CN114553694A - Vehicle OTA pushing system and method based on machine learning - Google Patents
Vehicle OTA pushing system and method based on machine learning Download PDFInfo
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- CN114553694A CN114553694A CN202210172476.8A CN202210172476A CN114553694A CN 114553694 A CN114553694 A CN 114553694A CN 202210172476 A CN202210172476 A CN 202210172476A CN 114553694 A CN114553694 A CN 114553694A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0813—Configuration setting characterised by the conditions triggering a change of settings
- H04L41/082—Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention relates to a vehicle OTA pushing system and a vehicle OTA pushing method based on machine learning, wherein the system comprises a vehicle-end OTA module, a vehicle machine, a sensing device, an OTA management platform and a pushing engine, wherein the OTA management platform and the pushing engine are arranged on a vehicle; the core thought of the method is that user upgrading parameters are collected every time an upgrading task is provided, and an OTA pushing model judges whether to push the OTA upgrading task according to the user upgrading parameters; meanwhile, the user upgrading parameter when the user selects OTA upgrading each time is defined as an upgrading hit parameter, the OTA pushing model is trained and upgraded by the upgrading hit parameter, the OTA pushing model is trained and perfected as the upgrading times of the user increase, and the opportunity of the OTA pushing model for judging the task needing pushing to be pushed is more in line with the upgrading habit of the user, so that the problem of low probability of selecting upgrading due to unreasonable pushing opportunity of OTA upgrading at present can be effectively solved, and the effects of improving the upgrading pushing rationality and vehicle OTA upgrading experience are achieved.
Description
Technical Field
The invention belongs to the technical field of vehicle OTA upgrading, and particularly relates to a vehicle OTA pushing system and method based on machine learning.
Background
OTA (Over-the-Air Technology) Over-the-Air download Technology is applied to automobiles, namely, Technology for realizing software updating on automobiles through mobile communication. The message pushing of the OTA upgrading is an indispensable link in the OTA upgrading, and a user selects upgrading or not upgrading according to the current situation after receiving the message pushing of the OTA upgrading, for example, Chinese patent CN202022612603.0 is a vehicle ECU upgrading system based on the OTA.
At present, when a user receives message push of vehicle OTA upgrade, the probability of selecting OTA upgrade is low, and the reason that the probability of selecting OTA upgrade by the user is low is mainly as follows: the OTA upgrade is unstable, the iterative content is unattractive to the user, and the OTA upgrade experience is poor. The OTA upgrading experience is mainly reflected in the OTA upgrading message pushing time, such as the OTA upgrading pushing time, environment, vehicle state and the like, when the pushing time does not accord with the habit of a user, the user can be caused to abandon the OTA upgrading, the OTA upgrading pushing is carried out on the user at the wrong time, and when the user pushes the vehicle in an emergency or pushes the vehicle at an unsuitable place, the user feels uncomfortable and even leads to the user complaint; the chinese patent CN202010805461.1 is an OTA upgrade control method and a TSP platform, in the OTA upgrade control method, a user may set a time for pushing an OTA upgrade, but the set push time is fixed and may not change with different car using scenarios, so that the set push time partially conflicts with the car using requirements or expectations of the user.
In addition, in order to improve the OTA upgrading experience of the vehicle and relieve the user's dislike caused by inappropriate pushing opportunity and scene, the current industry adopts a mode that the pushed user can select vehicle locking or time reservation for upgrading; however, this method also has the following disadvantages: firstly, the experience hysteresis is realized, the upgrading mode needs the user to further execute the selection operation of locking the vehicle for upgrading or reserving for upgrading after pushing, the actual improvement effect is not great for the complaint of the user caused by pushing, and the user does not need to further select locking the vehicle or reserving for upgrading; secondly, the push opportunity and scene still can not accord with the 'mind' of the user. This approach is really a unified process for all users, however, the time and scenario requirements for upgrade push are different for different users in practice.
Disclosure of Invention
Aiming at the defects in the prior art, the technical problem to be solved by the invention is to provide a vehicle OTA pushing system and method based on machine learning, so that the problem that the probability of selecting upgrading is low due to unreasonable pushing opportunity of OTA upgrading at present is solved, and the effects of improving the rationality of upgrading pushing and the experience of vehicle OTA upgrading are achieved.
In order to solve the technical problems, the invention adopts the following technical scheme:
a vehicle OTA pushing system based on machine learning comprises a vehicle-end OTA module, a vehicle machine, a sensing device, an OTA management platform and a pushing engine, wherein the vehicle-end OTA module, the vehicle machine and the sensing device are mounted on a vehicle; the vehicle-end OTA module is respectively and electrically connected with the vehicle machine and the sensing equipment, and is in wireless communication connection with the OTA management platform;
the vehicle-mounted terminal and the sensing equipment are used for acquiring user upgrading parameters and transmitting the user upgrading parameters to the vehicle-mounted OTA module, the vehicle-mounted OTA module is used for uploading the user upgrading parameters to the OTA management platform and executing an OTA upgrading task, the OTA management platform is used for uploading the user upgrading parameters to the pushing engine, pushing the OTA upgrading task to the vehicle-mounted OTA module and operating an OTA pushing model, the OTA pushing model is used for judging whether to push the OTA upgrading task according to the user upgrading parameters, and the pushing engine is used for training the OTA pushing model according to the user upgrading parameters.
Further, the user upgrade parameter refers to current scene data of the vehicle, and the scene data includes time information, position information, road information and vehicle state information.
Further, the car end OTA module is respectively connected with the car machine and the sensing equipment through a CAN bus or ETH communication.
The invention also comprises a vehicle OTA pushing method based on machine learning, which uses the vehicle OTA pushing system based on machine learning and comprises the following steps:
1) the OTA module at the vehicle end acquires user upgrading parameters through the vehicle machine and the sensing equipment;
2) the OTA management platform judges whether to push the OTA upgrading task, if yes, the step 3) is executed;
3) the OTA management platform pushes an OTA upgrading task to the vehicle-end OTA module;
4) the vehicle-end OTA module feeds back whether the user selects OTA upgrading to the OTA management platform, if yes, the step 6) is executed, and if not, the step 5) is executed;
5) the OTA module at the vehicle end keeps a pushing state and waits for the user to select OTA upgrading;
6) the push engine trains an OTA push model;
further, in step 2), the OTA management platform judges whether to push the OTA upgrade task according to the user upgrade parameter and the OTA push model.
Further, step 6) comprises the following sub-steps:
61) the vehicle-end OTA module acquires a user upgrading parameter when a user selects OTA upgrading through the vehicle machine and the sensing equipment;
62) defining the user upgrading parameters in the step 61) as upgrading hit parameters;
63) the vehicle-end OTA module transmits the upgrading hit parameters to the push engine through the OTA management platform;
64) the push engine trains the OTA push model using the upgrade hit parameters.
Compared with the prior art, the invention has the following beneficial effects:
the vehicle OTA pushing method based on machine learning is carried out based on the vehicle OTA pushing system based on machine learning, the core thought of the method is that when an upgrading task is carried out each time, user upgrading parameters, namely current environment data of a vehicle, are collected, and an OTA pushing model judges whether the OTA upgrading task is suitable to be pushed or not at the moment according to the user upgrading parameters; meanwhile, the user upgrading parameters when the user selects OTA upgrading each time are defined as upgrading hit parameters, the OTA pushing model is trained and upgraded by the upgrading hit parameters, the more times the user upgrades, the more perfect the OTA pushing model is trained, and therefore, whether the subsequent OTA pushing model is suitable for pushing an OTA upgrading task at the moment is judged according to the upgrading parameters of the user more accurately, the upgrading habit of the user is better met, the problem that the probability of selecting upgrading is low due to unreasonable pushing opportunity of OTA upgrading at present can be effectively solved, and the effects of improving the rationality of upgrading pushing and the OTA upgrading experience of the vehicle are achieved.
Drawings
Fig. 1 is a block diagram illustrating a vehicle OTA push system based on machine learning according to an embodiment;
fig. 2 is a flowchart of a vehicle OTA push method based on machine learning according to an embodiment.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example (b):
referring to fig. 1, a vehicle OTA push system based on machine learning includes a vehicle-end OTA module mounted on a vehicle, a vehicle machine (which may be replaced by an SOC having an internet function and a map function), a sensing device, an OTA management platform disposed at a cloud end, and a push engine; the vehicle-end OTA module is respectively connected with the vehicle machine and the sensing equipment through a CAN bus or ETH (Ethernet) in a communication way, and is in wireless communication connection with the OTA management platform;
the vehicle-mounted device and the sensing device are used for acquiring user upgrading parameters and transmitting the user upgrading parameters to the vehicle-mounted OTA module, the vehicle-mounted OTA module is used for uploading the user upgrading parameters to an OTA management platform and executing an OTA upgrading task, the OTA management platform is used for uploading the user upgrading parameters to a pushing engine, pushing the OTA upgrading task to the vehicle-mounted OTA module and running an OTA pushing model, the OTA pushing model is used for judging whether to push the OTA upgrading task according to the user upgrading parameters, and the pushing engine is used for training the OTA pushing model according to the user upgrading parameters;
the user upgrading parameters refer to current scene data of the vehicle, and the scene data comprise time information, position information, road information and vehicle state information.
Referring to fig. 2, the present invention further includes a vehicle OTA push method based on machine learning, which uses the vehicle OTA push system based on machine learning as described above, including the following steps:
1) the OTA module at the vehicle end acquires user upgrading parameters through the vehicle machine and the sensing equipment;
2) the OTA management platform judges whether to push an OTA upgrading task according to the user upgrading parameters and the OTA pushing model, if yes, the step 3) is executed;
3) the OTA management platform pushes an OTA upgrading task to the vehicle-end OTA module;
4) the vehicle-end OTA module feeds back whether the user selects OTA upgrading to the OTA management platform, if yes, the step 6) is executed, and if not, the step 5) is executed;
5) the OTA module at the vehicle end keeps a pushing state and waits for the user to select OTA upgrading;
6) the push engine trains an OTA push model; the method comprises the following substeps:
61) the vehicle-end OTA module acquires a user upgrading parameter when a user selects OTA upgrading through the vehicle machine and the sensing equipment;
62) defining the user upgrading parameters in the step 61) as upgrading hit parameters;
63) the vehicle-end OTA module transmits the upgrading hit parameters to the push engine through the OTA management platform;
64) the push engine trains the OTA push model using the upgrade hit parameters.
The vehicle OTA pushing method based on machine learning is carried out based on the vehicle OTA pushing system based on machine learning, the core thought of the method is that when an upgrading task is carried out each time, user upgrading parameters, namely current environment data of a vehicle, are collected, and an OTA pushing model judges whether the OTA upgrading task is suitable to be pushed or not at the moment according to the user upgrading parameters; meanwhile, the user upgrading parameters when the user selects OTA upgrading each time are defined as upgrading hit parameters, the OTA pushing model is trained and upgraded by the upgrading hit parameters, the more times the user upgrades, the more perfect the OTA pushing model is trained, and therefore, whether the subsequent OTA pushing model is suitable for pushing an OTA upgrading task at the moment is judged according to the upgrading parameters of the user more accurately, the upgrading habit of the user is better met, the problem that the probability of selecting upgrading is low due to unreasonable pushing opportunity of OTA upgrading at present can be effectively solved, and the effects of improving the rationality of upgrading pushing and the OTA upgrading experience of the vehicle are achieved.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (6)
1. A vehicle OTA push system based on machine learning is characterized in that: the system comprises a vehicle-end OTA module, a vehicle machine, a sensing device, an OTA management platform and a push engine, wherein the vehicle-end OTA module, the vehicle machine and the sensing device are carried on a vehicle; the vehicle-end OTA module is respectively and electrically connected with the vehicle machine and the sensing equipment, and is in wireless communication connection with the OTA management platform;
the vehicle-mounted terminal and the sensing equipment are used for acquiring user upgrading parameters and transmitting the user upgrading parameters to the vehicle-mounted OTA module, the vehicle-mounted OTA module is used for uploading the user upgrading parameters to the OTA management platform and executing an OTA upgrading task, the OTA management platform is used for uploading the user upgrading parameters to the pushing engine, pushing the OTA upgrading task to the vehicle-mounted OTA module and operating an OTA pushing model, the OTA pushing model is used for judging whether to push the OTA upgrading task according to the user upgrading parameters, and the pushing engine is used for training the OTA pushing model according to the user upgrading parameters.
2. The vehicle OTA push system based on machine learning of claim 1, wherein: the user upgrading parameters refer to current scene data of the vehicle, and the scene data comprises time information, position information, road information and vehicle state information.
3. The vehicle OTA push system based on machine learning of claim 1, wherein: and the vehicle-end OTA module is respectively connected with the vehicle machine and the sensing equipment through a CAN bus or ETH communication.
4. A vehicle OTA pushing method based on machine learning is characterized in that: use of a machine learning based vehicle OTA push system according to claims 1-3 comprising the steps of:
1) the OTA module at the vehicle end acquires user upgrading parameters through the vehicle machine and the sensing equipment;
2) the OTA management platform judges whether to push the OTA upgrading task, if yes, the step 3) is executed;
3) the OTA management platform pushes an OTA upgrading task to the vehicle-end OTA module;
4) the vehicle-end OTA module feeds back whether the user selects OTA upgrading to the OTA management platform, if yes, the step 6) is executed, and if not, the step 5) is executed;
5) the OTA module at the vehicle end keeps a pushing state and waits for the user to select OTA upgrading;
6) the push engine trains the OTA push model.
5. The vehicle OTA push method based on machine learning of claim 1, wherein the method comprises the following steps: in the step 2), the OTA management platform judges whether to push the OTA upgrading task according to the user upgrading parameters and the OTA pushing model.
6. The vehicle OTA push method based on machine learning of claim 1, wherein the method comprises the following steps: step 6) comprises the following substeps:
61) the vehicle-end OTA module acquires a user upgrading parameter when a user selects OTA upgrading through the vehicle machine and the sensing equipment;
62) defining the user upgrading parameters in the step 61) as upgrading hit parameters;
63) the vehicle-end OTA module transmits the upgrading hit parameters to the push engine through the OTA management platform;
64) the push engine trains the OTA push model using the upgrade hit parameters.
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| CN202210172476.8A CN114553694A (en) | 2022-02-24 | 2022-02-24 | Vehicle OTA pushing system and method based on machine learning |
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| CN202210172476.8A CN114553694A (en) | 2022-02-24 | 2022-02-24 | Vehicle OTA pushing system and method based on machine learning |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115567496A (en) * | 2022-09-21 | 2023-01-03 | 润芯微科技(江苏)有限公司 | OTA (over the air) upgrading method and system |
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| WO2018045958A1 (en) * | 2016-09-07 | 2018-03-15 | 深圳创维数字技术有限公司 | Method and system for upgrading wireless fidelity (wi-fi) device |
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| CN112667260A (en) * | 2020-12-31 | 2021-04-16 | 红石阳光（北京）科技股份有限公司 | OTA remote upgrading system and method based on intelligent brain |
| CN113364867A (en) * | 2021-06-03 | 2021-09-07 | 前海七剑科技(深圳)有限公司 | Vehicle upgrading method, device, equipment, vehicle and storage medium |
| CN113504925A (en) * | 2021-06-28 | 2021-10-15 | 中汽创智科技有限公司 | Over-the-air upgrading method, upgrading system and electronic equipment |
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- 2022-02-24 CN CN202210172476.8A patent/CN114553694A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2018045958A1 (en) * | 2016-09-07 | 2018-03-15 | 深圳创维数字技术有限公司 | Method and system for upgrading wireless fidelity (wi-fi) device |
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| CN112667260A (en) * | 2020-12-31 | 2021-04-16 | 红石阳光（北京）科技股份有限公司 | OTA remote upgrading system and method based on intelligent brain |
| CN113364867A (en) * | 2021-06-03 | 2021-09-07 | 前海七剑科技(深圳)有限公司 | Vehicle upgrading method, device, equipment, vehicle and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115567496A (en) * | 2022-09-21 | 2023-01-03 | 润芯微科技(江苏)有限公司 | OTA (over the air) upgrading method and system |
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