issues.

[0006] In a first aspect, the present invention provides a method for processing vehicle-road cooperative traffic big data, comprising the following steps:

[0007] S1. Collect vehicle information data on the road, mark the vehicle information data and transmit it to the cloud server;

[0008] S2. After pre-processing the vehicle information data on the cloud server, the vehicle status is identified and the position offset is corrected according to the vehicle status;

[0009] S3. The vehicle information data after position offset correction is stored in the cloud server.

[0010] Furthermore, the specific steps of step S1 are as follows:

[0011] S11. Collect real-time vehicle location information and motion status information through various sensors installed in the vehicle;

[0012] S12. The vehicle location information and vehicle status information are marked with a timestamp;

[0013] S13. Transmit the marked vehicle location information and vehicle status information to the cloud server in real time.

[0014] Furthermore, in step S11, the real-time location information of the vehicle includes latitude, longitude and altitude, and the vehicle motion state information includes the acceleration, speed and angular velocity of the vehicle;

[0015] The vehicle's latitude, longitude, and altitude are obtained through the GNN positioning module, the vehicle's angular velocity is obtained through the gyroscope, the vehicle's acceleration is obtained through the accelerometer, and the vehicle speed is obtained through the radar sensor.

[0016] Furthermore, the specific steps of step S2 are as follows:

[0017] S21. After the vehicle information data is standardized on the cloud server, it is cleaned to remove outliers and noise, and any data gaps are filled.

[0018] S22. Analyze the pre-processed vehicle information data to extract the vehicle speed, identify the vehicle speed greater than the speed threshold as a non-parked vehicle, and identify the vehicle speed less than or equal to the speed threshold as a parked vehicle;

[0019] S23. The vehicle information data is divided into parking data and non-parking data according to the vehicle status;

[0020] S24. Use the vehicle's motion state information to correct the parking data position offset;

[0021] S25. Correct the position offset of the non-parking data based on the predicted value and the true value of the vehicle information data.

[0022] Furthermore, the specific steps of step S21 are as follows:

[0023] S211. Standardize vehicle information data to eliminate the impact of different dimensions on data of different types;

[0024] S212. Calculate the Z-score for the standardized vehicle information data and remove outliers based on the Z-score;

[0025] S213. Use the Gaussian filter method to filter out the sudden noise in the vehicle information data using the following formula:

[0026]

[0027]

[0028] in, Represents vehicle position data, represents a weak Gaussian filter, represents a strong Gaussian filter, represents the weight, which depends on the noise level of the longitude and latitude in the vehicle location information data. is the offset of the vehicle position data before and after, , , is a predefined Gaussian kernel;

[0029] S214. Use Kalman filtering to estimate the optimal positioning state of vehicle information data;

[0030] S215. Use linear interpolation and extended null window algorithm to fill data gaps.

[0031] Furthermore, the specific steps of step S24 are as follows:

[0032] S241. Use Characterize the vehicle position information at time t, Indicates longitude, Indicates latitude, Indicates altitude;

[0033] S242. Use Characterize the vehicle speed at time t, Represents the speed in the x, y, and z directions respectively;

[0034] S243. Use Characterize the vehicle acceleration at time t, Represents the acceleration in the x, y, and z directions respectively;

[0035] S244. Use the following formula to correct the parking data for the speed at time t based on the speed and acceleration at time t-1:

[0036]

[0037] Among them, t is the current time, t-1 is the time before the current time, is the time difference between the current moment and the previous moment;

[0038] S245. Calculate the position offset between the current time t and the previous time t-1 using the following formula for the parking data. :

[0039] ;

[0040] S246. Calculate the offset-corrected position of the parking data using the following formula:

[0041]

[0042] Furthermore, the specific steps of step S25 are as follows:

[0043] S251. Set the offset coefficient and use the automatic parameter adjustment method to optimize the offset coefficient to obtain the optimal offset coefficient ;

[0044] S252. Use Characterize the vehicle position information at time t, Indicates longitude, Indicates latitude, Indicates altitude;

[0045] S253. Use Characterize the distance between the current time t and the previous time t-1 of the non-parking data;

[0046] S254. Use the optimal offset coefficient and the distance between the two moments Correction offset for non-parking data Perform the following normalization and tuning representation:

[0047] ;

[0048] S254. Calculate the offset-corrected position for the non-parking data using the following formula:

[0049]

[0050] In a second aspect, the present invention provides a doctor-patient interaction management device, comprising:

[0051] The data collection module is used to collect vehicle information data on the road, mark the vehicle information data and transmit it to the cloud server;

[0052] The data processing module is used to identify the vehicle status after pre-processing the vehicle information data on the cloud server, and to perform position offset correction according to the vehicle status;

[0053] The data storage module is used to store the vehicle information data that has completed position offset correction in the cloud server.

[0054] In a third aspect, the present invention provides an electronic device comprising a processor and a memory;

[0055] The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the electronic device executes the method described in the first aspect above.

[0056] In a fourth aspect, the present invention provides a storage medium;

[0057] The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0058] The beneficial effects of the present invention are:

[0059] The vehicle-road cooperative traffic big data processing method, system, equipment and medium provided by the present invention improve the vehicle positioning accuracy in the vehicle-road cooperative traffic big data scenario, reduce positioning error, and improve positioning reliability; the adaptive algorithm is adopted, which can automatically adjust parameters according to different environmental conditions to improve the adaptability of the system.

[0060] In addition, the design principle of the present invention is reliable and the structure is simple, and it can be used in various application scenarios such as autonomous driving, logistics tracking, and intelligent transportation.

[0061] It can be seen that compared with the prior art, the present invention has outstanding substantial features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0063] Figure 1 It is a flow chart of the vehicle-road cooperative traffic big data processing method of the present invention.

[0064] Figure 2 It is a schematic diagram of the vehicle-road cooperative traffic big data processing system of the present invention. DETAILED DESCRIPTION

[0065] The vehicle-road cooperative traffic big data processing method can be applied to one or more electronic devices, where the electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0066] An electronic device can be any electronic product that can interact with a user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), Internet Protocol Television (IPTV), etc. The network in which the device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0068] See also Figure 1 The following is a specific implementation of a method for processing vehicle-road collaborative traffic big data, including the following steps:

[0069] S1. Collect vehicle information data on the road, mark the vehicle information data and transmit it to the cloud server;

[0070] S2. After pre-processing the vehicle information data on the cloud server, the vehicle status is identified and the position offset is corrected according to the vehicle status;

[0071] S3. The vehicle information data after position offset correction is stored in the cloud server.

[0072] In this embodiment, the specific steps of step S1 are as follows:

[0073] S11. Real-time vehicle location information and motion status information are collected by various sensors provided in the vehicle; in step S11, the vehicle's real-time location information includes latitude, longitude, and altitude, and the vehicle's motion status information includes the vehicle's acceleration, speed, and angular velocity;

[0074] The GNN positioning module obtains the vehicle's latitude, longitude, and altitude, the gyroscope obtains the vehicle's angular velocity, the accelerometer obtains the vehicle's acceleration, and the radar sensor obtains the vehicle's speed;

[0075] S12. The vehicle location information and vehicle status information are marked with a timestamp;

[0076] S13 transmits the marked vehicle location information and vehicle status information to the cloud server in real time;

[0077] The specific steps of step S2 are as follows:

[0078] S21. After the vehicle information data is standardized on the cloud server, it is cleaned to remove outliers and noise, and any data gaps are filled.

[0079] The specific steps of step S21 are as follows:

[0080] S211. Standardize vehicle information data to eliminate the impact of different dimensions on data of different types;

[0081] S212. Calculate the Z-score for the standardized vehicle information data and remove outliers based on the Z-score;

[0082] S213. Use the Gaussian filter method to filter out the sudden noise in the vehicle information data using the following formula:

[0083]

[0084]

[0085] in, Represents vehicle position data, represents a weak Gaussian filter, represents a strong Gaussian filter, represents the weight, which depends on the noise level of the longitude and latitude in the vehicle location information data. is the offset of the vehicle position data before and after, , , is a predefined Gaussian kernel;

[0086] S214. Use Kalman filtering to estimate the optimal positioning state of vehicle information data;

[0087] S215. Fill data gaps using linear interpolation and extended null window algorithms;

[0088] S22. Analyze the pre-processed vehicle information data to extract the vehicle speed, identify the vehicle speed greater than the speed threshold as a non-parked vehicle, and identify the vehicle speed less than or equal to the speed threshold as a parked vehicle;

[0089] S23. The vehicle information data is divided into parking data and non-parking data according to the vehicle status;

[0090] S24. Use the vehicle's motion state information to correct the parking data position offset;

[0091] The specific steps of step S24 are as follows:

[0092] S241. Use Characterize the vehicle position information at time t, Indicates longitude, Indicates latitude, Indicates altitude;

[0093] S242. Use Characterize the vehicle speed at time t, Represents the speed in the x, y, and z directions respectively;

[0094] S243. Use Characterize the vehicle acceleration at time t, Represents the acceleration in the x, y, and z directions respectively;

[0095] S244. Use the following formula to correct the parking data for the speed at time t based on the speed and acceleration at time t-1:

[0096]

[0097] Among them, t is the current time, t-1 is the time before the current time, is the time difference between the current moment and the previous moment;

[0098] S245. Calculate the position offset between the current time t and the previous time t-1 using the following formula for the parking data. :

[0099] ;

[0100] S246. Calculate the offset-corrected position of the parking data using the following formula:

[0101] ;

[0102] S25. Correct the position offset of non-parking data based on the predicted value and the true value of the vehicle information data;

[0103] The specific steps of step S25 are as follows:

[0104] S251. Set the offset coefficient and use the automatic parameter adjustment method to optimize the offset coefficient to obtain the optimal offset coefficient ;

[0105] S252. Use Characterize the vehicle position information at time t, Indicates longitude, Indicates latitude, Indicates altitude;

[0106] S253. Use Characterize the distance between the current time t and the previous time t-1 of the non-parking data;

[0107] S254. Use the optimal offset coefficient and the distance between the two moments Correction offset for non-parking data Perform the following normalization and tuning representation:

[0108] ;

[0109] S254. Calculate the offset-corrected position for the non-parking data using the following formula:

[0110]

[0111] It should be understood that the order of execution of the steps in the above embodiment does not mean that the order of execution of the processes should be determined by their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0112] The following is an embodiment of the vehicle-road collaborative traffic big data processing system provided by the embodiments of the present disclosure. This system and the vehicle-road collaborative traffic big data processing methods of the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiments of the vehicle-road collaborative traffic big data processing system, please refer to the embodiments of the above-mentioned vehicle-road collaborative traffic big data processing methods.

[0113] like Figure 2 As shown, the system includes:

[0114] The data collection module is used to collect vehicle information data on the road, mark the vehicle information data and transmit it to the cloud server;

[0115] The data processing module is used to identify the vehicle status after pre-processing the vehicle information data on the cloud server, and to perform position offset correction according to the vehicle status;

[0116] The data storage module is used to store the vehicle information data that has completed position offset correction in the cloud server.

[0117] The vehicle-road cooperative traffic big data processing method provided by the present invention is a combination of the modules and algorithm steps of each example described in the embodiments disclosed herein, and can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0118] Those skilled in the art will appreciate that various aspects of the vehicle-road cooperative traffic big data processing method provided by the present invention can be implemented as a system, method, or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation that combines hardware and software aspects, which can be collectively referred to herein as a "circuit," "module," or "system."

[0119] In the non-transitory computer-readable storage medium storing the vehicle-road cooperative traffic big data processing method, it can be, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0120] Although the present invention has been described in detail with reference to the accompanying drawings and in conjunction with preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, persons of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and such modifications or substitutions shall be within the scope of the present invention. Any changes or substitutions that can be easily conceived by persons skilled in the art within the technical scope disclosed in the present invention shall be within the scope of protection of the present invention.

Claims

1. A method for processing vehicle-road cooperative traffic big data, characterized in that: The steps include: S1. Collect vehicle information data on the road, mark the vehicle information data and transmit it to the cloud server; S2. After pre-processing the vehicle information data on the cloud server, the vehicle status is identified and the position offset is corrected according to the vehicle status; The specific steps of step S2 are as follows: S21. After the vehicle information data is standardized on the cloud server, it is cleaned to remove outliers and noise, and any data gaps are filled. The specific steps of step S21 are as follows: S211. Standardize vehicle information data to eliminate the impact of different dimensions on data of different types; S212. Calculate the Z-score for the standardized vehicle information data and remove outliers based on the Z-score; S213. Use the Gaussian filter method to filter out the sudden noise in the vehicle information data using the following formula: in, Represents vehicle position data, represents a weak Gaussian filter, represents a strong Gaussian filter, represents the weight, which depends on the noise level of the longitude and latitude in the vehicle location information data. is the offset of the vehicle position data before and after, , , is a predefined Gaussian kernel; S214. Use Kalman filtering to estimate the optimal positioning state of vehicle information data; S215. Fill data gaps using linear interpolation and extended null window algorithms; S22. Analyze the pre-processed vehicle information data to extract the vehicle speed, identify the vehicle speed greater than the speed threshold as a non-parked vehicle, and identify the vehicle speed less than or equal to the speed threshold as a parked vehicle; S23. The vehicle information data is divided into parking data and non-parking data according to the vehicle status; S24. Use the vehicle's motion state information to correct the parking data position offset; S25. Correct the position offset of the non-parking data based on the predicted value and the actual value of the vehicle information data; S3. Store the vehicle information data with the position offset correction in the cloud server.

2. The method for processing big data of vehicle-road cooperative traffic according to claim 1, characterized in that: The specific steps of step S1 are as follows: S11. Collect real-time vehicle location information and motion status information through various sensors installed in the vehicle; S12. The vehicle location information and vehicle status information are marked with a timestamp; S13. Transmit the marked vehicle location information and vehicle status information to the cloud server in real time.

3. The method for processing big data of vehicle-road cooperative traffic according to claim 2, characterized in that: In step S11, the real-time location information of the vehicle includes latitude, longitude and altitude, and the vehicle motion state information includes the acceleration, speed and angular velocity of the vehicle; The vehicle's latitude, longitude, and altitude are obtained through the GNN positioning module, the vehicle's angular velocity is obtained through the gyroscope, the vehicle's acceleration is obtained through the accelerometer, and the vehicle speed is obtained through the radar sensor.

4. The method for processing vehicle-road cooperative traffic big data according to claim 2, characterized in that: The specific steps of step S24 are as follows: S241. Use Characterize the vehicle position information at time t, Indicates longitude, Indicates latitude, Indicates altitude; S242. Use Characterize the vehicle speed at time t, Represents the speed in the x, y, and z directions respectively; S243. Use Characterize the vehicle acceleration at time t, Represents the acceleration in the x, y, and z directions respectively; S244. Use the following formula to correct the parking data for the speed at time t based on the speed and acceleration at time t-1: Among them, t is the current time, t-1 is the time before the current time, is the time difference between the current moment and the previous moment; S245. Calculate the position offset between the current time t and the previous time t-1 using the following formula for the parking data. : ； S246. Calculate the offset-corrected position of the parking data using the following formula: 。 5. The method for processing big data of vehicle-road cooperative traffic according to claim 2, characterized in that: The specific steps of step S25 are as follows: S251. Set the offset coefficient and use the automatic parameter adjustment method to optimize the offset coefficient to obtain the optimal offset coefficient ; S252. Use Characterize the vehicle position information at time t, Indicates longitude, Indicates latitude, Indicates altitude; S253. Use Characterize the distance between the current time t and the previous time t-1 of the non-parking data; S254. Use the optimal offset coefficient and the distance between the two moments Correction offset for non-parking data Perform the following normalization and tuning representation: ； S254. Calculate the offset-corrected position for the non-parking data using the following formula: 。 6. A vehicle-road cooperative traffic big data processing system, based on the vehicle-road cooperative traffic big data processing method according to any one of claims 1 to 5, characterized in that: include: The data collection module is used to collect vehicle information data on the road, mark the vehicle information data and transmit it to the cloud server; The data processing module is used to identify the vehicle status after pre-processing the vehicle information data on the cloud server, and to perform position offset correction according to the vehicle status; The data storage module is used to store the vehicle information data that has completed position offset correction in the cloud server.

7. An electronic device, characterized in that: including processor and memory; The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the electronic device executes the method described in any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 5.

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