CN116016592A

CN116016592A - Automobile signal acquisition method and system based on edge calculation

Info

Publication number: CN116016592A
Application number: CN202211686387.1A
Authority: CN
Inventors: 黄亮; 沈亮
Original assignee: Ruizixing Shanghai Intelligent Technology Co ltd
Current assignee: Ruizixing Shanghai Intelligent Technology Co ltd
Priority date: 2022-12-27
Filing date: 2022-12-27
Publication date: 2023-04-25

Abstract

The application relates to an automobile signal acquisition method and system based on edge calculation, wherein the method comprises the following steps: leading in a signal source file by a cloud end, and generating configuration information according to the signal source file and a preset configuration template; the cloud end sends the configuration information to the vehicle end controller; the vehicle-end controller determines the message to be acquired according to the configuration information and acquires the corresponding bus message from the lower layer; and the vehicle-end controller performs multi-level caching and screening on the bus message according to the configuration information, compresses the screened data and uploads the compressed data to the cloud. The invention provides a strategy for carrying out edge screening based on signal attributes, which takes a cloud big data algorithm as a basis to send configuration information to a vehicle end; the vehicle end simplifies the collected data according to the configuration information, performs data screening and collection by locally utilizing an edge calculation strategy, and then compresses and uploads the data, thereby greatly reducing the storage and uploading amount of useless data and reducing the cost and improving the efficiency.

Description

Automobile signal acquisition method and system based on edge calculation

Technical Field

The application relates to the technical field of Internet of vehicles, in particular to an automobile signal acquisition method and system based on edge calculation.

Background

Along with the development of the intelligent and networking of automobiles, the complexity of the automobiles is increased rapidly, and the data volume of the network in the automobiles is also greatly increased, so that the collection volume of remote monitoring and fault data log is also greatly increased, thereby leading to the rise of the cost of the system and the rise of the data storage cost of a server side.

In the related art, the traditional recording method is a strategy of comparing original blind marks, the data volume of the automobile bus message recorded based on the strategy is large, one path of CAN bus needs 5GB of data in 1 day, the cost of local storage and data uploading is extremely high, and most of automobile configurations cannot meet the requirement, so that the automobile bus message CAN only be selectively recorded. The selective recording cannot ensure that the needed signals cannot be lost, and the situation that the needed signals are lost one by one frequently occurs, so that great difficulty is brought to fault analysis of the rear end, and the loss of large data analysis dimension is caused, and the mode is low in efficiency, high in cost and poor in effect. The intelligent automobile can be larger in information quantity for bus data transmission, and further the problem in the data storage and uploading process is amplified.

Disclosure of Invention

In order to overcome the problems existing in the related art to at least a certain extent, the application provides an automobile signal acquisition method and system based on edge calculation.

According to a first aspect of embodiments of the present application, there is provided an automotive signal acquisition method based on edge calculation, including:

leading in a signal source file by a cloud end, and generating configuration information according to the signal source file and a preset configuration template;

the cloud end sends the configuration information to the vehicle end controller;

the vehicle-end controller determines the message to be acquired according to the configuration information and acquires the corresponding bus message from the lower layer;

and the vehicle-end controller performs multi-level caching and screening on the bus message according to the configuration information, compresses the screened data and uploads the compressed data to the cloud.

Further, the signal source file includes at least one of: bus data, diagnostic data;

the generating configuration information according to the signal source file and the configuration template comprises the following steps:

analyzing the imported signal source file to obtain basic information; the base information includes at least one of: real-time data parameters of signal definition and diagnosis of an automobile bus;

inputting the basic information into a configuration template, and outputting a signal set to be acquired;

and configuring according to the signal set to generate configuration information.

Further, the configuring according to the signal set includes:

classifying according to the attribute of each signal in the signal set, and dividing the signal into two types of switch signals and numerical signals;

registering a switch signal according to an event, and configuring a maximum value, a minimum value and a typical value by a numerical signal;

setting a triggering mode of each signal and acquisition time periods before and after triggering;

generating, by the configuration generation tool, a data mirror structure;

the uploading period is configured to control the frequency of the acquisition.

Further, the method further comprises:

the cloud decompresses and analyzes the uploaded data packet, analyzes the data and judges the failure cause;

matching the acquired batch of vehicle data with fault events;

and analyzing the association relation between the data abnormality and the fault through an algorithm, extracting the data characteristic points of the potential fault, adding the data characteristic points into parameter configuration, and generating a configuration template.

Further, the method further comprises:

after configuration, data verification is carried out, and repeated configuration and illegal configuration are deleted;

after the verification is completed, generating a configuration structure file and a corresponding unique ID;

and issuing the configuration structure file to the appointed vehicle end controller according to the VIN of the appointed vehicle.

Further, the obtaining the corresponding bus message from the lower layer includes:

acquiring a message to be acquired from a lower layer through an API; and/or the number of the groups of groups,

sending a request through a diagnostic service to obtain a response, and extracting valid data in the response;

the message to be acquired is acquired by inquiring configuration information.

Further, the vehicle-end controller performs multi-level caching and screening on the bus message according to the configuration information, including:

storing the acquired latest bus message into a first-level cache;

converting the bus message in the first-level cache into an identifiable real signal value according to the configuration information, and storing the identifiable real signal value in the second-level cache;

and extracting change difference data according to the signals in the secondary cache, storing the change difference data in the secondary cache, and storing the change difference data in a flash to be stored in a file form.

Further, the method further comprises:

the edge model judges events according to the time line by accessing data in the secondary cache according to configured logic, and when the conditions are met, corresponding operation is executed;

the edge model is an algorithm model pre-stored in a vehicle-end controller; the corresponding operations include: triggering data uploading and/or executing a certain troubleshooting process.

Further, the method further comprises:

based on the signal value and the fault code, judging according to preset logic;

when the trigger condition is met, continuing to collect the designated time, compressing and storing the data, uploading event information and log at the same time, and retransmitting at intervals if log is not successfully uploaded.

According to a second aspect of embodiments of the present application, there is provided an automotive signal acquisition system based on edge calculation, including: cloud and vehicle end controllers;

the cloud end is used for importing a signal source file, generating configuration information according to the signal source file and a preset configuration template, and then sending the configuration information to the vehicle end controller;

the vehicle end controller is used for determining the message to be acquired according to the configuration information and acquiring a corresponding bus message from the lower layer; and the bus message is subjected to multi-level caching and screening according to the configuration information, and the screened data is compressed and then uploaded to a cloud.

The technical scheme provided by the embodiment of the application has the following beneficial effects:

the invention provides a strategy for carrying out edge screening based on signal attributes, which takes a cloud big data algorithm as a basis to send configuration information to a vehicle end; the vehicle end simplifies the collected data according to the configuration information, refines the changed and unchanged, normal and abnormal data, starts from the two dimensions, performs data screening and gathering by locally utilizing an edge computing strategy, and then compresses and uploads the data, thereby greatly reducing the useless data storage and uploading amount and reducing the cost and improving the efficiency.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

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

Fig. 1 is a schematic view of an application environment of an automotive signal acquisition method based on a configurable lightweight edge calculation according to an exemplary embodiment.

FIG. 2 is a flowchart illustrating a method of edge-calculation-based automotive signal acquisition, according to an exemplary embodiment.

FIG. 3 is an execution flow of an automobile signal acquisition based on a configurable lightweight edge calculation.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of methods and systems that are consistent with aspects of the present application, as detailed in the accompanying claims.

The automobile signal acquisition method based on edge calculation can be applied to an application environment shown in fig. 1, wherein the application environment comprises a cloud end and a local end (automobile end controller).

FIG. 2 is a flowchart illustrating a method of edge-calculation-based automotive signal acquisition, according to an exemplary embodiment. The method may comprise the steps of:

step S1, importing a signal source file by a cloud end, and generating configuration information according to the signal source file and a preset configuration template;

step S2, the cloud end sends the configuration information to a vehicle-end controller;

step S3, the vehicle-end controller determines the message to be acquired according to the configuration information, and acquires a corresponding bus message from the lower layer;

and S4, the vehicle-end controller performs multi-level caching and screening on the bus message according to the configuration information, and compresses and uploads the screened data to the cloud.

It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail with reference to the accompanying drawings.

As shown in fig. 1, a main architecture of a configurable lightweight edge-computed automotive signal acquisition is depicted. The architecture mainly comprises two parts, one part is a cloud end and the other part is a local end (in-vehicle controller).

The main tasks of the cloud end comprise two parts: a configuration and editing part and a data presentation model training part.

In some embodiments, the signal source file comprises at least one of: bus data, diagnostic data. The step S1 generates configuration information according to a signal source file and a configuration template, and comprises the following steps: analyzing the imported signal source file to obtain basic information; the base information includes at least one of: real-time data parameters of signal definition and diagnosis of an automobile bus; inputting the basic information into a configuration template, and outputting a signal set to be acquired; and configuring according to the signal set to generate configuration information.

In a more specific embodiment, the configuring according to the signal set includes the following steps: classifying according to the attribute of each signal in the signal set, and dividing the signal into two types of switch signals and numerical signals; registering a switch signal according to an event, and configuring a maximum value, a minimum value and a typical value by a numerical signal; setting a triggering mode of each signal and acquisition time periods before and after triggering; generating, by the configuration generation tool, a data mirror structure; the uploading period is configured to control the frequency of the acquisition.

The main tasks of configuration and model editing are to analyze input signal source files, such as signal definition of an automobile bus, real-time data parameters of diagnosis, then input the real-time data parameters into a configuration interface in a unified way, manually or model output signal sets required to be acquired, and the signal sets are classified into switch signals and numerical signals according to signal attributes after being acquired. The switch signal is registered according to the event, and the numerical signal needs to be configured with a maximum value, a minimum value and a typical value. Based on the above signals, the logic setting of the trigger condition and the combination condition are performed. A data mirror structure is then generated by the configuration generation tool, which directly maps a memory block (pool) in memory. And configuring an uploading period for controlling the acquisition frequency.

Model editing provides a visual model calculation formula for the calculation logic that sets the combining conditions.

The data display and model training part mainly analyzes and processes the result, decompresses the data packet uploaded by analysis, restores the data frame, displays the data frame to the UI interface, and is used for replaying analysis data and judging the failure cause.

The model training is to match the data of the batch vehicles with fault events, analyze the association relation between the data abnormality and the faults through an algorithm, extract the data characteristic points of potential faults, add the data characteristic points into parameter configuration, store the data characteristic points in the form of cases and take the data characteristic points as templates for subsequent issuing.

The vehicle end part is an execution end, and mainly works according to the configuration issued by the cloud end, extracts and refines layer by layer, acquires information meeting the acquisition requirement, and then performs data compression and uploading, and aims to perfectly reproduce useful data at the cloud end and perform display and big data analysis.

In some embodiments, the method further comprises: the cloud decompresses and analyzes the uploaded data packet, analyzes the data and judges the failure cause; matching the acquired batch of vehicle data with fault events; and analyzing the association relation between the data abnormality and the fault through an algorithm, extracting the data characteristic points of the potential fault, adding the data characteristic points into parameter configuration, and generating a configuration template.

In some embodiments, the method further comprises: after configuration, data verification is carried out, and repeated configuration and illegal configuration are deleted; after the verification is completed, generating a configuration structure file and a corresponding unique ID; and issuing the configuration structure file to the appointed vehicle end controller according to the VIN of the appointed vehicle.

In some embodiments, the obtaining the corresponding bus packet from the lower layer includes: acquiring a message to be acquired from a lower layer through an API; and/or sending a request through the diagnostic service to obtain a response, and extracting valid data in the response; the message to be acquired is acquired by inquiring configuration information.

In some embodiments, the vehicle-side controller performs multi-level caching and screening on the bus message according to the configuration information, including: storing the acquired latest bus message into a first-level cache; converting the bus message in the first-level cache into an identifiable real signal value according to the configuration information, and storing the identifiable real signal value in the second-level cache; and extracting change difference data according to the signals in the secondary cache, storing the change difference data in the secondary cache, and storing the change difference data in a flash to be stored in a file form.

In some embodiments, the method further comprises: the edge model judges events according to the time line by accessing data in the secondary cache according to configured logic, and when the conditions are met, corresponding operation is executed; the edge model is an algorithm model pre-stored in a vehicle-end controller; the corresponding operations include: triggering data uploading and/or executing a certain troubleshooting process.

In some embodiments, the method further comprises: based on the signal value and the fault code, judging according to preset logic; when the trigger condition is met, continuing to collect the designated time, compressing and storing the data, uploading event information and log at the same time, and retransmitting at intervals if log is not successfully uploaded.

The vehicle end comprises seven modules, which are respectively introduced as follows:

(1) The task management module is a master control module of a vehicle end, is a main management party of the SDK (Software Development Kit ), and has the main roles of acquiring configuration and local initialization of a docking cloud, scheduling and monitoring operation, acquisition and uploading data of an SDK package sub-module, and completing self-upgrading.

(2) The acquisition configuration is a configuration issued by the cloud, the configuration comprises a signal set to be acquired, a triggering mode and acquisition time periods before and after triggering, MAP rules of signal attributes and signals are defined, the MAP rules correspond to the cloud configuration, and the model algorithm is stored in a script array mode.

(3) The signal pool is a module for caching signals, the signal pool is stored according to three levels, the first level cache pool is used for storing the latest received bus message, the second level cache is used for extracting signals in the message, the third level cache is used for storing variation differences of the signals, and the signals are stored in a flash and stored in a file form once every 5 s.

(4) The bus acquisition and the diagnosis acquisition are an interface layer, acquire a required message to a lower layer through an API (Application Programming Interface, application program interface), or send a request acquisition response through a diagnosis service, extract effective data in the response, and acquire a message ID (identity) required to be acquired through configuration query.

(5) The signal extraction is to prune and analyze the useless information in the message according to the configuration to become identifiable real signal values which are stored in the signal pool 2;

(6) The edge model performs event discrimination according to the time line according to configured logic (logic conditions loaded from the cloud) by accessing the data in the signal pool 2, and when the conditions are met, performs corresponding operations, such as: triggering data uploading, such as executing a certain troubleshooting process, and the like, and further troubleshooting data acquisition is performed.

(7) The trigger monitoring is based on the judgment of the logic of the signal value and the fault code, log data is locally stored when the trigger condition is met, the upper layer is informed of event triggering, and the link is to send an event message first.

(8) The data uploading is to store and compress the data based on the signal pool 3, and then upload the compressed data to the cloud.

As shown in fig. 3, the entire flow is a closed loop flow.

Firstly, when data needs to be collected, bus data and diagnostic data need to be imported, wherein the bus information can be imported by directly adopting a DBC file, parameters of a message and a signal are contained in the DBC file, and the diagnostic data can be imported and obtained by using excel or ODX. After the parameters are imported, the parameters can be selected and configured through a template formed in the past, the data and the acquisition frequency required to be acquired can be manually configured and added, then the triggering condition and the time length required for front and back acquisition are configured, the triggering condition can be configured with signal values and can also be configured with fault code triggering, and the combination conditions of with or without the parameters can be supported. After the module is configured, data verification is carried out, repeated illegal configuration (such as overlapping signal positions and incorrect lengths, which mainly aim at operation errors input manually) is removed, and after the verification is finished, a configuration structure file and a configured unique ID (identity) are generated, wherein the configuration structure file and the configured unique ID are used for carrying out task matching with LOG data uploaded subsequently. After the configuration is completed, a desired vehicle input VIN (Vehicle Identification Number ) may be selected and issued to the vehicle end controller.

After the controller receives the configuration task, the controller performs configuration check, whether the configuration is complete, whether the configuration is matched with a vehicle model, then starts an initialization task, starts data receiving operation, and in the running process of the vehicle, the vehicle end controller transmits a required message to the SDK through the API, the SDK caches the message in the cache pool 1, and then the signal processing module extracts and analyzes the signal into the signal pool 2, so that the data volume is reduced. It should be noted that, in the signal pool 2, the trigger module determines whether the trigger condition is met by reading the signal in the signal pool 2, if so, the trigger condition continues to be collected for a specified time, the data is compressed and saved, the event information and log are uploaded, and if the log is not successfully uploaded (for example, the server is busy or the 4G signal is weak), the trigger condition is retransmitted at intervals.

And when the model condition is met, the data uploading and the associated flow execution depth fault detection can be triggered. Thereby acquiring more detailed and rich fault site data.

After the data is uploaded, the bus DBC and the diagnosis configuration data are acquired according to the configuration ID, the data is played back, a playback interface is friendly, the on-site restoration is carried out according to a time line, the problem is solved, the characteristics on the data can be operated while faults are found out, and a case template for detecting the subsequent similar problems is provided.

The uploaded data, fault codes, alarm lamps and maintenance cases are associated to perform large data analysis on large-batch vehicles, data features of faults to be generated are found out, and fault monitoring and data acquisition are performed iteratively, so that the probability of occurrence of potential faults can be predicted through data analysis before the faults occur, and the vehicle owners are prompted to enter a store for maintenance.

The core of the operations is business, and the platform provided by the invention can realize various flexible and intelligent monitoring means and reduce the operation cost.

In summary, the invention is an important link in the active after-sales service of the automobile, solves the problems of difficult data capture, more redundant data and great cost waste of the current host factory, can greatly reduce the data storage and the data uploading by more than 80 percent on the premise of meeting the after-sales maintenance requirement by the algorithm and the strategy of the invention, and reduces the cost and increases the efficiency of the host factory. Meanwhile, the configuration requirement and purchase cost of the server side are greatly reduced. Is a practical system with light weight.

The embodiment of the application also provides an automobile signal acquisition system based on edge calculation, which comprises: cloud and vehicle end controllers;

The specific steps in which the various modules perform operations in relation to the systems of the embodiments described above have been described in detail in relation to the embodiments of the method and are not described in detail herein. The modules in the automobile signal acquisition system can be realized in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.

It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.

It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.

The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.

In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims

1. An automobile signal acquisition method based on edge calculation is characterized by comprising the following steps:

2. The method of claim 1, wherein the signal source file comprises at least one of: bus data, diagnostic data;

3. The method of claim 2, wherein the configuring according to the set of signals comprises:

generating, by the configuration generation tool, a data mirror structure;

the uploading period is configured to control the frequency of the acquisition.

4. The method as recited in claim 1, further comprising:

matching the acquired batch of vehicle data with fault events;

5. The method as recited in claim 4, further comprising:

6. The method according to any one of claims 1-5, wherein the obtaining the corresponding bus message from the lower layer includes:

the message to be acquired is acquired by inquiring configuration information.

7. The method of claim 6, wherein the vehicle-side controller performs multi-level buffering and screening on the bus message according to the configuration information, including:

storing the acquired latest bus message into a first-level cache;

8. The method as recited in claim 7, further comprising:

9. The method as recited in claim 6, further comprising:

10. An edge calculation-based automotive signal acquisition system, comprising: cloud and vehicle end controllers;