CN115509585A - OTA system upgrading method based on Internet of vehicles - Google Patents

Abstract

The invention belongs to the field of intelligent networking, and particularly relates to an OTA system upgrading method based on Internet of vehicles. The vehicle software upgrading method based on the OTA captures vehicle software data through the internet equipment TBOX, extracts characteristic information through the data, matches the characteristic information with a corresponding solution, and upgrades the vehicle system OTA according to the conformity. The method of the invention can actively carry out silent upgrade processing before the problem is worsened, thereby preventing problem enlargement and problem aggravation. When the method is actually applied, the expansion can be completed according to different models and system upgrading schemes, the application scene is wide, and the expansibility is strong.

Description

OTA system upgrading method based on Internet of vehicles

Technical Field

The invention belongs to the field of intelligent networking, and particularly relates to an OTA system upgrading method based on Internet of vehicles.

Background

In the big data era, the integration of information technology and automobile problem solution is more and more intimate, the upgrading of a vehicle network and an OTA (over the air) becomes a mainstream trend, and the important role which is not negligible is played no matter the vehicle problem solution efficiency is improved or the high-quality customer service is provided. In a practical situation, software and hardware problem solving requires a customer to go to a maintenance station for on-site maintenance and treatment.

Disclosure of Invention

The invention aims to provide a systematic software problem processing and solving method, a novel problem solving mode is established, and a corresponding system model is used for carrying out intervention processing on early risks of vehicles. The method is based on the automobile networking technology, characteristic data analysis and OTA upgrading system model establishment, and after-sale vehicle data captured through the T-BOX is used for extracting characteristic information from the data, matching with the upgrading model, and performing targeted repair and solution on vehicle faults, so as to ensure stable and safe operation of the vehicle.

The specific technical scheme is as follows:

an OTA system upgrading method based on Internet of vehicles comprises the following steps:

step 1, collecting data: acquiring the whole vehicle software version data through a network connection module T-BOX, and uploading the data to a big data platform for storage;

step 2, setting screening conditions: performing data analysis and comparison through a big data platform, screening out characteristic information, and building a fault sample model through the characteristic data to perform detailed classification;

step 3, screening data batches: matching the captured data according to the built OTA upgrading system model, and preliminarily screening vehicle batches of typical characteristic data;

step 4, comparing the upgrade model: analyzing the vehicle state of the whole vehicle specific data; carrying out similarity matching with an OTA system upgrading model;

step 5, information analysis, confirmation and upgrading: if the step 4 is matched, the OTA is issued for upgrading according to a preset upgrading scheme if the step is confirmed to be matched; if the matching degree of the upgrading model is lower, whether a fault or a client operation problem exists is confirmed, the root cause of the problem of the vehicle with the fault is synchronously analyzed, the corresponding solution is matched according to the possible cause, and OTA upgrading model matching is carried out after the fault condition is eliminated until the system upgrading is completed.

Furthermore, an upgrade feedback is set in the step 5, namely the software version is compared with the cloud platform after the upgrade is finished, and if the upgrade is successful, specific upgrade information is fed back; and if the failure occurs, the OTA system is matched with the upgrading model again, and after the failure factor is eliminated, the OTA system is re-upgraded.

Further, the OTA upgrading platform framework is composed of three parts, namely a cloud end, a pipe end and a terminal: the cloud is cloud service, and is used for analyzing, displaying and operating the acquired vehicle data and providing a management platform for the Internet of vehicles service; the pipe end is a communication pipeline between the cloud platform and the remote terminal, and the vehicle factory cooperates with the operator to provide communication channel service; the terminal is a vehicle-mounted communication terminal, namely T-BOX, and is responsible for collecting data and uploading the data to the cloud and executing the operation of issuing an instruction from the cloud.

Further, the data platform page comprises four blocks of basic information statistics, energy consumption analysis, quality analysis and working condition analysis; the OTA upgrading model is built by means of the vehicle real-time data stream of the working condition analysis module, and main fault model characteristics are summarized.

The vehicle software upgrading method based on the OTA captures vehicle software data through the internet equipment TBOX, extracts characteristic information through the data, matches the characteristic information with a corresponding solution, and upgrades the vehicle system OTA according to the conformity. The method of the invention can actively carry out silent upgrade processing before the problem is worsened, thereby preventing problem enlargement and problem aggravation. When the method is actually applied, the expansion can be completed according to different models and system upgrading schemes, the application scene is wide, and the expansibility is strong.

Drawings

FIG. 1 TBOX feed problem model;

FIG. 2 is a diagram of the method steps of the present invention;

FIG. 3 is a flow chart of the method of the present invention;

FIG. 4 OTA upgrade platform architecture diagram;

FIG. 5 is a screenshot of a big data platform home page;

FIG. 6 is a screenshot of the fault code home page;

FIG. 7 is a screening data batch screenshot;

FIG. 8 data model comparison confirmation screenshot;

fig. 9 OTA upgrade feedback flow diagram;

FIG. 10 vehicle condition data export screenshot;

fig. 11 data compare confirmation screen shot.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.

The vehicle data are collected through the cloud platform based on a model built by cloud big data, and screening conditions are set for massive data.

And screening out the characteristic version data through mass data analysis, and classifying and dividing the batches through the characteristic data.

After the mass data are classified and divided, matching confirmation is carried out on the corresponding OTA upgrading scheme, and therefore whether the after-sale vehicle can execute the upgrading scheme or not is confirmed; the following TBOX feeding problem model is taken as an example (the flow is shown in fig. 1). The feed model upgrade scheme has several typical features: 1. the vehicle TBOX version is lower than the version of the OTA upgrading scheme model; 2. the SOC value of the vehicle is effectively and stably reduced within a limited duration period; 3. and continuously uploading the message which is naturally awakened in the non-starting state.

Through batch confirmation and circulating monitoring, the vehicle is confirmed to be in accordance with the OTA upgrading fault model, and then OTA upgrading repair is carried out on the vehicle through a built OTA upgrading system, so that stable operation of the vehicle is ensured, as shown in fig. 2 and 3.

The concrete description is as follows:

1. OTA upgrade platform architecture

The platform architecture (as shown in fig. 4) mainly comprises three parts, namely a cloud end, a pipe end and a terminal: the cloud is cloud service, and is used for analyzing, displaying and operating the acquired vehicle data and providing a management platform for the Internet of vehicles service; the pipe end is a communication pipeline between the cloud platform and the remote terminal, and the vehicle factory cooperates with the operator to provide communication channel service; the terminal is a vehicle-mounted communication terminal, namely T-BOX, and is responsible for collecting data and uploading the data to the cloud and executing the operation of issuing an instruction from the cloud.

2. Big data capture

The big data platform homepage (as shown in fig. 5) is constructed on the basis of a cloud database, and mass information stored by the cloud platform is visually displayed by utilizing computing and storage resources provided by the big data base platform according to business requirements and business modeling. The large data platform page mainly comprises four blocks of basic information statistics, energy consumption analysis, quality analysis and working condition analysis. The OTA upgrading model is mainly built by means of vehicle real-time data flow of the working condition analysis module, and main fault model characteristics are summarized.

3. Characterization model induction

An analysis engineer intelligently screens vehicle batch information and fault time through a software version or a DTC fault code (as shown in figure 6), analyzes specific data, extracts characteristic information, and establishes a specific OTA upgrading model according to the characteristic information and a data rule. For example, upgrade system models for feeds: by setting the SOC value (battery charge) 35 of the entire vehicle as a feeding threshold, a vehicle lot (as shown in fig. 7) in which the charge is reported to be 35 is derived. For the derived chassis number information, vehicle condition operation information of a single vehicle is derived in batches; and model analysis is carried out according to the historical data of the vehicles in the last month, so that the typical characteristics of the feed model are obtained: 1. the vehicle TBOX version is lower than the version of the OTA upgrading scheme model; 2. the SOC value falls effectively and steadily over a defined duration; 3. and continuously uploading the message which is naturally awakened in the non-starting state.

And building a corresponding power feeding problem upgrading model according to the typical data characteristics of power feeding. And comparing the data by taking the model as a sample so as to determine whether the vehicle has the fault risk of the type, and if the abnormal factor does exist, executing OTA software upgrading.

4. Data model comparison, analysis and confirmation

Taking fig. 8 as an example, according to the derived data stream: the version of the car software is 1.93, which is lower than the 2.10 version of the cloud platform; the vehicle continuously reports a low battery charge signal in 4 months and 18 days, starting from 20 to 13, and continuing to 20 to 23 for only 10 minutes, the battery charge is reduced from 63 to 60, other abnormal charge values are not doped in the battery charge, and the battery voltage is stabilized to 12V. The data satisfy the 2 nd characteristic of the feed data model; and under the condition that the vehicle is locked, data are uploaded all the time at 8 pm, but no corresponding action instruction is given in the data content, such as the action of opening and closing the vehicle door or the engine hood and the like. The data indicate that the hidden trouble that the electronic module of the whole vehicle is not dormant exists after the vehicle is powered off, and meanwhile, the 3 rd characteristic of the feed model is met.

5. Performing OTA upgrades

Therefore, for the type of vehicle, an OTA (over the air) upgrading model scheme for the power feeding problem is established, and the vehicle identification code is derived by using the cloud platform. A feed OTA upgrade scheme is automatically performed on such identification coded vehicles to prevent deep feed from damaging the battery, resulting in a failure to start.

When the cloud platform issues the task, synchronously detecting whether the vehicle meets the upgrading condition: whether the vehicle is flamed out, whether the electric quantity of the storage battery supports upgrading and the like, and after the upgrading conditions are confirmed to be met; the vehicle automatically executes decompression upgrading. And after the upgrading is finished, comparing the software version with the cloud platform, if the upgrading is successful, feeding back specific upgrading information, if the upgrading is failed, rolling back to the first step, re-detecting upgrading conditions, and after fault factors are eliminated, re-executing the OTA system upgrading.

Meanwhile, according to the conclusion analyzed by the data model, the problem hidden danger that the electronic module of the whole vehicle is not dormant can be directly positioned; in the traditional OTA upgrading mode, all after-sale vehicles are generally upgraded simply, but the vehicles cannot be accurately determined as required vehicles, and when a plurality of hardware modules need to be upgraded, the operation often causes resource waste and congestion and blockage of upgrading tasks.

Example 1

In the case of the actual occurrence using the OTA upgrade model, fig. 6 shows the fault code case: model screening under the limited condition that the vehicle is not started by taking the voltage value lower than 12V as a limited threshold value shows that after-market has many risks of failure of the type.

Take a certain 743 model as an example: through data retrieval stored in the remote terminal, a required data information flow and a time period are selected, and batch data export is carried out, as shown in fig. 10.

After information stream data is exported, main characteristic analysis and induction are carried out on the main data, and the auxiliary information supports and proves conclusions.

The following data of FIG. 11 is taken as an example: the main characteristic data are an SOC value, a power supply state of the whole vehicle, a door state and a battery voltage; the auxiliary information is other message actions of the whole vehicle; capturing the whole vehicle feeding information according to the characteristic data; and matching with the constructed model; by a detailed reading it can be found that: the vehicle performed the right front door opening operation at 10 o ' clock and 15 o ' clock on 2021/4/18 day, but did not start the vehicle (the entire vehicle power state was 0), and then performed the same operations at 6 o ' clock, 7 o ' clock and 9 o ' clock on 2021/4/19 day. Through subsequent data discovery, the whole vehicle is awakened by frequently opening the right front door within a week but is not powered on to start, so that the CAN bus of the whole vehicle is awakened frequently and cannot be recharged in time, and the voltage of the storage battery gradually approaches to a threshold value (11V) which cannot be started normally.

Grabbing through the feature types of the vehicle data, and matching and comparing with the feature model: confirming that the series of data streams conforms to one of the characteristic models of the feed: (1) the voltage of the vehicle is lower than 11.5V and stably drops; (2) the whole vehicle is not started for more than 10 days for a long time; and (3) uploading the awakening message for multiple times.

After confirming that the vehicle conforms to the feed model, OTA upgrading is carried out on the vehicles which conform to the model in the same batch in a targeted manner: for the type of vehicle, a uniform upgrading processing mechanism is established; the background automatically issues the vehicle of the type, OTA upgrade is carried out under the condition of the whole vehicle conforming to the upgrade, iteration and upgrade of the software version are completed in silence, and the risk of subsequent problems is fundamentally cut off.

2. Benefit effect

Through a similar big data analysis model, after-sale quality problems and customer satisfaction are obviously improved, for example, a CX743 vehicle model is taken, 48188 CX743 is produced in 2019, and before an automatic OTA model is established, after-sale storage batteries (809 yuan/station) are replaced to solve feeding problems. 1241 storage batteries are replaced in vehicles produced in 2019 in total, and the failure rate is 2.575%. Through carrying out discernment management and control and accurate OTA upgrading to after-sales, 41764 platform truck of 2020 annual production has only changed 37 batteries (fault rate 0.088%), and after-sales cost of maintenance has directly practiced thrift 105 ten thousand yuan.

Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (4)

1. An OTA system upgrading method based on Internet of vehicles is characterized in that: the method comprises the following steps:

step 1, collecting data: acquiring the whole vehicle software version data through a network connection module T-BOX, and uploading the data to a big data platform for storage;

step 2, setting screening conditions: performing data analysis and comparison through a big data platform, screening out characteristic information, and building a fault sample model through the characteristic information to perform detailed classification;

step 3, screening data batches: matching the captured data according to the established OTA upgrading system model, and preliminarily screening vehicle batches of typical characteristic data;

step 4, comparing the upgrading model: analyzing the vehicle state of the whole vehicle specific data; carrying out similarity matching with an OTA system upgrading model;

and 5, information analysis, confirmation and upgrading: if the step 4 is matched, OTA issuing upgrading is carried out according to a preset upgrading scheme if the step is confirmed to be matched; if the matching degree of the OTA upgrading model and the upgrading model is low, whether a fault or a client operation problem exists is confirmed, the root cause of the problem of the vehicle with the fault is synchronously analyzed, a corresponding solution is matched according to the possible cause, and OTA upgrading model matching is carried out after the fault condition is eliminated until the system upgrading is completed.

2. The OTA system upgrading method based on the Internet of vehicles according to claim 1, characterized in that: the step 5 is provided with upgrade feedback, namely the software version is compared with the cloud platform after the upgrade is finished, and if the upgrade is successful, specific upgrade information is fed back; and if the failure occurs, the OTA system is matched with the upgrading model again, and after the failure factor is eliminated, the OTA system is re-upgraded.

3. The OTA system upgrading method based on the Internet of vehicles as claimed in claim 1 or 2, wherein: the OTA upgrading platform framework consists of three parts, namely a cloud part, a pipe end and a terminal: the cloud is cloud service, and is used for analyzing, displaying and operating the acquired vehicle data and providing a management platform for the Internet of vehicles service; the pipe end is a communication pipeline between the cloud platform and the remote terminal, and the vehicle factory cooperates with the operator to provide communication channel service; the terminal is a vehicle-mounted communication terminal, namely T-BOX, and is responsible for collecting data, uploading the data to the cloud, executing the operation of issuing an instruction from the cloud and the like.

4. The OTA system upgrading method based on the Internet of vehicles according to claim 1 or 2, characterized in that: the big data platform page comprises four blocks of basic information statistics, energy consumption analysis, quality analysis and working condition analysis; the OTA upgrading model is built by means of the vehicle real-time data stream of the working condition analysis module, and main fault model characteristics are summarized.

Images