WO2024087206A1 - 数据处理***及其数据上传方法和数据处理方法 - Google Patents

数据处理***及其数据上传方法和数据处理方法 Download PDF

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Publication number
WO2024087206A1
WO2024087206A1 PCT/CN2022/128381 CN2022128381W WO2024087206A1 WO 2024087206 A1 WO2024087206 A1 WO 2024087206A1 CN 2022128381 W CN2022128381 W CN 2022128381W WO 2024087206 A1 WO2024087206 A1 WO 2024087206A1
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WIPO (PCT)
Prior art keywords
data
vehicle
cloud
queue
transmission channel
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PCT/CN2022/128381
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English (en)
French (fr)
Inventor
史业政
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深圳市锐明技术股份有限公司
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Application filed by 深圳市锐明技术股份有限公司 filed Critical 深圳市锐明技术股份有限公司
Priority to CN202280062268.0A priority Critical patent/CN117957819A/zh
Priority to PCT/CN2022/128381 priority patent/WO2024087206A1/zh
Publication of WO2024087206A1 publication Critical patent/WO2024087206A1/zh

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/14—Handling requests for interconnection or transfer
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/66—Arrangements for connecting between networks having differing types of switching systems, e.g. gateways
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/04—Error control

Definitions

  • the present application relates to the field of data processing, and in particular to a data processing system and a data uploading method and a data processing method thereof.
  • vehicle data is usually uploaded to the cloud through the vehicle-mounted device, and the cloud performs driving risk assessment on the vehicle.
  • vehicle data is usually uploaded to the cloud through the vehicle-mounted device, and the cloud performs driving risk assessment on the vehicle.
  • the cloud when uploading vehicle data to the cloud from the vehicle-mounted device, it is easy to have data loss and delay problems due to network anomalies, and when the cloud performs driving risk assessment on the vehicle, it is also easy to have message blocking problems due to network anomalies, which cannot meet the real-time and reliability requirements of vehicle driving risk assessment.
  • the embodiments of the present application provide a data processing system and its data upload method and data processing method, aiming to solve at least one of the problems in the prior art that when the vehicle-mounted device uploads the vehicle-mounted data to the cloud, there is a problem of data loss and delay due to network anomalies, and when the cloud performs driving risk assessment on the vehicle, there is a problem of message blocking due to network anomalies, and the real-time requirements and reliability requirements of the vehicle driving risk assessment cannot be met.
  • a first aspect of an embodiment of the present application provides a data uploading method, which is applied to a vehicle-mounted device end, including: when it is detected that a network failure occurs in the data transmission channel between the vehicle-mounted device end and the cloud and the network connection has been restored, obtaining a first locally recorded cloud-processed data record; traversing the vehicle data cached in the local data queue according to the first cloud-processed data record, and determining the data transmission starting point when the data transmission channel resumes data transmission according to the traversal result; starting from the data transmission starting point, obtaining target vehicle data in the local data queue in order, and uploading the target vehicle data to the cloud, wherein the cloud is used to perform a vehicle driving risk assessment based on the target vehicle data.
  • the local data queue includes a supplementary transmission queue and a real-time queue.
  • a network failure is detected in the data transmission channel between the vehicle-mounted device and the cloud, it also includes: real-time monitoring of the current network status of the data transmission channel; if the current network status of the data transmission channel has been restored to a network connection state, the vehicle-mounted data generated in real time by the vehicle-mounted device is cached in the real-time queue, and the vehicle-mounted data cached in the real-time queue is uploaded to the cloud in real time through the data transmission channel; if the current network status of the data transmission channel is in a network failure state, the vehicle-mounted data generated in real time by the vehicle-mounted device is cached in the supplementary transmission queue, and the vehicle-mounted data cached in the supplementary transmission queue is supplementally transmitted to the cloud through the data supplementary transmission channel.
  • the vehicle-mounted data cached in the supplementary transmission queue when the vehicle-mounted data cached in the supplementary transmission queue is supplementarily transmitted to the cloud through the data supplementary transmission channel, it includes: obtaining the processor memory usage information under the current operating state of the cloud; comparing the processor memory usage information with the preset supplementary transmission conditions to determine whether the supplementary transmission conditions are met under the current operating state of the cloud; if so, creating a data supplementary transmission channel between the vehicle-mounted device and the cloud, and supplementarily transmitting the vehicle-mounted data cached in the supplementary transmission queue to the cloud through the data supplementary transmission channel.
  • a third possible implementation method of the first aspect it also includes: receiving a first cloud-processed data record fed back by the cloud; identifying the processed vehicle data cached in the local data queue according to the cloud-processed data processing record, and deleting the processed vehicle data from the local data queue.
  • a second aspect of an embodiment of the present application provides a data processing method, characterized in that the data processing method is applied in the cloud, and the method includes: receiving target vehicle-mounted data uploaded in real time by a data transmission channel, the target vehicle-mounted data having corresponding queue sorting information; obtaining a second cloud-processed data record recorded in real time in the cloud; comparing the second cloud-processed data record with the queue sorting information corresponding to the target vehicle-mounted data to determine whether the target vehicle-mounted data is the cloud-processed data; if the target vehicle-mounted data is the cloud-processed data, deleting the target vehicle-mounted data; if the target vehicle-mounted data is the cloud-processed data, performing a vehicle driving risk assessment based on the target vehicle-mounted data and obtaining a driving risk assessment result generated by the vehicle based on the target vehicle-mounted data.
  • a vehicle driving risk assessment based on the target vehicle-mounted data after performing a vehicle driving risk assessment based on the target vehicle-mounted data and obtaining a driving risk assessment result generated by the vehicle based on the target vehicle-mounted data, it also includes: if the driving risk assessment result shows a high risk level, an alarm information carrying the driving risk assessment result is sent to the vehicle-mounted device in real time.
  • a second possible implementation method of the second aspect it also includes: receiving first vehicle-mounted data transmitted by a data supplement transmission channel; configuring a single batch processing quantity threshold according to processor memory usage information under the current operating state of the cloud; batch processing the first vehicle-mounted data according to the single batch processing quantity threshold, and obtaining a driving risk assessment result generated by the vehicle based on the first vehicle-mounted data.
  • a third possible implementation method of the second aspect after obtaining the driving risk assessment result generated by the vehicle based on the first vehicle-mounted data, it also includes: saving the driving risk assessment result generated by the vehicle based on the first vehicle-mounted data in the risk database in the cloud.
  • a third aspect of an embodiment of the present application provides a vehicle-mounted device end, characterized in that it includes a memory, a processor, a communication unit, and a computer program stored in the memory and executable on the processor, wherein: the communication unit is used to send and receive data or instructions to the cloud; the processor is used to execute the data upload method as described in any one of the first aspects.
  • the fourth aspect of an embodiment of the present application provides a cloud, including a memory, a processor, a communication unit, and a computer program stored in the memory and executable on the processor, wherein the cloud includes a communication unit and a processing unit, wherein: the communication unit is used to send and receive data or instructions to a vehicle-mounted device; the processor is used to execute a data processing method as described in any one of the second aspects.
  • the fifth aspect of an embodiment of the present application provides a data processing system, which includes a vehicle-mounted device and a cloud, wherein: the vehicle-mounted device is used to execute the data uploading method as described in any one of the first aspects; the cloud is used to execute the data processing method as described in any one of the second aspects.
  • a sixth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspect or the second aspect are implemented.
  • the embodiments of the present application have the following beneficial effects: when the vehicle-mounted device detects that a network failure has occurred in the data transmission channel between it and the cloud and the network connection has been restored, the vehicle-mounted device obtains the first locally recorded cloud-processed data record, and traverses the vehicle-mounted data cached in the local data queue according to the first cloud-processed data record to determine the data transmission starting point when the data transmission channel resumes data transmission, and then, starting from the data transmission starting point, the target vehicle-mounted data is obtained in the local data queue in order and uploaded to the cloud, which can avoid data loss and delay problems caused by network anomalies in the process of implementing driving risk assessment of the vehicle in the cloud, thereby ensuring the real-time performance of vehicle driving risk assessment in the cloud and improving the reliability of vehicle driving risk assessment in the cloud.
  • the cloud can delete the target vehicle-mounted data repeatedly uploaded by the vehicle-mounted device end according to the second cloud-processed data record recorded in real time, so as to avoid repeated data processing, and can batch process the vehicle-mounted data supplemented by the vehicle-mounted device end when its current operating state meets the supplementary transmission conditions, and can avoid message blocking problems caused by network abnormalities in the process of implementing driving risk assessment of the vehicle on the cloud, thereby ensuring the real-time performance of vehicle driving risk assessment implemented on the cloud and improving the reliability of implementing vehicle driving risk assessment on the cloud.
  • FIG1 is a basic method flow chart of a data uploading method provided by an embodiment of the present application.
  • FIG2 is a flow chart of a method for caching data after a network failure occurs in a data transmission channel in a data uploading method provided in an embodiment of the present application;
  • FIG. 3 is a flow chart of a method for supplementing vehicle data by a vehicle-mounted device end through a data supplementary transmission channel in a data uploading method provided in an embodiment of the present application;
  • FIG4 is a flow chart of a method for performing cache clearing on a vehicle-mounted device in a data uploading method provided in an embodiment of the present application
  • FIG5 is a basic method flow chart of a data processing method provided by an embodiment of the present application.
  • FIG6 is a flow chart of a method for processing supplementary vehicle data in the cloud in the data processing method provided in an embodiment of the present application;
  • FIG7 is a schematic diagram of the structure of a data uploading device provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of a data processing device provided in an embodiment of the present application.
  • FIG. 9 is a schematic diagram of an electronic device provided in accordance with an embodiment of the present application.
  • the embodiment of the present application provides a data processing system, based on which vehicle driving risk assessment can be implemented.
  • the data processing system includes an on-board device and a cloud.
  • a data transmission channel is established between the on-board device and the cloud, and the on-board device uploads the on-board data generated by the vehicle in real time to the cloud through the data transmission channel.
  • the cloud receives the on-board data uploaded by the on-board device through the data transmission channel, and performs vehicle driving risk assessment based on the on-board data.
  • FIG1 is a basic method flow chart of a data uploading method provided in an embodiment of the present application.
  • the data uploading method is applied in the vehicle-mounted device end, and is described in detail as follows:
  • the first cloud-processed data record recorded locally on the vehicle-mounted device is the cloud operation status information fed back by the cloud to the vehicle-mounted device, and can be actively sent by the cloud to the vehicle-mounted device.
  • the cloud will process the vehicle-mounted data in real time to implement real-time driving risk assessment of the vehicle.
  • the cloud will record the number of vehicle-mounted data currently processed. After processing a certain number of vehicle-mounted data, it will actively feed back a cloud-processed data record to the vehicle-mounted device.
  • the vehicle-mounted device After receiving the cloud-processed data record fed back by the cloud, the vehicle-mounted device will record the cloud-processed data record as the first cloud-processed data record locally. For example, when realizing the function of the cloud actively feeding back the cloud-processed data record to the vehicle-mounted device, a feedback threshold can be set in the cloud.
  • the cloud will actively feed back the cloud-processed data record to the vehicle-mounted device after processing each 50 vehicle-mounted data, that is, when the cloud processes the first 50 vehicle-mounted data, the cloud-processed data record fed back to the vehicle-mounted device is the first 50 vehicle-mounted data that have been processed, when the cloud processes the second 50 vehicle-mounted data, the cloud-processed data record fed back to the vehicle-mounted device is the first 100 vehicle-mounted data that have been processed, and so on. After receiving the cloud-processed data record fed back by the cloud, the vehicle-mounted device will record the cloud-processed data record locally.
  • the vehicle-mounted device in the process of implementing driving risk assessment of the vehicle in the cloud, it is necessary to establish a data transmission channel between the vehicle-mounted device and the cloud through a network connection, so that the vehicle-mounted device can upload the vehicle's on-board data to the cloud in real time through the data transmission channel, and then the cloud performs the vehicle driving risk assessment based on the real-time uploaded on-board data.
  • the vehicle-mounted device detects that a network failure has occurred in the data transmission channel between it and the cloud and the network connection has been restored, the current operating status of the cloud can be determined by obtaining the first cloud-processed data record recorded locally on the vehicle-mounted device, thereby determining the starting data position corresponding to when the vehicle-mounted device resumes data transmission. In this way, the problem of data loss due to network failure can be avoided.
  • S12 Traversing the vehicle data cached in the local data queue according to the first cloud-processed data record, and determining the data transmission starting point when the data transmission channel resumes data transmission according to the traversal result.
  • the vehicle-mounted device since the vehicle-mounted device generates vehicle-mounted data in real time as the vehicle is traveling, it is not affected by the network, that is, when a network failure occurs in the data transmission channel between the vehicle-mounted device and the cloud, the vehicle-mounted device will still generate vehicle-mounted data. Therefore, in this embodiment, the vehicle-mounted data generated in real time by the vehicle-mounted device will first be cached in the local data queue, and then the vehicle-mounted data will be uploaded to the cloud for processing in sequence according to the data sorting in the local data queue.
  • the data transmission starting point when the data transmission channel resumes data transmission can be determined by obtaining the first cloud-processed data record recorded locally on the vehicle-mounted device.
  • the first cloud-processed data record that was most recently fed back by the cloud and received by the vehicle-mounted device end can be found from the locally recorded first cloud-processed data record, and the data location that has been processed in the cloud and can be determined by the vehicle-mounted device end can be obtained from the first cloud-processed data record that was most recently fed back by the cloud.
  • the vehicle-mounted data cached in the local data queue are traversed, and the vehicle-mounted data corresponding to the data location that has been processed in the cloud and can be determined by the vehicle-mounted device end is found in the local data queue, and the location of the vehicle-mounted data is determined as the data transmission starting point when the data transmission channel resumes data transmission.
  • target vehicle data is obtained in the local data queue in order, and the target vehicle data is uploaded to the cloud, wherein the cloud is used to perform vehicle driving risk assessment based on the target vehicle data.
  • the local data queue when the local data queue caches the vehicle-mounted data, it sorts the vehicle-mounted data in the order of the time when the vehicle-mounted data is generated.
  • the data transmission is resumed on the vehicle-mounted device side, it can start from the data transmission starting point, and obtain the vehicle-mounted data after the data transmission starting point in the local data queue according to the sorting as the target vehicle-mounted data, and upload the target vehicle-mounted data to the cloud.
  • the data upload method can, when the vehicle-mounted device detects that a network failure has occurred in the data transmission channel between it and the cloud and the network connection has been restored, obtain the first cloud-processed data record recorded locally, and traverse the vehicle-mounted data cached in the local data queue according to the first cloud-processed data record to determine the data transmission starting point when the data transmission channel resumes data transmission, and then, starting from the data transmission starting point, obtain the target vehicle-mounted data in the local data queue in order and upload the target vehicle-mounted data to the cloud, which can avoid data loss and delay problems caused by network anomalies in the process of realizing driving risk assessment of the vehicle on the cloud, thereby ensuring the real-time performance of vehicle driving risk assessment realized in the cloud and improving the reliability of vehicle driving risk assessment realized in the cloud.
  • FIG. 2 is a flow chart of a method for the vehicle-mounted device to cache data after a network failure occurs in the data transmission channel in the data uploading method provided in the embodiment of the present application. The details are as follows:
  • two local data queues can be created on the vehicle device side.
  • One is a real-time queue and the other is a supplementary transmission queue.
  • the vehicle data cached in the real-time queue is uploaded to the cloud in real time through the data transmission channel, and the vehicle data cached in the supplementary transmission queue is supplemented to the cloud through the supplementary transmission channel.
  • the current network status of the data transmission channel can be monitored in real time.
  • the vehicle-mounted data generated in real time by the vehicle-mounted device end can be cached in the supplementary transmission queue.
  • the vehicle-mounted data generated in real time by the vehicle-mounted device end can be cached in the real-time queue. In this way, when the data transmission channel resumes data transmission, the vehicle-mounted data cached in the real-time queue can be uploaded to the cloud in real time through the data transmission channel.
  • a data supplementary transmission channel is created according to the supplementary transmission rules by setting supplementary transmission rules, and the vehicle-mounted data cached in the supplementary transmission queue is supplementary transmitted to the cloud through the data supplementary transmission channel.
  • the supplementary transmission rule can be set to determine whether to create a data supplementary transmission channel based on the real-time data processing capability of the cloud. If the cloud still has sufficient data processing capability while giving priority to processing the vehicle-mounted data uploaded in real time by the data transmission channel, a data supplementary transmission channel will be created, and the vehicle-mounted data cached in the supplementary transmission queue will be supplemented to the cloud through the data supplementary transmission channel.
  • the cloud can delay the transmission and delay the processing of the vehicle-mounted data generated by the vehicle-mounted device when the network fails, thereby achieving the purpose of giving priority to processing the vehicle-mounted data generated in real time by the vehicle-mounted device, and improving the real-time and reliability of the cloud's driving risk assessment of the vehicle.
  • a supplementary transmission queue can be created when the vehicle-mounted device detects that a network failure has occurred in the data transmission channel between it and the cloud, and the local data queue initially created by the vehicle-mounted device is configured as a real-time queue.
  • the vehicle-mounted device can first find the data location that has been processed in the cloud that can be determined by the vehicle-mounted device in the real-time queue based on the cloud-processed data processing record, and transfer the vehicle-mounted data cached after the data location at the current moment to the cache in the supplementary transmission queue.
  • the vehicle-mounted data after the data location is supplemented and transmitted to the cloud through the supplementary transmission channel for data evaluation and processing, which can avoid the problem of data loss due to network failure in the process of driving risk assessment of the vehicle in the cloud when the vehicle-mounted device is set with two data queues.
  • FIG3 is a flow chart of a method for supplementing vehicle data by the vehicle-mounted device end through the data supplementation channel in the data uploading method provided in the embodiment of the present application. The details are as follows:
  • S32 Compare the processor memory usage information with a preset supplementary transmission condition to determine whether the supplementary transmission condition is met under the current operation state of the cloud;
  • the data processing capacity of the cloud can be measured by the processor memory usage information in the current running state of the cloud.
  • the supplementary transmission condition can be pre-set on the vehicle-mounted device side, and the supplementary transmission condition can be a processor memory usage rate threshold.
  • the processor memory usage information in the current running state of the cloud is obtained from the cloud by sending a supplementary transmission request from the vehicle-mounted device side to the cloud, and the processor memory usage information in the current running state of the cloud can be expressed as the processor memory usage rate in the current running state of the cloud.
  • the processor memory usage information in the current running state of the cloud By comparing the processor memory usage information in the current running state of the cloud with the preset supplementary transmission condition, it is compared whether the processor memory usage rate in the current running state of the cloud is less than the processor memory usage rate threshold, wherein, when the processor memory usage rate in the current running state of the cloud is less than the processor memory usage rate threshold, it is judged that the supplementary transmission condition is met in the current running state of the cloud, otherwise, it is judged that the supplementary transmission condition is not met in the current running state of the cloud.
  • the supplementary transmission condition is met in the current running state of the cloud, a data supplementary transmission channel is created between the vehicle-mounted device side and the cloud, and then the vehicle-mounted data cached in the supplementary transmission queue is supplemented to the cloud through the data supplementary transmission channel.
  • This embodiment can delay the transmission and processing of the vehicle-mounted data generated by the vehicle-mounted device end when the network fails according to the real-time data processing capability of the cloud, thereby achieving the purpose of giving priority to the processing of the vehicle-mounted data generated in real time by the vehicle-mounted device end, and improving the real-time and reliability of the cloud-based driving risk assessment of the vehicle.
  • FIG. 4 is a flow chart of a method for performing cache clearing on the vehicle-mounted device in the data uploading method provided in the embodiment of the present application. The details are as follows:
  • S42 Identify the processed vehicle data cached in the local data queue according to the cloud processed data processing record, and delete the processed vehicle data from the local data queue.
  • the cloud will record the number of vehicle data currently processed during the real-time processing of vehicle data, and will actively feedback the cloud processed data record to the vehicle device end once each time a certain number of vehicle data is processed.
  • the vehicle device end After the vehicle device end receives the cloud processed data record fed back by the cloud, it can also perform cache cleaning on the local data queue of the vehicle device end according to the cloud processed data record, thereby releasing the data memory of the vehicle device end.
  • the vehicle device end can traverse the vehicle data cached in the local data queue according to the cloud processed data record, thereby identifying the processed vehicle data cached in the local data queue.
  • the sorting sequence number of each vehicle data cached in the local data queue is identified, and the vehicle data with a sorting sequence number less than 50 is identified as the processed vehicle data cached in the local data queue.
  • the processed vehicle data is deleted from the local data queue, thereby achieving the purpose of cache cleaning and freeing up memory.
  • FIG5 is a basic method flow chart of a data processing method provided in an embodiment of the present application.
  • the data processing method is applied in the cloud, as follows:
  • S51 receiving target vehicle-mounted data uploaded in real time through a data transmission channel, wherein the target vehicle-mounted data has corresponding queue sorting information;
  • the target vehicle-mounted data is the cloud-processed data
  • the target vehicle-mounted data is deleted; if the target vehicle-mounted data is the cloud-unprocessed data, a vehicle driving risk assessment is performed based on the target vehicle-mounted data and a driving risk assessment result generated by the vehicle based on the target vehicle-mounted data is obtained.
  • the cloud prioritizes the processing of the vehicle data transmitted in real time through the data transmission channel. While ensuring the ability to process the vehicle data transmitted in real time, if there is still sufficient data processing capacity, the vehicle data transmitted in supplementary data transmission channel is processed according to the sufficient data processing capacity.
  • the cloud when the cloud performs data processing, the target vehicle data uploaded in real time by the real-time queue is received through the data transmission channel. Among them, the target vehicle data has corresponding queue sorting information.
  • the cloud obtains the second cloud-processed data processing record recorded in real time, and compares the second cloud-processed data processing record with the queue sorting information corresponding to the target vehicle data to determine whether the target vehicle data is the cloud-processed data. If the target vehicle data is processed data, the target vehicle data is deleted. If the target vehicle data is unprocessed data, the vehicle driving risk assessment is performed according to the target vehicle data and the driving risk assessment result generated by the vehicle based on the target vehicle data is obtained.
  • the first cloud-processed data record received by the vehicle-mounted device is that the first 50 vehicle-mounted data have been processed.
  • the data transmission channel resumes data transmission it will start transmitting from the 51st vehicle-mounted data, while the cloud has processed up to the 57th vehicle-mounted data.
  • the second cloud-processed data processing record can be compared with the sorting information of the target vehicle-mounted data in the local data queue to determine that the 51st to 57th vehicle-mounted data in the target vehicle-mounted data are the cloud-processed data.
  • the cloud can delete the 51st to 57th vehicle-mounted data in the target vehicle-mounted data, start the vehicle driving risk assessment from the 58th vehicle-mounted data, and obtain the driving risk assessment result generated by the vehicle based on the vehicle-mounted data.
  • the driving risk assessment result can be expressed as a risk level, such as a high risk level and a low risk level.
  • the cloud obtains the driving risk assessment result generated by the vehicle based on the target vehicle data, it can also send an alarm message carrying the driving risk assessment result to the vehicle-mounted device in real time according to the risk level displayed by the driving risk assessment result when the driving risk assessment result is displayed as a high risk level.
  • FIG6 is a flow chart of a method for processing supplementary vehicle data in the cloud in the data processing method provided in the embodiment of the present application. The details are as follows:
  • S63 Batch process the first vehicle-mounted data according to the single batch processing quantity threshold to obtain a driving risk assessment result generated by the vehicle based on the first vehicle-mounted data.
  • the first vehicle-mounted data received by the cloud through the data retransmission channel is outdated historical data.
  • the cloud can use batch processing to perform vehicle driving risk assessment.
  • the single batch processing quantity threshold can be configured according to the processor memory usage information under the current operating state of the cloud.
  • the single batch processing quantity threshold can be calculated by the cloud based on the processor memory usage rate under its own current operating state.
  • the first vehicle-mounted data retransmitted by the data retransmission channel is divided into multiple batches according to the single batch processing quantity threshold, and the first vehicle-mounted data is batch processed by batches, thereby obtaining the driving risk assessment results generated by the vehicle based on the first vehicle-mounted data of each batch.
  • the cloud can save the driving risk assessment result generated by the vehicle based on the first vehicle-mounted data supplemented by the data supplementation channel in a risk database on the cloud after obtaining the driving risk assessment result generated by the vehicle based on the first vehicle-mounted data.
  • the alarm mechanism is not triggered regardless of whether the risk level is a high risk level.
  • FIG. 7 is a structural diagram of a data uploading device provided in an embodiment of the present application, which is applied in a vehicle-mounted device, and is described in detail as follows:
  • the data upload device includes: a first acquisition module 71, a determination module 72 and an upload module 73.
  • the first acquisition module 71 is used to first acquire the first locally recorded cloud-processed data record when it detects that a network failure has occurred in the data transmission channel between the vehicle-mounted device and the cloud and the network connection has been restored.
  • the determination module 72 is used to traverse the vehicle-mounted data cached in the local data queue according to the first cloud-processed data record, and determine the data transmission starting point when the data transmission channel resumes data transmission according to the traversal result.
  • the upload module 73 is used to start from the data transmission starting point, obtain the target vehicle-mounted data in the local data queue in order, and upload the target vehicle-mounted data to the cloud, wherein the cloud is used to perform vehicle driving risk assessment based on the target vehicle-mounted data.
  • the data uploading device corresponds one-to-one to the above-mentioned data uploading method, and will not be described in detail here.
  • FIG. 8 is a schematic diagram of the structure of a data processing device provided in an embodiment of the present application.
  • the data processing device is applied in the cloud, and is described in detail as follows:
  • the data processing device includes: a receiving module 81, a second acquisition module 82, a comparison module 83 and a processing module 84.
  • the receiving module 81 is used to receive the target vehicle-mounted data uploaded in real time by the data transmission channel, and the target vehicle-mounted data has corresponding queue sorting information.
  • the second acquisition module 82 is used to obtain the second cloud-processed data record recorded in real time by the cloud.
  • the comparison module 83 is used to compare the second cloud-processed data record with the queue sorting information corresponding to the target vehicle-mounted data to determine whether the target vehicle-mounted data is the cloud-processed data.
  • the processing module 84 is used to delete the target vehicle-mounted data if the target vehicle-mounted data is the cloud-processed data, and if the target vehicle-mounted data is the cloud-unprocessed data, perform a vehicle driving risk assessment based on the target vehicle-mounted data and obtain a driving risk assessment result generated by the vehicle based on the target vehicle-mounted data.
  • the data processing device corresponds one-to-one to the above-mentioned data processing method, and will not be described in detail here.
  • Figure 9 is a schematic diagram of an electronic device provided in an embodiment of the present application.
  • the electronic device can be a vehicle-mounted device or a cloud-based device, and the communication unit is used to send and receive data and signaling.
  • the electronic device 9 of this embodiment includes: a processor 91, a memory 92, and a computer program 93 stored in the memory 92 and executable on the processor 91, such as a data upload program or a data processing program.
  • the processor 91 executes the computer program 92, the steps in the above-mentioned data upload method embodiments or data processing methods are implemented.
  • the processor 91 executes the computer program 93, the functions of each module/unit in the above-mentioned device embodiments are implemented.
  • the computer program 93 may be divided into one or more modules/units, which are stored in the memory 92 and executed by the processor 91 to complete the present application.
  • the one or more modules/units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 93 in the electronic device 9.
  • the electronic device may include, but is not limited to, a processor 91 and a memory 92.
  • FIG9 is merely an example of the electronic device 9 and does not limit the electronic device 9.
  • the electronic device may include more or fewer components than shown in the figure, or may combine certain components, or different components.
  • the electronic device may also include an input/output device, a network access device, a bus, etc.
  • the processor 91 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or any conventional processor, etc.
  • the memory 92 may be an internal storage unit of the electronic device 9, such as a hard disk or memory of the electronic device 9.
  • the memory 92 may also be an external storage device of the electronic device 9, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 9.
  • the memory 92 may also include both an internal storage unit of the electronic device 9 and an external storage device.
  • the memory 92 is used to store the computer program and other programs and data required by the electronic device.
  • the memory 92 may also be used to temporarily store data that has been output or is to be output.
  • the technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration.
  • the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
  • the functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.
  • the disclosed devices/terminal equipment and methods can be implemented in other ways.
  • the device/terminal equipment embodiments described above are only schematic.
  • the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
  • the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium.
  • the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented.
  • the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form.
  • the computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electric carrier signal telecommunication signal and software distribution medium.
  • the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.
  • computer-readable media do not include electric carrier signals and telecommunication signals.

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Abstract

本申请提供一种数据处理***及其数据上传方法和数据处理方法，其中，数据上传方法包括检测到车载设备端与云端之间的数据传输通道发生网络故障并已恢复网络连接时，获取本地记载的第一云端已处理数据记录；根据第一云端已处理数据记录遍历本地数据队列中缓存的车载数据，根据遍历结果确定数据传输通道恢复数据传输时的数据传输起点；从数据传输起点开始，在本地数据队列中按照排序获取目标车载数据，并将目标车载数据上传至云端。基于该方法，可以在云端实现对车辆进行驾驶风险评估的过程中避免由于网络异常所导致的数据丢失和延迟问题，提高云端实现车辆驾驶风险评估的可靠性，保障云端实现车辆驾驶风险评估的实时性。

Description

数据处理***及其数据上传方法和数据处理方法 技术领域
本申请涉及数据处理领域，尤其涉及一种数据处理***及其数据上传方法和数据处理方法。
 背景技术
随着汽车行业的发展，越来越多人选择驾车出行，这给城市道路交通带来了很大的压力，交通事故也越来越多。目前，可以通过车辆驾驶风险评估技术来预防交通事故的发生。现有技术中，通常通过车载设备端上传车载数据到云端，由云端来对车辆进行驾驶风险评估。然而，在车载设备端上传车载数据到云端时容易因网络异常而存在数据丢失和延迟的问题，且云端对车辆进行驾驶风险评估时也容易因网络异常而存在消息阻塞的问题，无法满足车辆驾驶风险评估的实时性要求和可靠性要求。
 技术问题
有鉴于此，本申请实施例提供了一种数据处理***及其数据上传方法和数据处理方法，旨在至少解决现有技术中车载设备端上传车载数据到云端时容易因网络异常而存在数据丢失和延迟的问题、云端对车辆进行驾驶风险评估时容易因网络异常而存在消息阻塞的问题、以及无法满足车辆驾驶风险评估的实时性要求和可靠性要求等问题之一。
 技术解决方案
本申请实施例的第一方面提供了一种数据上传方法，所述数据上传方法应用在车载设备端，包括：检测到车载设备端与云端之间的数据传输通道发生网络故障并已恢复网络连接时，获取本地记载的第一云端已处理数据记录；根据所述第一云端已处理数据记录遍历本地数据队列中缓存的车载数据，根据遍历结果确定所述数据传输通道恢复数据传输时的数据传输起点；从所述数据传输起点开始，在所述本地数据队列中按照排序获取目标车载数据，并将所述目标车载数据上传至云端，其中，所述云端用于根据所述目标车载数据进行车辆驾驶风险评估。
结合第一方面，在第一方面的第一种可能实现方式中，所述本地数据队列包括补传队列和实时队列，检测到车载设备端与云端之间的数据传输通道发生网络故障之后，还包括：实时监听所述数据传输通道的当前网络状态；若所述数据传输通道的当前网络状态已恢复至网络连接状态，则将所述车载设备端实时产生的车载数据缓存至实时队列，所述实时队列中缓存的车载数据通过所述数据传输通道实时上传至所述云端；若所述数据传输通道的当前网络状态处于网络故障状态，则将所述车载设备端实时产生的车载数据缓存至补传队列，所述补传队列中缓存的车载数据通过数据补传通道补传至所述云端。
结合第一方面的第一种可能实现方式，在第一方面的第二种可能实现方式中，所述补传队列中缓存的车载数据通过数据补传通道补传至所述云端时，包括：获取所述云端当前运行状态下的处理器内存使用信息；将所述处理器内存使用信息与预设的补传条件进行比对，判断所述云端当前运行状态下是否满足补传条件；若满足，则创建所述车载设备端与所述云端之间的数据补传通道，通过所述数据补传通道将所述补传队列中缓存的车载数据补传至所述云端。
结合第一方面，在第一方面的第三种可能实现方式中，还包括：接收所述云端反馈的第一云端已处理数据记录；根据所述云端已处理数据处理记录识别出所述本地数据队列中缓存的已处理车载数据，将所述已处理车载数据从所述本地数据队列中删除。
本申请实施例的第二方面提供了一种数据处理方法，其特征在于，所述数据处理方法应用在云端，所述方法包括：接收数据传输通道实时上传的目标车载数据，所述目标车载数据具有对应的队列排序信息；获取所述云端实时记录的第二云端已处理数据记录；将所述第二云端已处理数据记录与所述目标车载数据对应的队列排序信息进行比对，判断所述目标车载数据是否为所述云端已处理数据；若所述目标车载数据为所述云端已处理数据，则将所述目标车载数据删除，若所述目标车载数据为所述云端未处理数据，则根据所述目标车载数据进行车辆驾驶风险评估并获得所述车辆基于所述目标车载数据生成的驾驶风险评估结果。
结合第二方面，在第二方面的第一种可能实现方式中，根据所述目标车载数据进行车辆驾驶风险评估并获得车辆基于所述目标车载数据生成的驾驶风险评估结果之后，还包括：若所述驾驶风险评估结果显示为高风险等级，则向所述车载设备端实时发送携带有所述驾驶风险评估结果的告警信息。
结合第二方面，在第二方面的第二种可能实现方式中，还包括：接收数据补传通道补传的第一车载数据；根据所述云端的当前运行状态下的处理器内存使用信息配置单次批处理数量阈值；按照所述单次批处理数量阈值对所述第一车载数据进行批处理，获得车辆基于所述第一车载数据生成的驾驶风险评估结果。
结合第二方面的第二种可能实现方式，在第二方面的第三种可能实现方式中，获得车辆基于所述第一车载数据生成的驾驶风险评估结果之后，还包括：将所述车辆基于所述第一车载数据生成的驾驶风险评估结果保存在所述云端的风险数据库中。
本申请实施例的第三方面提供了一种车载设备端，其特征在于，包括存储器、处理器、通讯单元以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其中：所述通讯单元用于向云端收发数据或指令；所述处理器用于执行如第一方面任一项所述的数据上传方法。
本申请实施例的第四方面提供了一种云端，包括存储器、处理器、通讯单元以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述云端包括通讯单元和处理单元，其中：所述通讯单元用于向车载设备端收发数据或指令；所述处理器用于执行如第二方面任一项所述的数据处理方法。
本申请实施例的第五方面提供了数据处理***，所述数据处理***包括车载设备端和云端，其中：所述车载设备端用于执行如第一方面任一项所述的数据上传方法；所述云端用于执行如第二方面任一项所述的数据处理方法。
本申请实施例的第六方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如第一方面或第二方面任一项所述方法的步骤。
 有益效果
本申请实施例与现有技术相比存在的有益效果是：车载设备端在检测到其与云端之间的数据传输通道发生网络故障并已恢复网络连接时，通过获取本地记载的第一云端已处理数据记录，根据第一云端已处理数据记录遍历本地数据队列中缓存的车载数据来确定数据传输通道恢复数据传输时的数据传输起点，进而从数据传输起点开始，在本地数据队列中按照排序获取目标车载数据并将目标车载数据上传至云端，可以在云端实现对车辆进行驾驶风险评估的过程中避免由于网络异常而导致的数据丢失和延迟问题，保证了云端实现车辆驾驶风险评估的实时性，提高了云端实现车辆驾驶风险评估的可靠性。
本申请实施例提供的数据处理方法中，云端可以根据其实时记录的第二云端已处理数据记录删除车载设备端重复上传的目标车载数据，避免数据重复处理，可以在自身当前运行状态满足补传条件的情况下批处理车载设备端补传的车载数据，可以在云端实现对车辆进行驾驶风险评估的过程中避免由于网络异常而导致的消息阻问题，保证了云端实现车辆驾驶风险评估的实时性，提高了云端实现车辆驾驶风险评估的可靠性。
 附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的一种数据上传方法的基本方法流程图；
图2为本申请实施例提供的数据上传方法中车载设备端在数据传输通道发生网络故障之后进行数据缓存的一种方法流程图；
图3为本申请实施例提供的数据上传方法中车载设备端通过数据补传通道补传车载数据时的一种方法流程图；
图4为本申请实施例提供的数据上传方法中车载设备端进行缓存清理时的一种方法流程图；
图5为本申请实施例提供的一种数据处理方法的基本方法流程图；
图6为本申请实施例提供的数据处理方法中云端处理补传的车载数据时的一种方法流程图；
图7为本申请实施例提供的一种数据上传装置的结构示意图；
图8为本申请实施例提供的一种数据处理装置的结构示意图；
图9是本申请一实施例提供的电子设备的示意图。
 本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定***结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的***、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为了说明本申请所述的技术方案，下面通过具体实施例来进行说明。
本申请实施例提供了一种数据处理***，基于该数据处理***可以实现在进行车辆驾驶风险评估。在本实施例中，该数据处理***中包含有车载设备端和云端。其中，车载设备端与云端之间建立有数据传输通道，车载设备端通过数据传输通道将车辆实时产生的车载数据上传给云端，云端通过数据传输通道接收车载设备端上传的车载数据，并根据车载数据进行车辆驾驶风险评估。
本申请的一些实施例中，请参阅图1，图1为本申请实施例提供的一种数据上传方法的基本方法流程图。数据上传方法应用在车载设备端中，详述如下：
S11：检测到车载设备端与云端之间的数据传输通道发生网络故障并已恢复网络连接时，获取本地记载的第一云端已处理数据记录。
本实施例中，车载设备端本地记载的第一云端已处理数据记录是云端反馈至车载设备端的云端运行状态信息，可以由云端主动发送给车载设备端。具体地，在云端实现对车辆进行驾驶风险评估的过程中，云端在接收到车载设备端实时发来的车载数据后，会实时处理该车载数据，以实现对车辆进行实时的驾驶风险评估。云端在实时处理车载数据的过程中，会记录当前已处理的车载数据数量，每处理完一定数量的车载数据后，会主动向车载设备端反馈一次云端已处理数据记录，车载设备端在接收到云端反馈的云端已处理数据记录后会将该云端已处理数据记录作为第一云端已处理数据记录记载在本地。示例性的，在实现云端主动向车载设备端反馈云端已处理数据记录的功能时，具体可以在云端设置反馈阈值，假设反馈阈值为50条车载数据，此时，云端会在每处理完50条车载数据后主动向车载设备端反馈一次云端已处理数据记录，即当云端处理完第一个50条车载数据时，向车载设备端反馈的云端已处理数据记录为已处理完前50条车载数据，当云端处理完第二个50条车载数据时，向车载设备端反馈的云端已处理数据记录为已处理完前100条车载数据，以此类推。车载设备端在接收到云端反馈的云端已处理数据记录，会将该云端已处理数据记录记载在本地。
本实施例中，在云端实现对车辆进行驾驶风险评估的过程中，需要在车载设备端与云端之间通过网络连接的方式建立数据传输通道，使得车载设备端可以通过数据传输通道实时地将车辆的车载数据上传至云端，然后由云端根据实时上传的车载数据进行车辆驾驶风险评估。在本实施例中，当车载设备端检测到其与云端之间的数据传输通道发生网络故障并已恢复网络连接时，可以通过获取车载设备端本地记载的第一云端已处理数据记录来判断云端当前的运行状态，从而确定车载设备端恢复数据传输时所对应的起始数据位置。以此，可以避免因网络故障而导致数据丢失的问题。
S12：根据所述第一云端已处理数据记录遍历本地数据队列中缓存的车载数据，根据遍历结果确定所述数据传输通道恢复数据传输时的数据传输起点。
本实施例中，由于车载设备端是随着车辆行驶过程实时产生车载数据的，不受网络影响，即在车载设备端与云端之间的数据传输通道发生网络故障的情况下，车载设备端依然会产生车载数据。因此在本实施例中，车载设备端实时产生的车载数据会先缓存到本地数据队列中，然后按照本地数据队列中的数据排序将车载数据依次上传至云端进行处理。在车载设备端，当检测到数据传输通道发生网络故障并已恢复网络连接时，可以通过获取车载设备端本地记载的第一云端已处理数据记录来确定数据传输通道恢复数据传输时的数据传输起点。具体地，可以从本地记载的第一云端已处理数据记录中找出车载设备端最近一次接收到的云端反馈的第一云端已处理数据记录，从该最近一次接收到的云端反馈的第一云端已处理数据记录中获得车载设备端可确定的云端已完成处理的数据位置，进而根据该车载设备端可确定的云端已完成处理的数据位置遍历本地数据队列中缓存的车载数据，在本地数据队列中查找出车载设备端可确定的云端已完成处理的数据位置所对应的车载数据，将该车载数据所在位置确定为数据传输通道恢复数据传输时的数据传输起点。
S13：从所述数据传输起点开始，在所述本地数据队列中按照排序获取目标车载数据，并将所述目标车载数据上传至云端，其中，所述云端用于根据所述目标车载数据进行车辆驾驶风险评估。
本实施例中，本地数据队列在缓存车载数据时，按照车载数据产生的时间先后排序，在车载设备端恢复数据传输时，可以从数据传输起点开始，在本地数据队列中按照排序，获取排在该数据传输起点之后的车载数据作为目标车载数据，将目标车载数据上传至云端。
以上可以看出，本申请实施例提供的数据上传方法可以在车载设备端检测到其与云端之间的数据传输通道发生网络故障并已恢复网络连接时，通过获取本地记载的第一云端已处理数据记录，根据第一云端已处理数据记录遍历本地数据队列中缓存的车载数据来确定数据传输通道恢复数据传输时的数据传输起点，进而从数据传输起点开始，在本地数据队列中按照排序获取目标车载数据并将目标车载数据上传至云端，可以在云端实现对车辆进行驾驶风险评估的过程中避免由于网络异常而导致的数据丢失和延迟问题，保证了云端实现车辆驾驶风险评估的实时性，提高了云端实现车辆驾驶风险评估的可靠性。
本申请的一些实施例中，请参阅图2，图2为本申请实施例提供的数据上传方法中车载设备端在数据传输通道发生网络故障之后进行数据缓存的一种方法流程图。详细如下：
S21：实时监听所述数据传输通道的当前网络状态；
S22：若所述数据传输通道的当前网络状态已恢复至网络连接状态，则将所述车载设备端实时产生的车载数据缓存至实时队列，所述实时队列中缓存的车载数据通过所述数据传输通道实时上传至所述云端；
S23：若所述数据传输通道的当前网络状态处于网络故障状态，则将所述车载设备端实时产生的车载数据缓存至补传队列，所述补传队列中缓存的车载数据通过数据补传通道补传至所述云端。
本实施例中，为避免在云端实现对车辆进行驾驶风险评估的过程中由于网络异常而容易致使导致消息阻塞问题，可以在车载设备端创建两个本地数据队列。其中，一个为实时队列，另一个为补传队列。缓存在实时队列中的车载数据通过数据传输通道实时上传至云端，缓存在补传队列中的车载数据则通过补传通道补传至云端。
在本实施例中，在创建补传队列后，可以通过实时监听数据传输通道的当前网络状态，在数据传输通道的当前网络状态处于网络故障状态时，将车载设备端实时产生的车载数据缓存至补传队列中，在数据传输通道的当前网络状态已恢复至网络连接状态时，将所述车载设备端实时产生的车载数据缓存至实时队列中。以此，可以在数据传输通道恢复数据传输时，实现通过数据传输通道将实时队列中缓存的车载数据实时上传至云端。而针对于补传队列中缓存的车载数据，则通过设定补传规则的方式，按照补传规则来创建数据补传通道，通过数据补传通道将补传队列中缓存的车载数据补传至云端。在本实施例中，补传规则可以设定为根据云端实时的数据处理能力来判断是否创建数据补传通道，若云端优先处理数据传输通道实时上传的车载数据的情况下仍具备富足的数据处理能力，则创建数据补传通道，通过数据补传通道将补传队列缓存的车载数据补传至云端，实现云端可以对网络故障时车载设备端产生的车载数据进行延迟传输和延迟处理，从而达到优先处理车载设备端实时产生的车载数据的目的，提高了云端对车辆进行驾驶风险评估的实时性和可靠性。
本申请的一些实施例中，补传队列可以在车载设备端检测到其与云端之间的数据传输通道发生网络故障时创建，而车载设备端初始创建的本地数据队列配置为实时队列。在创建补传队列后，可以先根据云端已处理数据处理记录，在实时队列中查找出车载设备端可确定的云端已完成处理的数据位置，并将当前时刻该数据位置之后缓存的车载数据转移至补传队列中缓存。以此实现将该数据位置之后的车载数据通过补传通道补传至云端进行数据评估处理，可以避免车载设备端设置有两个数据队列的情况下云端实现对车辆进行驾驶风险评估的过程中可能存在因网络故障而丢失数据的问题。
本申请的一些实施例中，请参阅图3，图3为本申请实施例提供的数据上传方法中车载设备端通过数据补传通道补传车载数据时的一种方法流程图。详细如下：
S31：获取所述云端当前运行状态下的处理器内存使用信息；
S32：将所述处理器内存使用信息与预设的补传条件进行比对，判断所述云端当前运行状态下是否满足补传条件；
S33：若满足，则创建所述车载设备端与所述云端之间的数据补传通道，通过所述数据补传通道将所述补传队列中缓存的车载数据补传至所述云端。
本实施例中，云端的数据处理能力可以通过云端当前运行状态下的处理器内存使用信息来衡量。在本实施例中，可以在车载设备端预先设置补传条件，补传条件可以为处理器内存使用率阈值。由车载设备端向云端发送补传请求的方式，从云端获取得到云端当前运行状态下的处理器内存使用信息，云端当前运行状态下的处理器内存使用信息可以表示为云端当前运行状态下的处理器内存使用率。通过将云端当前运行状态下的处理器内存使用信息与预设的补传条件进行比对，比对云端当前运行状态下的处理器内存使用率是否小于处理器内存使用率阈值，其中，当云端当前运行状态下的处理器内存使用率小于处理器内存使用率阈值时，判断云端当前运行状态下满足补传条件，否则，判断云端当前运行状态下不满足补传条件。在云端当前运行状态下满足补传条件时，创建车载设备端与云端之间的数据补传通道，然后通过该数据补传通道将补传队列中缓存的车载数据补传至云端。本实施例可以根据云端实时的数据处理能力来对网络故障时车载设备端产生的车载数据进行延迟传输和延迟处理，从而达到优先处理车载设备端实时产生的车载数据的目的，提高了云端对车辆进行驾驶风险评估的实时性和可靠性。
本申请的一些实施例中，请参阅图4，图4为本申请实施例提供的数据上传方法中车载设备端进行缓存清理时的一种方法流程图。详细如下：
S41：接收所述云端反馈的第一云端已处理数据记录；
S42：根据所述云端已处理数据处理记录识别出所述本地数据队列中缓存的已处理车载数据，将所述已处理车载数据从所述本地数据队列中删除。
本实施例中，云端在实时处理车载数据的过程中，会记录当前已处理的车载数据数量，每处理完一定数量的车载数据后，会主动向车载设备端反馈一次云端已处理数据记录。当车载设备端接收到云端反馈的云端已处理数据记录后，还可以根据该云端已处理数据记录对车载设备端的本地数据队列进行缓存清理，从而释放车载设备端的数据内存。示例性的，在车载设备端接收到云端反馈的云端已处理数据记录后，车载设备端可以根据该云端已处理数据记录遍历本地数据队列中缓存的车载数据，从而识别出本地数据队列中缓存的已处理车载数据，例如，向车载设备端反馈的云端已处理数据记录为已处理完前50条车载数据，则识别本地数据队列中缓存的每条车载数据的排序序号，将排序序号小于50的车载数据识别为本地数据队列中缓存的已处理车载数据。在识别出本地数据队列中缓存的已处理车载数据后，将该已处理车载数据从本地数据队列中删除，从而实现缓存清理、释放内存的目的。
本申请的一些实施例中，请参阅图5，图5为本申请实施例提供的一种数据处理方法的基本方法流程图。该数据处理方法应用在云端中，详细如下：
S51：接收数据传输通道实时上传的目标车载数据，所述目标车载数据具有对应的队列排序信息；
S52：获取所述云端实时记录的第二云端已处理数据记录；
S53：将所述第二云端已处理数据记录与所述目标车载数据对应的队列排序信息进行比对，判断所述目标车载数据是否为所述云端已处理数据；
S54：若所述目标车载数据为所述云端已处理数据，则将所述目标车载数据删除，若所述目标车载数据为所述云端未处理数据，则根据所述目标车载数据进行车辆驾驶风险评估并获得车辆基于所述目标车载数据生成的驾驶风险评估结果。
本实施例中，云端优先处理数据传输通道实时传来的车载数据，在保证具有处理实时传来的车载数据的能力下，若仍具备富足的数据处理能力，则根据富足的数据处理能力处理数据补传通道补传来的车载数据。在本实施例中，云端进行数据处理时，通过数据传输通道接收实时队列实时上传的目标车载数据。其中，目标车载数据具有对应的队列排序信息。由于车载设备端接收到的第一云端已处理数据记录与云端实时记录的第二云端已处理数据记录存在时间差，因此，云端接收到目标车载数据后，获取其实时记录的第二云端已处理数据处理记录，通过将第二云端已处理数据处理记录与目标车载数据所对应的队列排序信息进行比对，以此判断目标车载数据是否为云端已处理数据。若目标车载数据为已处理数据，则将目标车载数据删除，若目标车载数据为未处理数据，则根据目标车载数据进行车辆驾驶风险评估并获得车辆基于目标车载数据生成的驾驶风险评估结果。示例性的，假设车载设备端与云端之间的数据传输通道在传输地58条车载数据至云端时发生网络故障，那么此时，车载设备接收到的第一云端已处理数据记录为已处理完前50条车载数据，数据传输通道恢复数据传输时会从第51条车载数据开始传输，而云端则已经处理到第57条车载数据。在此种情况下，可以通过将第二云端已处理数据处理记录与目标车载数据在所述本地数据队列中的排序信息进行比对，以此判断出目标车载数据中第51至第57条车载数据为云端已处理数据。此时，云端可以将目标车载数据中第51至第57条车载数据删除，第58条车载数据开始进行车辆驾驶风险评估，并获取车辆基于车载数据生成的驾驶风险评估结果。
本申请的一些实施例中，驾驶风险评估结果可以表示为风险等级，例如高风险等级和低风险等级。云端获取车辆基于目标车载数据生成的驾驶风险评估结果后，还可以根据驾驶风险评估结果所显示的风险等级，在驾驶风险评估结果显示为高风险等级时，向车载设备端实时发送携带有该驾驶风险评估结果的告警信息。
本申请的一些实施例中，请参阅图6，图6为本申请实施例提供的数据处理方法中云端处理补传的车载数据时的一种方法流程图。详细如下：
S61：接收数据补传通道补传的第一车载数据；
S62：根据所述云端的当前运行状态下的处理器内存使用信息配置单次批处理数量阈值；
S63：按照所述单次批处理数量阈值对所述第一车载数据进行批处理，获得车辆基于所述第一车载数据生成的驾驶风险评估结果。
本实施例中，云端通过数据补传通道接收到的第一车载数据为已过时的历史数据，针对这些补传的第一车载数据，云端可以采用批处理的方式进行车辆驾驶风险评估。具体地，在云端优先处理数据传输通道实时上传的车载数据的情况下仍具备富足的数据处理能力时，可以根据云端的当前运行状态下的处理器内存使用信息配置单次批处理数量阈值。该单次批处理数量阈值可以由云端根据其自身当前运行状态下的处理器内存使用率计算获得。云端获得单次批处理数量阈值后，按照单次批处理数量阈值将数据补传通道补传来的第一车载数据划分成多个批次，按批次对该第一车载数据进行批处理，从而获取车辆基于各批次的第一车载数据生成的驾驶风险评估结果。
本申请的一些实施例中，云端针对于数据补传通道补传来的第一车载数据，在获得车辆基于该第一车载数据生成的驾驶风险评估结果后，可以将该驾驶风险评估结果保存在云端的风险数据库中。在本实施例中，由于第一车载数据为已过时的历史数据，在生成对应的驾驶风险评估结果后，无论风险等级是否为高风险等级，均不触发告警机制。
可以理解的是，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
本申请的一些实施例中，请参阅图7，图7为本申请实施例提供的一种数据上传装置的结构示意图，应用在车载设备端中，详述如下：
数据上传装置包括：第一获取模块71、确定模块72以及上传模块73。其中，所述第一获取模块71用于检测到车载设备端与云端之间的数据传输通道发生网络故障并已恢复网络连接时，第一获取本地记载的第一云端已处理数据记录。所述确定模块72用于根据所述第一云端已处理数据记录遍历本地数据队列中缓存的车载数据，根据遍历结果确定所述数据传输通道恢复数据传输时的数据传输起点。所述上传模块73用于从所述数据传输起点开始，在所述本地数据队列中按照排序获取目标车载数据，并将所述目标车载数据上传至云端，其中，所述云端用于根据所述目标车载数据进行车辆驾驶风险评估。
所述数据上传装置，与上述的数据上传方法一一对应，此处不再赘述。
本申请的一些实施例中，请参阅图8，图8为本申请实施例提供的一种数据处理装置的结构示意图，该数据处理装置应用在云端中，详述如下：
数据处理装置包括：接收模块81、第二获取模块82、比对模块83以及处理模块84。其中，所述接收模块81用于接收数据传输通道实时上传的目标车载数据，所述目标车载数据具有对应的队列排序信息。所述第二获取模块82用于获取所述云端实时记录的第二云端已处理数据记录。所述比对模块83用于将所述第二云端已处理数据记录与所述目标车载数据对应的队列排序信息进行比对，判断所述目标车载数据是否为所述云端已处理数据。所述处理模块84用于若所述目标车载数据为所述云端已处理数据，则将所述目标车载数据删除，若所述目标车载数据为所述云端未处理数据，则根据所述目标车载数据进行车辆驾驶风险评估并获得车辆基于所述目标车载数据生成的驾驶风险评估结果。
所述数据处理装置，与上述的数据处理方法一一对应，此处不再赘述。
本申请的一些实施例中，请参阅图9，图9是本申请一实施例提供的电子设备的示意图，该电子设备可以为车载设备端，也可以为云端，通讯单元用于收发数据和信令。如图9所示，该实施例的电子设备9包括：处理器91、存储器92以及存储在所述存储器92中并可在所述处理器91上运行的计算机程序93，例如数据上传程序或数据处理程序。所述处理器91执行所述计算机程序92时实现上述各个数据上传方法实施例或数据处理方法中的步骤。或者，所述处理器91执行所述计算机程序93时实现上述各装置实施例中各模块/单元的功能。
示例性的，所述计算机程序93可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器92中，并由所述处理器91执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序93在所述电子设备9中的执行过程。
所述电子设备可包括，但不仅限于，处理器91、存储器92。本领域技术人员可以理解，图9仅仅是电子设备9的示例，并不构成对电子设备9的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述电子设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器91可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器 (Digital Signal Processor，DSP)、专用集成电路 (Application Specific Integrated Circuit，ASIC)、现成可编程门阵列 (Field-Programmable Gate Array，FPGA) 或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器92可以是所述电子设备9的内部存储单元，例如电子设备9的硬盘或内存。所述存储器92也可以是所述电子设备9的外部存储设备，例如所述电子设备9上配备的插接式硬盘，智能存储卡（Smart Media Card, SMC），安全数字（Secure Digital, SD）卡，闪存卡（Flash Card）等。进一步地，所述存储器92还可以既包括所述电子设备9的内部存储单元也包括外部存储设备。所述存储器92用于存储所述计算机程序以及所述电子设备所需的其他程序和数据。所述存储器92还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述***中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器（ROM，Read-Only Memory）、随机存取存储器（RAM，Random Access Memory）、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (12)

  1. 一种数据上传方法，其特征在于，所述数据上传方法应用在车载设备端，包括：
    检测到车载设备端与云端之间的数据传输通道发生网络故障并已恢复网络连接时，获取本地记载的第一云端已处理数据记录；
    根据所述第一云端已处理数据记录遍历本地数据队列中缓存的车载数据，根据遍历结果确定所述数据传输通道恢复数据传输时的数据传输起点；
    从所述数据传输起点开始，在所述本地数据队列中按照排序获取目标车载数据，并将所述目标车载数据上传至云端，其中，所述云端用于根据所述目标车载数据进行车辆驾驶风险评估。
  2. 根据权利要求1所述的数据上传方法，其特征在于，所述本地数据队列包括补传队列和实时队列，检测到车载设备端与云端之间的数据传输通道发生网络故障之后，还包括：
    实时监听所述数据传输通道的当前网络状态；
    若所述数据传输通道的当前网络状态已恢复至网络连接状态，则将所述车载设备端实时产生的车载数据缓存至实时队列，所述实时队列中缓存的车载数据通过所述数据传输通道实时上传至所述云端；
    若所述数据传输通道的当前网络状态处于网络故障状态，则将所述车载设备端实时产生的车载数据缓存至补传队列，所述补传队列中缓存的车载数据通过数据补传通道补传至所述云端。
  3. 根据权利要求2所述的数据上传方法，其特征在于，所述补传队列中缓存的车载数据通过数据补传通道补传至所述云端时，包括：
    获取所述云端当前运行状态下的处理器内存使用信息；
    将所述处理器内存使用信息与预设的补传条件进行比对，判断所述云端当前运行状态下是否满足补传条件；
    若满足，则创建所述车载设备端与所述云端之间的数据补传通道，通过所述数据补传通道将所述补传队列中缓存的车载数据补传至所述云端。
  4. 根据权利要求1所述的数据上传方法，其特征在于，还包括：
    接收所述云端反馈的第一云端已处理数据记录；
    根据所述云端已处理数据处理记录识别出所述本地数据队列中缓存的已处理车载数据，将所述已处理车载数据从所述本地数据队列中删除。
  5. 一种数据处理方法，其特征在于，所述数据处理方法应用在云端，所述方法包括：
    接收数据传输通道实时上传的目标车载数据，所述目标车载数据具有对应的队列排序信息；
    获取所述云端实时记录的第二云端已处理数据记录；
    将所述第二云端已处理数据记录与所述目标车载数据对应的队列排序信息进行比对，判断所述目标车载数据是否为所述云端已处理数据；
    若所述目标车载数据为所述云端已处理数据，则将所述目标车载数据删除，若所述目标车载数据为所述云端未处理数据，则根据所述目标车载数据进行车辆驾驶风险评估并获得车辆基于所述目标车载数据生成的驾驶风险评估结果。
  6. 根据权利要求5所述的数据处理方法，其特征在于，根据所述目标车载数据进行车辆驾驶风险评估并获得车辆基于所述目标车载数据生成的驾驶风险评估结果之后，还包括：
    若所述驾驶风险评估结果显示为高风险等级，则向所述车载设备端实时发送携带有所述驾驶风险评估结果的告警信息。
  7. 根据权利要求5所述的数据处理方法，其特征在于，还包括：
    接收数据补传通道补传的第一车载数据；
    根据所述云端的当前运行状态下的处理器内存使用信息配置单次批处理数量阈值；
    按照所述单次批处理数量阈值对所述第一车载数据进行批处理，获得车辆基于所述第一车载数据生成的驾驶风险评估结果。
  8. 根据权利要求7所述的数据处理方法，其特征在于，获得车辆基于所述第一车载数据生成的驾驶风险评估结果之后，还包括：
    将所述车辆基于所述第一车载数据生成的驾驶风险评估结果保存在所述云端的风险数据库中。
  9. 一种车载设备端，其特征在于，包括存储器、处理器、通讯单元以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其中：
    所述通讯单元用于向云端收发数据或指令；
    所述处理器用于执行如权利要求1-4任一项所述的数据上传方法。
  10. 一种云端，其特征在于，包括存储器、处理器、通讯单元以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述云端包括通讯单元和处理单元，其中：
    所述通讯单元用于向车载设备端收发数据或指令；
    所述处理器用于执行如权利要求5-8任一项所述的数据处理方法。
  11. 一种数据处理***，其特征在于，所述数据处理***包括车载设备端和云端，其中：
    所述车载设备端用于执行如权利要求1-4任一项所述的数据上传方法；
    所述云端用于执行如权利要求5-8任一项所述的数据处理方法。
  12. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1-4，或5-8任一项所述方法的步骤。
PCT/CN2022/128381 2022-10-28 2022-10-28 数据处理***及其数据上传方法和数据处理方法 WO2024087206A1 (zh)

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