CN117201404A - Vehicle data uploading method, device and storage medium - Google Patents
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Abstract
The application relates to a vehicle data uploading method, device and storage medium, wherein the method comprises the following steps: determining the uploading frequency of data, and transmitting the uploading frequency to a vehicle end; receiving data sent by the vehicle end according to the uploading frequency; acquiring the number of vehicle ends of current uploading data and a preset number threshold, updating the frequency of uploading data by the vehicle ends to obtain updated uploading frequency in response to the number of the vehicle ends of uploading data exceeding the number threshold, and transmitting the updated uploading frequency to the vehicle ends; and receiving data sent by the vehicle side according to the updated uploading frequency. The application can coordinate the uploading of vehicle data and the reasonable allocation of data processing resources, and reduce the pressure of a server.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a method, an apparatus, and a storage medium for uploading vehicle data.
Background
At present, along with the rapid development of the internet of vehicles, the current automobiles are more and more intelligent, most of the automobiles start to be connected into the internet of vehicles, and the purposes of intelligent monitoring and intelligent analysis are achieved through communication with the cloud in the running process of the vehicles. As more and more vehicles access the internet, more and more data are generated by the internet of vehicles, the cloud concurrency is greater, and the access and processing pressure of the cloud is greater. If 20 ten thousand vehicles run on the road at the same time, vehicle state and diagnosis data are reported every 10 seconds in the running process, and the size of a single data packet is about 2K-3K, 20 ten thousand requests are received and processed every 10 seconds for the cloud end, and about 500M of data are received and processed. If 200 ten thousand vehicles are on-line at the same time, the cloud end receives and processes 200 ten thousand requests and responses every 10 seconds, and receives and processes about 5G of data.
Because the internet of vehicles data has a certain periodicity such as early peak, afternoon peak and late peak, the data traffic is concentrated, and the data traffic is scattered and smaller in the rest time. If the number of servers, the memory of the servers and the configuration are simply increased, the rest of the time is wasted for server resources, although the data processing and analysis for peak periods is effective. At the same time, the normal vehicle state report and the normal diagnosis data analysis and storage have low real-time requirements, users often do not pay attention to the data in the driving process, and users generally pay attention to the recording and analysis of the whole journey after the journey is finished.
Therefore, how to coordinate the uploading of vehicle data and the reasonable allocation of data processing resources, and reduce the pressure of the server are technical problems to be solved.
Disclosure of Invention
Based on the above, the application provides a vehicle data uploading method, device and storage medium, so as to solve the problems in the prior art.
In a first aspect, there is provided a vehicle data uploading method, the method comprising:
determining the uploading frequency of data, and transmitting the uploading frequency to a vehicle end;
Receiving data sent by the vehicle end according to the uploading frequency;
acquiring the number of vehicle ends of current uploading data and a preset number threshold, updating the frequency of uploading data by the vehicle ends to obtain updated uploading frequency in response to the number of the vehicle ends of uploading data exceeding the number threshold, and transmitting the updated uploading frequency to the vehicle ends;
and receiving data sent by the vehicle side according to the updated uploading frequency.
According to an implementation manner of the embodiment of the present application, the receiving the data sent by the vehicle end according to the upload frequency includes:
the message middleware receives data sent by the vehicle end, wherein the data sent by the vehicle end carries identification information of the vehicle end;
determining a data partition corresponding to the identification information of the vehicle end, wherein the message middleware is divided into a plurality of partitions for storing data in advance;
storing the data sent by the vehicle end into a data partition corresponding to the identification information of the vehicle end, and storing the identification information of the vehicle end and the corresponding data partition into a redis cache;
and acquiring the data sent by the vehicle end from the data partition of the message middleware.
According to an implementation manner of the embodiment of the present application, the acquiring the data sent by the vehicle end from the data partition of the message middleware includes:
determining a total data amount of data acquired from the message middleware;
determining a first data volume obtained from each data partition on average according to the number of the data partitions in the message middleware and the total data volume;
and simultaneously acquiring the data of the first data volume from each data partition according to the predetermined data acquisition frequency.
According to an implementation manner of the embodiment of the present application, the acquiring the data sent by the vehicle end from the data partition of the message middleware includes:
receiving vehicle operation information sent by a vehicle end, wherein the vehicle operation ending information carries identification information of the corresponding vehicle end;
responding to the vehicle running information to represent that the vehicle stops running, acquiring corresponding identification information of a vehicle end, and acquiring a target data partition corresponding to the identification information of the vehicle end from the redis cache;
and acquiring all data corresponding to the identification information of the vehicle end from the target data partition.
According to an implementation manner of the embodiment of the present application, the obtaining, from the target data partition, all data corresponding to the identification information of the vehicle end includes:
determining the frequency of acquiring data from the message middleware and the quantity of the acquired data;
and acquiring data corresponding to the identification information of the vehicle end in the target data partition based on the frequency of acquiring the data from the message middleware and the quantity of acquiring the data until all the data corresponding to the identification information of the vehicle end in the target data partition are acquired.
According to an implementation manner of the embodiment of the present application, the receiving the data sent by the vehicle side according to the updated upload frequency includes:
receiving target data obtained by combining the offset data according to the full data of the first moment by the vehicle end;
the method comprises the steps that the starting time of a specified time period corresponding to the updated uploading frequency is a first time, the end time of the specified time period is a second time, data generated by a vehicle at the first time is full-quantity data of the first time, and data generated by the vehicle at the second time is full-quantity data of the second time; the offset data characterizes a data offset between the full data at the first time and the full data at the second time.
In a second aspect, there is provided a vehicle data uploading method, the method comprising:
receiving uploading frequency of data sent by a cloud;
transmitting data to the cloud according to the uploading frequency;
receiving updated uploading frequency of data sent by a cloud;
and sending data to the cloud according to the updated uploading frequency.
According to an implementation manner of the embodiment of the present application, the sending data to the cloud according to the updated upload frequency includes:
determining a designated time period corresponding to the updated uploading frequency according to the updated uploading frequency;
determining the starting time of the appointed time period as a first time, the ending time of the appointed time period as a second time, wherein the data generated by the vehicle at the first time is the total data of the first time, and the data generated by the vehicle at the second time is the total data of the second time;
obtaining offset data according to the data offset between the full-scale data at the first moment and the full-scale data at the second moment;
combining the offset data according to the full data at the first moment to obtain target data;
and sending the target data to a cloud.
In a third aspect, there is provided a computer device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores computer instructions executable by the at least one processor to enable the at least one processor to perform the method as referred to in the first or second aspect above.
In a fourth aspect, there is provided a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as referred to in the first or second aspect above.
According to the technical content provided by the embodiment of the application, the uploading frequency of the data is determined, the uploading frequency is sent to the vehicle end, the data sent by the vehicle end according to the uploading frequency is received, the number of the vehicle ends uploading the data currently and a preset number threshold value are obtained, the frequency of the vehicle end uploading the data is updated if the number of the vehicle ends uploading the data exceeds the number threshold value, the updated uploading frequency is obtained, the updated uploading frequency is sent to the vehicle end, and finally the data sent by the vehicle end according to the updated uploading frequency is received. The application can dynamically adjust the frequency of reporting the vehicle data according to the number of vehicles accessed by the current cloud in real time, thereby coordinating the uploading of the vehicle data, reasonably distributing data processing resources and reducing the pressure of a server
Drawings
FIG. 1 is a flow chart of a method of uploading vehicle data in one embodiment;
FIG. 2 is one of the flow charts of vehicle data upload in one embodiment;
FIG. 3 is a second flow chart of vehicle data upload in one embodiment;
FIG. 4 is a third flow chart of vehicle data upload in one embodiment;
FIG. 5 is a fourth flow chart of vehicle data upload in one embodiment;
FIG. 6 is a flow chart of a method of uploading vehicle data according to another embodiment;
fig. 7 is a schematic structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Fig. 1 is a flowchart of a vehicle data uploading method according to an embodiment of the present application, including steps 101 to 104. In the vehicle data uploading method in the prior art, as shown in fig. 2, a vehicle end uploads vehicle data to a device access end of a cloud end, and then the device access end forwards the vehicle data to a cloud end big data system, and the cloud end big data system performs data analysis processing. When a user needs to inquire data information such as vehicle driving behaviors, the user acquires data from a cloud big data system through a service system. However, as more and more data is generated by the internet of vehicles, the cloud concurrency is greater, and the access and processing pressure of the cloud is greater.
Fig. 1 is a flowchart of a vehicle data uploading method according to an embodiment of the present application, as shown in fig. 1, the method may include the following steps:
step 101: and determining the uploading frequency of the data, and transmitting the uploading frequency to the vehicle side.
Specifically, in this embodiment, the cloud is used as an execution body, as shown in fig. 3, a coordinator system is set in the cloud to coordinate uploading of vehicle data, and the coordinator system first determines the reporting frequency of the vehicle data and sends the uploading frequency to the vehicle end. For example, the frequency of vehicle data reporting is once every 10 seconds. When the vehicle starts, the vehicle end obtains the reporting frequency of the vehicle data from the equipment access end of the cloud end, the equipment access end accesses the coordinator system to obtain the frequency of the reporting data, the reporting frequency is sent to the vehicle end, the vehicle end sends the data to the cloud end big data system according to the reporting frequency, and the cloud end big data system analyzes and processes the data.
Step 102: and receiving data sent by the vehicle side according to the uploading frequency.
Specifically, as shown in fig. 3, the vehicle end obtains the reporting frequency of the vehicle data from the device access end of the cloud end, the device access end accesses the coordinator system to obtain the frequency of the reporting data, and sends the reporting frequency to the vehicle end, the vehicle end sends the data to the cloud end big data system according to the reporting frequency, and the cloud end receives the data sent by the vehicle end according to the uploading frequency and analyzes and processes the data by the cloud end big data system.
Step 103: the method comprises the steps of obtaining the number of vehicle ends uploading data currently and a preset number threshold, updating the frequency of uploading the data by the vehicle ends in response to the fact that the number of the vehicle ends uploading the data exceeds the number threshold, obtaining updated uploading frequency, and sending the updated uploading frequency to the vehicle ends.
Specifically, the cloud coordinator system presets a quantity threshold value of the quantity of the vehicle ends for uploading data, acquires the quantity of the vehicle ends for uploading data currently and the preset quantity threshold value, updates the frequency of uploading data by the vehicle ends if the quantity of the vehicle ends for uploading data exceeds the quantity threshold value, obtains updated uploading frequency, and sends the updated uploading frequency to the vehicle ends. Wherein the updated uploading frequency is slower than the previous uploading frequency, for example, the original uploading frequency is 10 seconds, 1 time of complete data is collected and reported, and the updating is that the complete data is reported every 20 seconds. And sending the updated uploading frequency to the vehicle end, and continuously uploading data by the vehicle end more than the updated uploading frequency.
And if the vehicle access quantity exceeds a certain threshold, dynamically adjusting the data reporting frequency according to the number of the vehicles accessed in real time by the current cloud. The cloud coordinator system records and formulates a vehicle-end load balancing strategy, including reporting frequency and reporting time of vehicle-end data. For example, the load balancing policy includes 4 frequencies: reporting once for 10s, reporting once for 20s, reporting once for 30s, and reporting once for 60 s. The system defaults that the reporting frequency is reported once for 10 seconds, the reporting time of the frequency is sequentially and circularly allocated in turn according to the sequence from 0 to 9 of the access vehicles, namely, if 10 vehicles exist, the system is uniformly allocated according to 0 to 9 seconds, the 11 th vehicle continues to allocate the reporting time for 0 seconds, the 12 th vehicle continues to allocate the reporting time for 1 second, and so on; similarly, when the reporting frequency is 20s, the vehicle data reporting time is sequentially and circularly allocated in turn according to the sequence from 0 to 19 of the access vehicle, when the reporting frequency is 30s, the vehicle data reporting time is sequentially and circularly allocated in turn according to the sequence from 0 to 29 of the access vehicle, and when the reporting frequency is 60s, the vehicle data reporting time is sequentially and circularly allocated in turn according to the sequence from 0 to 59 of the access vehicle.
Before all the vehicle ends report data, the vehicle ends firstly interact with the equipment access end, the equipment access end obtains the reporting frequency of the current vehicle from the coordinator, and the time of reporting the data of the current vehicle is calculated according to the access sequence. When the coordinator checks that the on-line vehicle exceeds a certain threshold (such as 10 ten thousand, 20 ten thousand and 50 ten thousand …), the vehicle end load balancing strategy needs to be updated, and the coordinator responds to the equipment access end and responds to the vehicle end according to the on-line vehicle reassignment reporting frequency and time, and the vehicle end updates the reporting frequency and the reporting time in real time.
Step 104: and receiving data sent by the vehicle side according to the updated uploading frequency.
Specifically, if the vehicle access amount exceeds a certain threshold, the data reporting frequency can be dynamically adjusted according to the current cloud real-time access vehicle number, and then the data sent by the vehicle terminal according to the updated uploading frequency is received. For example, the default vehicle end collects and reports 1 complete data every 10 seconds, the adjustment frequency is that the vehicle end reports every 20 seconds, at this time, the vehicle end still keeps collecting and buffering the vehicle data every 10 seconds, and the collected 2 complete data is reported at the same time, so that concurrency for the cloud end is reduced by half. Therefore, concurrent requests to the cloud can be effectively reduced.
It can be seen that, in the embodiment of the application, the uploading frequency is sent to the vehicle end by determining the uploading frequency of the data, the vehicle end receives the data sent by the vehicle end according to the uploading frequency, the number of the vehicle end uploading the data currently and the preset number threshold value are obtained, the frequency of the vehicle end uploading the data is updated if the number of the vehicle end uploading the data exceeds the number threshold value in response to the number of the vehicle end uploading the data, the updated uploading frequency is obtained, the updated uploading frequency is sent to the vehicle end, and finally the vehicle end receives the data sent by the vehicle end according to the updated uploading frequency. According to the method and the system for reporting the vehicle data, the frequency of reporting the vehicle data can be dynamically adjusted according to the number of vehicles accessed by the current cloud in real time, so that uploading of the vehicle data is coordinated, data processing resources are reasonably distributed, and the pressure of a server is reduced.
In one embodiment of the present application, the receiving vehicle side in step 104 sends data according to the updated upload frequency, including: receiving target data obtained by a vehicle end according to the total data and the offset data at the first moment; the starting time of the appointed time period corresponding to the updated uploading frequency is a first time, the end time of the appointed time period is a second time, the data generated by the vehicle at the first time is the full-quantity data of the first time, and the data generated by the vehicle at the second time is the full-quantity data of the second time; the offset data characterizes a data offset between the full data at the first time and the full data at the second time.
Specifically, the updated uploading frequency is, for example, 20s for uploading data once, the corresponding designated time period is 20s, the starting time of the designated time period is recorded as the first time, and the ending time of the designated time period is recorded as the second time. The data generated by the vehicle at the first time is the full-scale data of the first time, namely, the data generated by the vehicle at the starting point in the time period of 20 s. Such as: mileage: 1200, gps: [119,80], temperature 26, …
The data generated by the vehicle at the second time is the full-scale data at the second time, that is, the data generated by the vehicle at the end of the 20s period. Such as: mileage: 1201 gps: [119,80.5], temperature 26, …
The offset data characterizes a data offset between the full data at the first time and the full data at the second time. Such as: the offset data is: mileage +1, gps [ +0, +0.5], temperature: +0 …
The target data obtained by combining the full data with the offset data at the first moment are as follows: mileage: [1200, +1] gps [ [119,80], [ +0, +0.5] ], temperature [ [26, +0],
the following full data are simply exemplified:
full data 1 at first moment { frequency: 10s, mileage: 1200, gps: [119,80], temperature: 26, … };
full data 2 at the second time instant { frequency: 10s, mileage: 1201 gps: [119,80.5], temperature 26, … };
Offset data: mileage +1201, gps+0.5;
after combining the two data into one piece, obtaining target data obtained by combining the total data and the offset data of the vehicle end according to the first moment:
target data { frequency: 20s, mileage: [1200, +1], gps [ [119,80], [ +0, +0.5] ], temperature [ [26, +0], … }
For most of the status report data, the cloud end is not required to analyze and process immediately, the real-time requirement is not high, the user generally cannot pay attention to the driving behavior analysis in the driving process, and the user cannot feel even if a certain delay exists. And if the vehicle access quantity exceeds a certain threshold, dynamically adjusting the data reporting frequency according to the current cloud real-time access vehicle quantity. The default vehicle end collects and reports 1 complete data every 10 seconds, the adjustment frequency is that every 20 seconds is reported, at this time, the vehicle end still keeps collecting the vehicle data every 10 seconds and caches the data, and the collected data of 2 times are reported simultaneously after the collected data of 2 times, so that concurrency to the cloud is reduced by half. Therefore, concurrent requests to the cloud can be effectively reduced. The concurrency can be effectively reduced for reporting from 10 seconds to 30 seconds, but in practice, the total amount of I/O caused by reporting 1 time of collected data every 10 seconds and reporting twice of collected data every 20 seconds to the cloud is unchanged, i.e. the I/O of the cloud is not reduced. Because the data reported by a single vehicle is usually continuous and comprises hardware equipment state, mileage, gps, oil quantity, electric quantity, temperature, speed and the like, if the report of a plurality of complete data can be processed into the report of a single complete data plus a data offset, the size of the reported data packet can be effectively reduced. The data offset is shown with a "+/-value", and the data collected at two intervals is recorded as "+0" without change, so that the size of a single data packet is greatly reduced.
In one embodiment of the present application, the receiving vehicle side in step 104 sends data according to the upload frequency, including: step 1041: the message middleware receives data sent by a vehicle end, wherein the data sent by the vehicle end carries identification information of the vehicle end; step 1042: determining a data partition corresponding to the identification information of the vehicle end, wherein a plurality of partitions for storing data are divided in advance in the message middleware; step 1043: storing the data sent by the vehicle end into a data partition corresponding to the identification information of the vehicle end, and storing the identification information of the vehicle end and the corresponding data partition into a redis cache; step 1044: and acquiring the data sent by the vehicle side from the data partition of the message middleware.
Specifically, as shown in fig. 4, after receiving data, the device access terminal first checks whether the data is compressed, decompresses the compressed data to restore the data into single original complete data, and then delivers the data to the big data system. The concurrency and the I/O reduction are mainly aimed at the transmission of the Internet of vehicles data from the public network to the equipment access end, and the transmission of the data to the intranet is carried out, so that the concurrency and the I/O of the data still keep the original quantity. Because the real-time requirements of data analysis, user driving and vehicle diagnosis analysis are not high, if a buffer is added to the big data system at the equipment access end and the flow of data entering the big data system can be controlled, the concurrency and I/O pressure of the data analysis processing of the big data system can be reduced. Thus, the cloud sets a message middleware (kafka) between the device access terminal and the coordinator system, as shown in fig. 4. The message middleware receives data sent by the vehicle end, and the data sent by the vehicle end carries identification information of the vehicle end.
Next, a data partition corresponding to the identification information of the vehicle side is determined, wherein a plurality of partitions for storing data are divided in advance in the message middleware. kafka includes a specific Topic (Topic) and Partition (Partition) and provides an interface for the device access to obtain the Topic (Topic) and Partition (Partition) to which the vehicle data should be delivered. The coordinator system sets the kafka location of the reported data delivery to 100 Topic [ topic_0, topic_1, …, topic_99 ], and each Topic contains 10 partitions [ partition_0, partition_1, …, partition_9 ]. When the vehicle data is reported, the coordinator puts the vehicle data into a fixed Topic fixed Partition corresponding to the identification information of the vehicle, and the vehicle data is delivered from the partition_0 of the topic_0 to the partition_9 of the topic_99 after one round of delivery.
And storing the data sent by the vehicle end into a data partition corresponding to the identification information of the vehicle end, and storing the identification information of the vehicle end and the corresponding data partition into a redis cache. The redis cache is an open-source log-type Key-Value database which is written and supported by ANSIC language and can be based on memory and can be persistent. Considering the efficiency of the device access terminal to acquire the Topic (Topic) and Partition (Partition) data which a specific vehicle should deliver, the vehicle information and the Topic (Topic) and Partition (Partition) which should be delivered are cached to the Redis. The method comprises the steps that firstly, a device access terminal reads a theme (Topic) and a Partition (Partition) which need to be delivered of vehicle data from a Redis cache, if the theme (Topic) and the Partition (Partition) which are not distributed and should be delivered are not obtained, which indicate that a vehicle which is just on line is not distributed, a coordinator interface is obtained from a coordinator, and the coordinator is responsible for calculating the theme (Topic) and the Partition (Partition) which need to be delivered, responds to the device access terminal and stores the theme (Topic) and the Partition (Partition) into the Redis cache.
And finally, acquiring the data sent by the vehicle end from the data partition of the message middleware.
In one embodiment of the present application, the step 1044 of obtaining data sent by the vehicle end from the data partition of the message middleware includes: determining a total data amount of the acquired data from the message middleware; determining a first data volume obtained from each data partition on average according to the number of the data partitions and the total data volume in the message middleware; and simultaneously acquiring the data of the first data amount from each data partition according to the predetermined data acquisition frequency.
Specifically, the message middleware kafka further comprises a flow control module which bears a flow control function and is responsible for controlling the flow of vehicle data into the big data system, so that the situation that the data processing capacity of the big data system is suddenly increased and resources are not used enough due to the overlarge flow in the peak period is avoided. The module firstly communicates with the big data system, determines the frequency of acquiring data from the message middleware and the quantity of the acquired data, and obtains a reasonable flow threshold value, for example, 5 ten thousand pieces of data can be processed per second currently, and then the coordinator consumes kafka according to the speed of acquiring 5 ten thousand pieces of data per second and forwards the kafka to the big data system. And in the subsequent communication process, the service end flow control module always keeps heartbeat with the big data system, and acquires a proper flow threshold value of the processing data of the current big data system through the heartbeat to change the push flow.
First, the total amount of data to acquire data from the message middleware (e.g., acquire 5 ten thousand pieces of data) is determined. Then, based on the number of data acquired from the message middleware (e.g., 5 ten thousand pieces) and the number of data partitions in the message middleware (e.g., 1000 pieces), data (i.e., 50 pieces) that averages the first data amount acquired from each data partition in the message middleware is determined. Thereafter, a first amount of data (i.e., 50) is acquired from each data partition, i.e., 50 data per second, according to a predetermined frequency of data acquisition from the message middleware (e.g., once per second). Thus, the big data system consumes data uniformly in each data partition at an acquisition rate of 50 data per second.
In the embodiment of the application, since the data reported by the vehicle is uniformly distributed on the Kafka theme (Topic) and the Partition (Partition), the flow control can also uniformly consume the corresponding theme (Topic) and Partition (Partition). Specifically, the total number of all the partitions (Partition) is 1000, namely, the data is uniformly distributed into the 1000 partitions (Partition) in the unit of vehicle, wherein the total number of all the current topics is 100, namely, 10 partitions (Partition) below each Topic. If 10 ten thousand vehicles are currently on-line, then on average each Partition (Partition) receives 100 data delivery of the vehicles. Flow control is the control of the consumption per second consumption rate for each Partition (Partition).
In one embodiment of the present application, the step 1044 of obtaining data sent by the vehicle end from the data partition of the message middleware includes: receiving vehicle operation information sent by a vehicle end, wherein the vehicle operation ending information carries identification information of the corresponding vehicle end; responding to the vehicle running information to represent that the vehicle stops running, acquiring the corresponding identification information of the vehicle end, and acquiring a target data partition corresponding to the identification information of the vehicle end from a redis cache; and acquiring all data corresponding to the identification information of the vehicle end from the target data partition.
Specifically, on the basis of the above embodiment, the vehicle data may flow into the big data system uniformly with a certain flow rate in a load balancing manner. All vehicle data are fair and have no priority, and when a certain vehicle journey is over, the data can be backlogged in kafka due to the flow control in the peak period, so that the final data processing and analysis delay is caused, and the user is not good in finally checking the driving analysis and related reports.
The analysis results such as driving behavior analysis and diagnosis of the cloud end may not be required in the running process of the vehicle, but when the user finishes the journey, the user may be more familiar to see the driving journey, the change of the vehicle condition and the like in a few minutes, and the flow control during the rush hour may cause that the analysis results cannot be seen in time when the single vehicle finishes the running because the data are uniform and have no priority in processing and analysis. If the end of the user journey is taken as a signal, the data of the current vehicle in the cloud service flow is processed preferentially, and the processing and analysis of the current vehicle data are completed by concentrating resources in a short time, so that better experience can be brought to the current user. As shown in fig. 5, the cloud end receives vehicle operation information sent by the vehicle end, where the vehicle operation end information carries identification information of the corresponding vehicle end. And responding to the vehicle running information to represent that the vehicle stops running, acquiring the corresponding identification information of the vehicle end, and acquiring the target data partition corresponding to the identification information of the vehicle end from the redis cache. And then acquiring all data corresponding to the identification information of the vehicle end from the target data partition. And forwarding the data to a big data system of the cloud, analyzing and processing the big data system, and timely sending a processing result to the client when receiving a query instruction sent by a user.
According to the embodiment of the application, the end of the user journey is taken as a signal, the data in the cloud service flow of the current vehicle is processed preferentially, and the processing and analysis of the current vehicle data are completed by concentrating resources in a short time, so that better experience is brought to the current user.
In one embodiment of the present application, the acquiring all data corresponding to the identification information of the vehicle side from the target data partition in the above embodiment includes: determining the frequency of acquiring data from the message middleware and the quantity of the acquired data; and acquiring the data corresponding to the identification information of the vehicle end in the target data partition based on the frequency of acquiring the data from the message middleware and the number of the acquired data until all the data corresponding to the identification information of the vehicle end in the target data partition are acquired.
Specifically, when receiving information of the end of a certain vehicle journey, inquiring a Kafka theme (Topic) and a Partition (Partition) where current vehicle data are located and a Partition maximum offset of the Partition, namely, the data quantity of the Partition (Partition) at the current moment, suspending consumption of other partitions (Partition), and completing quick consumption and forwarding of the data of the Partition (Partition) where the vehicle is located by a centralized server resource in a short time until the recorded offset data are consumed, and recovering an equilibrium consumption Partition (Partition) mode of the Kafka.
First, the frequency of data acquisition from the message middleware (e.g., once per second) and the amount of data acquisition (e.g., fifty thousand pieces) are determined. Then, data corresponding to the identification information of the vehicle side is acquired in the target data partition (i.e., data is acquired in the target data partition at a speed of fifty thousand pieces per second) based on the frequency (i.e., once per second) of acquiring data from the message middleware and the number of acquiring data (i.e., fifty thousand pieces per second), until the data corresponding to the identification information of the vehicle side in the target data partition is all acquired.
According to the embodiment of the application, when the journey of the user is finished, the Partition where the current vehicle data is located is inquired, all data transmission resources are allocated to the data transmission of the Partition, the data transmission of other partitions (Partition) is suspended, the central server resources complete the rapid data transmission of the Partition where the vehicle is located in a short time until all data corresponding to the identification information of the vehicle end in the target data Partition are acquired, the balanced data transmission of other partitions of kafka is restored, the data in the cloud service flow of the current vehicle is processed preferentially, and the processing and analysis of the current vehicle data are completed by the central resources in a short time, so that better experience can be brought to the current user.
According to the specific embodiment provided by the application, the technical scheme provided by the application can have the following advantages:
the method comprises the steps of determining the uploading frequency of data, sending the uploading frequency to a vehicle end, receiving the data sent by the vehicle end according to the uploading frequency, obtaining the number of the vehicle ends uploading the data currently and a preset number threshold, updating the frequency of the vehicle ends uploading the data if the number of the vehicle ends uploading the data exceeds the number threshold, obtaining the updated uploading frequency, sending the updated uploading frequency to the vehicle end, and finally receiving the data sent by the vehicle end according to the updated uploading frequency. According to the method and the system for reporting the vehicle data, the frequency of reporting the vehicle data can be dynamically adjusted according to the number of vehicles accessed by the current cloud in real time, so that uploading of the vehicle data is coordinated, data processing resources are reasonably distributed, and the pressure of a server is reduced. When the user journey is finished, inquiring the subarea where the current vehicle data is located, distributing all data transmission resources to the data transmission of the subarea, suspending the data transmission of other subareas (Partition), finishing the data rapid transmission of the subarea where the vehicle is located by the centralized server resources in a short time until all the data corresponding to the identification information of the vehicle end in the target data subarea are acquired, recovering the balanced data transmission of other subareas of kafka, preferentially processing the data of the current vehicle in the cloud service flow, and finishing the processing and analysis of the current vehicle data by the centralized resources in a short time, thereby bringing better experience to the current user.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited in the present application, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
Fig. 6 is a flowchart of a vehicle data uploading method according to an embodiment of the present application, as shown in fig. 6, the method may include the following steps:
step 601: receiving uploading frequency of data sent by cloud
Step 602: transmitting data to the cloud according to the uploading frequency;
step 603: receiving updated uploading frequency of data sent by a cloud;
step 604: and sending data to the cloud according to the updated uploading frequency.
Specifically, in this embodiment, the vehicle end is taken as an execution main body, the vehicle end obtains the reporting frequency of the vehicle data from the device access end of the cloud end, and the cloud end receives the data sent by the vehicle end according to the uploading frequency and analyzes and processes the data by the cloud end big data system. Before all the vehicle ends report data, the vehicle ends firstly interact with the equipment access end, the equipment access end obtains the reporting frequency of the current vehicle from the coordinator, and the time of reporting the data of the current vehicle is calculated according to the access sequence. When the coordinator checks that the on-line vehicle exceeds a certain threshold (such as 10 ten thousand, 20 ten thousand and 50 ten thousand …), the vehicle end load balancing strategy needs to be updated, and the coordinator responds to the equipment access end and responds to the vehicle end according to the on-line vehicle reassignment reporting frequency and time, and the vehicle end updates the reporting frequency and the reporting time in real time. The cloud coordinator system presets a quantity threshold value of the quantity of the vehicle ends for uploading data, acquires the quantity of the vehicle ends for uploading data currently and the preset quantity threshold value, updates the frequency of uploading data by the vehicle ends if the quantity of the vehicle ends for uploading data exceeds the quantity threshold value, obtains updated uploading frequency, and sends the updated uploading frequency to the vehicle ends. Wherein the updated uploading frequency is slower than the previous uploading frequency, for example, the original uploading frequency is 10 seconds, 1 time of complete data is collected and reported, and the updating is that the complete data is reported every 20 seconds. And sending the updated uploading frequency to the vehicle end, and continuously uploading data by the vehicle end more than the updated uploading frequency. The vehicle receives updated uploading frequency of the data sent by the cloud end, and sends the data to the cloud end according to the updated uploading frequency.
In one embodiment, sending data to the cloud according to the updated upload frequency includes: determining a designated time period corresponding to the updated uploading frequency according to the updated uploading frequency; determining the starting time of a designated time period as a first time, the ending time of the designated time period as a second time, wherein the data generated by the vehicle at the first time is the full-scale data of the first time, and the data generated by the vehicle at the second time is the full-scale data of the second time; obtaining offset data according to data offset between the full-scale data at the first moment and the full-scale data at the second moment; obtaining target data according to the total data and the offset data at the first moment; and sending the target data to the cloud.
Specifically, the updated uploading frequency is, for example, 20s for uploading data once, the corresponding designated time period is 20s, the starting time of the designated time period is recorded as the first time, and the ending time of the designated time period is recorded as the second time. The data generated by the vehicle at the first time is the full-scale data of the first time, namely the data generated by the vehicle at the starting point in the time slot of 20s, and the data generated by the vehicle at the second time is the full-scale data of the second time, namely the data generated by the vehicle at the end point in the time slot of 20 s. The offset data characterizes a data offset between the full data at the first time and the full data at the second time.
The following full data are simply exemplified:
full data 1 at first moment { frequency: 10s, mileage: 1200, gps: [119,80], temperature: 26, … };
full data 2 at the second time instant { frequency: 10s, mileage: 1201 gps: [119,80.5], temperature 26, … };
offset data: mileage +1201, gps+0.5;
after combining the two data into one piece, obtaining target data obtained by combining the total data and the offset data of the vehicle end according to the first moment:
target data { frequency: 20s, mileage: [1200, +1], gps [ [119,80], [ +0, +0.5] ], temperature [ [26, +0], … }
For most of the status report data, the cloud end is not required to analyze and process immediately, the real-time requirement is not high, the user generally cannot pay attention to the driving behavior analysis in the driving process, and the user cannot feel even if a certain delay exists. And if the vehicle access quantity exceeds a certain threshold, dynamically adjusting the data reporting frequency according to the current cloud real-time access vehicle quantity. The default vehicle end collects and reports 1 complete data every 10 seconds, the adjustment frequency is that every 20 seconds is reported, at this time, the vehicle end still keeps collecting the vehicle data every 10 seconds and caches the data, and the collected data of 2 times are reported simultaneously after the collected data of 2 times, so that concurrency to the cloud is reduced by half. Therefore, concurrent requests to the cloud can be effectively reduced. The concurrency can be effectively reduced for reporting from 10 seconds to 30 seconds, but in practice, the total amount of I/O caused by reporting 1 time of collected data every 10 seconds and reporting twice of collected data every 20 seconds to the cloud is unchanged, i.e. the I/O of the cloud is not reduced. Because the data reported by a single vehicle is usually continuous and comprises hardware equipment state, mileage, gps, oil quantity, electric quantity, temperature, speed and the like, if the report of a plurality of complete data can be processed into the report of a single complete data plus a data offset, the size of the reported data packet can be effectively reduced. The data offset is shown with a "+/-value", and the data collected at two intervals is recorded as "+0" without change, so that the size of a single data packet is greatly reduced.
According to the specific embodiment provided by the application, the technical scheme provided by the application can have the following advantages:
the method comprises the steps of determining the uploading frequency of data, sending the uploading frequency to a vehicle end, receiving the data sent by the vehicle end according to the uploading frequency, obtaining the number of the vehicle ends uploading the data currently and a preset number threshold, updating the frequency of the vehicle ends uploading the data if the number of the vehicle ends uploading the data exceeds the number threshold, obtaining the updated uploading frequency, sending the updated uploading frequency to the vehicle end, and finally receiving the data sent by the vehicle end according to the updated uploading frequency. According to the method and the system for reporting the vehicle data, the frequency of reporting the vehicle data can be dynamically adjusted according to the number of vehicles accessed by the current cloud in real time, so that uploading of the vehicle data is coordinated, data processing resources are reasonably distributed, and the pressure of a server is reduced. When the user journey is finished, inquiring the subarea where the current vehicle data is located, distributing all data transmission resources to the data transmission of the subarea, suspending the data transmission of other subareas (Partition), finishing the data rapid transmission of the subarea where the vehicle is located by the centralized server resources in a short time until all the data corresponding to the identification information of the vehicle end in the target data subarea are acquired, recovering the balanced data transmission of other subareas of kafka, preferentially processing the data of the current vehicle in the cloud service flow, and finishing the processing and analysis of the current vehicle data by the centralized resources in a short time, thereby bringing better experience to the current user.
The same and similar parts of the above embodiments are all referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
It should be noted that, in the embodiment of the present application, the use of user data may be involved, and in practical application, user specific personal data may be used in the schemes described herein within the scope allowed by applicable legal regulations under the condition that the applicable legal regulations of the country are met (for example, the user explicitly agrees, the user is explicitly notified, the user is explicitly authorized, etc.).
According to an embodiment of the present application, the present application also provides a computer device, a computer-readable storage medium. The application also provides a computer device comprising at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores computer instructions executable by the at least one processor to enable the at least one processor to perform the vehicle data uploading method of any of the embodiments described above.
As shown in fig. 7, is a block diagram of a computer device according to an embodiment of the present application. Computer equipment is intended to represent various forms of digital computers or mobile devices. Wherein the digital computer may comprise a desktop computer, a portable computer, a workstation, a personal digital assistant, a server, a mainframe computer, and other suitable computers. The mobile device may include a tablet, a smart phone, a wearable device, etc.
As shown in fig. 7, the computer device 700 includes a computing unit 701, a ROM 702, a RAM 703, a bus 704, and an input/output (I/O) interface 705, and the computing unit 701, the ROM 702, and the RAM 703 are connected to each other through the bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The computing unit 701 may perform various processes in the method embodiments of the present application according to computer instructions stored in a Read Only Memory (ROM) 702 or computer instructions loaded from a storage unit 708 into a Random Access Memory (RAM) 703. The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. The computing unit 701 may include, but is not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), as well as any suitable processor, controller, microcontroller, etc. In some embodiments, the methods provided by embodiments of the present application may be implemented as a computer software program tangibly embodied on a computer-readable storage medium, such as storage unit 708.
The RAM 703 may also store various programs and data required for the operation of the device 700. Part or all of the computer program may be loaded and/or installed onto the device 700 via the ROM 702 and/or the communication unit 709.
An input unit 706, an output unit 707, a storage unit 708, and a communication unit 709 in the computer apparatus 700 may be connected to the I/O interface 705. Wherein the input unit 706 may be, for example, a keyboard, mouse, touch screen, microphone, etc.; the output unit 707 may be, for example, a display, a speaker, an indicator light, or the like. The device 700 is capable of exchanging information, data, and the like with other devices through the communication unit 709.
It should be noted that the device may also include other components necessary to achieve proper operation. It is also possible to include only the components necessary to implement the inventive arrangements, and not necessarily all the components shown in the drawings.
Various implementations of the systems and techniques described here can be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof.
Computer instructions for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer instructions may be provided to a computing unit 701 such that the computer instructions, when executed by the computing unit 701, such as a processor, cause the steps involved in embodiments of the method of the present application to be performed.
The present application also provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the vehicle data uploading method according to any of the above embodiments.
The computer readable storage medium provided by the present application may be a tangible medium that may contain, or store, computer instructions for performing the steps involved in the method embodiments of the present application. The computer readable storage medium may include, but is not limited to, storage media in the form of electronic, magnetic, optical, electromagnetic, and the like.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (10)
1. A method of uploading vehicle data, the method comprising:
determining the uploading frequency of data, and transmitting the uploading frequency to a vehicle end;
receiving data sent by the vehicle end according to the uploading frequency;
acquiring the number of vehicle ends of current uploading data and a preset number threshold, updating the frequency of uploading data by the vehicle ends to obtain updated uploading frequency in response to the number of the vehicle ends of uploading data exceeding the number threshold, and transmitting the updated uploading frequency to the vehicle ends;
and receiving data sent by the vehicle side according to the updated uploading frequency.
2. The vehicle data uploading method according to claim 1, wherein the receiving the data sent by the vehicle side according to the uploading frequency includes:
the message middleware receives data sent by the vehicle end, wherein the data sent by the vehicle end carries identification information of the vehicle end;
determining a data partition corresponding to the identification information of the vehicle end, wherein the message middleware is divided into a plurality of partitions for storing data in advance;
storing the data sent by the vehicle end into a data partition corresponding to the identification information of the vehicle end, and storing the identification information of the vehicle end and the corresponding data partition into a redis cache;
And acquiring the data sent by the vehicle end from the data partition of the message middleware.
3. The method for uploading vehicle data according to claim 2, wherein the obtaining the data sent by the vehicle end from the data partition of the message middleware includes:
determining a total data amount of data acquired from the message middleware;
determining a first data volume obtained from each data partition on average according to the number of the data partitions in the message middleware and the total data volume;
and simultaneously acquiring the data of the first data volume from each data partition according to the predetermined data acquisition frequency.
4. The method for uploading vehicle data according to claim 2, wherein the obtaining the data sent by the vehicle end from the data partition of the message middleware includes:
receiving vehicle operation information sent by a vehicle end, wherein the vehicle operation ending information carries identification information of the corresponding vehicle end;
responding to the vehicle running information to represent that the vehicle stops running, acquiring corresponding identification information of a vehicle end, and acquiring a target data partition corresponding to the identification information of the vehicle end from the redis cache;
And acquiring all data corresponding to the identification information of the vehicle end from the target data partition.
5. The vehicle data uploading method according to claim 4, wherein the acquiring all data corresponding to the identification information of the vehicle side from the target data partition includes:
determining the frequency of acquiring data from the message middleware and the quantity of the acquired data;
and acquiring data corresponding to the identification information of the vehicle end in the target data partition based on the frequency of acquiring the data from the message middleware and the quantity of acquiring the data until all the data corresponding to the identification information of the vehicle end in the target data partition are acquired.
6. The vehicle data uploading method according to claim 1, wherein the receiving the data sent by the vehicle side according to the updated uploading frequency includes:
receiving target data obtained by combining the offset data according to the full data of the first moment by the vehicle end;
the method comprises the steps that the starting time of a specified time period corresponding to the updated uploading frequency is a first time, the end time of the specified time period is a second time, data generated by a vehicle at the first time is full-quantity data of the first time, and data generated by the vehicle at the second time is full-quantity data of the second time; the offset data characterizes a data offset between the full data at the first time and the full data at the second time.
7. A method of uploading vehicle data, the method comprising:
receiving uploading frequency of data sent by a cloud;
transmitting data to the cloud according to the uploading frequency;
receiving updated uploading frequency of data sent by a cloud;
and sending data to the cloud according to the updated uploading frequency.
8. The vehicle data uploading method according to claim 7, wherein the sending data to the cloud according to the updated uploading frequency includes:
determining a designated time period corresponding to the updated uploading frequency according to the updated uploading frequency;
determining the starting time of the appointed time period as a first time, the ending time of the appointed time period as a second time, wherein the data generated by the vehicle at the first time is the total data of the first time, and the data generated by the vehicle at the second time is the total data of the second time;
obtaining offset data according to the data offset between the full-scale data at the first moment and the full-scale data at the second moment;
combining the offset data according to the full data at the first moment to obtain target data;
and sending the target data to a cloud.
9. A computer device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores computer instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6 or 7-8.
10. A computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any of claims 1-6 or 7-8.
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| CN202311249591.1A CN117201404A (en) | 2023-09-26 | 2023-09-26 | Vehicle data uploading method, device and storage medium |
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