CN113641874A - Data acquisition processing system and data acquisition processing method - Google Patents
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Abstract
The invention relates to a data acquisition processing system and a data acquisition processing method, which are composed of a vehicle-end controller and a cloud server, wherein the vehicle-end controller is connected with an environment sensing system and a vehicle bus system on a vehicle and is used for acquiring environment sensing data and vehicle driving state data and uploading the data to the cloud server through a network. The data acquisition processing method of the data acquisition processing system comprises vehicle-end data acquisition processing and cloud data storage management analysis, data value identification is directly carried out at a data source end, the evaluation criterion of the value has certain universality and stability, the value can be continuously found, and then the data is acquired, wherein the acquired data is valuable data; the data acquisition flow cost and the data storage cost are reduced, the data processing and analyzing time is saved, and a database directly provided with data indexes is established; due to the stability and universality of the value judgment reference, the method can be operated at the data source end for a long time without frequent updating.
Description
Technical Field
The invention belongs to the technical field of automatic driving, and particularly relates to a data acquisition and processing system and a data acquisition and processing method.
Background
At present, a big data technology is widely applied to various industries, but the data volume is large, the data value density is low, a large data volume is often collected and accumulated, and when data of a certain aspect is analyzed, the data can not be found or valuable data can be found with great effort just like a sea fishing needle. On one hand, the flow and the data storage space are wasted for collecting and storing the mass data; on the other hand, the difficulty is caused for using and extracting valuable data.
In many industries, especially the automobile industry, a general data acquisition and processing mode is to collect and store all data, and then to find out the required data for analysis through data cleaning. The prior art discloses a method and a system for evaluating a rule control algorithm and a computer storage medium, wherein the method for evaluating the rule control algorithm comprises the following steps: the vehicle end identifies an abnormal scene according to vehicle data, wherein the vehicle data comprises external data and internal data; after the abnormal scene is identified by the vehicle end, generating abnormal scene data and transmitting the abnormal scene data to the lower end of the line; and the lower line end constructs a virtual scene according to the abnormal scene data, and evaluates the rule control algorithm corresponding to the abnormal scene based on the virtual scene. The method is mainly used for carrying out data cleaning and machine learning training or virtual scene construction and is used for collecting machine learning training data. The technology does not give clear description on the design of data acquisition events and data labels, and has no database comparison duplication checking function.
Disclosure of Invention
The invention aims to provide a data acquisition and processing system for directly identifying data value at a data source end and a data acquisition and processing method of the data acquisition and processing system, so as to solve the problems of data storage and data flow resource waste caused by undifferentiated data acquisition, incapability of retrieving large data after acquisition and storage and low utilization rate.
The purpose of the invention is realized by the following technical scheme:
a data acquisition processing system is composed of a vehicle-end controller and a cloud server; the vehicle-end controller is connected with an environment sensing system and a vehicle bus system on the vehicle and is used for acquiring environment sensing data and vehicle running state data; the cloud server is provided with a data receiving and transmitting module, a storage module, a retrieval module and a playback analysis module and is used for receiving, transmitting, storing, retrieving and performing playback analysis on data; the vehicle-end controller uploads the data to a cloud server through a network; and the vehicle-end controller is connected with the driver state monitoring system and is used for acquiring the driver control data.
A data acquisition processing method of a data acquisition processing system comprises vehicle-end data acquisition processing and cloud data storage management analysis;
the vehicle-end data acquisition and processing method comprises the following steps:
a1, acquiring and recording environmental perception sensor data, vehicle running state data and driver control data of a recent period in real time by a vehicle-end controller;
a2, analyzing data of the environmental perception sensor, and triggering the next analysis process if an event with low identification credibility or a difference value between the automatic driving track or speed planning data and the actual track and speed of the driver exceeds a threshold value or the driver takes over the event during automatic driving;
a3, intercepting data of a period of time before and after the event occurs and current event information from the environmental perception sensor data, and extracting key information from the environmental perception sensor data and the vehicle driving state data to form a scene label;
a4, comparing the scene data labels generated in the step A3 with a scene label database stored by a cloud server, if the scene data labels have the same scene, adding 1 to the scene data count corresponding to the database, if the accumulated count value of the scene labels is less than a calibration value, compressing and packaging the data and the scene labels within the calibration time before and after the event occurs, and uploading the data and the scene labels to the cloud server, otherwise, discarding the scene labels;
the cloud data storage management analysis comprises the following steps:
b1, the cloud server receives the uploaded data, extracts scene tags in the data, respectively stores the scene tags and the original data into a scene tag database and an original database, and establishes links;
b2, searching the scene label content, inputting a search keyword, finding corresponding original data for analysis, and displaying the original data in a curve, icon, video stream and other modes;
and B3, collecting data uploaded by a plurality of vehicles, updating the label database according to the data labels uploaded by each vehicle each time, wherein the label database comprises corresponding count values, and updating the label database in the vehicle-end controller through the network after the update.
Further, in step a2, the low-reliability event includes the reliability of the lane line identification signal, the reliability of the obstacle object identification signal, and the like, and when the reliability value thereof is lower than a certain calibration value, the low-reliability event of the signal is considered to occur.
Further, the calibration value is initially set to 30%.
Further, step A2, the difference between the automated driving trajectory or speed planning data and the actual trajectory and speed at which the driver is driving exceeds a threshold.
Still further, the trajectory lateral deviation is greater than 1 meter or the velocity deviation is greater than 30 km/h.
Further, step a3, the key information includes target information, road information, and vehicle handling information.
Furthermore, the target information is that pedestrians cross the road, the road information is a 2-lane straight road section, and the vehicle control information is that the vehicle is braked by 50% and turns by 90 degrees.
Further, in step a4, the accumulated count value of the scene is less than a certain calibration value, and the calibration time before and after the event is a certain calibration value before the event and a certain calibration value after the event.
Furthermore, the calibration value is initially set to 50, and the calibration time before and after the event is 60 seconds before the event and 30 seconds after the event. Compared with the prior art, the invention has the beneficial effects that:
according to the data acquisition processing system and the data acquisition processing method, data value identification is directly carried out at a data source end, the evaluation criterion of the value has certain universality and stability, the value can be continuously found, and then the data are acquired, wherein the acquired data are valuable data; the data acquisition flow cost and the data storage cost are reduced, the data processing and analyzing time is saved, and a database directly provided with data indexes is established; due to the stability and universality of the value judgment reference, the method can be operated at the data source end for a long time without frequent updating.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram of a data acquisition and processing system according to the present invention;
FIG. 2 is a flow chart of data acquisition by the vehicle end controller;
fig. 3 is a flow chart of cloud server data processing.
Detailed Description
The invention is further illustrated by the following examples:
the present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The invention relates to a stable data source processing method and a system which can continuously and automatically find problems and record uploading problems, and are designed for driving vehicles like people based on the ultimate goal of automatic driving. The method is mainly used in the automobile electric control industry or the automatic driving industry and used for solving the related problems of data acquisition and data management.
As shown in fig. 1, the data acquisition and processing system of the present invention is composed of a controller at a vehicle end and a cloud server used in cooperation with the controller.
The vehicle-end controller is connected with an environment sensing system and a vehicle bus system on the vehicle, can also comprise a driver state monitoring system, can acquire environment sensing sensor data, vehicle running state data and driver control data, and can also upload the data to the cloud server through a network.
The cloud server is provided with a data receiving and transmitting module, a storage module, a retrieval module and a playback analysis module and is used for receiving, transmitting, storing, retrieving and performing playback analysis on data; and the vehicle-end controller uploads the data to the cloud server through a network.
The data acquisition and processing method comprises vehicle-end data acquisition and processing and cloud data storage management and analysis. The vehicle-end data acquisition and processing comprises data acquisition and data processing of a vehicle-end controller and a method for triggering and labeling events. The cloud data storage management analysis comprises data storage, retrieval and analysis methods of a cloud server.
As shown in fig. 2, specifically, the vehicle-end data acquisition and processing includes the following steps:
step 1: the method comprises the following steps that a vehicle-end controller collects and records environmental perception sensor data, vehicle running state data and driver control data in a recent period of time in real time, processes and analyzes the data, firstly, the environmental perception sensor data are analyzed, and if an event with low recognition quality (reliability) occurs, the next analysis process is triggered;
step 2: extracting target information in use from the environmental perception sensor data, and generating a scene label of the data through processing;
step 3, intercepting data of a period of time before and after the event occurs and current event information from the environmental perception sensor data, and extracting key information from the environmental perception sensor data and the vehicle driving state data to form a scene data label;
and 4, comparing the scene data labels generated in the step 3 with a scene label database stored by a cloud server, if the scene data labels have the same scene, adding 1 to the scene data count corresponding to the database, if the accumulated count value of the scene labels is less than a certain calibration value, compressing and packaging the data of the certain calibration value before the event occurs and the data of the certain calibration value after the event occurs and the scene labels, uploading the data to the cloud server, and otherwise, discarding the scene labels.
As shown in fig. 3, the cloud data storage management analysis includes the following steps:
step 1: the cloud server receives the uploaded data;
step 2: extracting scene labels in the data, respectively storing the scene labels and the original data into a scene label database and an original database, and establishing links;
and step 3: the cloud server has a data retrieval function, and corresponding original data can be found for analysis through retrieval of the scene tag content, for example, a retrieval keyword is input: the pedestrians cross the road +2 lanes + go straight, so that the corresponding data can be found;
and 4, step 4: the cloud server has a data playback function and can display original data in a curve mode, an icon mode, a video stream mode and the like;
and 5: the cloud server can collect data uploaded by multiple vehicles, updates the tag database according to the data tags uploaded by each vehicle at each time, comprises corresponding count values, and updates the tag database in the vehicle-end controller through the network.
Example 1
Vehicle end data acquisition and processing:
step 1: the method comprises the following steps that a vehicle-end controller collects and records environment perception sensor data, vehicle running state data and driver control data in a recent period of time in real time, processes and analyzes the data, firstly, the environment perception sensor data are analyzed, and if the reliability of lane line identification signals is lower than 30%, the next analysis process is triggered;
step 2: pedestrian road passing information is extracted from the data of the environmental perception sensor, and a scene label of the data is generated through processing;
step 3, intercepting data of a period of time before and after the event occurs and current event information from the environmental perception sensor data, and extracting key information from the environmental perception sensor data and the vehicle driving state data to form a scene data label;
and 4, comparing the scene data tags generated in the step 3 with a scene tag database stored in a cloud server, if the scene data tags have the same scene, adding 1 to the scene data count corresponding to the database, if the accumulated count value of the scene tags is less than 50, compressing and packaging the data and the scene tags 60 seconds before the event occurs and 30 seconds after the event occurs, and uploading the data and the scene tags to the cloud server, otherwise, discarding the scene tags.
Cloud data storage management analysis:
step 1: the cloud server receives the uploaded data;
step 2: extracting scene labels in the data, respectively storing the scene labels and the original data into a scene label database and an original database, and establishing links;
and step 3: the cloud server has a data retrieval function, and corresponding original data can be found for analysis through retrieval of the scene tag content, for example, a retrieval keyword is input: when a pedestrian crosses the road, the corresponding data can be found;
and 4, step 4: the cloud server has a data playback function and can display original data in a curve mode, an icon mode, a video stream mode and the like;
and 5: the cloud server can collect data uploaded by multiple vehicles, updates the tag database according to the data tags uploaded by each vehicle at each time, comprises corresponding count values, and updates the tag database in the vehicle-end controller through the network. Example 2
Vehicle end data acquisition and processing:
step 1: the method comprises the following steps that a vehicle-end controller collects and records environmental perception sensor data, vehicle running state data and driver control data in a recent period of time in real time, processes and analyzes the data, firstly, the environmental perception sensor data are analyzed, and if the transverse deviation of an automatic driving track is larger than 1 meter and the speed deviation is larger than 30km/h, the next analysis process is triggered;
step 2: extracting 2-lane straight road section information from the environmental perception sensor data, and generating a scene label of the data through processing;
step 3, intercepting data of a period of time before and after the event occurs and current event information from the environmental perception sensor data, and extracting key information from the environmental perception sensor data and the vehicle driving state data to form a scene data label;
and 4, comparing the scene data tags generated in the step 3 with a scene tag database stored in a cloud server, if the scene data tags have the same scene, adding 1 to the scene data count corresponding to the database, if the accumulated count value of the scene tags is less than 50, compressing and packaging the data and the scene tags 60 seconds before the event occurs and 30 seconds after the event occurs, and uploading the data and the scene tags to the cloud server, otherwise, discarding the scene tags.
Cloud data storage management analysis:
step 1: the cloud server receives the uploaded data;
step 2: extracting scene labels in the data, respectively storing the scene labels and the original data into a scene label database and an original database, and establishing links;
and step 3: the cloud server has a data retrieval function, and corresponding original data can be found for analysis through retrieval of the scene tag content, for example, a retrieval keyword is input: 2 lanes, corresponding data can be found;
and 4, step 4: the cloud server has a data playback function and can display original data in a curve mode, an icon mode, a video stream mode and the like;
and 5: the cloud server can collect data uploaded by multiple vehicles, updates the tag database according to the data tags uploaded by each vehicle at each time, comprises corresponding count values, and updates the tag database in the vehicle-end controller through the network.
Example 3
Vehicle end data acquisition and processing:
step 1: the method comprises the following steps that a vehicle-end controller collects and records environment perception sensor data, vehicle running state data and driver control data in a recent period of time in real time, processes and analyzes the data, firstly, the environment perception sensor data are analyzed, and if a driver takeover event occurs during automatic driving, the next analysis process is triggered;
step 2: extracting information of pedestrians crossing a road, a straight road section of 2 lanes, 50% of brakes and 90% of steering from the data of the environment perception sensor, and processing the information to generate a scene label of the data;
step 3, intercepting data of a period of time before and after the event occurs and current event information from the environmental perception sensor data, and extracting key information from the environmental perception sensor data and the vehicle driving state data to form a scene data label;
and 4, comparing the scene data tags generated in the step 3 with a scene tag database stored in a cloud server, if the scene data tags have the same scene, adding 1 to the scene data count corresponding to the database, if the accumulated count value of the scene tags is less than 50, compressing and packaging the data and the scene tags 60 seconds before the event occurs and 30 seconds after the event occurs, and uploading the data and the scene tags to the cloud server, otherwise, discarding the scene tags.
Cloud data storage management analysis:
step 1: the cloud server receives the uploaded data;
step 2: extracting scene labels in the data, respectively storing the scene labels and the original data into a scene label database and an original database, and establishing links;
and step 3: the cloud server has a data retrieval function, corresponding original data can be found for analysis through retrieval of scene tag contents, and retrieval keywords are input: the pedestrians cross the road +2 lanes + go straight, so that the corresponding data can be found;
and 4, step 4: the cloud server has a data playback function and can display original data in a curve mode, an icon mode, a video stream mode and the like;
and 5: the cloud server can collect data uploaded by multiple vehicles, updates the tag database according to the data tags uploaded by each vehicle at each time, comprises corresponding count values, and updates the tag database in the vehicle-end controller through the network.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A data acquisition processing system, characterized by: the system is composed of a vehicle-end controller and a cloud server; the vehicle-end controller is connected with an environment sensing system and a vehicle bus system on the vehicle and is used for acquiring sensor data and bus data; the cloud server is provided with a data receiving and transmitting module, a storage module, a retrieval module and a playback analysis module and is used for receiving, transmitting, storing, retrieving and performing playback analysis on data; the vehicle-end controller uploads the data to a cloud server through a network; and the vehicle-end controller is connected with the driver state monitoring system and is used for acquiring the driver state data.
2. A data acquisition processing method of a data acquisition processing system is characterized in that: the method comprises the steps of vehicle-end data acquisition and processing and cloud data storage management analysis;
the vehicle-end data acquisition and processing method comprises the following steps:
a1, acquiring and recording environmental perception sensor data, vehicle running state data and driver control data of a recent period in real time by a vehicle-end controller;
a2, analyzing data of the environmental perception sensor, and triggering the next analysis process if an event with low identification credibility or a difference value between the automatic driving track or speed planning data and the actual track and speed of the driver exceeds a threshold value or the driver takes over the event during automatic driving;
a3, intercepting data of a period of time before and after the event occurs and current event information from the environmental perception sensor data, and extracting key information from the environmental perception sensor data and the vehicle driving state data to form a scene label;
a4, comparing the scene data labels generated in the step A3 with a scene label database stored by a cloud server, if the scene data labels have the same scene, adding 1 to the scene data count corresponding to the database, if the accumulated count value of the scene labels is less than a calibration value, compressing and packaging the data and the scene labels within the calibration time before and after the event occurs, and uploading the data and the scene labels to the cloud server, otherwise, discarding the scene labels;
the cloud data storage management analysis comprises the following steps:
b1, the cloud server receives the uploaded data, extracts scene tags in the data, respectively stores the scene tags and the original data into a scene tag database and an original database, and establishes links;
b2, searching the scene label content, inputting a search keyword, finding corresponding original data for analysis, and displaying the original data in a curve, icon, video stream and other modes;
and B3, collecting data uploaded by a plurality of vehicles, updating the label database according to the data labels uploaded by each vehicle each time, wherein the label database comprises corresponding count values, and updating the label database in the vehicle-end controller through the network after the update.
3. The data acquisition processing method of the data acquisition processing system according to claim 2, characterized in that: and step A2, the low-reliability event comprises the reliability of the lane line identification signal, the reliability of the obstacle target identification signal and the like, and when the reliability value is lower than a certain calibration value, the low-reliability event of the signal is considered to occur.
4. The data acquisition processing method of the data acquisition processing system according to claim 3, characterized in that: the calibration value is initially set to 30%.
5. The data acquisition processing method of the data acquisition processing system according to claim 2, characterized in that: step a2, the difference between the automated driving trajectory or speed planning data and the actual trajectory and speed at which the driver is driving exceeds a threshold.
6. The data acquisition processing method of the data acquisition processing system according to claim 5, wherein: the track lateral deviation is more than 1 meter or the speed deviation is more than 30 km/h.
7. The data acquisition processing method of the data acquisition processing system according to claim 2, characterized in that: step a3, the key information including target information, road information, and vehicle handling information.
8. The data acquisition processing method of the data acquisition processing system according to claim 7, wherein: the target information is that pedestrians cross the road, the road information is a 2-lane straight road section, and the vehicle control information is that the brake is 50% and the steering is 90 degrees.
9. The data acquisition processing method of the data acquisition processing system according to claim 2, characterized in that: step A4, the accumulated count value of the scene is less than a certain calibration value, and the calibration time before and after the event is a certain calibration value before the event and a certain calibration value after the event.
10. The data acquisition processing method of the data acquisition processing system according to claim 9, characterized in that: the calibration value is initially set to 50, and the calibration time before and after the occurrence of the event is 60 seconds before the occurrence of the event and 30 seconds after the occurrence of the event.
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