CN114329035A - Data processing method and device, storage medium, processor and electronic device - Google Patents
Data processing method and device, storage medium, processor and electronic device Download PDFInfo
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Abstract
The invention discloses a data processing method, a data processing device, a storage medium, a processor and an electronic device. The data processing method comprises the following steps: acquiring vehicle information and a preset rule set, wherein the vehicle information at least comprises one of the following components: the method comprises the following steps that a preset rule set is used for representing a preset condition for determining that a vehicle is in a calibration state; judging whether the vehicle information meets a preset condition, determining that the vehicle is in a calibration state under the condition that the vehicle information meets the preset condition, and generating an acquisition instruction; acquiring target data based on an acquisition instruction, wherein the acquisition instruction is used for acquiring the target data, and the target data at least comprises one of the following data: point cloud data, vehicle body data, image data and visual perception data; generating case data based on the target data; the case data is returned to the server. The invention solves the technical problem of slow data transmission of vehicles in the related art.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data processing method, an apparatus, a storage medium, a processor, and an electronic apparatus.
Background
With the development of algorithms, computing power and big data, artificial intelligence starts to enter a high-speed development period. As an extension and application of artificial intelligence technology in the automobile industry and the traffic field, the deep learning algorithm of artificial intelligence is widely applied to the perception system of vehicle automatic driving. The perception system needs mass marked data samples as training sets for identifying objects, and accurate identification of traffic participants (motor vehicles, pedestrians, non-motor vehicles and the like) can be achieved through supervised learning of a large amount of data.
The massive sensing data (camera data and lidar data) required by the autopilot sensing system faces two major challenges: firstly, tens of test fleets need to be established for acquiring mass sensing data, and different road working conditions such as urban roads, national roads, provincial roads, expressways and rural roads are covered, different weather conditions such as sunny days, rainy days, cloudy days and haze are covered, and different scene conditions of environments such as tunnels, elevated buildings and roundabouts are covered. Such a method of acquiring perceptual data requires a significant amount of time and resources. Secondly, for a single vehicle, the generation amount of camera data and laser radar data is huge, the daily generated data amount of the single vehicle reaches the TB level, and data return is generally carried out in the prior art in a form of mobile hard disk and hard disk express delivery. According to the data acquisition method, data are not screened at the vehicle end, the data volume is large, and worthless data exist. Data are transmitted in an express delivery mode, and the data are easy to damage.
Therefore, the prior art becomes a current key problem for rapidly and automatically transmitting data collected by a vehicle end. In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a data processing method and device, a storage medium, a processor and an electronic device, and at least solves the technical problem of slow data transmission of vehicles in the related art.
According to an embodiment of the present invention, a data processing method is provided, including: acquiring vehicle information and a preset rule set, wherein the vehicle information at least comprises one of the following components: the method comprises the following steps that a preset rule set is used for representing a preset condition for determining that a vehicle is in a calibration state; judging whether the vehicle information meets a preset condition, determining that the vehicle is in a calibration state under the condition that the vehicle information meets the preset condition, and generating an acquisition instruction; acquiring target data based on an acquisition instruction, wherein the acquisition instruction is used for acquiring the target data, and the target data at least comprises one of the following data: point cloud data, vehicle body data, image data and visual perception data; generating case data based on the target data; the case data is returned to the server.
Optionally, acquiring the target data based on the acquisition instruction includes: collecting point cloud data, vehicle body data, image data and visual perception data in real time by taking a first preset time period as a cycle; and determining point cloud data, vehicle body data, image data and visual perception data in a second time period as target data when the acquisition instruction is generated, wherein the starting point of the second time period is earlier than the starting point of the first preset time period, and the end point of the second time period is later than the end point of the first preset time period.
Optionally, the real-time collection of the point cloud data, the vehicle body data, the image data and the visual perception data by taking the first preset time period as a cycle comprises: image data were acquired using a J3 core plate.
Optionally, generating case data based on the target data comprises: and packaging and encrypting the target data by using the core A chip to generate case data.
Optionally, returning the case data to the server comprises: and returning the case data to the server by utilizing the Ethernet chip.
Optionally, the method further comprises: obtaining calibration data, wherein the calibration data at least comprises one of the following: determining the time when the vehicle is in the calibration state, determining the times of the time when the vehicle is in the calibration state, and determining the position of the vehicle when the vehicle is in the calibration state; and returning the calibration data to the server.
According to an embodiment of the present invention, there is also provided a data processing apparatus, including: the acquisition module is used for acquiring target data, wherein the target data at least comprises one of the following data: point cloud data, vehicle body data, image data and visual perception data; a determination module to generate case data based on the target data; the transmission module is used for returning the case data to the server; and the storage module is used for storing the target data and the case data.
According to an embodiment of the present invention, there is further provided a non-volatile storage medium having a computer program stored therein, wherein the computer program is configured to execute the processing method of data in any one of the preceding items when running.
There is further provided, according to an embodiment of the present invention, a processor configured to execute a program, where the program is configured to execute, when running, the data processing method in any one of the foregoing methods.
According to an embodiment of the present invention, there is also provided an electronic apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the data processing method through the computer program.
In the embodiment of the invention, the vehicle information is acquired and a preset rule set is adopted, and the vehicle information at least comprises one of the following components: the method comprises the following steps that driving parameter information of a vehicle, driving condition information of the vehicle and external perception information of the vehicle are obtained, a preset rule set is used for representing preset conditions for determining that the vehicle is in a calibration state, whether the vehicle information meets the preset conditions or not is judged, the vehicle is determined to be in the calibration state under the condition that the vehicle information meets the preset conditions, a collection instruction is generated, target data are collected based on the collection instruction, the collection instruction is used for collecting the target data, and the target data at least comprise one of the following data: the method comprises the steps of point cloud data, vehicle body data, image data and visual perception data, finally, case data are generated based on target data, the case data are returned to a server, the purpose of screening the collected target data on a vehicle side is achieved, data at all times do not need to be collected and transmitted to the server, the value of the target data is improved, an engineer can adaptively obtain the target data of vehicles under different scene conditions by configuring vehicle information and a preset rule set, the collected data are transmitted without adopting a hard disk, mass data transmission can be achieved through remote uploading, and the technical problem that data transmission of the vehicles in the related technology is slow is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a computer terminal of a data processing method according to an alternative embodiment of the present invention;
FIG. 2 is a flow diagram of a method of processing data in accordance with an alternative embodiment of the present invention;
FIG. 3 is a flow diagram of a method of processing data in accordance with an alternative embodiment of the present invention;
FIG. 4 is a block diagram of a method of processing data applied to a vehicle in accordance with an alternate embodiment of the present invention;
fig. 5 is a block diagram of a data processing apparatus according to an alternative embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations 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, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with one embodiment of the present invention, there is provided an embodiment of a method of data processing, it being noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
The method embodiments may be performed in an electronic device or similar computing device that includes a memory and a processor in a vehicle. Taking the example of an electronic device operating on a vehicle, as shown in fig. 1, the electronic device of the vehicle may include one or more processors 102 (the processors may include, but are not limited to, Central Processing Units (CPUs), Graphics Processing Units (GPUs), Digital Signal Processing (DSP) chips, Microprocessors (MCUs), programmable logic devices (FPGAs), neural Network Processors (NPUs), Tensor Processors (TPUs), Artificial Intelligence (AI) type processors, etc.) and a memory 104 for storing data. Optionally, the electronic device of the automobile may further include a transmission device 106, an input-output device 108, and a display device 110 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 1 is merely an illustration and is not intended to limit the structure of the electronic device of the vehicle. For example, the electronic device of the vehicle may also include more or fewer components than described above, or have a different configuration than described above.
The memory 104 can be used for storing computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the data processing method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, implementing the information processing method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The display device 110 may be, for example, a touch screen type Liquid Crystal Display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of the mobile terminal. In some embodiments, the mobile terminal has a Graphical User Interface (GUI) with which a user can interact by touching finger contacts and/or gestures on a touch-sensitive surface, where the human-machine interaction function optionally includes the following interactions: executable instructions for creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, emailing, call interfacing, playing digital video, playing digital music, and/or web browsing, etc., for performing the above-described human-computer interaction functions, are configured/stored in one or more processor-executable computer program products or readable storage media.
The present embodiment provides a method for processing data of an electronic device operating in a vehicle, and fig. 2 is a flowchart of a method for processing data according to an embodiment of the present invention, and as shown in fig. 2, the flowchart includes the following steps:
step S10, obtaining vehicle information and a preset rule set, where the vehicle information at least includes one of the following: the method comprises the following steps that a preset rule set is used for representing a preset condition for determining that a vehicle is in a calibration state;
step S20, judging whether the vehicle information meets a preset condition, determining that the vehicle is in a calibration state and generating an acquisition instruction under the condition that the vehicle information meets the preset condition;
step S30, acquiring target data based on an acquisition instruction, where the acquisition instruction is used to acquire the target data, and the target data at least includes one of the following: point cloud data, vehicle body data, image data and visual perception data;
step S40, generating case data based on the target data;
step S50, return the case data to the server.
Through the steps, the vehicle information and the preset rule set are obtained, and the vehicle information at least comprises one of the following steps: the method comprises the following steps that driving parameter information of a vehicle, driving condition information of the vehicle and external perception information of the vehicle are obtained, a preset rule set is used for representing preset conditions for determining that the vehicle is in a calibration state, whether the vehicle information meets the preset conditions or not is judged, the vehicle is determined to be in the calibration state under the condition that the vehicle information meets the preset conditions, a collection instruction is generated, target data are collected based on the collection instruction, the collection instruction is used for collecting the target data, and the target data at least comprise one of the following data: the method comprises the steps of point cloud data, vehicle body data, image data and visual perception data, finally, case data are generated based on target data, the case data are returned to a server, the purpose of screening the collected target data on a vehicle side is achieved, data at all times do not need to be collected and transmitted to the server, the value of the target data is improved, an engineer can adaptively obtain the target data of vehicles under different scene conditions by configuring vehicle information and a preset rule set, the collected data are transmitted without adopting a hard disk, mass data transmission can be achieved through remote uploading, and the technical problem that data transmission of the vehicles in the related technology is slow is solved.
The running parameter information includes information such as speed information and vehicle operation. For example, the running parameter information includes deceleration, and the preset rule set acquired by the vehicle from the cloud is set to determine that the vehicle is in a calibration state when the deceleration exceeds a threshold, that is, when the deceleration of the vehicle exceeds the threshold, the vehicle is marked as the calibration state, and the acquisition instruction is generated. It is reasonable to conclude to those skilled in the art that deceleration may be replaced by acceleration. The driving parameters may also include driver takeover information. The driving condition information at least comprises vehicle front collision early warning FCW action information, vehicle automatic emergency braking system AEB braking information and adaptive cruise system ACC information. The external perception information at least comprises vehicle identification object information, for example, a vehicle camera detects a human-like object and shoots an image with the object, and the image is processed by an algorithm to be used as the vehicle identification object information to participate in judgment with a preset rule. For example, the preset rule set acquired by the vehicle from the cloud is set to determine that the vehicle is in the calibration state when the human-like object is detected, that is, when the external perception information of the vehicle is that the human-like object is detected, the vehicle is marked as the calibration state, and the acquisition instruction is generated. Through the preset rule set which can be configured at the cloud end, the calibration state can be triggered according to different driving information, the target data is collected, and the flexibility and the convenience for collecting the test data by engineering personnel are greatly improved.
Optionally, in step S30, acquiring the target data based on the acquisition instruction includes:
step S301, point cloud data, vehicle body data, image data and visual perception data are collected in real time by taking a first preset time period as a cycle;
preferably, the first preset time period is 5s, that is, the vehicle collects the image data at 5s time intervals in real time, and the point cloud data, the vehicle body data and the visual perception data are cached into the memory.
Step S302, point cloud data, vehicle body data, image data and visual perception data in a second time period are determined as target data when the acquisition instruction is generated, wherein the starting point of the second time period is earlier than the starting point of the first preset time period, and the end point of the second time period is later than the end point of the first preset time period.
Specifically, the point cloud data, the vehicle body data, the image data and the visual perception data which are cached into the memory in the second time period are stored into the nonvolatile memory in a determining mode.
In an optional embodiment, the second time period is a time interval between a previous first preset time period and a next preset time period when the acquisition instruction is generated.
Optionally, in step S301, the acquiring point cloud data, vehicle body data, image data, and visual perception data in real time with a first preset time period as a cycle includes: image data were acquired using a J3 core plate.
It should be noted that the J3 core chip is mainly responsible for intercepting and processing images acquired by the camera, storing the images into the memory in an ethernet transmission form, and compressing the target data in an h.264 format.
In one exemplary embodiment, generating case data based on the target data in step S40 includes: and packaging and encrypting the target data by using the core A chip to generate case data.
It should be noted that the core a chip is mainly responsible for packing and encrypting the acquired target data.
Optionally, in step S50, returning the case data to the server includes: and returning the case data to the server by utilizing the Ethernet chip.
The case data are transmitted to the server by utilizing the Ethernet chip, so that the remote transmission of the data can be realized, and the problem of low transmission rate caused by adopting Bluetooth transmission and ZigBee transmission can be solved.
In one exemplary embodiment, the method further comprises: obtaining calibration data, wherein the calibration data at least comprises one of the following: determining the time when the vehicle is in the calibration state, determining the times of the time when the vehicle is in the calibration state, and determining the position of the vehicle when the vehicle is in the calibration state; and returning the calibration data to the server.
That is to say, when the vehicle is determined to be in the calibration state, the calibration data at the moment is collected, so that the time, the position and the frequency of the vehicle in the calibration state can be analyzed and determined by a cloud testing person.
Fig. 3 is a schematic diagram of a data processing method according to an alternative embodiment of the present invention. The specific flow of the data processing method applied to the vehicle is as follows:
step S1, the vehicle is normally powered on, and the automatic driving area controller and the central gateway start to work;
in step S2, the central gateway system starts to operate normally. The central gateway monitors the driving behavior (driving parameter information) of a driver, typical driving conditions (driving condition information) and the recognition result (external perception information) of a sensor perception algorithm;
step S3, acquiring a preset rule set;
for example, the preset rule set includes a vehicle deceleration threshold and a deceleration rule set, and the maximum threshold of the vehicle deceleration is first configured by the cloud end rule set and then sent to the central gateway. After receiving the new vehicle deceleration rule screening rule, the central gateway starts to monitor the deceleration behavior of the vehicle in real time;
step S4, collecting point cloud data, vehicle body data, image data and visual perception data in real time by taking a first preset time period as a cycle;
in an exemplary embodiment, the image capturing module and the data packing module buffer data in the memory according to a period of 5s, preferably, the first preset time period is 5 s;
step S5, judging whether the vehicle information meets a preset condition, determining that the vehicle is in a calibration state and generating an acquisition instruction under the condition that the vehicle information meets the preset condition;
that is to say, the edge computing node of the central gateway monitors that deceleration of the vehicle is greater than a fixed threshold, that is, the vehicle is deemed to have sudden braking behavior, that is, it is determined that vehicle information (deceleration) meets a preset condition (greater than the fixed threshold), and the edge computing node of the central gateway triggers the data to send the data to the data packing module and the picture intercepting module (that is, generates an acquisition instruction);
step S6, point cloud data, vehicle body data, image data and visual perception data in a second time period when the acquisition instruction is generated are determined as target data;
optionally, when receiving an input of a trigger signal of an edge computing node of the central gateway, the image capture module automatically stores image data segments of the previous 5s and the next 5s at this time, and compresses the original image data into an h.264 format data packet. Preferably, the second time period is the first 5s and the last 5s of the determination that the vehicle is in the calibration state.
Step S7, packaging and encrypting the target data, and generating case data based on the target data;
the data packaging module packages the compressed picture data (namely image data), the perception result data (namely visual perception data) and the vehicle CAN information (namely vehicle body data) in a time stamp and time synchronization mode to form a vehicle deceleration case segment, encrypts all the data, and stores the encrypted data in the nonvolatile memory.
Step S8, returning the case data to the server;
the data transfer master node invokes the entire data packet of the vehicle deceleration case segment and transfers the data from the autopilot domain controller to the central gateway in the form of an ethernet.
In step S9, the data transmission receives the case data from the nodes and merges and stores the trigger record data (calibration data) of the edge computing nodes in the nonvolatile memory of the central gateway.
And step S10, after the user finishes using the vehicle, the uploading agent module is responsible for uploading data to the automatic driving cloud terminal in the form of a private network during the idle time after the vehicle is shut down. FIG. 4 is a block diagram of a method of processing data applied to a vehicle in accordance with an alternate embodiment of the present invention; as shown in fig. 4, the module mainly includes: an autonomous driving domain controller, a central gateway, and a collection device. The acquisition equipment comprises a forward-looking camera, an angle radar, a front radar, a chassis CAN data acquisition unit and a front intelligent camera. Preferably, the angular radars are provided in four.
It should be noted that the non-volatile memory refers to a memory in which data is not lost when the computer is unexpectedly turned off, and is mainly divided into block addressing (such as flash memory) and byte addressing (phase change memory).
The central gateway controller is a core component in a whole vehicle electronic and electrical architecture, CAN be used as a data exchange hub of a whole vehicle network, CAN route data networks such as CAN, lin, MOST \ fLEXRAY, Ethernet buses and the like in different networks, and a gateway is a central node of in-vehicle communication, is connected with MOST of electric control power supplies in the whole vehicle, supports various bus systems, and CAN realize cross-domain function integration, basic routing communication, protocol translation and extraction and integration of in-vehicle data.
The edge computing means that an open platform integrating network, computing, storage and application core capabilities is adopted near one side of an object or a data source to provide nearest-end service nearby. The application program is initiated at the edge side, and the network service response can be generated more quickly. The cloud computing may access historical data of the edge computing. For the internet of things, edge computing is adopted, so that much control can be realized through local equipment without being handed to a cloud end, and the processing process is in a local edge computing layer of a vehicle.
As shown in fig. 3 and 4, the data processing method is applied to the vehicle as follows:
step S1, obtaining vehicle information and a preset rule set, and judging whether the vehicle information meets a preset condition;
the vehicle edge computing node acquires and supervises vehicle information of the vehicle in real time, and realizes triggering logic for acquiring data (acquiring target data) based on an intelligent driving rule set (namely a preset rule set).
In the process of judging whether the vehicle information meets the preset conditions, the triggering logic is 3 types: first, based on driver driving behavior, such as: behaviors such as rapid acceleration, rapid deceleration, manual take-over and the like; second, based on typical driving conditions, such as: the method comprises the following steps that the FCW action, the automatic emergency braking system AEB and the adaptive cruise system ACC of the vehicle are early-warned when the vehicle is in front collision; thirdly, the vehicle sensor senses that the recognition result is seriously deviated from a normal value or senses and recognizes an unknown object.
Step S2, collecting target data based on the collection instruction;
the image intercepting module ensures that image data fragments of the intelligent camera 5s time interval before online recording are cached in real time, automatically stores the image data fragments of the former 5s and the latter 5s at the moment when the triggering signal input of the edge computing node is received, and compresses the original image data into an H.264 format data packet.
Step S3, generating case data based on the target data;
and the data packaging module packages the compressed picture data (namely image data), perception result data (visual perception data) and vehicle CAN information data (namely vehicle body data) in a time stamp and time synchronization mode, encrypts all the data, and stores the encrypted data in the nonvolatile memory.
And step S4, the data transmission main node extracts the stored data and transmits the data from the automatic driving area controller to the central gateway through the form.
In step S5, the data transmission receives data from the nodes and merges and stores the trigger record data (i.e. calibration data) of the edge computing nodes in the nonvolatile memory of the central gateway.
And step S6, the uploading agent module is responsible for uploading data to the automatic driving cloud terminal in a private network mode during idle time after the vehicle is shut down.
In step S7, the autopilot cloud may configure the preset rule set according to the type of data required by the autopilot cloud, that is, the autopilot cloud may configure the rule set of the artificial intelligence perception data according to the type of data required by the autopilot cloud.
In steps S2, S3, and S4, the autopilot domain controller includes 3 chips, which are an R core (responsible for transmission of target data and case data), an a core (responsible for data calculation, grouping, and encryption), and a J3 core (responsible for processing and intercepting of image data), respectively.
By adopting the technical scheme, the problems that the target data are difficult to obtain and resources are limited by adopting a test motorcade in the prior art are solved, meanwhile, the method can realize the intelligent extraction of the target data and return the target data in a network form, so that the data acquisition efficiency is improved, and the method for intelligently screening the sensing data (namely acquiring the data of the determined vehicle in a calibration state) at the vehicle end has the advantages of small data volume, high data safety and high data value.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a data processing apparatus is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, and details of which have been already described are omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram of a data processing apparatus according to an embodiment of the present invention, as shown in fig. 5, the apparatus includes: an obtaining module 51, configured to obtain target data, where the target data at least includes one of the following: point cloud data, vehicle body data, image data and visual perception data; a determination module 52 for generating case data based on the target data; a transmission module 53 for returning the case data to the server; and a storage module 54 for storing the target data and the case data.
Through the device, the vehicle information is acquired and a preset rule set is adopted, and the vehicle information at least comprises one of the following steps: the method comprises the following steps that driving parameter information of a vehicle, driving condition information of the vehicle and external perception information of the vehicle are obtained, a preset rule set is used for representing preset conditions for determining that the vehicle is in a calibration state, whether the vehicle information meets the preset conditions or not is judged, the vehicle is determined to be in the calibration state under the condition that the vehicle information meets the preset conditions, a collection instruction is generated, target data are collected based on the collection instruction, the collection instruction is used for collecting the target data, and the target data at least comprise one of the following data: the method comprises the steps of point cloud data, vehicle body data, image data and visual perception data, finally, case data are generated based on target data, the case data are returned to a server, the purpose of screening the collected target data on a vehicle side is achieved, data at all times do not need to be collected and transmitted to the server, the value of the target data is improved, an engineer can adaptively obtain the target data of vehicles under different scene conditions by configuring vehicle information and a preset rule set, the collected data are transmitted without adopting a hard disk, mass data transmission can be achieved through remote uploading, and the technical problem that data transmission of the vehicles in the related technology is slow is solved.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
step S1, obtaining vehicle information and a preset rule set, where the vehicle information at least includes one of the following: the method comprises the following steps that a preset rule set is used for representing a preset condition for determining that a vehicle is in a calibration state;
step S2, judging whether the vehicle information meets a preset condition, determining that the vehicle is in a calibration state and generating an acquisition instruction under the condition that the vehicle information meets the preset condition;
step S3, acquiring target data based on an acquisition instruction, where the acquisition instruction is used to acquire the target data, and the target data at least includes one of the following: point cloud data, vehicle body data, image data and visual perception data;
step S4, generating case data based on the target data;
step S5, return the case data to the server.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide a processor for executing a program, wherein the program is configured to perform the steps in any of the above method embodiments when executed.
Alternatively, in this embodiment, the processor may be configured to store a computer program for executing the following steps:
step S1, obtaining vehicle information and a preset rule set, where the vehicle information at least includes one of the following: the method comprises the following steps that a preset rule set is used for representing a preset condition for determining that a vehicle is in a calibration state;
step S2, judging whether the vehicle information meets a preset condition, determining that the vehicle is in a calibration state and generating an acquisition instruction under the condition that the vehicle information meets the preset condition;
step S3, acquiring target data based on an acquisition instruction, where the acquisition instruction is used to acquire the target data, and the target data at least includes one of the following: point cloud data, vehicle body data, image data and visual perception data;
step S4, generating case data based on the target data;
step S5, return the case data to the server.
Embodiments of the present invention also provide an electronic device comprising a memory in which a computer program is stored and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
step S1, obtaining vehicle information and a preset rule set, where the vehicle information at least includes one of the following: the method comprises the following steps that a preset rule set is used for representing a preset condition for determining that a vehicle is in a calibration state;
step S2, judging whether the vehicle information meets a preset condition, determining that the vehicle is in a calibration state and generating an acquisition instruction under the condition that the vehicle information meets the preset condition;
step S3, acquiring target data based on an acquisition instruction, where the acquisition instruction is used to acquire the target data, and the target data at least includes one of the following: point cloud data, vehicle body data, image data and visual perception data;
step S4, generating case data based on the target data;
step S5, return the case data to the server.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute 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 Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (10)
1. A method for processing data, comprising:
acquiring vehicle information and a preset rule set, wherein the vehicle information at least comprises one of the following information: the preset rule set is used for representing preset conditions for determining that the vehicle is in a calibration state;
judging whether the vehicle information meets the preset condition, determining that the vehicle is in a calibration state under the condition that the vehicle information meets the preset condition, and generating an acquisition instruction;
acquiring target data based on the acquisition instruction, wherein the acquisition instruction is used for acquiring the target data, and the target data at least comprises one of the following data: point cloud data, vehicle body data, image data and visual perception data;
generating case data based on the target data;
and returning the case data to the server.
2. The method of processing data of claim 1, wherein acquiring target data based on the acquisition instruction comprises:
collecting the point cloud data, the vehicle body data, the image data and the visual perception data in real time by taking a first preset time period as a cycle;
and determining the point cloud data, the vehicle body data, the image data and the visual perception data in a second time period as the target data when the acquisition instruction is generated, wherein the starting point of the second time period is earlier than the starting point of the first preset time period, and the end point of the second time period is later than the end point of the first preset time period.
3. The data processing method of claim 2, wherein the collecting the point cloud data, the vehicle body data, the image data and the visual perception data in real time with a first preset time period as a cycle comprises:
the image data was acquired using a J3 core plate.
4. The method of processing data of claim 1, wherein generating case data based on the target data comprises:
and packaging and encrypting the target data by using the core A chip to generate the case data.
5. The method of processing data of claim 1, wherein returning the case data to a server comprises:
and returning the case data to the server by utilizing an Ethernet chip.
6. The method of processing data according to claim 1, the method further comprising:
obtaining calibration data, wherein the calibration data comprises at least one of: determining the time when the vehicle is in a calibration state, determining the times of the time when the vehicle is in the calibration state, and determining the position of the vehicle when the vehicle is in the calibration state;
and returning the calibration data to the server.
7. An apparatus for data processing, comprising:
the acquisition module is used for acquiring target data, wherein the target data at least comprises one of the following data: point cloud data, vehicle body data, image data and visual perception data;
a determination module to generate case data based on the target data;
the transmission module is used for returning the case data to the server;
and the storage module is used for storing the target data and the case data.
8. A non-volatile storage medium, characterized in that a computer program is stored in the storage medium, wherein the computer program is arranged to execute the method of processing data according to any of claims 1 to 6 when running.
9. A processor for running a program, wherein the program is arranged to perform the method of processing data as claimed in any one of claims 1 to 6 when running.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and the processor is configured to execute the computer program to perform the method of processing data as claimed in any one of claims 1 to 6.
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