CN116112549A - Vehicle scene recommendation method and related equipment - Google Patents
Vehicle scene recommendation method and related equipment Download PDFInfo
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Abstract
The embodiment of the application provides a vehicle scene recommendation method and related equipment, and mainly aims to solve the problems that a hard coding mode is adopted in a vehicle end engine in the existing vehicle networking recommendation platform scheme, the scheme can only make fixed scene recommendation according to an offline calculation result, but cannot make real-time recommendation according to a scene in which a user is currently located, namely, the user scene recommendation cannot be dynamically configured and cannot be recommended in real time. The method comprises the following steps: acquiring vehicle environment parameter information; dynamically configuring scene recommendation rules through a rule configuration page to obtain real-time scene recommendation rules; based on the vehicle environment parameter information and the real-time scene recommendation rule, scene recommendation information is obtained.
Description
Technical Field
The invention relates to the technical field of Internet of vehicles, in particular to a vehicle scene recommendation method and related equipment.
Background
With the continuous development of the technology in the field of internet of vehicles in China, in an automobile starting state, an automobile machine side can display fixed recommended content for a user according to factory preset settings or cloud configured settings, wherein the traditional automobile industry internet of vehicles recommended platform scheme mostly adopts a hard coding mode of an engine at the automobile side to realize the functions, the implementation mode can only conduct fixed scene recommendation according to an offline calculation result, and cannot acquire environment parameters according to an actual scene where the user is currently located so as to conduct scene content recommendation according with the current actual situation, namely, a user scene recommendation function cannot be dynamically configured, and a scene recommendation function cannot be conducted in real time.
Disclosure of Invention
In view of the above problems, the present patent proposes a vehicle scene recommendation method, which adopts an architecture integrating a vehicle end and a cloud end, and is divided into a vehicle end engine and a cloud end engine. The cloud engine adopts the visual configuration of scene rules and the real-time recommendation technology, so that the scene recommendation content is dynamically pushed to the vehicle end in real time. The vehicle-end engine is responsible for collecting parameters such as weather, environment and road conditions of a user in real time, reporting the parameters to the cloud engine, and obtaining scene recommended content issued by the cloud engine and displaying the scene recommended content to the user. The method realizes the dynamic visual configuration of the scene recommendation content, and dynamically adapts to the key characteristics of the current scene data of the user and the like to perform scene recommendation. To solve at least one of the above technical problems, in a first aspect, the present invention provides a vehicle scene recommendation method, which includes:
acquiring vehicle environment parameter information;
dynamically configuring scene recommendation rules through a rule configuration page to obtain real-time scene recommendation rules;
based on the vehicle environment parameter information and the real-time scene recommendation rule, scene recommendation information is obtained.
Optionally, dynamically configuring the scene recommendation rule through the rule configuration page includes:
logic to implement scene recommendation rules based on the Drools language.
Optionally, logic for implementing a scene recommendation rule based on a Drools language includes:
the logic is converted to a Drools language and transmitted to the database, wherein the configuration is a dynamic configuration of the associated database.
Optionally, based on the vehicle environment parameter information and the real-time scene recommendation rule, scene recommendation information is acquired:
the vehicle environment parameters and the real-time scene recommendation rules are logically transmitted to the application to cause the application to calculate based on the received data, wherein the application is developed based on the flank framework.
Optionally, acquiring the vehicle environment parameter information includes:
and data monitoring is carried out through Kafka so as to acquire vehicle environment parameter information in real time.
Optionally, the method further comprises:
performing at least one of adding, deleting, or modifying functions for a vehicle scenario based on a rule configuration page, and/or
And based on the rule configuration page, inputting custom condition information to obtain scene recommendation information after condition filtering.
Optionally, acquiring the vehicle environment parameter information includes:
acquiring an internal environment parameter and an external environment parameter of a vehicle, wherein the internal environment information parameter comprises: at least one of a driving position information parameter, a passenger information parameter, a vehicle current information parameter, and a characteristic date parameter, and the external environment parameters include: at least one of weather information parameters, environment information parameters, and road information parameters.
In a second aspect, an embodiment of the present invention further provides an apparatus for intelligent robot diagnosis, including:
the acquisition unit is used for acquiring vehicle environment parameter information;
the configuration unit is used for dynamically configuring the scene recommendation rules through the rule configuration page so as to obtain real-time scene recommendation rules;
and the pushing unit is used for acquiring scene recommendation information based on the vehicle environment parameter information and the real-time scene recommendation rule.
In order to achieve the above object, according to a third aspect of the present invention, there is provided a computer-readable storage medium including a stored program, wherein the above-described vehicle scene recommendation method is implemented when the program is executed by a processor.
In order to achieve the above object, according to a fourth aspect of the present invention, there is provided an electronic device including at least one processor, and at least one memory connected to the processor; the processor is used for calling the program instructions in the memory and executing the vehicle scene recommendation method.
By means of the technical scheme, the vehicle scene recommending method is divided into a vehicle end engine and a cloud engine by adopting a vehicle end and cloud integrated architecture. The cloud engine adopts the visual configuration of scene rules and the real-time recommendation technology, so that the scene recommendation content is dynamically pushed to the vehicle end in real time. The vehicle-end engine is responsible for collecting parameters such as weather, environment and road conditions of a user in real time, reporting the parameters to the cloud engine, and obtaining scene recommended content issued by the cloud engine and displaying the scene recommended content to the user. The method realizes the dynamic visual configuration of the scene recommendation content, and dynamically adapts to the key characteristics of the current scene data of the user and the like to perform scene recommendation. According to the method, the vehicle environment parameter information is obtained, the scene recommendation rule is dynamically configured through the rule configuration page, and the scene recommendation information is finally obtained based on the vehicle environment parameter information and the real-time scene recommendation rule.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a flow chart of a vehicle scene recommendation method according to an embodiment of the present invention;
fig. 2 shows a schematic block diagram of a vehicle scene recommendation device according to an embodiment of the present invention;
fig. 3 shows a schematic block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
In order to solve the problems that the current car networking recommendation platform scheme adopts a hard coding mode for a car end engine, fixed scene recommendation can only be performed according to the offline calculation result, real-time recommendation can not be performed according to the current scene of a user, namely the user scene recommendation can not be dynamically configured and can not be performed in real time. The embodiment of the invention provides a vehicle scene recommendation method. Fig. 1 shows a flowchart of a vehicle scene recommendation method 100 according to an embodiment of the present invention. As shown in fig. 1, the method 100 includes:
s101, acquiring vehicle environment parameter information.
It should be noted that, the above-mentioned actual application scenario may be that the vehicle starts the vehicle by remote start or by unlocking the vehicle to enter the cockpit, and the data acquisition engine at the vehicle end is powered on to start, so as to acquire the identifiable information around the vehicle and in the vehicle, and convert the identifiable information into corresponding parameter information according to the identifiable information, and store the parameter information in the data storage device of the vehicle, so as to upload the cloud subsequently.
For example, the above-mentioned environmental parameter acquisition may be that image information and audio information that can be acquired by recognition are acquired and analyzed through an image and audio acquisition device that is external to the current vehicle or can acquire the external environment, for example, during the running process of the vehicle, the surrounding vehicle quantity parameter information and position parameter information are acquired to acquire road condition information, that is, the external environmental parameter information.
The above-mentioned acquiring environmental parameters may also be, for example, acquiring and analyzing image information and audio information that can be acquired by being identified through the current vehicle interior or an image and audio acquisition device that can acquire the internal environment, for example, acquiring noise parameter information and driver behavior parameter information in the vehicle in a starting state of the vehicle to acquire personnel information in the vehicle, that is, internal environmental parameter information. And providing rich parameter information for the operation of scene recommendation for the subsequent cloud according to the information but not limited to the information.
S102, dynamically configuring scene recommendation rules through a rule configuration page to obtain real-time scene recommendation rules.
It should be noted that, according to the method, through the visual rule configuration page at the cloud, the type and logic judgment of the scene recommendation content acquired by the vehicle end are subjected to visual parameter modification, the scene recommendation rule subjected to the parameter modification is updated and stored in the database, and the server continuously calls the updated scene recommendation rule in the database according to the set frequency.
Illustratively, for example, under the newly added scene function page,
editing and modifying scene basic information, rule definition, pushing strategy, pushing content and the like in a visual rule configuration page.
The visual basic information page can carry out custom editing modification on scene names;
for scene categories: sub-scenes/independent scenes, selecting;
editing and modifying specific years, months, days, time periods, minutes and seconds of effective time and failure time;
and carrying out scene remark explanation on the current scene.
The condition group type can be selected in the visual rule definition page;
selecting a condition group corresponding to the condition group type;
further configuration of conditional options.
In the visualized pushing strategy page, a pushing terminal can be selected;
the method comprises the steps of selecting the vehicle types under the vehicle type group corresponding to the pushing terminal in a subdivision mode;
and setting push frequency and push time interval parameters.
In the visualized push content page, the scene display duration can be configured;
configuring scene aging;
editing and modifying the execution content associated with the pushing strategy according to the basic information and rule definition.
According to the configuration, the cloud can edit and modify the content and rules of scene pushing with high flexibility, and a rear-end programmer is not required to perform secondary coding and testing on the logic rules of the bottom layer.
S103, acquiring scene recommendation information based on the vehicle environment parameter information and the real-time scene recommendation rule.
Based on the steps S101 and S102, the cloud end performs integration calculation by inputting the provided rich parameters according to the step S101 and calling the real-time scene recommendation rule of the step S102, and finally pushes the real-time scene recommendation information to the current vehicle end, and the vehicle end acquires push content through the Internet of vehicles equipment, namely acquires the scene recommendation information.
It should be noted that, the cloud end invokes the scene rule in step S102 in real time through the configured computing program, and the invoking frequency is adjusted according to the scene timeliness configured in the visualized pushing policy page in step S102. The method has the advantages that the real-time scenes of the vehicle end can be dynamically and real-time recommended according to the cloud configuration rules, the real-time scenes are closer to the current real scenes of the user and the vehicle, and the user experience is improved. The scene recommendation information is content which is based on the scene where the current actual scene is located, and is related to the scene according to the acquired environment parameters and used for improving user experience.
For example, the scene recommendation information can be that a birthday blessing push is carried out on a vehicle driver on a display screen of a vehicle end, and the push content can be that the birthday blessing push is carried out from top to bottom or from left to right in a text form; or carrying out birthday blessing broadcasting in a voice mode.
For example, the scene recommendation information can also be a prompt that the child lock of the vehicle is not locked for the driver on the display screen of the vehicle end, so as to guide the driver to perform the vehicle driving safety operation. Specifically, in this embodiment, it is detected that a child exists in the vehicle, and the current child lock is not locked, so that a display screen at the vehicle end prompts the driver to perform locking operation on the child lock, so as to ensure the safety of the child during driving.
For example, the scene recommendation information can be fatigue driving pushing, and fatigue driving content pushing is performed according to the fatigue degree of a driver and whether the driver is in a highway section, such as light fatigue (high speed/non-high speed) and medium/heavy fatigue (high speed/non-high speed). The vehicle cabin opening and relieving modes are adjusted corresponding to different fatigue states, and can be seat heating, seat ventilation, seat massage, air conditioning self-adaptive cold/warm air and the like. The method can also automatically start playing the corresponding relaxing music to relax nerves for the moderate/severe fatigue state, and additionally display the navigation route of the nearest service station for the driver in the moderate/severe fatigue state on the high-speed road section.
In the above scheme, it can be ensured that when a vehicle scene recommending method is executed under complex conditions, vehicle environment parameter information can be acquired so as to conveniently configure scene recommending rules through a rule configuration page, so that the problem that the current vehicle industry internet of vehicles recommending platform scheme mainly adopts a hard-coding mode of a vehicle end engine to realize a scene recommending function is solved, the realizing mode can only carry out fixed scene recommending according to the result of offline calculation, and cannot acquire environment parameters according to the actual scene where a user is currently located so as to further carry out scene content recommending according with the current actual situation, namely, the user scene recommending function cannot be dynamically configured and cannot carry out the scene recommending function in real time is solved.
In some embodiments, the configuring of the scene recommendation rules by the rule configuration page at step S102 may include: s201, logic for realizing scene recommendation rules based on a Drools language.
It should be noted that, the visual configuration of the scene recommendation rule and the real-time scene recommendation technology firstly utilizes the Drools expression language between the computer state and the user state to summarize the recommendation scene logic, and can support writing into the database in a mode of interface dynamic configuration, and the rule writing using Drools language has the advantages that the service rule is separated from the system code, the centralized management of the service rule is realized, the service rule can be expanded and maintained at any time without restarting the service, the service rule can be dynamically modified, the requirement change can be responded quickly, and meanwhile, the rule engine is relatively independent and only processes the service rule, so that the service analyst can also participate in editing and maintaining the service rule of the system, the cost and risk of hard coding the service rule are reduced, and the purposes of acquiring the recommendation scene rule in real time and configuring the visualization are achieved.
Drools language: DRL (Drools Rule Language) is simply called Drools ' rule language, when using Drools ' rule engine, the already configured rules are typically placed in a drl suffix file, then Drools ' rule engine reloads the rules, and the suffix drl file is typically configured as follows:
package is the package name;
import is the class imported;
function is optional, defined function;
query is an optional, defined query;
declare is optional, declaration;
global is an optional, defined global variable;
the rule "rule name 1" is followed by the attribute of "rule name 1";
a when post-connection condition;
then follow the action executed after meeting the condition;
end is over.
In some embodiments, the logic for implementing the scene recommendation rule based on the Drools language in step S201 may include: step S301, converting the logic into a Drools language and transmitting the Drools language to a database, wherein the configuration is a dynamic configuration of an associated database.
It should be noted that, because the rule logic of scene recommendation is written by using the Drools language, the scene recommendation rule can be dynamically modified when the scene recommendation rule configured in real time on the visual rule configuration page is called by the cloud, the change of the scene recommendation rule is responded quickly, the rule logic after modification is converted into the Drools language and transmitted to the database to be stored, the consistency of the database is realized, the overall stability of the method is enhanced, and the situation that the recommendation content corresponding to the scene recommendation rule before the change is pushed by the cloud is avoided to a great extent.
Database coherency (Database Consistency), i.e., the result of a transaction execution must be to change the database from one coherency state to another. Ensuring database consistency means that when a transaction is completed, all data must be brought to a consistent state, and in a relational database, all rules must be applied to the modification of the transaction in order to maintain the integrity of all data.
In some embodiments, when the above method is executed, step S103 may include, based on the vehicle environment parameter information and the real-time scene recommendation rule, acquiring the scene recommendation information: step S401, the vehicle environment parameters and the real-time scene recommendation rule logic are transmitted to an application so that the application can calculate based on the received data, wherein the application is developed based on the Flink framework.
It should be noted that, the application is a real-time scene computing program, and the cloud end transmits the received environmental parameter information collected according to the vehicle end and the configured scene recommendation rule to the program through starting the scene computing program, and the program is developed based on the link framework. In a specific embodiment of the scheme, no matter a vehicle in life is static or in vehicle driving, the surrounding environment is usually complex, the scene of a driver and passengers in the vehicle is also changeable instantaneously, the scheme aims to allocate pages according to a visual rule, real-time scene recommendation is carried out on a vehicle end, larger information throughput is generated, certain timeliness of data calculation and transmission is required to be maintained, and based on the point, an application program for cloud responsible calculation is developed based on a Flink frame, and the advantage is that the Flink is a distributed streaming data processing frame integrating high throughput, low delay and high performance in the current open source community, and the service logic requirement of the scheme can be well adapted. Other same type of processing engines, such as Apache Spark, can only compromise high throughput and high performance characteristics, but not low latency characteristics; while the streaming computing framework Apache Storm can only support low latency and high performance characteristics, but cannot meet the high throughput requirements. Meanwhile, the framework can support window calculation based on event time semantics, namely time generated by using events, the event-driven mechanism enables events to arrive in an out-of-order manner, a streaming system can calculate accurate results, the timeliness of the events when the events are originally generated is maintained, the influence of network transmission or a hardware system is avoided as much as possible, the stability and the accuracy of the scheme are maintained, and further, the framework also supports state management, namely intermediate result data of operators are stored in a memory or a file system in the streaming calculation process, the current result can be calculated from the previous state after the next event enters the operators, therefore, the process step of intermediate calculation is not required to be saved, the calculation efficiency of a cloud is greatly improved, the resource consumption of the data calculation process is reduced, and the real-time scene recommended content is more efficient.
In some embodiments, the step S101 of acquiring the vehicle environment parameter information may include:
step S501, data interception is performed through Kafka to obtain vehicle environment parameter information in real time.
It should be noted that, in the above method, the scene parameter information is transmitted to the cloud computing program after the data is monitored and captured by using Kafka, and according to step S401, the characteristics associated with the scheme are known to have three standards of high throughput, low delay and high performance, so in the aspect of the data monitoring technology of the scheme, kafka is used for implementation.
Kafka, a distributed message queue, has high performance, persistence, multi-copy backup, lateral expansion capabilities. In the scheme, a producer, namely data collected by a vehicle end, transmits a message to a queue; the consumer, i.e. the computing program configured in the cloud in the present solution, takes the message from the queue to perform service logic. The Kafka technology can be used for decoupling, peak clipping and asynchronous processing in the scheme.
Meanwhile, kafka has the capability of efficiently processing huge message flows, can ensure sub-second message delay while processing a large amount of data, and can well adapt to the requirement of the scheme on data monitoring.
In some embodiments, the method further comprises, when executed:
step S601, executing at least one function of adding, deleting or modifying to the vehicle scene based on the rule configuration page, and/or inputting custom condition information based on the rule configuration page to obtain scene recommendation information after condition filtering.
For example, the rule configuration page in the visualization may edit the content as mentioned in step S102 by adding scenes, may delete scenes from the configured scenes, and may edit and modify the configured scenes. Based on the above operation, the condition can be set and filtered on the rule definition page, for example, the birthday blessing push for the vehicle side is carried out on the vehicle owner, the condition type is firstly configured as a cloud condition group/vehicle side condition group, then the user portrait is selected for secondary screening and filtering under the configuration of the cloud condition group, and the condition is reconfigured, if the condition is the birthday of the vehicle owner; new filtering conditions such as lunar calendar/solar calendar, vehicle owner gender, etc. can also be added;
after the operation is carried out, programs developed based on the Flink framework are associated with the database, and scene rule configuration logic in the database is correspondingly added, deleted and modified.
In some embodiments, when the above method is performed, the step S101 of acquiring the vehicle environment parameter information may include: s701, acquiring an internal environment parameter and an external environment parameter of a vehicle, where the internal environment information parameter includes: at least one of a driving position information parameter, a passenger information parameter, a vehicle current information parameter, and a characteristic date parameter, and the external environment parameters include: at least one of weather information parameters, environment information parameters, and road information parameters.
It should be noted that the present solution can push rich scene recommendation information, so the types and conditions of the acquired information are also various, and when the vehicle end performs data acquisition, the environmental parameter information is divided into two types, namely an internal environmental parameter and an external environmental parameter, to be respectively acquired.
Illustratively, according to the acquired driving position information parameters, carrying out corresponding distraction driving reminding;
according to the acquired passenger information parameters, capturing states of other people in the vehicle, for example, acquiring noise decibels in the vehicle, filtering and judging sounds, and then reminding a child lock and pacifying crying;
correspondingly carrying out low electric quantity/low oil quantity reminding according to the current information parameters of the collected vehicle, and simultaneously displaying the corresponding driving mileage of the current residual electric quantity/oil quantity;
according to the acquired characteristic date, scene recommendation information of birthdays/annual celebrations/festival/solar terms of the user is carried out;
according to the acquired weather information parameters, weather early warning or broadcasting matched with the weather parameter information is correspondingly carried out;
according to the acquisition environment and the road information parameters, road condition broadcasting, future road condition prediction and path planning suggestion are correspondingly carried out;
it should be noted that, as an implementation of the method shown in fig. 1 and the related embodiments, the embodiment of the present invention further provides a vehicle scene recommendation device, which is configured to implement the method shown in fig. 1 and the related embodiments. The embodiment of the device corresponds to the embodiment of the method, and for convenience of reading, details of the embodiment of the method are not repeated one by one, but it should be clear that the device in the embodiment can correspondingly realize all the details of the embodiment of the method. Fig. 2 shows a schematic block diagram of a vehicle scene recommendation device 20 according to an embodiment of the present invention. As shown in fig. 2, the apparatus 20 includes:
an acquisition unit 21 for acquiring vehicle environment parameter information;
a detection unit 22, configured to dynamically configure the scene recommendation rules through a rule configuration page, so as to obtain real-time scene recommendation rules;
a matching unit 23, configured to obtain scene recommendation information based on the vehicle environment parameter information and the real-time scene recommendation rule.
By means of the technical scheme, the embodiment of the invention provides a vehicle scene recommendation method, for the scheme of the current vehicle networking recommendation platform in the automobile industry, a scene recommendation function is realized by adopting a hard coding mode of an engine at a vehicle end, the realization mode can only carry out fixed scene recommendation according to the result of offline calculation, and can not carry out scene content recommendation conforming to the current actual situation according to the current actual scene acquisition environment parameters of a user, namely the problem that the user scene recommendation function can not be dynamically configured and the scene recommendation function can not be carried out in real time is solved, and the vehicle end and cloud end integrated architecture is adopted and is divided into a vehicle end engine and a cloud end engine. The cloud engine adopts the visual configuration of scene rules and the real-time recommendation technology, so that the scene recommendation content is dynamically pushed to the vehicle end in real time. The vehicle-end engine is responsible for collecting parameters such as weather, environment and road conditions of a user in real time, reporting the parameters to the cloud engine, and obtaining scene recommended content issued by the cloud engine and displaying the scene recommended content to the user. The method realizes the dynamic visual configuration of the scene recommendation content, and dynamically adapts to the key characteristics of the current scene data of the user and the like to perform scene recommendation.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. One or more kernels can be arranged, and a vehicle scene recommending method is realized by adjusting kernel parameters, so that the problem that the current vehicle user scene recommendation cannot be dynamically configured and cannot be recommended in real time is solved.
The embodiment of the invention provides a storage medium, on which a program is stored, which when executed by a processor, implements a vehicle scene recommendation method.
The embodiment of the invention provides a processor, which is used for running a program, wherein the program runs to execute a vehicle scene recommendation method.
Fig. 3 shows a schematic block diagram of an electronic device 30 according to an embodiment of the invention. As shown in fig. 3, the electronic device 30 comprises at least one processor 31, at least one memory 32 connected to the processor 31, a bus 33; wherein, the processor 31 and the memory 32 complete the communication with each other through the bus 33; the processor 31 is configured to invoke the program instructions in the memory 32 to perform the vehicle scene recommendation method described above.
The device herein may be a server, PC, PAD, cell phone, etc.
The present application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: acquiring vehicle environment parameter information; dynamically configuring scene recommendation rules through a rule configuration page to obtain real-time scene recommendation rules; and acquiring scene recommendation information based on the vehicle environment parameter information and the scene recommendation rule.
Further, configuring the scene recommendation rule through the rule configuration page further includes:
acquiring vehicle environment parameter information;
dynamically configuring scene recommendation rules through a rule configuration page to obtain real-time scene recommendation rules;
based on the vehicle environment parameter information and the real-time scene recommendation rule, scene recommendation information is obtained.
Further, the dynamic configuration of the scene recommendation rule through the rule configuration page further includes:
logic to implement scene recommendation rules based on the Drools language.
Further, logic for implementing a scene recommendation rule based on a Drools language further includes:
the logic is converted to a Drools language and transmitted to the database, wherein the configuration is a dynamic configuration of the associated database.
Further, scene recommendation information is acquired based on the vehicle environment parameter information and the real-time scene recommendation rule:
the vehicle environment parameters and the real-time scene recommendation rules are logically transmitted to the application to cause the application to calculate based on the received data, wherein the application is developed based on the flank framework.
Further, acquiring vehicle environment parameter information includes:
and data monitoring is carried out through Kafka so as to acquire vehicle environment parameter information in real time.
Further, the method further comprises the following steps:
performing at least one of adding, deleting, or modifying functions for a vehicle scenario based on a rule configuration page, and/or
And based on the rule configuration page, inputting custom condition information to obtain scene recommendation information after condition filtering.
Further, acquiring vehicle environment parameter information includes:
acquiring an internal environment parameter and an external environment parameter of a vehicle, wherein the internal environment information parameter comprises: at least one of a driving position information parameter, a passenger information parameter, a vehicle current information parameter, and a characteristic date parameter, and the external environment parameters include: at least one of weather information parameters, environment information parameters, and road information parameters.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A vehicle scene recommendation method, characterized by comprising:
acquiring vehicle environment parameter information;
dynamically configuring scene recommendation rules through a rule configuration page to obtain real-time scene recommendation rules;
and acquiring scene recommendation information based on the vehicle environment parameter information and the real-time scene recommendation rule.
2. The method according to claim 1, characterized in that: the dynamic configuration of the scene recommendation rules through the rule configuration page comprises the following steps:
logic to implement the scene recommendation rules based on the Drools language.
3. The method according to claim 2, characterized in that: logic for implementing the scene recommendation rules based on a Drools language, including:
the logic is converted into a Drools language and transmitted to a database, wherein the configuration is a dynamic configuration of an associated database.
4. A method according to any one of claims 1 to 3, characterized in that: the acquiring scene recommendation information based on the vehicle environment parameter information and the real-time scene recommendation rule includes:
and logically transmitting the vehicle environment parameters and the real-time scene recommendation rules to an application so that the application calculates based on the received data, wherein the application is developed based on a Flink framework.
5. A method according to any one of claims 1 to 3, characterized in that: the obtaining the vehicle environment parameter information includes:
and carrying out data monitoring through Kafka to acquire the vehicle environment parameter information in real time.
6. A method according to any one of claims 1 to 3, further comprising:
based on the rule configuration page, performing at least one of adding, deleting, or modifying functions for the vehicle scene, and/or
And based on the rule configuration page, inputting user-defined condition information to obtain the scene recommendation information after condition filtering.
7. A method according to any one of claims 1 to 3, characterized in that: the obtaining the vehicle environment parameter information includes:
acquiring an internal environment parameter and an external environment parameter of the vehicle, wherein the internal environment information parameter comprises: at least one of a driving position information parameter, a passenger information parameter, a vehicle current information parameter, and a characteristic date parameter, wherein the external environment parameters include: at least one of weather information parameters, environment information parameters, and road information parameters.
8. A vehicle scene recommendation apparatus, characterized by comprising:
the acquisition unit is used for acquiring vehicle environment parameter information;
the configuration unit is used for dynamically configuring the scene recommendation rules through the rule configuration page so as to obtain real-time scene recommendation rules;
and the pushing unit is used for acquiring scene recommendation information based on the vehicle environment parameter information and the real-time scene recommendation rule.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the vehicle scene recommendation method according to any one of claims 1 to 7 is implemented when the program is executed by a processor.
10. An electronic device comprising at least one processor and at least one memory coupled to the processor; wherein the processor is configured to invoke program instructions in the memory to perform the vehicle scene recommendation method of any of claims 1 to 7.
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| CN202310080498.6A CN116112549A (en) | 2023-01-13 | 2023-01-13 | Vehicle scene recommendation method and related equipment |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105320766A (en) * | 2015-10-28 | 2016-02-10 | 百度在线网络技术（北京）有限公司 | Information pushing method and apparatus |
| WO2021000734A1 (en) * | 2019-07-02 | 2021-01-07 | 腾讯科技（深圳）有限公司 | Message pushing method, storage medium, and server |
| CN113934926A (en) * | 2021-09-18 | 2022-01-14 | 青岛海尔科技有限公司 | Recommendation method and device for interactive scene and electronic equipment |
| CN114036390A (en) * | 2021-11-17 | 2022-02-11 | 中国第一汽车股份有限公司 | Scene service recommendation method and device, electronic equipment and storage medium |
| CN114430424A (en) * | 2021-12-31 | 2022-05-03 | 联通智网科技股份有限公司 | Method and device for processing big data of Internet of vehicles and storage medium |
| CN114880569A (en) * | 2022-05-18 | 2022-08-09 | 中国第一汽车股份有限公司 | Recommendation control method and device for vehicle, electronic equipment, system and storage medium |
| CN115130002A (en) * | 2022-07-22 | 2022-09-30 | 上海鱼尔网络科技有限公司 | Recommendation request processing method and device, computer equipment and storage medium |
-
2023
- 2023-01-13 CN CN202310080498.6A patent/CN116112549A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105320766A (en) * | 2015-10-28 | 2016-02-10 | 百度在线网络技术（北京）有限公司 | Information pushing method and apparatus |
| WO2021000734A1 (en) * | 2019-07-02 | 2021-01-07 | 腾讯科技（深圳）有限公司 | Message pushing method, storage medium, and server |
| CN113934926A (en) * | 2021-09-18 | 2022-01-14 | 青岛海尔科技有限公司 | Recommendation method and device for interactive scene and electronic equipment |
| CN114036390A (en) * | 2021-11-17 | 2022-02-11 | 中国第一汽车股份有限公司 | Scene service recommendation method and device, electronic equipment and storage medium |
| CN114430424A (en) * | 2021-12-31 | 2022-05-03 | 联通智网科技股份有限公司 | Method and device for processing big data of Internet of vehicles and storage medium |
| CN114880569A (en) * | 2022-05-18 | 2022-08-09 | 中国第一汽车股份有限公司 | Recommendation control method and device for vehicle, electronic equipment, system and storage medium |
| CN115130002A (en) * | 2022-07-22 | 2022-09-30 | 上海鱼尔网络科技有限公司 | Recommendation request processing method and device, computer equipment and storage medium |
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