WO2024060965A1 - 动态配置业务逻辑的方法、云平台、车辆及存储介质 - Google Patents

动态配置业务逻辑的方法、云平台、车辆及存储介质 Download PDF

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Publication number
WO2024060965A1
WO2024060965A1 PCT/CN2023/116420 CN2023116420W WO2024060965A1 WO 2024060965 A1 WO2024060965 A1 WO 2024060965A1 CN 2023116420 W CN2023116420 W CN 2023116420W WO 2024060965 A1 WO2024060965 A1 WO 2024060965A1
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Prior art keywords
meta
scenario
business logic
vehicle
factor
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English (en)
French (fr)
Inventor
吴爱勇
任国昌
张世华
王津生
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Great Wall Motor Co Ltd
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Great Wall Motor Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/60—Software deployment
    • G06F8/65—Updates
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/70—Software maintenance or management
    • G06F8/71—Version control; Configuration management

Definitions

  • This application relates to the field of vehicle technology, and in particular, to a method of dynamically configuring business logic, a cloud platform, a vehicle and a storage medium.
  • This application aims to solve at least one of the technical problems existing in the prior art. To this end, the purpose of this application is to propose a method, cloud platform, vehicle and storage medium for dynamically configuring business logic.
  • the vehicle in this application upgrades the software system in the vehicle by receiving configuration messages sent by the cloud platform, which can quickly respond to demand changes, without the need for software developers to do repetitive and tedious development work, so that non-technical personnel can also participate in editing and maintenance.
  • the system's business rules eliminate the need for OTA upgrade iterations, which can reduce the cost and risk of software system upgrades.
  • This application proposes a method for dynamically configuring business logic, which is applied to cloud platforms and includes: receiving configuration information; and dynamically configuring a scenario decision tree according to the configuration information to generate a configuration message, and sending the configuration message Delivered to the vehicle, the scenario decision tree is obtained by scenario-based business logic through a preset event library.
  • the method for dynamically configuring business logic may also have the following additional technical features:
  • the scenario decision tree is obtained by contextualizing the business logic through a preset event library, including: determining multiple meta-scenarios according to the business logic and the preset event library; determining the meta-scenarios corresponding to the multiple meta-scenarios.
  • the identification factor and scene model of each meta-scene in the meta-scene determine the decision-making factor and execution factor corresponding to the scene model; according to the meta-scenario, the identification factor, the scene model, the decision-making factor and The execution factors construct the scenario decision tree.
  • the identification factor includes a meta-event, a first operator and a first parameter; the decision-making factor includes a meta-fact, a second operator and a second parameter; and the execution factor includes a meta-transaction and a third parameter.
  • dynamically configuring the scene decision tree according to the configuration information to generate a configuration message includes: configuring the configuration information of the identification factor corresponding to each meta-scenario in the plurality of meta-scenarios, and the configuration information of the identification factor corresponding to the scenario model.
  • the configuration information of the decision factor and the configuration information of the execution factor are written in the configuration message.
  • the form of the configuration message includes at least one of domain definition language DSL, decision table XSL and rule flow JBPM.
  • first parameter, the second parameter and the third parameter are determined according to a data mining algorithm.
  • the software in the vehicle is configured by dynamically configuring the scenario decision tree set on the cloud platform to obtain a configuration message, and sending the configuration message to the vehicle.
  • the system When the system is upgraded, it can quickly respond to demand changes, eliminating the need for software developers to do repetitive and tedious development work. Non-technical personnel can also participate in editing and maintaining the business rules of the system.
  • this application also proposes a method of dynamically configuring business logic, which is applied to vehicles, including: receiving data issued by the cloud platform.
  • Configuration message wherein the configuration message is obtained by the cloud platform dynamically configuring a scenario decision tree according to the configuration information, and the scenario decision tree is obtained by scenario-based business logic through a preset event library; parse the The configuration message is configured to obtain business rules corresponding to the business logic; and the business rules are executed.
  • the method for dynamically configuring business logic may also have the following additional technical features:
  • parsing the configuration message to obtain business rules corresponding to the business logic includes: registering a listening identification factor, and combining the first operator to perform meta-scene identification; and analyzing the meta-scenario and/or meta-event. Carry out conflict identification and make scenario decisions to obtain decision factors and execution factors.
  • executing the business rules includes: executing corresponding transactions in the scenario model based on the execution factors.
  • the monitoring identification factor before registering the monitoring identification factor, it also includes: connecting and opening up the basic atomic capabilities of the scene, and the basic atomic capabilities of the scene include meta-events, meta-facts and meta-transactions.
  • the vehicle upgrades the software system in the vehicle by receiving configuration messages sent by the cloud platform, and can quickly respond to demand changes without requiring software developers to do repetitive and tedious development work.
  • This allows non-technical personnel to participate in editing and maintaining the business rules of the system without the need for OTA upgrade iterations, which can reduce the cost and risk of software system upgrades.
  • this application also proposes a cloud platform, including: a receiving module for receiving configuration information; a configuration module for dynamically configuring the scene decision tree according to the configuration information to generate a configuration message, and The configuration message is sent to the vehicle, where the scenario decision tree is obtained by scenario-based business logic through a preset event library.
  • the cloud platform of the embodiment of the present application by dynamically configuring the scene decision tree set on the cloud platform, obtaining the configuration message, and sending it to the vehicle to upgrade the software system in the vehicle, it can quickly respond to demand changes.
  • software developers There is no need for software developers to do repetitive and tedious development work, so non-technical personnel can also participate in editing and maintaining the business rules of the system.
  • OTA upgrade iterations there is no need to perform OTA upgrade iterations, which can reduce the cost and risk of software system upgrades.
  • cloud platform may also have the following additional technical features:
  • the scenario decision tree is obtained by scenario-based business logic through a preset event library, including: determining multiple meta-scenarios according to the business logic; determining the meta-scenario corresponding to each meta-scenario in the multiple meta-scenarios. identifying factors and scene models; determining decision factors and execution factors corresponding to the scene model; and constructing the scene decision tree according to the meta-scenario, the identification factors, the scene model, the decision factors and the execution factors .
  • the identification factor includes a meta-event, a first operator and a first parameter; the decision-making factor includes a meta-fact, a second operator and a second parameter; and the execution factor includes a meta-transaction and a third parameter.
  • this application also proposes a vehicle, including: a receiving module for receiving a configuration message sent by the cloud platform, where the configuration message is the cloud platform's dynamic configuration of the scene decision tree. Obtained, the scenario decision tree is obtained by contextualizing the business logic through the preset event library; the parsing module is used to parse the configuration message to obtain the business rules corresponding to the business logic; and the execution module is used to Execute the business rules described.
  • the vehicle upgrades the software system in the vehicle by receiving configuration messages sent by the cloud platform, and can quickly respond to demand changes, without the need for software developers to do repetitive and tedious development work, so that non-technical personnel can also You can participate in editing and maintaining the business rules of the system without the need for OTA upgrade iterations, which can reduce the cost and risk of software system upgrades.
  • this application also proposes a computer-readable storage medium.
  • a program for dynamically configuring business logic is stored on the computer-readable storage medium.
  • the program for dynamically configuring business logic is executed by a processor, the following is implemented: The method for dynamically configuring business logic described in any of the above embodiments.
  • the configuration message is obtained by dynamically configuring the scenario decision tree set on the cloud platform, and The configuration message is sent to the vehicle to configure the software system in the vehicle. Upgrading can quickly respond to demand changes, without the need for software developers to do repetitive and tedious development work, allowing non-technical personnel to participate in editing and maintaining the business rules of the system. At the same time, there is no need to perform OTA upgrade iterations, which can reduce the cost and risk of software system upgrades. .
  • Figure 1 is a flow chart of a method for dynamically configuring business logic according to an embodiment of the present application
  • Figure 2 is a schematic structural diagram of a scenario decision tree according to an embodiment of the present application.
  • Figure 3 is a flow chart of a method for dynamically configuring business logic according to another embodiment of the present application.
  • Figure 4 is a schematic block diagram of a cloud platform according to an embodiment of the present application.
  • Figure 5 is a schematic block diagram of a vehicle according to one embodiment of the present application.
  • the cloud platform When upgrading and updating the software system in the embodiment of this application, the cloud platform obtains a scenario decision tree by contextualizing the business logic, dynamically configures the scenario decision tree to obtain a configuration message, and sends the configuration message to Using the vehicle to upgrade the software system in the vehicle can solve the problem of developers having to do repetitive and tedious development work when facing business needs and changes, while avoiding the risks that exist when performing OTA upgrades.
  • Figure 1 is a flow chart of a method for dynamically configuring business logic according to an embodiment of the present application. As shown in Figure 1, a method of dynamically configuring business logic for cloud platforms includes the following steps:
  • Step S1 Receive configuration information.
  • This configuration information can be set by product or operations personnel according to the business logic of the software system. Specifically, the configuration information is relevant parameters of the business logic.
  • Step S2 Dynamically configure the scene decision tree according to the configuration information to generate a configuration message, and send the configuration message to the vehicle; where the scene decision tree is obtained by scenario-based business logic through the preset event library.
  • the preset event library includes multiple preset events, each preset event includes at least one configuration information, and the business logic is composed of multiple preset events.
  • Scenarioization refers to regularizing business logic. Scenario is the way of regularizing business logic.
  • the scenario decision tree obtained by scenarioizing business logic can be used to describe the business logic. That is, the business logic of the software is separated from the code to form an independent Business rules can be dynamically modified through scenario decision trees when business logic changes to quickly respond to demand changes, allowing non-technical personnel to participate in editing and maintaining the business rules of the system, while reducing the cost and risk of software system upgrades. .
  • a scene is a fragment of something that happens in a certain time and space with a beginning and an end, that is, what happened at what time and place, what is the character's mood, what does he want to do next, and what actions will he take in order to achieve the goal? What purpose.
  • the car owner is traveling by car, the center of the vehicle The car owner suddenly cannot access the Internet, and the car owner will call the customer service phone for help. After receiving the car owner's request for help, the customer service will urgently seek engineers to help solve the problem.
  • the business logic is scenario-based, the scenario shown in Table 1 can be obtained.
  • the event library in this scenario includes facts, events, and transactions.
  • the facts include Who, When, and Where, the events include What, and the transactions include How; the configuration information of Who includes the car owner or customer service, and the configuration information of When includes no mobile phone during self-driving travel. Stand or office hours, Where configuration information includes unfamiliar cities or offices, What configuration information includes the central control unit suddenly being unable to access the Internet or the car owner seeking emergency help. Engineers help solve the problem.
  • the configuration message is generated by the cloud platform by dynamically configuring the scenario decision tree based on the configuration information.
  • the configuration message can take the form of at least one of domain definition language DSL, decision table XSL, and rule flow JBPM. Different configuration information can obtain different configuration messages.
  • DSL domain definition language
  • decision table XSL decision table XSL
  • rule flow JBPM rule flow JBPM
  • Different configuration information can obtain different configuration messages.
  • the scenario decision tree is obtained by contextualizing business logic through a preset event library, including: determining multiple meta-scenarios based on the business logic and the preset event library; determining the corresponding meta-scenarios corresponding to the multiple meta-scenarios.
  • the identification factor and scene model of each meta-scene in the scene determine the decision-making factor and execution factor corresponding to the scene model; build a scene decision tree based on the meta-scenario, identification factor, scene model, decision-making factor and execution factor.
  • the preset event library includes a plurality of meta-scenarios, an identification factor and a scene model corresponding to each meta-scenario in the plurality of meta-scenarios, and a decision-making factor and execution factor corresponding to the scene model.
  • the meta-scene is the smallest granular scene, consisting of identification factors and scene models.
  • business logic can include multiple dynamically connected meta-scenarios.
  • the identification factor contains the set of all events used to identify whether the scene can be triggered.
  • the scene model consists of decision factors and execution factors.
  • the decision factors include all condition sets used to determine whether the current scene can be executed.
  • the execution factors include all action instructions that need to be executed for the target touch point.
  • the cloud platform dynamically configures business logic through scenario decision trees to quickly respond to demand changes, allowing non-technical personnel to participate in editing and maintaining the business rules of the system, while reducing the cost and risk of software system upgrades.
  • the configuration information of the meta-scene includes, for example, whether the vehicle is in a traffic jam state
  • the configuration information of the identification factor includes, for example, the navigation system of the vehicle
  • the configuration information of the decision factor includes, for example, the air conditioning status of the vehicle
  • the configuration information of the execution factor includes Turns the external circulation mode of the vehicle's air conditioner on or off.
  • the vehicle's navigation system determines that the vehicle is in a traffic jam, and triggers the decision factor to detect the vehicle's air conditioning status.
  • the corresponding execution factor is to turn off the external circulation mode of the vehicle air conditioner and open the internal circulation mode of the vehicle air conditioner. At this time, the vehicle executes the transaction of turning off the outer circulation mode of the vehicle air conditioner and turning on the inner circulation mode of the vehicle air conditioner according to the execution factor.
  • the corresponding execution factor is not to change the circulation mode of the vehicle's air conditioner.
  • the vehicle's navigation system determines that the vehicle is not in a traffic jam state, and triggers the decision factor to detect the vehicle's air conditioning state.
  • the corresponding execution factor is to not change the circulation mode of the vehicle air conditioner; when the vehicle's air conditioner is not in the external circulation mode, the corresponding execution factor is to turn on the external circulation mode of the vehicle air conditioner.
  • the vehicle executes Factor execute the transaction of turning on the external circulation mode of the vehicle air conditioner.
  • identification factors include meta-events, first operators, and first parameters; decision-making factors include meta-facts, second operators, and second parameters; and execution factors include meta-transactions. and the third parameter.
  • meta-events only describe simple actions or state changes, such as navigation -> entering tunnel/exiting tunnel; meta-facts are minimal that cannot or do not need to be subdivided.
  • Fact elements such as vehicle status -> gear value is in P gear, meta-transactions are the smallest granularity transactions that cannot or do not need to be subdivided, such as multimedia -> playing audio (online music).
  • ) of meta-events, and the second operator is applied to the mathematical operations (>, ⁇ , ) and logical operations of meta-facts Operations (&&,
  • the first operator needs to be used to perform logical operations on each meta-event.
  • the result of the logical operation satisfies the trigger
  • the decision factor is triggered to determine whether the current scenario has a set of conditions that can be executed.
  • the second operator needs to be used to perform logical operations on each meta-fact.
  • the vehicle executes the action instruction corresponding to the execution factor. Therefore, when there are multiple meta-events and multiple meta-facts, the vehicle executes the action instructions corresponding to the execution factors through the logical operations of each meta-event and each meta-fact.
  • the configuration information of the meta-scene includes, for example, whether the vehicle is in a traffic jam and the air quality inside the vehicle.
  • the configuration information of the corresponding identification factors includes, for example, the vehicle's navigation system and air quality detection sensors.
  • the configuration of the corresponding decision factors includes, for example, the air conditioning status of the vehicle and the closed status of the vehicle windows.
  • the configuration information of the corresponding execution factors includes, for example, turning on or off the external circulation mode of the vehicle air conditioning and opening or closing the vehicle windows.
  • the vehicle's navigation system determines that the vehicle is in a traffic jam, and the air quality detection sensor in the vehicle detects that the air quality index in the vehicle is greater than the preset value (i.e., the first parameter), that is, whether the vehicle is in a traffic jam and the vehicle is in a traffic jam. Carry out logical && operation (i.e. the first operator) on the air quality within.
  • the decision factor is triggered to detect the vehicle's air conditioning status and the closed status of the windows.
  • operation i.e., the second operator
  • the vehicle's air conditioning state and the closed state of the window and the corresponding execution factor is to turn off the external circulation of the air conditioner. mode or close the windows. That is, in a scene, if there are multiple meta-events and multiple meta-facts at the same time, the logical operation of each meta-event is performed through the first operator.
  • the trigger decision factor is Determine whether the current scenario has an executable set of conditions.
  • the second operator is used to perform logical operations on each meta-fact.
  • the vehicle executes the action instruction corresponding to the execution factor.
  • the scenario decision tree is dynamically configured according to the configuration information to generate a configuration message, including: the configuration information of the identification factor corresponding to each meta-scenario in multiple meta-scenarios, and the scenario model The corresponding configuration information of the decision factor and the configuration information of the execution factor are written into the configuration message.
  • the form of the configuration message is as follows, where Scene is the scene, meta_scene is the meta scene, recognition_factor is the recognition factor, meta_event is the meta event, op is the operator, param is the parameter, scene_model is the scene model, and decision_factor is Decision factor, meta_fact is a meta-fact, action_factor is an execution factor, and meta_tranction is a meta-transaction.
  • the configuration message includes: meta-scenario, identification factor, meta-events, operators, and parameters corresponding to the identification factor, decision-making factors, meta-facts, operators, and parameters corresponding to the decision-making factor, execution factor, corresponding to the execution factor meta-transactions and parameters.
  • the configuration information of the identification factors corresponding to each meta-scenario in the multiple meta-scenarios as well as the configuration information and execution factors of the decision factors corresponding to the scenario model are
  • the configuration information is written into the configuration message to form independent business rules, so that when the business logic changes, the configuration message can be modified to quickly respond to the change in requirements, so that non-technical personnel can also participate in editing and maintaining the business rules of the system. At the same time, It can reduce the cost and risk of software system upgrade.
  • the first parameter, the second parameter and the third parameter are determined according to a data mining algorithm.
  • the first parameter is, for example, the temperature at which the vehicle air conditioner is turned on.
  • the vehicle air conditioning usage needs can be predicted and reminded based on the data mining algorithm.
  • the temperature of the air conditioner can be automatically adjusted after the person turns on the air conditioner in the car according to the person's usual habit of using the air conditioner. This makes the vehicle more intelligent when configuring business logic.
  • the method of dynamically configuring business logic in the embodiment of the present application by receiving configuration information, dynamically configuring the scenario decision tree set on the cloud platform to obtain a configuration message, and sending the configuration message to the vehicle to configure the vehicle.
  • Upgrading the software system can quickly respond to demand changes, without the need for software developers to do repetitive and tedious development work, so that non-technical personnel can also participate in editing and maintaining the business rules of the system.
  • there is no need to perform OTA upgrade iterations which can reduce the cost of software system upgrades. costs and risks.
  • FIG. 3 is a flow chart of a method of dynamically configuring business logic according to an embodiment of the present application. As shown in Figure 3, a method of dynamically configuring business logic is applied to vehicles. The method includes the following steps:
  • Step S10 Receive the configuration message sent by the cloud platform.
  • the configuration message is obtained by the cloud platform dynamically configuring the scenario decision tree based on the configuration information.
  • the scenario decision tree is obtained by scenario-based business logic through the preset event library. of.
  • the configuration message is generated by the cloud platform by dynamically configuring the scenario decision tree based on the configuration information.
  • the configuration message can take the form of at least one of domain definition language DSL, decision table XSL, and rule flow JBPM. Different configuration information can obtain different configuration packets.
  • DSL domain definition language
  • decision table XSL decision table XSL
  • rule flow JBPM rule flow JBPM
  • Different configuration information can obtain different configuration packets.
  • Step S20 Parse the configuration message to obtain business rules corresponding to the business logic.
  • configuration messages separate business logic from code and form independent business rules. That is, after the vehicle parses the configuration message, it executes according to the meta-scenario in the configuration message, the identification factor, the meta-events, operators and parameters corresponding to the identification factor, and the decision-making factor, which corresponds to the meta-facts, operators and parameters of the decision-making factor.
  • Factors, meta-transactions and parameters corresponding to execution factors control relevant components in the vehicle to perform corresponding actions.
  • Step S30 Execute business rules, including: executing corresponding transactions in the scenario model based on execution factors.
  • the configuration message is parsed to obtain business rules corresponding to the business logic, including: registering a monitoring identification factor, and performing meta-scene identification in combination with a first operator; performing conflict identification on meta-scenes and/or meta-events, and performing scene decisions to obtain decision factors and execution factors.
  • meta-scenarios there may be multiple meta-scenarios at the same time, such as whether the vehicle is in a traffic jam or the air quality in the car. Therefore, when multiple meta-scenarios exist, conflict identification is required. For example, you can determine which meta-scenario corresponds to the meta-transaction to be executed first by setting the priority of the meta-scenario. Similarly, when there are multiple meta-events, conflict identification is also required to determine which meta-transaction in the same meta-scenario is executed first. Therefore, when there are multiple meta-scenarios, the vehicle is allowed to undergo conflict identification and the execution order of meta-transactions corresponding to the meta-scenarios is determined.
  • the meta-scene includes, for example, whether the vehicle is in a traffic jam and the air quality inside the vehicle.
  • the corresponding identification factors include, for example, the vehicle's navigation system and air quality detection sensors.
  • the corresponding decision factors for example, include the vehicle's air conditioning status and the vehicle's air quality.
  • the closed state of the window, the corresponding execution factors include, for example, turning on or off the external circulation mode of the vehicle air conditioner and opening or closing the vehicle window.
  • the decision factor is triggered first to detect the closed state of the window, and when When the car window is open, the corresponding execution factor is to close the car window.
  • the method before registering the monitoring identification factor, further includes: connecting and opening up the scene-based atomic capabilities.
  • the scene-based atomic capabilities include meta-events, meta-facts, and meta-transactions.
  • the docking and opening up of the basic atomic capabilities of the scene includes the enabling capabilities of the corresponding components in the vehicle that correspond to meta-events, meta-facts, and meta-transactions.
  • the meta-event is, for example, the vehicle's navigation system
  • the meta-fact is, for example, the vehicle's air-conditioning status
  • the meta-transaction is, for example, turning on or off the external circulation mode of the vehicle's air conditioner.
  • the vehicle upgrades the software system in the vehicle by receiving configuration messages sent by the cloud platform, and can quickly respond to demand changes without requiring software developers to do repetitive and tedious development work.
  • This allows non-technical personnel to participate in editing and maintaining the business rules of the system without the need for OTA upgrade iterations, which can reduce the cost and risk of software system upgrades.
  • a further embodiment of the present application also discloses a cloud platform 100, including: a receiving module 11 and a configuration module 12.
  • the receiving module 11 is used to receive configuration information.
  • the configuration module 12 is configured to dynamically configure the scene decision tree according to the configuration information to generate a configuration message, and deliver the configuration message to the vehicle.
  • the scenario decision tree is obtained by scenario-based business logic through a preset event library.
  • the scenario decision tree is obtained by scenario-based business logic through a preset event library, including: determining multiple meta-scenarios according to the business logic; determining the corresponding meta-scenario for each of the multiple meta-scenarios. Identify factors and scene models of meta-scenarios; determine decision factors and execution factors corresponding to the scene model; build scene decision trees based on meta-scenarios, recognition factors, scene models, decision factors, and execution factors.
  • the identification factor includes a meta-event, a first operator and a first parameter; the decision-making factor includes a meta-fact, a second operator and a second parameter; and the execution factor includes a meta-transaction and a third parameter.
  • the scenario decision tree is dynamically configured according to the configuration information to generate a configuration message, including: the configuration information of the identification factor corresponding to each meta-scenario in multiple meta-scenarios, the scenario model corresponding to The configuration information of the decision factor and the configuration information of the execution factor are written in the configuration message.
  • the first parameter, the second parameter and the third parameter are determined according to a data mining algorithm.
  • the cloud platform 100 of the embodiment of the present application by receiving configuration information, dynamically configuring the scenario decision tree set on the cloud platform to obtain a configuration message, and sending the configuration message to the vehicle to configure the software system in the vehicle. Upgrading can quickly respond to demand changes, without the need for software developers to do repetitive and tedious development work, allowing non-technical personnel to participate in editing and maintaining the business rules of the system. At the same time, there is no need to perform OTA upgrade iterations, which can reduce the cost and risk of software system upgrades. .
  • a further embodiment of the present application further discloses a vehicle 200, comprising: a receiving module 21, a parsing module 22 and an execution module 23.
  • the receiving module 21 is used to receive a configuration message sent by a cloud platform, wherein the configuration message is obtained by the cloud platform dynamically configuring a scenario decision tree, and the scenario decision tree is obtained by scenario-based business logic through a preset event library.
  • the parsing module 22 is used to parse the configuration message to obtain business rules corresponding to the business logic.
  • the execution module 23 is used to execute business rules.
  • parsing the configuration message to obtain business rules corresponding to the business logic includes: registering a listening identification factor, and combining it with the first operator to perform meta-scene identification; performing meta-scenario and/or meta-event identification. Conflict identification, scenario decision-making, and decision-making factors and execution factors are obtained.
  • executing business rules includes: executing corresponding transactions in the scenario model based on execution factors.
  • the listening identification factor before registering the listening identification factor, it also includes connecting and opening up the scene-based atomic capabilities.
  • the scene-based atomic capabilities include meta-events, meta-facts, and meta-transactions.
  • the vehicle upgrades the software system in the vehicle by receiving configuration messages sent by the cloud platform, and can quickly respond to demand changes, without the need for software developers to do repetitive and tedious development work, so that non-technical personnel can also You can participate in editing and maintaining the business rules of the system without the need for OTA upgrade iterations, which can reduce the cost and risk of software system upgrades.
  • a further embodiment of the present application also discloses a computer-readable storage medium.
  • the computer-readable storage medium stores a program for dynamically configuring business logic.
  • the program for dynamically configuring business logic is executed by a processor, it implements any of the above.
  • the configuration message is obtained by dynamically configuring the scenario decision tree set on the cloud platform, and The configuration message is sent to the vehicle to upgrade the software system in the vehicle, which can quickly respond to demand changes and eliminate the need for software developers to do repetitive and tedious development work.
  • This allows non-technical personnel to participate in editing and maintaining the business rules of the system, and does not require Carrying out OTA upgrade iterations can reduce the cost and risk of software system upgrades.

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Abstract

本申请公开了一种动态配置业务逻辑的方法、云平台、车辆及存储介质。该方法包括：接收配置信息；和根据该配置信息对场景决策树进行动态配置以生成配置报文，并将该配置报文下发至车辆，其中，该场景决策树由通过预设事件库将业务逻辑进行场景化得到。

Description

动态配置业务逻辑的方法、云平台、车辆及存储介质
相关申请的交叉引用
本申请要求在2022年9月20日提交至中国国家知识产权局、申请号为202211148052.4、名称为“动态配置业务逻辑的方法、云平台、车辆及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及车辆技术领域，尤其是涉及一种动态配置业务逻辑的方法、云平台、车辆及存储介质。
背景技术
随着软件定义汽车和驾舱智能化的演进革新，软件赋能驾舱智能化的趋势日益凸显。然而，软件领域中的一个最大的需求就是变更(包括迭代更新)，无数的软件开发者都在实现需求和应对变更的道路上做着重复繁琐的开发工作，使得开发者面临如此频繁的变更时相当痛苦。另外，传统的软件变更需要OTA(Over-the-Air Technology，空中下载技术)升级迭代，OTA升级迭代除费用成本外，还具备一定的风险。
发明内容
本申请旨在至少解决现有技术中存在的技术问题之一。为此，本申请的目的在于提出一种动态配置业务逻辑的方法、云平台、车辆及存储介质。
本申请中的车辆通过接收云平台下发的配置报文对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
本申请提出的一种动态配置业务逻辑的方法，应用于云平台，包括：接收配置信息；和根据所述配置信息对场景决策树进行动态配置以生成配置报文，并将所述配置报文下发至车辆，其中，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到。
另外，根据本申请实施例的动态配置业务逻辑的方法，还可以具有如下附加的技术特征：
进一步地，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到，包括：根据所述业务逻辑和所述预设事件库，确定多个元场景；确定对应于所述多个元场景中的每一个元场景的识别因子和场景模型；确定对应于所述场景模型的决策因子和执行因子；根据所述元场景、所述识别因子、所述场景模型、所述决策因子和所述执行因子构建所述场景决策树。
进一步地，所述识别因子包括元事件、第一算子和第一参数；所述决策因子包括元事实、第二算子和第二参数；和所述执行因子包括元事务和第三参数。
进一步地，根据所述配置信息对场景决策树进行动态配置以生成配置报文，包括：将所述多个元场景中的每一个元场景对应的识别因子的配置信息，所述场景模型对应的决策因子的配置信息和所述执行因子的配置信息写入所述配置报文中。
进一步地，所述配置报文采用的形式包括领域定义语言DSL、决策表XSL和规则流JBPM中的至少一种。
进一步地，所述第一参数、所述第二参数和所述第三参数根据数据挖掘算法确定。
根据本申请实施例的动态配置业务逻辑的方法，通过对设置于云平台上的场景决策树进行动态配置以得到配置报文，并将所述配置报文下发至车辆来对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
针对上述存在的问题，本申请还提出一种动态配置业务逻辑的方法，应用于车辆，包括：接收云平台下发的 配置报文，其中，所述配置报文为所述云平台根据配置信息对场景决策树进行动态配置得到的，所述场景决策树为通过预设事件库将业务逻辑进行场景化得到；解析所述配置报文以得到对应于所述业务逻辑的业务规则；和执行所述业务规则。
另外，根据本申请实施例的动态配置业务逻辑的方法，还可以具有如下附加的技术特征：
进一步地，解析所述配置报文以得到对应于所述业务逻辑的业务规则，包括：注册监听识别因子，并结合第一算子进行元场景识别；和对所述元场景和/或元事件进行冲突识别，并进行场景决策，得到决策因子和执行因子。
进一步地，执行所述业务规则，包括：基于所述执行因子，执行场景模型中对应的事务。
进一步地，在注册监听识别因子之前，还包括：进行场景基础原子能力的对接打通，所述场景基础原子能力包括元事件、元事实和元事务。
根据本申请实施例的动态配置业务逻辑的方法，车辆通过接收云平台下发的配置报文对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
针对上述存在的问题，本申请还提出一种云平台，包括：接收模块，用于接收配置信息；配置模块，用于根据所述配置信息对场景决策树进行动态配置以生成配置报文，并将所述配置报文下发至车辆，其中，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到。
根据本申请实施例的云平台，通过对设置于云平台上的场景决策树进行动态配置，得到配置报文，并下发至车辆来对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
另外，根据本申请实施例的云平台，还可以具有如下附加的技术特征：
进一步地，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到，包括：根据业务逻辑，确定多个元场景；确定对应于所述多个元场景中的每一个元场景的识别因子和场景模型；确定对应于所述场景模型的决策因子和执行因子；和根据所述元场景、所述识别因子、所述场景模型、所述决策因子和执行因子构建所述场景决策树。
进一步地，所述识别因子包括元事件、第一算子和第一参数；所述决策因子包括元事实、第二算子和第二参数；和所述执行因子包括元事务和第三参数。
针对上述存在的问题，本申请还提出一种车辆，包括：接收模块，用于接收云平台下发的配置报文，其中，所述配置报文为所述云平台对场景决策树进行动态配置得到的，所述场景决策树为通过预设事件库将业务逻辑进行场景化得到的；解析模块，用于解析配置报文以得到对应于所述业务逻辑的业务规则；和执行模块，用于执行所述业务规则。
根据本申请实施例的车辆，车辆通过接收云平台下发的配置报文对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
针对上述存在的问题，本申请还提出一种计算机可读存储介质，所述计算机可读存储介质上存储有动态配置业务逻辑的程序，所述动态配置业务逻辑的程序被处理器执行时实现如上述任一实施例所述的动态配置业务逻辑的方法。
根据本申请实施例的计算机可读存储介质，其上存储的动态配置业务逻辑的程序被处理器执行时，通过对设置于云平台上的场景决策树进行动态配置以得到配置报文，并将所述配置报文下发至车辆来对车辆中的软件系统 进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
本申请的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
本申请的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解，其中：
图1是根据本申请一个实施例的动态配置业务逻辑的方法的流程图；
图2是根据本申请一个实施例的场景决策树的结构示意图；
图3是根据本申请另一个实施例的动态配置业务逻辑的方法的流程图；
图4是根据本申请一个实施例的云平台的示意性框图。
图5是根据本申请一个实施例的车辆的示意性框图。
具体实施方式
为了能够更加详尽地了解本申请实施例的特点与技术内容，下面结合附图对本申请实施例的实现进行详细阐述，所附附图仅供参考说明之用，并非用来限定本申请实施例。在以下的技术描述中，为方便解释起见，通过多个细节以提供对所披露实施例的充分理解。然而，在没有这些细节的情况下，一个或多个实施例仍然可以实施。在其它情况下，为简化附图，熟知的结构和装置可以简化展示。
下面参考图1-图5描述根据本申请实施例的动态配置业务逻辑的方法、云平台、车辆及存储介质。
目前，软件定义汽车和驾舱智能化的趋势日益凸显，而汽车软件领域中一个最大的需求就是变更(包括迭代更新)，无数的软件开发者都在实现需求和应对变更的道路上做着重复繁琐的开发工作，使得开发者面临如此频繁的变更时相当痛苦。此外，汽车远程升级技术OTA(Over-the-Air)是将更新软件上传到OTA中心，然后OTA中心无线传输更新软件到车辆端，最后车辆端自动更新软件。这导致升级流程复杂，成本高，且存在较大的升级失败的风险。
本申请实施例在进行软件系统的升级和更新时，云平台通过将业务逻辑进行场景化来得到场景决策树，对场景决策树进行动态配置以得到配置报文，并将配置报文下发至车辆来对车辆中的软件系统进行升级，可以解决在面对业务需求和变更时，开发人员需要做重复繁琐的开发工作的问题，同时避免在进行OTA升级时存在的风险。
图1是根据本申请一个实施例的动态配置业务逻辑的方法的流程图。如图1所示，一种动态配置业务逻辑的方法，用于云平台，该方法包括以下步骤：
步骤S1：接收配置信息。该配置信息可以由产品或运营人员根据软件系统的业务逻辑设置。具体地，配置信息为业务逻辑的相关参数。
步骤S2：根据配置信息对场景决策树进行动态配置以生成配置报文，并将配置报文下发至车辆；其中，场景决策树由通过预设事件库将业务逻辑进行场景化得到。
具体而言，预设事件库包括多个预设事件，每个预设事件包括至少一个配置信息，业务逻辑由多个预设事件组成。场景化是指将业务逻辑规则化，场景即是业务逻辑规则化的方式，将业务逻辑进行场景化得到的场景决策树可以用来描述业务逻辑，即将软件的业务逻辑从代码中分离，形成独立的业务规则，以在业务逻辑发生变化时，通过场景决策树动态修改业务规则来快速响应需求变更，使得非技术人员也可以参与编辑维护系统的业务规则，同时能够降低软件系统升级的代价和风险。
示例性地，场景是在某个时空下发生的有开始、有结尾的事情片段，即什么时间什么地点发生了什么事，人物心情怎样，接下来他想做什么，会有什么动作，为了达到什么目的。具体地，当车主在自驾旅行中，车辆的中 控突然无法上网，车主会拨打客服电话寻求帮助，客服在接到车主的求助后，会紧急寻求工程师协助解决，则将该业务逻辑进行场景化后，可得到如表1所示的场景。该场景中的事件库包括事实、事件和事务，其中，事实包括Who、When、Where，事件包括What，事务包括How；Who的配置信息包括车主或客服，When的配置信息包括自驾旅行中无手机支架或办公时间，Where的配置信息包括陌生城市或办公室，What的配置信息包括中控突然无法上网或车主紧急求助中控突然无法上网，How的配置信息包括车停路边拨打客服电话或紧急寻求工程师协助解决。
表1
具体而言，配置报文由云平台根据配置信息对场景决策树进行动态配置生成，配置报文可以采用的形式包括领域定义语言DSL、决策表XSL和规则流JBPM等中的至少一种。不同的配置信息可以得到不同的配置报文，当配置信息发生变化时，只需要在云平台对场景决策树进行配置，由云平台生成配置报文后，将配置报文下发至车辆，由车辆对配置报文进行解析执行，以避免在业务需求(业务流程)发生变更时，软件开发人员做重复繁琐的开发工作，同时无需进行OTA(Over-the-Air Technology，空中下载技术)升级迭代即可实现需求的变更，以降低软件系统升级的代价和风险。
在本申请的一个实施例中，场景决策树由通过预设事件库将业务逻辑进行场景化得到，包括：根据业务逻辑和预设事件库，确定多个元场景；确定对应于该多个元场景中的每一个元场景的识别因子和场景模型；确定对应于该场景模型的决策因子和执行因子；根据元场景、识别因子、场景模型、决策因子和执行因子构建场景决策树。
具体而言，预设事件库包括多个元场景、对应于该多个元场景中的每一个元场景的识别因子和场景模型、对应于该场景模型的决策因子和执行因子。如图2所示，元场景是最小粒度的场景，由识别因子和场景模型构成。根据时间顺序的不同，业务逻辑可以包括多个动态串联的元场景。识别因子包含所有用于识别该场景是否可以被触发的事件集。场景模型由决策因子和执行因子构成，其中，决策因子包含所有用于判定当前场景是否可以执行的条件集，执行因子包含针对目标触点需要执行的所有动作指令。由此，云平台通过场景决策树动态配置业务逻辑来快速响应需求变更，使得非技术人员也可以参与编辑维护系统的业务规则，同时能够降低软件系统升级的代价和风险。
在具体实施例中，元场景的配置信息例如包括车辆是否处于堵车状态，识别因子的配置信息例如包括车辆的导航系统，决策因子的配置信息例如包括车辆的空调状态，执行因子的配置信息例如包括打开或关闭车辆空调的外循环模式。则当车辆处于堵车状态时，为了避免车外的汽车尾气污染车内的空气，车辆的导航系统判断车辆处于堵车状态，则触发决策因子去检测车辆的空调状态。当车辆的空调处于外循环模式时，对应的执行因子为关闭车辆空调的外循环模式，打开车辆空调的内循环模式。此时，车辆根据执行因子，执行关闭车辆空调的外循环模式，打开车辆空调的内循环模式的事务。当车辆的空调不处于外循环模式时，对应的执行因子为不改变车辆空调的循环模式。同样的，当车辆由堵车状态变为不堵车状态时，为了使车内空气与室外空气保持流通，车辆的导航系统判断车辆不处于堵车状态，则触发决策因子去检测车辆的空调状态，当车辆的空调处于外循环模式时，对应的执行因子为不改变车辆空调的循环模式；当车辆的空调不处于外循环模式时，对应的执行因子为打开车辆空调的外循环模式，此时车辆根据执行因子，执行打开车辆空调的外循环模式的事务。
在本申请的一个实施例中，如图2所示，识别因子包括元事件、第一算子和第一参数；决策因子包括元事实、第二算子和第二参数；执行因子包括元事务和第三参数。
具体而言，元事件仅描述简单的动作或状态的改变，如导航->进入隧道/驶出隧道；元事实是不能或不必再分的最小 事实元素，如车辆状态->档位值位于P档，元事务是不能或不必再分的最小颗粒度的事务，如多媒体->播放音频(在线音乐)。第一算子应用于元事件的数学运算(>,<,＝)以及逻辑运算(&&，||、|)，第二算子应用于元事实的数学运算(>,<,＝)以及逻辑运算(&&，||、|)。在一个场景中，可能同时存在多个元事件和/或多个元事实，因此当存在多个元事件时，需要通过第一算子进行各个元事件的逻辑运算，当逻辑运算的结果满足触发决策因子的条件时，则触发决策因子以判定当前场景是否有可以执行的条件集。当存在多个元事实时，需要通过第二算子进行各个元事实的逻辑运算，当逻辑运算的结果满足触发执行因子的条件时，车辆执行对应于执行因子的动作指令。由此，当存在多个元事件和多个元事实时，车辆经过各个元事件和各个元事实的逻辑运算，执行对应于执行因子的动作指令。
在具体实施例中，元场景的配置信息例如包括车辆是否处于堵车状态和车内的空气质量，对应的识别因子的配置信息例如包括车辆的导航系统和空气质量检测传感器，对应的决策因子的配置信息例如包括车辆的空调状态和车窗的关闭状态，对应的执行因子的配置信息例如包括打开或关闭车辆空调的外循环模式和打开或关闭车窗。当车辆处于堵车状态时，车辆的导航系统判断车辆处于堵车状态，车内的空气质量检测传感器检测车内的空气质量指数大于预设数值(即第一参数)，即将车辆是否处于堵车状态和车内的空气质量进行逻辑&&运算(即第一算子)。当车辆的导航系统判断车辆处于堵车状态，且车内的空气质量检测传感器检测车内的空气质量指数大于预设数值时，触发决策因子去检测车辆的空调状态和车窗的关闭状态。当车辆的空调处于外循环模式或者车窗处于打开状态时，即将车辆的空调状态和车窗的关闭状态进行逻辑||运算(即第二算子)，对应的执行因子为关闭空调的外循环模式或关闭车窗。即在一个场景中，若同时存在多个元事件和多个元事实，通过第一算子进行各个元事件的逻辑运算，当逻辑运算的结果满足触发决策因子的条件时，则触发决策因子以判定当前场景是否有可以执行的条件集。当存在多个元事实时，通过第二算子进行各个元事实的逻辑运算，当逻辑运算的结果满足触发执行因子的条件时，车辆执行对应于执行因子的动作指令。
在本申请的一个实施例中，根据配置信息对场景决策树进行动态配置以生成配置报文，包括：将多个元场景中的每一个元场景对应的识别因子的配置信息，以及该场景模型对应的决策因子的配置信息和执行因子的配置信息写入配置报文中。
在具体实施例中，配置报文的形式如下，其中，Scene是场景，meta_scene是元场景，recognition_factor是识别因子，meta_event是元事件，op是算子，param是参数，scene_model是场景模型，decision_factor是决策因子，meta_fact是元事实，action_factor是执行因子，meta_tranction是元事务。具体地，配置报文包括：元场景，识别因子，对应于识别因子的元事件、算子和参数，决策因子，对应于决策因子的元事实、算子和参数，执行因子，对应于执行因子的元事务和参数。通过对场景决策树进行动态配置，即将多个元场景串联起来，并将多个元场景中的每一个元场景对应的识别因子的配置信息以及该场景模型对应的决策因子的配置信息和执行因子的配置信息写入配置报文中，形成独立的业务规则，以在业务逻辑发生变化时，通过修改配置报文来快速响应需求变更，使得非技术人员也可以参与编辑维护系统的业务规则，同时能够降低软件系统升级的代价和风险。
在本申请的一个实施例中，第一参数、第二参数和第三参数根据数据挖掘算法确定。
具体而言，第一参数例如为车辆空调打开的温度。当调节第一参数时，可以基于数据挖掘算法，进行车辆空调使用需求的预判与提醒。在具体实施例中，当车内的摄像头检测到驾驶人员为特定人员时，根据该人员平时使用空调的习惯，当该人员打开车内空调后，可以自动调节空调的温度。由此，使得车辆在配置业务逻辑时，更加智能化。
根据本申请实施例的动态配置业务逻辑的方法，通过接收配置信息，对设置于云平台上的场景决策树进行动态配置以得到配置报文，并将配置报文下发至车辆来对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
本申请的进一步实施例还公开了另一种动态配置业务逻辑的方法。图3是根据本申请一个实施例的动态配置业务逻辑的方法的流程图，如图3所示，一种动态配置业务逻辑的方法，应用于车辆，该方法包括以下步骤：
步骤S10：接收云平台下发的配置报文，其中，配置报文为云平台根据配置信息对场景决策树进行动态配置得到的，场景决策树为通过预设事件库将业务逻辑进行场景化得到的。
具体而言，配置报文由云平台根据配置信息对场景决策树进行动态配置生成，配置报文可以采用的形式包括领域定义语言DSL、决策表XSL和规则流JBPM等中的至少一种。不同的配置信息可以得到不同的配置报文。当配置信息发生变化时，只需要在云平台对场景决策树进行配置，由云平台生成配置报文后，将配置报文下发至车辆，由车辆对配置报文进行解析执行，以避免在业务需求(业务流程)发生变更时，软件开发人员做重复繁琐的开发工作，同时无需进行OTA升级迭代即可实现需求的变更，以降低软件系统升级的代价和风险。
步骤S20：解析配置报文以得到对应于业务逻辑的业务规则。
具体而言，配置报文把业务逻辑从代码中分离，形成独立的业务规则。即车辆解析配置报文后，根据配置报文中的元场景，识别因子，对应于识别因子的元事件、算子和参数，决策因子，对应于决策因子的元事实、算子和参数，执行因子、对应于执行因子的元事务和参数，控制车辆中的相关部件执行相应的动作。
步骤S30：执行业务规则，包括：基于执行因子，执行场景模型中对应的事务。
在本申请的一个实施例中，解析配置报文以得到对应于业务逻辑的业务规则，包括：注册监听识别因子，并结合第一算子进行元场景识别；对元场景和/或元事件进行冲突识别，并进行场景决策以得到决策因子和执行因子。
具体而言，在一个场景中，可能同时存在多个元场景，例如车辆是否处于堵车状态、车内的空气质量，因此当存在多个元场景时，需要进行冲突识别。例如可以通过设定元场景的优先级来确定先执行哪个元场景对应的元事务。同样的，当存在多个元事件时，也需要进行冲突识别，以确定先执行同一元场景中的哪个元事务。由此，当存在多个元场景时，使得车辆经过冲突识别，确定元场景对应的元事务的执行顺序。
在具体实施例中，元场景例如包括车辆是否处于堵车状态和车内的空气质量，对应的识别因子例如包括车辆的导航系统和空气质量检测传感器，对应的决策因子例如包括车辆的空调状态和车窗的关闭状态，对应的执行因子例如包括打开或关闭车辆空调的外循环模式和打开或关闭车窗。若车辆是否处于堵车状态的优先级低于车内的空气质量的优先级，则当车辆处于堵车状态且空气质量指数大于预设数值时，优先触发决策因子去检测车窗的关闭状态，且当车窗处于打开状态时，对应的执行因子为关闭车窗。
在本申请的一个实施例中，在注册监听识别因子之前，该方法还包括：进行场景基础原子能力的对接打通，场景基础原子能力包括元事件、元事实、元事务。
具体而言，场景基础原子能力的对接打通包括车辆中对应于元事件、元事实、元事务的相应部件具有可以使能的能力。在具体实施例中，元事件例如为车辆的导航系统，元事实例如为车辆的空调状态，元事务例如为打开或关闭车辆空调的外循环模式，则在车辆解析配置报文之前，应保证车辆的导航系统、车辆的空调处于可被软件使能的状态，以在接收到配置报文后，能够进行车辆中软件系统的升级。
根据本申请实施例的动态配置业务逻辑的方法，车辆通过接收云平台下发的配置报文对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
如图4所示，本申请的进一步实施例还公开了一种云平台100，包括：接收模块11和配置模块12。其中，接收模块11用于接收配置信息。配置模块12用于根据该配置信息对场景决策树进行动态配置以生成配置报文，并将该配置报文下发至车辆。其中，该场景决策树由通过预设事件库将业务逻辑进行场景化得到。
在本申请的一个实施例中，场景决策树由通过预设事件库将业务逻辑进行场景化得到，包括：根据业务逻辑，确定多个元场景；确定对应于该多个元场景中的每一个元场景的识别因子和场景模型；确定对应于场景模型的决策因子和执行因子；根据元场景、识别因子、场景模型、决策因子和执行因子构建场景决策树。
在本申请的一个实施例中，识别因子包括元事件、第一算子和第一参数；决策因子包括元事实、第二算子和第二参数；执行因子包括元事务和第三参数。
在本申请的一个实施例中，根据配置信息对场景决策树进行动态配置以生成配置报文，包括：将多个元场景中的每一个元场景对应的识别因子的配置信息、场景模型对应的决策因子的配置信息和执行因子的配置信息写入配置报文中。
在本申请的一个实施例中，第一参数、第二参数和第三参数根据数据挖掘算法确定。
需要说明的是，本申请实施例的云平台在进行动态配置业务逻辑时，其具体实现方式与本申请实施例的用于云平台的动态配置业务逻辑的方法的具体实现方式类似，具体请参见方法部分的描述，为了减少冗余，此处不再赘述。
根据本申请实施例的云平台100，通过接收配置信息，对设置于云平台上的场景决策树进行动态配置以得到配置报文，并将配置报文下发至车辆来对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
如图5所示，本申请的进一步实施例还公开了一种车辆200，包括：接收模块21、解析模块22和执行模块23。其中，接收模块21用于接收云平台下发的配置报文，其中，配置报文为云平台对场景决策树进行动态配置得到的，场景决策树为通过预设事件库将业务逻辑进行场景化得到的。解析模块22用于解析配置报文以得到对应于业务逻辑的业务规则。执行模块23用于执行业务规则。
在本申请的一个实施例中，解析配置报文以得到对应于业务逻辑的业务规则，包括：注册监听识别因子，并结合第一算子进行元场景识别；对元场景和/或元事件进行冲突识别，并进行场景决策，得到决策因子和执行因子。
在本申请的一个实施例中，执行业务规则，包括：基于执行因子，执行场景模型中对应的事务。
在本申请的一个实施例中，在注册监听识别因子之前，还包括进行场景基础原子能力的对接打通，场景基础原子能力包括元事件、元事实、元事务。
需要说明的是，本申请实施例的车辆在进行动态配置业务逻辑时，其具体实现方式与本申请实施例的用于车辆的动态配置业务逻辑的方法的具体实现方式类似，具体请参见方法部分的描述，为了减少冗余，此处不再赘述。
根据本申请实施例的车辆，车辆通过接收云平台下发的配置报文对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
本申请的进一步实施例还公开了一种计算机可读存储介质，该计算机可读存储介质上存储有动态配置业务逻辑的程序，该动态配置业务逻辑的程序被处理器执行时实现如上述任一实施例所述的动态配置业务逻辑的方法。
根据本申请实施例的计算机可读存储介质，其上存储的动态配置业务逻辑的程序被处理器执行时，通过对设置于云平台上的场景决策树进行动态配置以得到配置报文，并将配置报文下发至车辆来对车辆中的软件系统进行升级，可以快速响应需求变更，无需软件开发人员做重复繁琐的开发工作，使得非技术人员也可以参与编辑维护系统的业务规则，同时无需进行OTA升级迭代，能够降低软件系统升级的代价和风险。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示意性实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。
尽管已经示出和描述了本申请的实施例，本领域的普通技术人员可以理解：在不脱离本申请的原理和宗旨的情况下 可以对这些实施例进行多种变化、修改、替换和变型，本申请的范围由权利要求及其等同物限定。

Claims (15)

  1. 一种动态配置业务逻辑的方法，其特征在于，应用于云平台，包括：
    接收配置信息；和
    根据所述配置信息对场景决策树进行动态配置以生成配置报文，并将所述配置报文下发至车辆，其中，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到。
  2. 根据权利要求1所述的动态配置业务逻辑的方法，其特征在于，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到，包括：
    根据所述业务逻辑和所述预设事件库，确定多个元场景；
    确定对应于所述多个元场景中的每一个元场景的识别因子和场景模型；
    确定对应于所述场景模型的决策因子和执行因子；和
    根据所述元场景、所述识别因子、所述场景模型、所述决策因子和所述执行因子构建所述场景决策树。
  3. 根据权利要求2所述的动态配置业务逻辑的方法，其特征在于，
    所述识别因子包括元事件、第一算子和第一参数；
    所述决策因子包括元事实、第二算子和第二参数；和
    所述执行因子包括元事务和第三参数。
  4. 根据权利要求2或3所述的动态配置业务逻辑的方法，其特征在于，根据所述配置信息对场景决策树进行动态配置以生成配置报文，包括：
    将所述多个元场景中的每一个元场景对应的所述识别因子的配置信息、所述场景模型对应的决策因子的配置信息和所述执行因子的配置信息写入所述配置报文中。
  5. 根据权利要求1-4中任一项所述的动态配置业务逻辑的方法，其特征在于，所述配置报文采用的形式包括领域定义语言DSL、决策表XSL和规则流JBPM中的至少一种。
  6. 根据权利要求3或4所述的动态配置业务逻辑的方法，其特征在于，所述第一参数、所述第二参数和所述第三参数根据数据挖掘算法确定。
  7. 一种动态配置业务逻辑的方法，其特征在于，用于车辆，包括：
    接收云平台下发的配置报文，其中，所述配置报文为所述云平台根据配置信息对场景决策树进行动态配置得到的，所述场景决策树为通过预设事件库将业务逻辑进行场景化得到；
    解析所述配置报文以得到对应于所述业务逻辑的业务规则；和
    执行所述业务规则。
  8. 根据权利要求7所述的动态配置业务逻辑的方法，其特征在于，解析所述配置报文以得到对应于所述业务逻辑的业务规则，包括：
    注册监听识别因子，并结合第一算子进行元场景识别；和
    对所述元场景和/或元事件进行冲突识别，并进行场景决策，以得到决策因子和执行因子。
  9. 根据权利要求8所述的动态配置业务逻辑的方法，其特征在于，执行所述业务规则，包括：
    基于所述执行因子，执行所述场景模型中对应的事务。
  10. 根据权利要求8或9所述的动态配置业务逻辑的方法，其特征在于，在注册监听识别因子之前，还包括：
    进行场景基础原子能力的对接打通，所述场景基础原子能力包括元事件、元事实和元事务。
  11. 一种云平台，其特征在于，包括：
    接收模块，用于接收配置信息；和
    配置模块，用于根据所述配置信息对场景决策树进行动态配置以生成配置报文，并将所述配置报文下发至车 辆，其中，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到。
  12. 根据权利要求11所述的云平台，其特征在于，所述场景决策树由通过预设事件库将业务逻辑进行场景化得到，包括：
    根据业务逻辑，确定多个元场景；
    确定对应于所述多个元场景中的每一个元场景的识别因子和场景模型；
    确定对应于所述场景模型的决策因子和执行因子；和
    根据所述元场景、所述识别因子、所述场景模型、所述决策因子和执行因子构建所述场景决策树。
  13. 根据权利要求12所述的云平台，其特征在于，
    所述识别因子包括元事件、第一算子和第一参数；
    所述决策因子包括元事实、第二算子和第二参数；和
    所述执行因子包括元事务和第三参数。
  14. 一种车辆，其特征在于，包括：
    接收模块，用于接收云平台下发的配置报文，其中，所述配置报文为所述云平台对场景决策树进行动态配置得到的，所述场景决策树为通过预设事件库将业务逻辑进行场景化得到的；
    解析模块，用于解析配置报文以得到对应于所述业务逻辑的业务规则；和
    执行模块，用于执行所述业务规则。
  15. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有动态配置业务逻辑的程序，所述动态配置业务逻辑的程序被处理器执行时实现如权利要求1-6中任一项所述的动态配置业务逻辑的方法，或者，实现如权利要求7-10中任一项所述的动态配置业务逻辑的方法。
PCT/CN2023/116420 2022-09-20 2023-09-01 动态配置业务逻辑的方法、云平台、车辆及存储介质 Ceased WO2024060965A1 (zh)

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