Data collection method, device, equipment, storage medium and chip

Abstract

The present disclosure relates to a data collection method, device, equipment, storage medium and chip, wherein the method includes: when a rule triggering condition is met, traversing the rule interpreter that matches the rule triggering condition and all the scene rules managed by the rule interpreter, the rule triggering condition includes a change in the operating state of the electronic device, and / or reaching a preset sampling period, the scene rule is used to indicate the data collection requirements of the corresponding scene; when it is determined that there is a target scene rule, triggering corresponding data collection based on the target scene rule, the target scene rule is a rule among all the scene rules managed by the rule interpreter. In this way, it can solve the technical problems in the related technology such as the inability to cope with complex scenes or logic processing, low flexibility and scalability.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technology, and in particular to a data collection method, device, equipment, storage medium and chip. Background Art

[0002] In the continuous iteration and upgrading of autonomous driving technology, data closed loop plays a core driving role in the development of technology. By building an efficient and compliant data collection, processing and feedback mechanism, the maturity and optimization of autonomous driving technology can be greatly accelerated, bringing users a safer, more convenient and intelligent travel experience. In particular, as autonomous driving software shifts from rules to models and from modularization to fusion integration, data-driven plays an increasingly important role in autonomous driving systems. Collecting and transmitting data from various scenarios through mass-produced vehicles to help the rapid iteration of autonomous driving technology has become a key technology in the field of autonomous driving.

[0003] At present, the data collection solutions provided by related technologies, for example, have a set of trigger functions built into the autonomous driving data collection equipment to start data collection; wherein the trigger function is a set of mathematical expressions of vehicle status, which is used to determine whether the desired data collection scenario is met based on the vehicle status. The trigger condition judgment of data collection in the above scheme only uses simple mathematical expression calculations, which cannot cope with complex scenarios or logical processing, and has relatively low flexibility and scalability. Summary of the invention

[0004] In order to overcome the problems existing in the related technologies, the present disclosure provides a data collection method, device, equipment, storage medium and chip to solve the technical problems in the above-mentioned related technologies such as the inability to cope with complex scenarios or logical processing, and the low flexibility and scalability.

[0005] According to a first aspect of an embodiment of the present disclosure, there is provided a data collection method, comprising:

[0006] When a rule triggering condition is met, traverse the rule interpreter that matches the rule triggering condition and all scenario rules managed by the rule interpreter, wherein the rule triggering condition includes a change in the operating state of the electronic device and / or a preset sampling period is reached, and the scenario rule is used to indicate the data collection requirements of the corresponding scenario;

[0007] When it is determined that there is a target scenario rule, corresponding data collection based on the target scenario rule is triggered, and the target scenario rule is a rule among all scenario rules managed by the rule interpreter.

[0008] In some embodiments, before the triggering performs corresponding data collection based on the target scene rule, the method further includes:

[0009] When the rule triggering condition includes a change in the operating state of the electronic device, determining a target scenario rule that matches the changed operating state from all scenario rules managed by the rule interpreter; and / or,

[0010] When the rule triggering condition includes reaching the preset sampling period, if the electronic device has data update, a target scene rule matching the updated data is determined from all scene rules managed by the rule interpreter.

[0011] In some embodiments, all scenario rules managed by the rule interpreter are of the same type.

[0012] In some embodiments, traversing the rule interpreter matching the rule triggering condition and all scenario rules managed by the rule interpreter includes:

[0013] Traversing the rule interpreters that match the rule triggering condition and creating a running environment for the rule interpreters;

[0014] All scenario rules managed by the rule manager are traversed, and all scenario rules are parsed using the rule manager.

[0015] In some embodiments, the method further comprises:

[0016] The target data obtained by the data collection is uploaded to the cloud.

[0017] In some embodiments, the method further comprises:

[0018] After all rule interpreters are deployed, all the rule interpreters are loaded;

[0019] An operating environment is created for each of the rule interpreters to initialize each of the rule interpreters.

[0020] In some embodiments, the method further comprises:

[0021] After all scenario rules are deployed, load all the scenario rules;

[0022] After successfully performing a legality check on any of the scenario rules, mapping any of the scenario rules to a corresponding rule interpreter to initialize any of the scenario rules;

[0023] Among them, the scene rules of the same type correspond to one rule interpreter.

[0024] In some embodiments,

[0025] When the type of the scenario rule includes a mathematical calculation type, the rule interpreter is a first type rule interpreter; or,

[0026] When the type of the scenario rule includes a logic judgment type, the rule interpreter is a second type rule interpreter; or,

[0027] When the type of the scenario rule includes a specific scenario algorithm type, the rule interpreter is a third type rule interpreter.

[0028] In some embodiments, the rule interpreter is deployed independently.

[0029] According to a second aspect of an embodiment of the present disclosure, there is provided a data acquisition device, comprising:

[0030] a processing module configured to traverse a rule interpreter matching the rule triggering condition and all scenario rules managed by the rule interpreter when a rule triggering condition is met, wherein the rule triggering condition includes a change in the operation state of the electronic device and / or a preset sampling period is reached, and the scenario rule is used to indicate a data collection requirement for a corresponding scenario;

[0031] The processing module is further configured to trigger corresponding data collection based on the target scenario rule when determining that there is a target scenario rule, wherein the target scenario rule is a rule among all scenario rules managed by the rule interpreter.

[0032] For the contents not introduced or described in the embodiments of the present disclosure, please refer to the relevant introduction in the aforementioned method embodiments, and the embodiments of the present disclosure are not limited thereto.

[0033] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory for storing processor executable instructions; wherein the processor is configured to execute the executable instructions to implement the steps of the above-mentioned data acquisition method.

[0034] In some embodiments, the electronic device includes a scheduler, a rule interpreter, and a data collector.

[0035] In some embodiments, the electronic device further includes a data uploader.

[0036] In some embodiments, the electronic device includes at least one of: a vehicle, a robot, or a drone.

[0037] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the data acquisition method provided in the first aspect of the present disclosure are implemented.

[0038] According to a fifth aspect of an embodiment of the present disclosure, a chip is provided, comprising: a processor and an interface; the processor is used to read instructions to execute the steps of the above-mentioned data acquisition method.

[0039] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: when the electronic device meets the rule triggering condition, it traverses the rule interpreter that matches the rule triggering condition and all the scene rules managed by the rule interpreter, the rule triggering condition includes the change of the running state of the electronic device, and / or the arrival of the preset sampling period, and the scene rule is used to indicate the data collection requirements of the corresponding scene; when it is determined that there is a target scene rule, it triggers the corresponding data collection based on the target scene rule, and the target scene rule is a rule among all the scene rules managed by the rule interpreter. The present disclosure manages the scene rules by introducing a programmable rule interpreter, which can greatly improve the description ability of various data collection scenes, meet the data collection requirements of all known scenes, and make the data collection have the characteristics of high scalability, good flexibility and wide adaptability. At the same time, it can also solve the technical problems in the related technology that the simple mathematical expression method is used to trigger data collection, which cannot cope with complex scenes or logical processing, and has low flexibility and scalability.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0042] Figure 1 It is a schematic diagram of an application scenario according to an exemplary embodiment.

[0043] Figure 2 It is a schematic diagram of a framework of a data acquisition system according to an exemplary embodiment.

[0044] Figure 3 It is a flowchart of a data collection method according to an exemplary embodiment.

[0045] Figure 4 The figure is a schematic diagram of a traversal process according to an exemplary embodiment.

[0046] Figure 5 The figure is a schematic diagram of a process of determining a target scene rule according to an exemplary embodiment.

[0047] Figure 6The figure is a schematic diagram of an initialization flow of a rule interpreter according to an exemplary embodiment.

[0048] Figure 7 The figure is a schematic diagram of a scene rule initialization process according to an exemplary embodiment.

[0049] Figure 8 It is a structural schematic diagram of a data acquisition device according to an exemplary embodiment.

[0050] Fig. 9 The diagram is a schematic structural diagram of an electronic device according to an exemplary embodiment.

[0051] Fig.10 It is a schematic structural diagram of a vehicle according to an exemplary embodiment.

[0052] Fig.11 The figure is a schematic diagram showing the structure of a chip according to an exemplary embodiment. DETAILED DESCRIPTION

[0053] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0054] It should be noted that all actions of acquiring signals, information or data in the present disclosure are performed with the authorization given by the owner of the corresponding device.

[0055] In the continuous iteration and upgrading of intelligent driving technology, data closed loop plays a core driving role in the development of technology. Especially in the mass production of high-level autonomous driving, the construction of efficient data closed loop is particularly important. Among them, data collection in the autonomous driving industry usually has the following methods:

[0056] First, special data collection. Use a dedicated data collection fleet to obtain more realistic and high-precision full-volume sensor data. Support large-scale, long-term continuous data collection in different regions and scenarios. But it usually does not include closed-loop control information, and it is not very good at covering long-tail scenarios. The closed-loop control information here may refer to information used for subsequent operations such as data mining and algorithm training in autonomous driving; the long-tail scenarios here refer to long-tail scenarios that appear in autonomous driving, such as driving scenarios and objects that vehicles will encounter in most cases, such as traffic lights, pedestrians, etc.; this disclosure does not make too many restrictions and details here.

[0057] Second, mass production data collection. Use mass production vehicles to complete data collection. Due to the large number of mass production vehicles, it is good at discovering corner cases. However, due to network and hardware resources, it is impossible to collect all data. Usually, it is necessary to set trigger conditions, such as collecting data for targeted scenarios with limited duration and limited channels, etc. This data can include control information, etc.

[0058] Third, simulation generation. Directly generate various required driving scenarios, and then let the virtual vehicle drive in the generated scenarios. This can take into account the characteristics of both special data acquisition and mass production data acquisition, especially in the given boundary conditions, it can quickly obtain a large amount of high-precision true value data.

[0059] The data collection solution provided by the related technology can start data collection through a trigger function. However, the judgment of this trigger condition only uses a simple mathematical expression calculation, which cannot cope with complex scenarios / logic processing, and has relatively low flexibility and scalability.

[0060] Considering relatively complex scenarios, such as users being dissatisfied with the automatic parking effect and manually parking the vehicle again after the automatic parking is completed, the data collection needs of such complex scenarios cannot be judged and satisfied by simple mathematical expressions. Moreover, with the gradual popularization of autonomous driving applications, more and more complex driving scene data need to be further mined, which puts higher requirements on the logical ability and scalability of data collection.

[0061] In order to solve the above problems, the present disclosure provides a data collection method, device, equipment, storage medium and chip. The following first introduces some applicable embodiments of the present disclosure.

[0062] See also Figure 1 FIG. 1 is a schematic diagram of an application scenario according to an exemplary embodiment. Figure 1 The application scenario shown includes a local system 10 of an electronic device (e.g., a vehicle-side system in autonomous driving technology) and a cloud system 20. It is understandable that in the field of autonomous driving, the local system 10 can be run in a vehicle to provide autonomous driving related functions, such as active safety, intelligent driving assistance, data collection, etc. The present disclosure focuses on the data collection function of the local system 10, which can include a scenario rule list 100 and a data engine 200.

[0063] The above-mentioned scenario rule list 100 may include one or more scenario rules, which may be scenario rules that are pre-customized according to actual conditions. The scenario rule list defines various scenarios where data collection needs to be performed, such as sudden acceleration, sudden deceleration, automatic parking anomalies and other scenarios in the field of autonomous driving. The above-mentioned scenario rule list 100 is usually issued by the cloud system 20 on demand. For example, the cloud system 20 can prepare a corresponding scenario rule list according to various data collection requirements and issue it to electronic devices, etc. Optionally, the cloud system 20 can also add, delete or change the content in the list as needed according to actual conditions, but it does not affect the functional operation that can be perceived by the device user. The present disclosure will illustrate this with examples below.

[0064] The data engine 200 can use the above-mentioned scene rule list 100 as input, perform scene rule matching, and collect various required data (such as status, sensor data, or internal data of device operation, etc.) from the electronic device. Optionally, the data engine 200 can also upload the collected data to the cloud system 20.

[0065] The cloud system 20 is mainly responsible for assisting the realization of related functions of electronic devices. In the data collection scenario, it mainly involves two functions: data collection operation, data center and data mining. Among them, the data collection operation is mainly responsible for making corresponding scene rule lists according to various data collection needs, and sending them to electronic devices (such as mass-produced vehicles, etc.). In the field of autonomous driving, the content in the above list can also be added or deleted according to actual needs, but it does not affect the operation of the autonomous driving function that can be perceived by the vehicle-side users. The data center and data mining are mainly responsible for receiving the collected data uploaded by electronic devices (such as the vehicle side) and providing it to downstream for data consumption, such as data mining, algorithm training, etc., which are not excessively limited and described in this disclosure.

[0066] The electronic devices involved in the present disclosure may also be referred to as terminal devices (terminal), user equipment (UE), mobile station (MS), mobile terminal (MT), etc. The terminal device may be a car with communication function, a smart car, a robot, a drone, a mobile phone (Mobile Phone), a wearable device, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control (Industrial Control), a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid (Smart Grid), a wireless terminal device in transportation safety (Transportation Safety), a wireless terminal device in smart city (Smart City), a wireless terminal device in smart home (Smart Home) or other Internet of Things devices, etc. The embodiments of the present disclosure do not limit the specific technology and specific device form adopted by the electronic device.

[0067] See also Figure 2 FIG. 1 is a schematic diagram showing a data acquisition system according to an exemplary embodiment. Figure 2 The system shown may include: a scenario rule table 100, a rule engine 300, a data collector 400 and a data uploader 500. The rule engine 300 may include a scheduler 301 and at least one rule interpreter 302. Figure 1 According to the functions, the data engine 200 can be subdivided into the rule engine 300, the data collector 400 and the data uploader 500. That is, the data engine 200 can include the rule engine 300, the data collector 400 and the data uploader 500. They are introduced below.

[0068] The above-mentioned scenario rule table 100 may include one or more scenario rules, for example, the diagram includes scenario rule 1, scenario rule 2, ... scenario rule n, where n is a custom positive integer. There may be multiple scenario rules included in the scenario rule table, and each scenario rule corresponds to a data collection requirement for a clearly specified scenario. For example, in the sudden deceleration scenario of Navigate On Autopilot (NOA), it is necessary to collect video data from cameras around the vehicle within a preset time, collect internal data of vehicle operation (such as collecting sensor data to determine whether the vehicle brakes automatically or manually), etc., and the present disclosure does not make too many restrictions and descriptions on this. The above-mentioned scenario rule table / scenario rules are usually issued by the cloud or built into the system at the factory, and they can be added, deleted, and changed according to actual needs.

[0069] It can be understood that the above-mentioned scenario rules can be of different types, that is, the above-mentioned scenario rule list can include different types of scenario rules to suit different scenarios / occasions. For example, mathematical calculation expressions are suitable for scenarios with large calculation amounts and high requirements for delay performance; logical judgment expressions are suitable for scenarios with complex user scenarios (for example, there are scene superpositions) and the need for complex logical judgments; there are also some specific scenario / field algorithm rule descriptions, etc. Optionally, the present disclosure can use a programmable scripting language (i.e., the rule interpreter below) to interpret and understand the same type of scenario rules. For example, the Expression Toolkit (ExprTK) script is mainly used for scenario rules of various mathematical calculations, Lua scripts are used for scenario rules of complex logical judgments, and support vector machines (Support Vector Machine, SVM) are used for scenario rules of specific scenario / field algorithms, and so on.

[0070] The rule engine 300 is mainly used to understand the scene rules and schedule the execution of programmable scripts according to the strategy to determine whether the current state of the electronic device (such as a vehicle) matches one or some scene rules. In the specific implementation, the rule engine 300 needs to interpret or parse these scene rules according to the type of scene rules. The present disclosure can introduce a rule interpreter 302 to perform the understanding and interpretation / parsing of the scene rules. The rule interpreter 302 can also be called a programmable rule interpreter, which matches the type of scene rules and is responsible for interpreting / parsing the scene rules of this type. The relationship between the rule interpreter 302 and the scene rules is a one-to-many mapping relationship. Specifically, a rule interpreter 302 can process at least one scene rule of the same type. Different types of scene rules correspond to different rule interpreters 302, and the same type of scene rules correspond to one rule interpreter 302. The present disclosure does not limit the number of the rule interpreters 302, for example, there can be one or more, and the rule interpreter 302 can be expanded according to actual needs to support new types of needs. For example, a new type of lighter rule interpreter is added on a low computing power platform.

[0071] For example, when the type of the above-mentioned scenario rules includes a mathematical calculation type, the above-mentioned rule interpreter may be, for example, a first-class rule interpreter using a programmable language such as ExprTK. When the type of the above-mentioned scenario rules includes a logical judgment type, the above-mentioned rule interpreter may be, for example, a second-class rule interpreter using a programmable language such as Lua. For another example, when the type of the above-mentioned scenario rules includes a specific scenario / domain algorithm type, the above-mentioned rule interpreter may be, for example, a third-class rule interpreter using SVM. This disclosure does not make too many limitations and details on this.

[0072] The present disclosure also designs a highly scalable scheduler 301 as a scheduling framework for the rule interpreter 302, providing plug-and-play functionality for the rule interpreter 302. The above scheduler 301 can dynamically load independent rule interpreters 302 during the initialization phase, and create an independent operating environment for the rule interpreter 302, so that each rule interpreter 302 can be deployed separately / independently, without interfering with each other, and is easy to add or uninstall.

[0073] It should be noted that each programmable language in the present disclosure can meet a type of data collection scenario with common needs. For example, some scenarios require a lot of mathematical operations, some scenarios require complex logical judgments, some need to rely on statistical data, and some hope to run on low-computing platforms, etc. Each programmable language will correspond to a rule interpreter 302, which is used to understand and interpret such scenario rules. The above-mentioned rule interpreter 302 can also be expanded as needed, and the framework layer (scheduler 301) provides it with plug-and-play capabilities, which also reflects that the disclosed solution has good scalability and can meet the data discovery and collection of known scenarios. It also has the characteristics of high scalability, good flexibility, and wide adaptability.

[0074] The above-mentioned data collector 400 is mainly responsible for executing corresponding data collection operations after a certain scene rule / s is matched successfully. For example, corresponding data collection can be performed according to different collection channels, different collection durations or other auxiliary information, etc. This disclosure does not make too many restrictions and details on this.

[0075] The data uploader 500 is mainly responsible for uploading the data collected by the data collector 400 to the cloud system 20 at an appropriate time (for example, when the user's upload operation is detected). The data uploaded here may include but is not limited to supporting data validity verification, secure channel transmission or breakpoint resume, etc., and the present disclosure does not make too many restrictions and details on this. The validity verification here can be, for example, a legality verification of the data to be uploaded, such as whether the collected data supports this model, etc.; or it can carry a verification code to facilitate verification at the receiving end, etc. Breakpoint resume can mean continuing to transmit data at the disconnection point of the upload without having to start over.

[0076] Based on the above examples, see Figure 3 FIG. 1 is a flow chart of a data collection method according to an exemplary embodiment. Figure 3 The method shown can be applied to Figure 1 to Figure 2 In the electronic device shown, the method may include the following implementation steps:

[0077] S301. When a rule triggering condition is met, traverse the rule interpreter that matches the rule triggering condition and all scenario rules managed by the rule interpreter, wherein the rule triggering condition includes a change in the operating state of the electronic device and / or reaching a preset sampling period, and the scenario rule is used to indicate the data collection requirements of the corresponding scenario.

[0078] In the present disclosure, the above-mentioned rule triggering condition may refer to the triggering condition that is pre-customized and set by the system or user according to actual needs, which is used to determine whether to schedule the scene rule. For example, the triggering condition may include but is not limited to at least one of the following: the operating state of the electronic device changes, reaches a preset sampling period or other custom triggering conditions, etc. The above-mentioned sampling period may refer to the period of data collection that is pre-customized and set by the system or user according to actual needs, for example, it may be an experience value set according to user experience, or a statistical value calculated based on a series of experimental data, etc., and the present disclosure does not make too many restrictions and details on this. The operating state of the above-mentioned electronic device may refer to the state or data used to describe the electronic device. For example, taking the electronic device as a vehicle as an example, the operating state of the above-mentioned vehicle may include but is not limited to the braking state, parking state, acceleration state, deceleration state or other custom states of the vehicle. The present disclosure does not limit the number of the above-mentioned operating states, which may be one or more, that is, for example, when one or more operating states of the electronic device are detected to change, subsequent operations can be continued.

[0079] The above-mentioned scenario rules can be pre-customized and configured by the cloud or the system according to actual conditions, and can be used to indicate the data collection requirements in the corresponding scenarios. For example, in the sudden deceleration scenario of Navigate On Autopilot (NOA), it is necessary to collect video data from cameras around the vehicle within a preset time period, collect internal data of the vehicle operation (such as collecting sensor data to determine whether the vehicle brakes automatically or manually), etc.

[0080] In actual applications, when the above-mentioned scheduler 301 in the present disclosure detects / judges that the above-mentioned rule triggering conditions are met, in order to save resource overhead, the present disclosure can traverse the rule interpreter 302 that matches the above-mentioned rule triggering conditions and all the scenario rules managed by the rule interpreter 302. The present disclosure does not limit the number of the rule interpreters 302, which can be one or more, and can be determined according to actual conditions. Without considering resource overhead, the present disclosure can traverse all / all rule interpreters 302 in the system and all scenario rules managed by each rule interpreter 302. For the convenience of describing the present disclosure, the following text uses a rule interpreter 302 as an example to explain the relevant content, but it does not constitute a limitation. Among them, the present disclosure does not limit the implementation method of traversing the above-mentioned rule interpreter 302 and all the scenario rules managed by it. For example, please refer to Figure 4 FIG. 1 is a schematic diagram of a traversal process according to an exemplary embodiment. Figure 4 The flowchart shown may include the following implementation steps:

[0081] S401, traverse the rule interpreters that match the rule triggering condition.

[0082] The above-mentioned rule interpreter may refer to a rule interpreter for processing a certain type of scenario rules, which may be related to the above-mentioned rule triggering conditions. Specifically, when the above-mentioned rule triggering conditions include a change in the operating state of the electronic device, the above-mentioned rule interpreter may be a rule interpreter deployed in the system for processing the information related to the above-mentioned changed operating state. For example, taking the above-mentioned operating state as acceleration, the above-mentioned rule interpreter may be a programmable interpreter deployed in the system for processing the scenario rules related to the acceleration state. When the above-mentioned rule triggering conditions include the above-mentioned arrival of the preset sampling period, the above-mentioned rule interpreter may be all / all rule interpreters deployed in the system. Similarly, for the convenience of describing the present disclosure, the following text takes a rule interpreter 302 as an example to explain the relevant content, but it does not constitute a limitation.

[0083] S402: Create a running environment for the rule interpreter.

[0084] S403: Traverse all scenario rules managed by the rule manager.

[0085] S404: Utilize the rule manager to parse all scenario rules managed by the rule manager.

[0086] In the present disclosure, the scheduler 301 can dynamically load the rule interpreter 302 in the form of a dynamic library, and create an independent operating environment for the rule interpreter 302, so that the rule interpreters 302 can be independently deployed, do not interfere with each other, and can be easily added or uninstalled. Further, the scheduler 301 can traverse all scenario rules managed by the rule interpreter 302, and use the rule interpreter 302 to understand and interpret these scenario rules.

[0087] S302: When it is determined that a target scenario rule exists, trigger corresponding data collection based on the target scenario rule, where the target scenario rule is a rule among all scenario rules managed by the rule interpreter.

[0088] After determining the target scene rules, the scheduler 301 can trigger / notify the data collector 400 to collect data according to the target scene rules, thereby obtaining the collected target data. The present disclosure does not limit the specific implementation of the above data collection. For example, the data collector 400 can collect data according to different collection channels, different collection durations or other auxiliary information.

[0089] By implementing the embodiments of the present disclosure, when the electronic device (specifically, the scheduler 301 in the electronic device) meets the rule triggering condition, it traverses the rule interpreter that matches the rule triggering condition and all the scene rules managed by the rule interpreter, wherein the rule triggering condition includes a change in the operating state of the electronic device and / or reaching a preset sampling period, and the scene rule is used to indicate the data collection requirements of the corresponding scene; when it is determined that there is a target scene rule, it triggers the corresponding data collection based on the target scene rule, and the target scene rule is a rule among all the scene rules managed by the rule interpreter. The present disclosure manages the scene rules by introducing a programmable rule interpreter, which can greatly improve the description capability of various data collection scenes, meet the data collection requirements of all known scenes, and make the data collection have the characteristics of high scalability, good flexibility and wide adaptability. At the same time, it can also solve the technical problems in the related technology that the simple mathematical expression method is used to trigger data collection, which cannot cope with complex scenes or logical processing, and has low flexibility and scalability.

[0090] The present disclosure does not limit the specific implementation of the above step S302. For example, the present disclosure can first determine whether there is a target scene rule in the system. After determining that the above target scene rule exists, the data collector 400 is triggered / notified to perform corresponding data collection according to the above target scene rule. Among them, the above target scene rule is a scene rule among all scene rules managed by the above rule interpreter 302. The present disclosure does not limit the number of the above target scene rules. It can determine one or more target scene rules based on actual conditions, and the present disclosure does not make too many limitations and descriptions on this.

[0091] The present disclosure does not limit the above-mentioned target scene rule judgment / determination implementation method. For example, see Figure 5 FIG. 1 is a schematic diagram of a process for determining a target scene rule according to an exemplary embodiment. Figure 5 The flowchart shown may include the following implementation steps:

[0092] S501: When the rule triggering condition includes a change in the operating state of the electronic device, determine a target scenario rule that matches the changed operating state from all scenario rules managed by the rule interpreter.

[0093] It can be understood that each scenario rule in the present disclosure has corresponding input parameters, and different scenario rules may correspond to different input parameters. The input parameters may include, for example, the operating state of the electronic device, internal data or other data of the electronic device operation, etc. Accordingly, the present disclosure can determine the scenario rules that match it based on the corresponding input parameters. Based on this, when the above-mentioned rule triggering conditions include a change in the operating state of the above-mentioned electronic device, the present disclosure can select / determine the target scenario rule that matches the above-mentioned changed operating state from all the scenario rules managed by the above-mentioned rule interpreter 302 according to the changed operating state of the electronic device.

[0094] S502: When the rule triggering condition includes reaching a preset sampling period, if the electronic device has data update, determine a target scenario rule that matches the updated data from all scenario rules managed by the rule interpreter.

[0095] When the rule triggering conditions include reaching the preset sampling period, the present disclosure can first determine whether there is data update in the electronic device. Specifically, taking the electronic device as a vehicle as an example, the present disclosure can determine whether the vehicle has data update, such as whether the vehicle has status updates such as sudden acceleration or sudden deceleration. When it is determined that the electronic device has data update, the target scene rule that matches the updated data can be selected / determined from all scene rules managed by the rule interpreter 302. On the contrary, when it is determined that there is no data update in the electronic device, no operation can be performed or the process can be directly ended.

[0096] It should be noted that the present disclosure may select any one of the above steps S501 and S502 for execution according to actual needs, or may execute both of the above solutions. When both of the above solutions are executed, the present disclosure does not limit the execution order of the above S501 and S502. For example, the present disclosure may execute step S502 first and then step S501, etc., and the present disclosure does not make too many limitations and descriptions on this.

[0097] Some optional embodiments of the present disclosure are described below.

[0098] In some optional embodiments, after acquiring the target data, the data collector 400 may upload the target data to the cloud system 20 through the data uploader 500. Optionally, when uploading the target data, the data uploader 500 may perform operations such as data validity verification, secure channel transmission, breakpoint continuation or other custom data operations. For the description of the validity verification, secure channel transmission and breakpoint continuation, please refer to the above Figure 2Optionally, after executing the embodiment of the present disclosure, the next rule triggering condition may be met and the above embodiment of the present disclosure may be repeatedly executed, and the present disclosure will not make too many limitations and descriptions on this.

[0099] In some other optional embodiments, the present disclosure may initialize the above rule engine 300 (specifically including the scheduler 301 and the rule interpreter 302 in the engine). Optionally, the present disclosure may also initialize the above scenario rule table 100 (specifically including all scenario rules in the list). Figure 6 FIG. 1 is a schematic diagram of an initialization process of a rule interpreter according to an exemplary embodiment. Figure 6 The flowchart shown may include the following implementation steps:

[0100] S601: After all rule interpreters are deployed, load all the rule interpreters.

[0101] S602: Create a running environment for each of the rule interpreters to initialize each of the rule interpreters.

[0102] When the system starts, the initialization of the rule engine 300 can be entered first, and then the initialization of the scene rule list 100 can be entered. During the initialization process of the rule engine 300, the scheduler 301 can first complete the deployment of all rule interpreters 302 in the set first directory, and then dynamically load all rule interpreters 302 in the form of a dynamic library, and create an independent operating environment for each rule interpreter 302 in all rule interpreters 302, so that each rule interpreter 302 can be deployed independently and does not interfere with each other. After the operating environment is created, the rule engine 300 (specifically including the scheduler 301 and the rule interpreter 302) is initialized such as parameter configuration, thereby completing the initialization of the above rule engine 300 (specifically including the scheduler 301 and the rule interpreter 302).

[0103] Please also see Figure 7 FIG. 1 is a schematic diagram of a scene rule initialization process according to an exemplary embodiment. Figure 7 The flowchart shown may include the following implementation steps:

[0104] S701: After all scenario rules are deployed, all scenario rules are loaded.

[0105] S702: After successfully performing a validity check on any of the scenario rules, map any of the scenario rules to a corresponding rule interpreter to initialize any of the scenario rules; wherein the scenario rules of the same type correspond to one rule interpreter.

[0106] During the initialization process of the scene rule list 100 (including each scene rule in the list), the scheduler 301 can first complete the deployment of all scene rules (i.e., the above-mentioned scene rule list 100) in the set second directory, and then load all the scene rules in the list. Then, the rule interpreter 302 is used to check the legality of the corresponding scene rules; the present disclosure does not limit the specific implementation method of the above-mentioned legality check, such as checking whether there is a logical judgment error in the scene rule, or checking whether the scene rule matches the electronic device (such as the vehicle model), etc., and the present disclosure does not make too many restrictions and details on this. After the legality check of any of the above-mentioned scene rules is successfully performed, the present disclosure can map any scene rule to the corresponding rule interpreter 302 according to the type of the scene rule; similarly, according to the above principle, the present disclosure can perform legality checks and rule interpreter mapping on all scene rules in the list. Optionally, the input parameters in the scene rules can also be initialized and configured, so as to complete the initialization of the above-mentioned scene rule list 100 (including all scene rules). For the introduction of the above-mentioned rule interpreter 302 and the above-mentioned scene rules, please refer to the above-mentioned Figures 1 to 6 The relevant introduction in the embodiments will not be repeated here.

[0107] Optionally, after completing the initialization of the above-mentioned rule engine 300 and the above-mentioned scene rule list 100, the above-mentioned data collector 400 and the above-mentioned data uploader 500 can also be initialized respectively, for example, the respective configuration parameters of the above-mentioned data collector 400 and the above-mentioned data uploader 500 can be initialized, etc., and the present disclosure does not make too many limitations and descriptions on this.

[0108] It can be seen that the embodiment of the present disclosure greatly improves the description ability of scene rules for data collection scenes by introducing multiple types of rule interpreters 302, and is no longer limited to certain simple scenes. This provides a more solid foundation for data collection of massive scene data. By introducing a highly scalable scheduler 301, the addition and expansion of scene rules is greatly facilitated, and can be efficiently deployed according to actual business needs; this provides possibilities for the upcoming popularization of high-order autonomous driving, end-to-end large models and other applications. In a specific implementation, when the electronic device (specifically, the scheduler 301 in the electronic device) meets the rule triggering condition, it traverses the rule interpreter matching the rule triggering condition and all the scene rules managed by the rule interpreter, and the rule triggering condition includes a change in the operating state of the electronic device, and / or reaching a preset sampling period, and the scene rule is used to indicate the data collection requirements of the corresponding scene; when it is determined that there is a target scene rule, the corresponding data collection based on the target scene rule is triggered, and the target scene rule is a rule among all the scene rules managed by the rule interpreter. The present disclosure introduces a programmable rule interpreter to manage scene rules, which can greatly improve the description capability of various data collection scenes, meet the data collection needs of all known scenes, and make data collection have the characteristics of high scalability, good flexibility and wide adaptability. At the same time, it can also solve the technical problems of the related technology that uses simple mathematical expressions to trigger data collection, such as the inability to cope with complex scenes or logic processing, low flexibility and scalability, etc.

[0109] Based on the above examples, see Figure 8 FIG. 1 is a schematic diagram showing the structure of a data acquisition device according to an exemplary embodiment. Figure 8 The device shown can be applied to an electronic device, and the device may include a processing module 801. Among them:

[0110] The processing module 801 is configured to traverse the rule interpreter matching the rule triggering condition and all the scenario rules managed by the rule interpreter when the rule triggering condition is met, wherein the rule triggering condition includes a change in the operation state of the electronic device and / or a preset sampling period is reached, and the scenario rule is used to indicate the data collection requirements of the corresponding scenario;

[0111] The processing module 801 is further configured to trigger corresponding data collection based on the target scenario rule when determining that there is a target scenario rule, and the target scenario rule is a rule among all scenario rules managed by the rule interpreter.

[0112] In some embodiments, before the triggering performs corresponding data collection based on the target scene rule, the processing module 801 is further configured to:

[0113] When the rule triggering condition includes a change in the operating state of the electronic device, determining a target scenario rule that matches the changed operating state from all scenario rules managed by the rule interpreter; and / or,

[0114] When the rule triggering condition includes reaching the preset sampling period, if the electronic device has data update, a target scene rule matching the updated data is determined from all scene rules managed by the rule interpreter.

[0115] In some embodiments, all scenario rules managed by the rule interpreter are of the same type.

[0116] In some embodiments, the processing module 801 is configured to:

[0117] Traversing the rule interpreters that match the rule triggering condition and creating a running environment for the rule interpreters;

[0118] All scenario rules managed by the rule manager are traversed, and all scenario rules are parsed using the rule manager.

[0119] In some embodiments, the device further includes a transceiver module 802. The transceiver module 802 is configured to upload the target data obtained by the data collection to the cloud.

[0120] In some embodiments, the processing module 801 is further configured to:

[0121] After all rule interpreters are deployed, all the rule interpreters are loaded;

[0122] An operating environment is created for each of the rule interpreters to initialize each of the rule interpreters.

[0123] In some embodiments, the processing module 801 is further configured to:

[0124] After all scenario rules are deployed, load all the scenario rules;

[0125] After successfully performing a legality check on any of the scenario rules, mapping any of the scenario rules to a corresponding rule interpreter to initialize any of the scenario rules;

[0126] Among them, the scene rules of the same type correspond to one rule interpreter.

[0127] In some embodiments,

[0128] When the type of the scenario rule includes a mathematical calculation type, the rule interpreter is a first type rule interpreter; or,

[0129] When the type of the scenario rule includes a logic judgment type, the rule interpreter is a second type rule interpreter; or,

[0130] When the type of the scenario rule includes a specific scenario algorithm type, the rule interpreter is a third type rule interpreter.

[0131] In some embodiments, the rule interpreter is deployed independently.

[0132] For the contents not introduced or described in the embodiments of the present disclosure, please refer to the relevant description in the aforementioned method embodiments, which will not be repeated here.

[0133] By implementing the embodiments of the present disclosure, when the rule triggering conditions are met, the above-mentioned device traverses the rule interpreter that matches the rule triggering conditions and all the scene rules managed by the rule interpreter, wherein the rule triggering conditions include changes in the operating state of the electronic device and / or reaching a preset sampling period, and the scene rules are used to indicate the data collection requirements of the corresponding scene; when it is determined that there are target scene rules, the corresponding data collection based on the target scene rules is triggered, and the target scene rules are the rules of all the scene rules managed by the rule interpreter. The present disclosure manages the scene rules by introducing a programmable rule interpreter, which can greatly improve the description ability of various data collection scenes, meet the data collection requirements of all known scenes, and make the data collection have the characteristics of high scalability, good flexibility and wide adaptability. At the same time, it can also solve the technical problems in the related technology that the simple mathematical expression method is used to trigger data collection, such as the inability to cope with complex scenes or logical processing, low flexibility and scalability.

[0134] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and the program instructions, when executed by a processor, implement the steps of the data acquisition method provided by the present disclosure.

[0135] Fig. 9 9 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment. For example, the electronic device 900 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0136] Reference Fig. 9, the electronic device 900 may include one or more of the following components: a processing component 902 , a memory 904 , a power component 906 , a multimedia component 908 , an audio component 910 , an input / output interface 912 , a sensor component 914 , and a communication component 916 .

[0137] The processing component 902 generally controls the overall operation of the device 900, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the above-mentioned data acquisition method. In addition, the processing component 902 may include one or more modules to facilitate the interaction between the processing component 902 and other components. For example, the processing component 902 may include a multimedia module to facilitate the interaction between the multimedia component 908 and the processing component 902.

[0138] The memory 904 is configured to store various types of data to support operations on the device 900. Examples of such data include instructions for any application or method operating on the device 900, contact data, phone book data, messages, pictures, videos, etc. The memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0139] The power supply component 906 provides power to the various components of the device 900. The power supply component 906 can include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 900.

[0140] The multimedia component 908 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front camera and / or a rear camera. When the electronic device 900 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0141] The audio component 910 is configured to output and / or input audio signals. For example, the audio component 910 includes a microphone (MIC), and when the device 900 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 904 or sent via the communication component 916. In some embodiments, the audio component 910 also includes a speaker for outputting audio signals.

[0142] The input / output interface 912 provides an interface between the processing component 902 and the peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0143] The sensor assembly 914 includes one or more sensors for providing various aspects of status assessment for the device 900. For example, the sensor assembly 914 can detect the open / closed state of the device 900, the relative positioning of components, such as the display and keypad of the device 900, and the sensor assembly 914 can also detect the position change of the device 900 or a component of the device 900, the presence or absence of user contact with the device 900, the orientation or acceleration / deceleration of the device 900, and the temperature change of the device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0144] The communication component 916 is configured to facilitate wired or wireless communication between the device 900 and other devices. The device 900 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0145] In an exemplary embodiment, the device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-mentioned data acquisition method.

[0146] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, and the instructions can be executed by a processor 920 of the device 900 to complete the above-mentioned upper data acquisition method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0147] In addition to being an independent electronic device, the above-mentioned device can also be a part of an independent electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be an IC or a collection of multiple ICs; the chip can include but is not limited to the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), SOC (System on Chip, SoC), etc. The above-mentioned integrated circuit or chip can be used to execute executable instructions (or codes) to implement the above-mentioned data acquisition method. The executable instructions can be stored in the integrated circuit or chip, or can be obtained from other devices or equipment, such as the integrated circuit or chip including a processor, a memory, and an interface for communicating with other devices. The executable instruction can be stored in the memory, and when the executable instruction is executed by the processor, the above-mentioned data acquisition method can be implemented; alternatively, the integrated circuit or chip can receive the executable instruction through the interface and transmit it to the processor for execution, so as to implement the above-mentioned data acquisition method.

[0148] By implementing the embodiments of the present disclosure, when the electronic device meets the rule triggering condition, it traverses the rule interpreter that matches the rule triggering condition and all the scene rules managed by the rule interpreter, the rule triggering condition includes a change in the operating state of the electronic device, and / or reaching a preset sampling period, and the scene rule is used to indicate the data collection requirements of the corresponding scene; when it is determined that there is a target scene rule, it triggers the corresponding data collection based on the target scene rule, and the target scene rule is a rule among all the scene rules managed by the rule interpreter. The present disclosure manages the scene rules by introducing a programmable rule interpreter, which can greatly improve the description ability of various data collection scenes, meet the data collection requirements of all known scenes, and make the data collection have the characteristics of high scalability, good flexibility and wide adaptability. At the same time, it can also solve the technical problems in the related technology that the simple mathematical expression method is used to trigger data collection, such as the inability to cope with complex scenes or logical processing, low flexibility and scalability.

[0149] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above data collection method when executed by the programmable device.

[0150] See also Fig.10 , Fig.10 6 is a functional block diagram of a vehicle 600 shown in an exemplary embodiment. The vehicle 600 can be configured in a fully or partially autonomous driving mode. For example, the vehicle 600 can obtain environmental information about its surroundings through the perception system 620, and obtain an autonomous driving strategy based on the analysis of the surrounding environmental information to achieve fully autonomous driving, or present the analysis results to the user to achieve partially autonomous driving.

[0151] The vehicle 600 may include various subsystems, such as an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. Optionally, the vehicle 600 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and component of the vehicle 600 may be interconnected by wire or wirelessly.

[0152] In some embodiments, infotainment system 610 may include communication system 611 , entertainment system 612 , and navigation system 613 .

[0153] The communication system 611 may include a wireless communication system that can communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system may use 3G cellular communication, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system may communicate with a wireless local area network (WLAN) using WiFi. In some embodiments, the wireless communication system may communicate directly with the device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system may include one or more dedicated short range communications (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside stations.

[0154] The entertainment system 612 may include a display device, a microphone and speakers. Users can listen to the radio and play music in the car based on the entertainment system; or connect the mobile phone to the vehicle and project the mobile phone's screen on the display device. The display device may be touch-screen, and users can operate by touching the screen.

[0155] In some cases, the user's voice signal can be obtained through a microphone, and based on the analysis of the user's voice signal, the user can control certain aspects of the vehicle 600, such as adjusting the temperature inside the vehicle, etc. In other cases, music can be played to the user through a speaker.

[0156] The navigation system 613 may include a map service provided by a map provider, thereby providing navigation of the driving route for the vehicle 600. The navigation system 613 may be used in conjunction with the vehicle's global positioning system 621 and inertial measurement unit 622. The map service provided by the map provider may be a two-dimensional map or a high-precision map.

[0157] The perception system 620 may include several sensors that sense information about the environment around the vehicle 600. For example, the perception system 620 may include a global positioning system 621 (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU) 622, a laser radar 623, a millimeter wave radar 624, an ultrasonic radar 625, and a camera 626. The perception system 620 may also include sensors of the internal systems of the monitored vehicle 600 (for example, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors can be used to detect objects and their corresponding characteristics (position, shape, direction, speed, etc.). This detection and recognition is a key function for the safe operation of the vehicle 600.

[0158] The global positioning system 621 is used to estimate the geographic location of the vehicle 600 .

[0159] The inertial measurement unit 622 is used to sense the change of the posture of the vehicle 600 based on the inertial acceleration. In some embodiments, the inertial measurement unit 622 may be a combination of an accelerometer and a gyroscope.

[0160] Lidar 623 utilizes lasers to sense objects in the environment in which vehicle 600 is located. In some embodiments, Lidar 623 may include one or more laser sources, a laser scanner, and one or more detectors, among other system components.

[0161] The millimeter wave radar 624 uses radio signals to sense objects in the surrounding environment of the vehicle 600. In some embodiments, in addition to sensing objects, the millimeter wave radar 624 can also be used to sense the speed and / or heading of the object.

[0162] The ultrasonic radar 625 may sense objects around the vehicle 600 using ultrasonic signals.

[0163] The camera device 626 is used to capture image information of the surrounding environment of the vehicle 600. The camera device 626 may include a monocular camera, a binocular camera, a structured light camera, a panoramic camera, etc. The image information acquired by the camera device 626 may include a static image or video stream information.

[0164] The decision control system 630 includes a computing system 631 that analyzes and makes decisions based on the information obtained by the perception system 620. The decision control system 630 also includes a vehicle controller 632 that controls the power system of the vehicle 600, and a steering system 633, a throttle 634 and a braking system 635 for controlling the vehicle 600.

[0165] The computing system 631 can be operated to process and analyze various information obtained by the perception system 620 in order to identify targets, objects and / or features in the surrounding environment of the vehicle 600. Targets may include pedestrians or animals, and objects and / or features may include traffic signals, road boundaries and obstacles. The computing system 631 may use object recognition algorithms, Structure from Motion (SFM) algorithms, video tracking and other technologies. In some embodiments, the computing system 631 can be used to map the environment, track objects, estimate the speed of objects, and so on. The computing system 631 can analyze the various information obtained and derive a control strategy for the vehicle.

[0166] The vehicle controller 632 can be used to coordinate the control of the vehicle's power battery and engine 641 to improve the power performance of the vehicle 600.

[0167] The steering system 633 is operable to adjust the forward direction of the vehicle 600. For example, in one embodiment, it may be a steering wheel system.

[0168] The throttle 634 is used to control the operating speed of the engine 641 and thereby control the speed of the vehicle 600 .

[0169] Braking system 635 is used to control the deceleration of vehicle 600. Braking system 635 can use friction to slow down wheel 644. In some embodiments, braking system 635 can convert the kinetic energy of wheel 644 into electric current. Braking system 635 can also take other forms to slow down the rotation speed of wheel 644 to control the speed of vehicle 600.

[0170] The drive system 640 may include components that provide powered movement for the vehicle 600. In one embodiment, the drive system 640 may include an engine 641, an energy source 642, a transmission system 643, and wheels 644. The engine 641 may be an internal combustion engine, an electric motor, an air compression engine, or a combination of other types of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine 641 converts the energy source 642 into mechanical energy.

[0171] Examples of energy source 642 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 642 can also provide energy to other systems of vehicle 600.

[0172] The transmission system 643 can transmit the mechanical power from the engine 641 to the wheels 644. The transmission system 643 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission system 643 may also include other devices, such as a clutch. Among them, the drive shaft may include one or more shafts that can be coupled to one or more wheels 644.

[0173] Some or all functions of the vehicle 600 are controlled by a computing platform 650. The computing platform 650 may include at least one processor 651 that may execute instructions 653 stored in a non-transitory computer-readable medium such as a memory 652. In some embodiments, the computing platform 650 may also be a plurality of computing devices that control individual components or subsystems of the vehicle 600 in a distributed manner.

[0174] The processor 651 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor 651 may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof. Figure 6Functionally illustrated processor, memory and other elements of the computer in the same block, but those of ordinary skill in the art will appreciate that the processor, computer or memory may actually include multiple processors, computers or memories that may or may not be stored in the same physical housing. For example, the memory may be a hard drive or other storage media that is located in a housing different from the computer. Therefore, references to processors or computers will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Different from using a single processor to perform the steps described herein, some components such as steering components and deceleration components may each have their own processors that only perform calculations related to the functions specific to the components.

[0175] In the embodiment of the present disclosure, the processor 651 can execute the above-mentioned data collection method.

[0176] In various aspects described herein, processor 651 may be located remote from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle and others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.

[0177] In some embodiments, the memory 652 may include instructions 653 (e.g., program logic) that may be executed by the processor 651 to perform various functions of the vehicle 600. The memory 652 may also include additional instructions, including instructions to send data to, receive data from, interact with, and / or control one or more of the infotainment system 610, the perception system 620, the decision control system 630, and the drive system 640.

[0178] In addition to instructions 653, memory 652 may also store data such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, and other information. Such information may be used by vehicle 600 and computing platform 650 during operation of vehicle 600 in autonomous, semi-autonomous, and / or manual modes.

[0179] The computing platform 650 may control functions of the vehicle 600 based on input received from various subsystems, such as the drive system 640, the perception system 620, and the decision control system 630. For example, the computing platform 650 may utilize input from the decision control system 630 in order to control the steering system 633 to avoid an obstacle detected by the perception system 620. In some embodiments, the computing platform 650 may be operable to provide control over many aspects of the vehicle 600 and its subsystems.

[0180] Alternatively, one or more of the above-mentioned components may be installed or associated separately from the vehicle 600. For example, the memory 652 may exist partially or completely separately from the vehicle 600. The above-mentioned components may be communicatively coupled together in a wired and / or wireless manner.

[0181] Optionally, the above components are only examples. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 6 It should not be understood as limiting the embodiments of the present disclosure.

[0182] An autonomous vehicle traveling on a road, such as vehicle 600 above, can identify objects in its surroundings to determine adjustments to the current speed. Objects can be other vehicles, traffic control devices, or other types of objects. In some examples, each identified object can be considered independently, and based on the object's respective characteristics, such as its current speed, acceleration, spacing from the vehicle, etc., can be used to determine the speed to be adjusted by the autonomous vehicle.

[0183] Optionally, the vehicle 600 or a perception and computing device associated with the vehicle 600 (e.g., computing system 631, computing platform 650) can predict the behavior of the identified object based on the characteristics of the identified object and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of each other, so all the identified objects can also be considered together to predict the behavior of a single identified object. The vehicle 600 can adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous vehicle can determine what stable state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors can also be considered to determine the speed of the vehicle 600, such as the lateral position of the vehicle 600 in the road on which it is traveling, the curvature of the road, the proximity of static and dynamic objects, and the like.

[0184] In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may also provide instructions to modify the steering angle of vehicle 600 so that the autonomous vehicle follows a given trajectory and / or maintains a safe lateral and longitudinal distance from objects near the autonomous vehicle (e.g., vehicles in adjacent lanes on the road).

[0185] The vehicle 600 may be any type of travel vehicle, such as a car, a truck, a motorcycle, a bus, a ship, an airplane, a helicopter, an RV, a train, etc., and the present disclosure does not specifically limit the vehicle.

[0186] See also Fig.11 FIG. 1 is a schematic diagram showing the structure of a chip according to an exemplary embodiment. Fig.11 The chip 700 shown includes a processor 701 and an interface 702. Optionally, it may also include a memory 703. The number of the processor 701 may be one or more, and the number of the interface 702 may be more than one.

[0187] In one embodiment, for a case where a chip is used to implement the method embodiment of the present disclosure:

[0188] The interface 702 is used to receive or output signals;

[0189] The processor 701 is used to execute part or all of the content in the data collection method embodiment.

[0190] It is understandable that the processor in the embodiment of the present disclosure can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DigitalSignal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0191] It is understandable that the memory in the embodiments of the present disclosure may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0192] It should be noted here that the description of the above storage medium, device and chip embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of the present disclosure, please refer to the description of the method embodiments of the present disclosure for understanding.

[0193] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0194] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data collection method, characterized in that: include: When a rule triggering condition is met, traverse the rule interpreter that matches the rule triggering condition and all scenario rules managed by the rule interpreter, wherein the rule triggering condition includes a change in the operating state of the electronic device and / or a preset sampling period is reached, and the scenario rule is used to indicate the data collection requirements of the corresponding scenario; When it is determined that there is a target scenario rule, triggering corresponding data collection based on the target scenario rule, the target scenario rule being a rule among all scenario rules managed by the rule interpreter, and the target scenario rule being related to the rule triggering condition; Wherein, the method further comprises: When the rule triggering condition includes a change in the operating state of the electronic device, determining a target scenario rule that matches the changed operating state from all scenario rules managed by the rule interpreter; and / or, When the rule triggering condition includes reaching the preset sampling period, if the electronic device has data update, a target scene rule matching the updated data is determined from all scene rules managed by the rule interpreter.

2. The method according to claim 1, characterized in that All scenario rules managed by the rule interpreter are of the same type.

3. The method according to claim 1, characterized in that The traversal of the rule interpreter matching the rule triggering condition and all scenario rules managed by the rule interpreter includes: Traversing the rule interpreters that match the rule triggering condition and creating a running environment for the rule interpreters; All scenario rules managed by the rule interpreter are traversed, and all scenario rules are parsed using the rule interpreter.

4. The method according to claim 1, characterized in that The method further comprises: The target data obtained by the data collection is uploaded to the cloud.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: After all rule interpreters are deployed, all the rule interpreters are loaded; An operating environment is created for each of the rule interpreters to initialize each of the rule interpreters.

6. The method according to any one of claims 1 to 4, characterized in that The method further comprises: After all scenario rules are deployed, load all the scenario rules; After successfully performing a legality check on any of the scenario rules, mapping any of the scenario rules to a corresponding rule interpreter to initialize any of the scenario rules; Among them, the scene rules of the same type correspond to one rule interpreter.

7. The method according to claim 6, characterized in that When the type of the scenario rule includes a mathematical calculation type, the rule interpreter is a first type rule interpreter; or, When the type of the scenario rule includes a logic judgment type, the rule interpreter is a second type rule interpreter; or, When the type of the scenario rule includes a specific scenario algorithm type, the rule interpreter is a third type rule interpreter.

8. The method according to any one of claims 1 to 4, characterized in that The rule interpreter is deployed independently.

9. A data acquisition device, characterized in that: include: a processing module configured to traverse a rule interpreter matching the rule triggering condition and all scenario rules managed by the rule interpreter when a rule triggering condition is met, wherein the rule triggering condition includes a change in the operation state of the electronic device and / or a preset sampling period is reached, and the scenario rule is used to indicate a data collection requirement for a corresponding scenario; The processing module is further configured to trigger corresponding data collection based on the target scenario rule when determining that there is a target scenario rule, wherein the target scenario rule is a rule among all scenario rules managed by the rule interpreter, and the target scenario rule is related to the rule triggering condition; Among them, it also includes: When the rule triggering condition includes a change in the operating state of the electronic device, determining a target scenario rule that matches the changed operating state from all scenario rules managed by the rule interpreter; and / or, When the rule triggering condition includes reaching the preset sampling period, if the electronic device has data update, a target scene rule matching the updated data is determined from all scene rules managed by the rule interpreter.

10. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the executable instructions to implement the steps of the method described in any one of claims 1 to 9.

11. The device according to claim 10, characterized in that The electronic device includes a scheduler, a rule interpreter and a data collector.

12. The device according to claim 11, characterized in that The electronic device also includes a data uploader.

13. The device according to any one of claims 10 to 12, characterized in that The electronic device includes at least one of the following: a vehicle, a robot or a drone.

14. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

15. A chip, characterized in that: The method comprises a processor and an interface; the processor is used to read instructions to execute the method according to any one of claims 1 to 8.

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