Vehicle-mounted signal analysis model building method, storage medium and electronic device

Abstract

The invention discloses a vehicle-mounted signal analysis model building method, a storage medium and an electronic device, and relates to the technical field of automotive electronics. The method comprises the following steps: acquiring a network protocol file; analyzing the network protocol file, and extracting signal data from the network protocol file; on the basis of a preset naming rule, prefix information or suffix information is spliced for the signal data to obtain spliced signal data, and the prefix information or suffix information is used for describing the property of the signal data; filling the spliced signal data into the initial signal analysis template to obtain a target signal analysis template; and according to a preset building rule and the target signal analysis template, building to obtain a signal analysis model. According to the invention, the technical problem of low construction efficiency of the signal analysis model in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics technology, and more specifically, to a method for building an in-vehicle signal analysis model, a storage medium, and an electronic device. Background Technology

[0002] Currently, the signal transmission between onboard controllers and communication modules in electric vehicles is increasing, which significantly increases the workload of signal analysis. Traditional methods rely on engineers manually building signal analysis models, which is not only time-consuming but also prone to errors when dealing with a large number of signals, such as missing interfaces, duplicate assignments, or incorrect assignments.

[0003] No effective solution has yet been proposed to address the aforementioned technical issues. Summary of the Invention

[0004] This invention provides a method for building a vehicle signal analysis model, a storage medium, and an electronic device to at least solve the technical problem of low efficiency in building signal analysis models in related technologies.

[0005] According to one embodiment of the present invention, a method for building a vehicle signal analysis model is provided, comprising: acquiring a network protocol file, wherein the network protocol file is used to record parameters and definitions involved in vehicle network communication; extracting signal data from the network protocol file by analyzing the network protocol file; concatenating prefix information or suffix information to the signal data based on a preset naming rule to obtain concatenated signal data, wherein the prefix information or suffix information is used to describe the properties of the signal data; filling the concatenated signal data into an initial signal analysis template to obtain a target signal analysis template, wherein the initial signal analysis template is created based on a preset format standard; and building a signal analysis model according to preset building rules and the target signal analysis template.

[0006] Optionally, the method for building the vehicle signal analysis model also includes: parsing the network protocol file based on an automated script to obtain the structure and syntax of the network protocol file; and determining the preset logic of the network protocol file based on the structure and syntax.

[0007] Optionally, signal data is extracted from the network protocol file by analyzing the network protocol file, including: determining a field extraction protocol based on preset logic, wherein the field extraction protocol is the extraction rule corresponding to the automated script; analyzing the network protocol file based on the field extraction protocol, and extracting the signal data.

[0008] Optionally, the signal data is spliced ​​with prefix or suffix information based on a preset naming rule to obtain spliced ​​signal data, including: determining the prefix or suffix information based on the nature of the signal data; and splicing the signal data with the corresponding prefix or suffix information by calling a splicing statement to obtain spliced ​​signal data, wherein the spliced ​​signal data conforms to the preset naming rule.

[0009] Optionally, the method for building an on-board signal analysis model further includes: determining the template display content based on a preset format standard, wherein the template display content includes: a custom signal name column, a bus signal name column, and a signal data type column; and creating an initial signal analysis template based on the template display content.

[0010] Optionally, filling the spliced ​​signal data into the initial signal analysis template to obtain the target signal analysis template includes: determining the preset display position of the spliced ​​signal data in the initial signal analysis template based on preset logic; and filling the spliced ​​signal data into the initial signal analysis template according to the preset display position to obtain the target signal analysis template.

[0011] Optionally, a signal analysis model is constructed according to preset construction rules and a target signal analysis template, including: acquiring spliced ​​signal data in the target signal analysis template; determining the module types of multiple modules and the preset step size between multiple modules by traversing the spliced ​​signal data; determining the position parameters of multiple modules based on the module types and preset step sizes; setting the signal line connection method between multiple modules according to preset construction rules and position parameters; and constructing multiple modules based on the signal line connection method to obtain the signal analysis model.

[0012] According to one embodiment of the present invention, an apparatus for building a vehicle signal analysis model is also provided, comprising: an acquisition module for acquiring a network protocol file, wherein the network protocol file is used to record parameters and definitions involved in vehicle network communication; an extraction module for extracting signal data from the network protocol file by analyzing the network protocol file; a splicing module for splicing prefix information or suffix information to the signal data based on a preset naming rule to obtain spliced ​​signal data, wherein the prefix information or suffix information is used to describe the properties of the signal data; a filling module for filling the spliced ​​signal data into an initial signal analysis template to obtain a target signal analysis template, wherein the initial signal analysis template is created based on a preset format standard; and a building module for building a signal analysis model according to preset building rules and the target signal analysis template.

[0013] According to one embodiment of the present invention, a computer-readable storage medium is also provided, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the vehicle signal analysis model building method described above when running on a computer or processor.

[0014] According to one embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to execute the vehicle signal analysis model building method of any of the above claims.

[0015] According to one embodiment of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the vehicle signal analysis model building method described in any of the above claims.

[0016] In this embodiment of the invention, a network protocol file is obtained, which records the parameters and definitions involved in vehicle network communication; signal data is extracted from the network protocol file by analysis; prefix or suffix information is concatenated to the signal data based on a preset naming rule to obtain concatenated signal data, wherein the prefix or suffix information is used to describe the properties of the signal data; the concatenated signal data is filled into an initial signal parsing template to obtain a target signal parsing template, wherein the initial signal parsing template is created based on a preset format standard; a signal parsing model is built according to preset building rules and the target signal parsing template, thereby achieving the goal of efficiently and accurately processing vehicle signals, thus significantly improving development efficiency and reducing human error, and solving the technical problem of low efficiency in building signal parsing models in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0018] Figure 1 This is a flowchart of a method for building a vehicle signal analysis model according to one embodiment of the present invention;

[0019] Figure 2 This is a flowchart of an automatic construction method for an on-board controller interface parsing model according to one embodiment of the present invention;

[0020] Figure 3 This is a flowchart of a network protocol file analysis and signal extraction method according to one embodiment of the present invention;

[0021] Figure 4 This is a flowchart of a signal name automatic splicing method according to one embodiment of the present invention;

[0022] Figure 5 This is a flowchart of an automated signal analysis model construction method according to one embodiment of the present invention;

[0023] Figure 6 This is a structural block diagram of an in-vehicle signal analysis model building device according to one embodiment of the present invention. Detailed Implementation

[0024] For ease of understanding, some concepts related to embodiments of the present invention are illustrated below for reference.

[0025] A signal parsing model refers to the process and structure of extracting signal data from vehicle network communication protocols and formatting, converting, and decoding it. Signal parsing models are typically composed of software algorithms, aiming to transform complex, raw network signals into a data format understandable by the application layer, enabling the onboard controller to correctly interpret and process the signal data. The accuracy and efficiency of the signal parsing model directly affect the response speed and safety of the vehicle system.

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. In the description of these embodiments, unless otherwise stated, "a plurality of" means two or more. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to one embodiment of the present invention, an embodiment of a method for building a vehicle signal analysis model is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This method embodiment can be executed in an electronic device, similar control device, or system that includes a memory and a processor. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the electronic device may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the electronic device. For example, the electronic device may include more or fewer components than described above, or have a different configuration than described above.

[0030] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), or an artificial intelligence (AI) processor. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0031] The memory can be used to store computer programs, such as the computer program corresponding to the vehicle signal analysis model building method in this embodiment of the invention. The processor implements the vehicle signal analysis model building method by running the computer program stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to electronic devices via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] Communication devices are used to receive or send data via a network. Specific examples of such networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet.

[0033] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0034] This embodiment provides a method for building an in-vehicle signal analysis model running on an electronic device. Figure 1 This is a flowchart of a method for building a vehicle signal analysis model according to one embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0035] Step S10: Obtain the network protocol file, wherein the network protocol file is used to record the parameters and definitions involved in vehicle network communication;

[0036] In this embodiment of the invention, the network protocol file refers to a document or data file that records in detail the communication rules and parameters between various electronic control units inside the vehicle. Specifically, for vehicle network communication, the network protocol file usually includes the following typical types: (1) DBC file (Controller Area Network, CAN Database File), which is used to define the signals and data on the controller area network bus. The DBC file contains the signal name, number, data type, signal length, transmission period, signal description, and the signal mapping method on the bus, and is an important reference when building a CAN signal parsing model. (2) LDF file (Local Interconnect Network, LIN Description File) is a description file of the local interconnect network bus, used to define the topology, signal type, signal encoding, signal transmission period, signal data field length, and other parameters of the LIN network.

[0037] As can be seen, by acquiring network protocol files, automated scripts can accurately identify the characteristics of each signal, thereby enabling the rapid and automatic construction of signal analysis models.

[0038] Step S12: Extract signal data from the network protocol file by analyzing the network protocol file;

[0039] In this embodiment of the invention, signal data refers to various information and parameters describing the characteristics of communication signals, which are parsed and extracted from network protocol files (such as DBC files and LDF files). For example, signal data includes, but is not limited to: signal name, signal code, signal length, signal start bit, signal direction, signal data type, and signal description.

[0040] Analyzing network protocol files and extracting signal data from them can be understood as using appropriate tools (such as parsing libraries or custom scripts) to open and read network protocol files (such as DBC or LDF files), analyzing the structure and format of the network protocol files, and extracting signal data according to the signal formats defined in the network protocol files. Each signal data includes, but is not limited to, the signal name, encoding, data type, range, unit, and transmission period.

[0041] It can be seen that by accurately extracting signal data, the subsequent model building can be based on accurate communication specifications, thereby improving the efficiency and accuracy of signal processing and reducing errors and time costs in the development process.

[0042] Step S14: Based on the preset naming rules, prefix information or suffix information is concatenated to the signal data to obtain concatenated signal data, wherein the prefix information or suffix information is used to describe the properties of the signal data;

[0043] In this embodiment of the invention, preset naming rules are used to ensure that signal names reflect both the nature and purpose of the signal, and also follow a certain format for easy management and identification. Preset naming rules generally include elements such as signal type indication, signal function description, and signal direction indication, ensuring the consistency and clarity of signal names.

[0044] Prefix information consists of descriptive characters or strings added before the original signal data to emphasize a particular attribute or function of the signal, such as its source, nature, input, or output. For example, "PortIn_" can be used as a prefix for a received signal, indicating that the signal data is a signal entering the vehicle controller; "Ctrl_" may be used as a prefix for a signal emitted by the controller, indicating that the signal is associated with a specific controller, and this is not limited here.

[0045] Postfix information consists of descriptive characters or strings that follow the original signal data, used to further refine the signal's purpose or behavior. For example, "_PortOut" might indicate that the signal data is a signal output by a controller; "_Status" might indicate that the signal carries status information, etc., and there are no restrictions here.

[0046] Concatenated signal data refers to signal data formed by combining preset prefix and suffix information with original signal data and then using automated scripts or algorithms to concatenate strings.

[0047] Based on preset naming rules, prefix or suffix information is added to the signal data to obtain spliced ​​signal data. This can be understood as combining the original signal data name with prefix or suffix information based on a predetermined naming standard. This ensures that each signal data can correctly add descriptive information according to the preset rules, thereby making the generated signal name more complete and standardized.

[0048] For example, for the original signal data "TempSensor", according to the preset naming rules, the prefix information can be set to "PortIn_", and the resulting spliced ​​signal data is "PortIn_TempSensor". The spliced ​​signal data not only clearly indicates the input nature of the signal, but also retains the original descriptiveness of the signal, making it convenient to identify and use in the model environment. There are no restrictions on this.

[0049] As can be seen, through the above steps, the signal data in the embodiments of the present invention, under the processing of automated scripts, obtains a more descriptive name that conforms to the preset specifications, which is beneficial for signal identification and management in complex vehicle systems, and improves development efficiency and model building accuracy.

[0050] Step S16: Fill the spliced ​​signal data into the initial signal parsing template to obtain the target signal parsing template, wherein the initial signal parsing template is created based on a preset format standard;

[0051] In this embodiment of the invention, the initial signal parsing template is a pre-designed template file with a specific format and structure, typically existing in tabular form (such as an Excel file, which is not limited here). The initial signal parsing template is used to organize and record signal data in the vehicular network. It includes multiple columns, such as signal name, signal code, signal type, signal length, and signal start position, each column corresponding to a specific attribute of the signal. The structure of the initial signal parsing template is created based on a preset format standard, ensuring template compatibility and standardization.

[0052] The target signal parsing template is formed by automatically processing the initial signal parsing template with spliced ​​signal data to fill in the initial template. The target signal parsing template contains complete signal information, including the name, properties, and other key attributes of the signal data, and can be directly used for subsequent signal parsing model building.

[0053] Predefined format standards refer to the rules and structure that are predefined to create the initial signal parsing template. Predefined format standards typically include details such as the template layout, column names, and data types, ensuring that all signal data can be correctly identified and processed in the model.

[0054] Filling the spliced ​​signal data into the initial signal parsing template to obtain the target signal parsing template can be understood as reading the spliced ​​signal data according to the automated script and writing the spliced ​​signal data into the corresponding position of the initial signal parsing template according to the preset format standard to obtain the target signal parsing template.

[0055] It can be seen that by obtaining the target signal analysis template and then carrying out further model building and debugging work based on the target signal analysis template, the efficiency and accuracy of vehicle controller software development can be improved.

[0056] Step S18: Based on the preset construction rules and target signal analysis template, a signal analysis model is constructed.

[0057] In this embodiment of the invention, the preset building rules are predefined guiding principles and methods used to automatically build a signal analysis model based on the signal data in the target signal analysis template.

[0058] The signal analysis model built according to the preset building rules and the target signal analysis template can be understood as automatically building the signal analysis model by reading the data of the target signal analysis template based on the automated script and following the preset building rules.

[0059] As can be seen, by following the above steps and building a signal analysis model based on the preset building rules and target signal analysis template, we can not only ensure the accuracy and consistency of signal data, but also improve the automation level of signal analysis model building, significantly improve development efficiency, and reduce the risk of human error.

[0060] Figure 2 This is a flowchart of an automatic construction method for an on-board controller interface parsing model according to one embodiment of the present invention, such as... Figure 2 As shown, firstly, the network protocols such as CAN / LIN that need to be converted are obtained, their content is analyzed, and the distribution patterns of corresponding signal content are found, thus creating a signal conversion template. Key information from the protocol is extracted using an automated script and written into the template. Secondly, according to the defined naming rules, an automated script extracts the content of the corresponding columns in the template, adds appropriate prefixes and suffixes, calls MATLAB statements to perform signal splicing, and writes it back to the corresponding column of the signal name in the Excel template. Finally, according to the construction rules of the signal parsing module, an automated script is written to extract all necessary content from the template, set the model's position and parameters, set connection lines based on signal handles, and complete the corresponding module layout and definition, thereby automatically building the signal parsing module.

[0061] Specifically, firstly, based on the files obtained for building the signal parsing module, such as DBC and LDF files, a code viewing tool is used to analyze the underlying code logic, facilitating subsequent script development. Next, according to the requirements of the signal parsing module, an Excel template for signal parsing parameters is created, including columns for custom signal names, bus signal names, and signal data types. An automated script is then written based on the code logic in the signal protocol file and the created signal template to extract the corresponding signal content from the protocol and paste it into the corresponding columns of the template. Then, according to a standardized signal naming rule, an automated script is written to extract the relevant information from the signal template and add the corresponding prefixes and suffixes. MATLAB statements are then used to concatenate the fields according to the naming rules and write them back to the corresponding columns of the template. Finally, based on the signal parsing module's construction logic, an automated script is written to extract the corresponding signal columns from the signal template. By setting module parameters and signal line association statements, the signal parsing module is built, and the template data is traversed to automatically build the parsing models for all signals.

[0062] Through the above steps, the network protocol file is first obtained, which records the parameters and definitions involved in vehicle network communication. Next, the network protocol file is analyzed to extract signal data. Then, based on preset naming rules, prefix or suffix information is appended to the signal data to obtain concatenated signal data, where the prefix or suffix information describes the properties of the signal data. Next, the concatenated signal data is filled into an initial signal parsing template to obtain a target signal parsing template, where the initial signal parsing template is created based on a preset format standard. Finally, according to preset building rules and the target signal parsing template, a signal parsing model is built, achieving the goal of efficiently and accurately processing vehicle signals. This significantly improves development efficiency, reduces human error, and solves the technical problem of low efficiency in signal parsing model building in related technologies.

[0063] Optionally, the method for building a vehicle signal analysis model may also include the following steps:

[0064] Step S11: Parse the network protocol file based on the automated script to obtain the structure and syntax of the network protocol file;

[0065] Step S13: Determine the preset logic of the network protocol file based on its structure and syntax.

[0066] In this embodiment of the invention, the preset logic refers to the processing rules and procedures formed by the automated script based on the structure and syntax parsing results of the network protocol file, which are intended to guide the automatic construction of the signal parsing model.

[0067] Parsing network protocol files using automated scripts to obtain their structure and syntax can be understood as using automated scripts (such as Python or MATLAB scripts) to analyze network protocol files (e.g., DBC or LDF files). Specifically, automated scripts read the content of network protocol files, identify and parse the file format, including the file's hierarchical structure, syntax elements, keywords, and data fields, thereby understanding the organization and specific details of the network protocol.

[0068] Determining the preset logic of network protocol files based on structure and syntax can be understood as follows: after obtaining the structure and syntax of the network protocol files, the automated script transforms the structure and syntax into specific guiding principles that can be used for model building, and then determines the preset logic of the network protocol files based on these specific guiding principles.

[0069] As can be seen, the above steps not only achieve effective parsing of network protocol files, but also provide a logical foundation for building signal parsing models, thereby improving the development efficiency and quality of signal parsing models.

[0070] Optionally, in step S12, signal data is extracted from the network protocol file by analyzing the network protocol file, including the following execution steps:

[0071] Step S121: Determine the field extraction protocol based on preset logic, wherein the field extraction protocol is the extraction rule corresponding to the automated script;

[0072] Step S122: Analyze the network protocol file based on the field extraction protocol and extract the signal data.

[0073] In this embodiment of the invention, the field extraction protocol refers to the rules and guidelines followed by the automated script when processing network protocol files, which are used to accurately identify and extract key signal data from the files.

[0074] The field extraction protocol based on preset logic can be understood as designing a set of specific rules or algorithms based on the structure and syntax features of the network protocol file obtained from the analysis, in order to guide the automated script on how to effectively identify and extract key signal fields from the file.

[0075] Analyzing network protocol files based on field extraction protocols and extracting signal data can be understood as applying field extraction protocols in automated scripts to analyze network protocol files and extract signal-related data fields from the files.

[0076] It can be seen that by implementing a field extraction protocol based on preset logic, the automated script can accurately and efficiently extract signal data from network protocol files, which not only greatly reduces the need for manual intervention, but also significantly improves the speed and accuracy of signal information extraction.

[0077] Figure 3 This is a flowchart of a network protocol file analysis and signal extraction method according to one embodiment of the present invention, such as... Figure 3As shown, firstly, obtain the files (such as DBC files, LDF files, etc.) used to build the signal parsing module, and analyze the underlying code logic using a code viewing tool. Next, based on the requirements of the signal parsing module, create an Excel template for signal parsing parameters, including columns for custom signal names, bus signal names, and signal data types. Then, based on the code logic in the signal protocol file and the created signal template, write an automated script to extract the corresponding signal content from the protocol into the corresponding columns of the template. Finally, based on the code logic in the signal protocol file and the created signal template, write a MATLAB script to extract the signal bus name, signal data type, and other required signal content from the code into corresponding arrays, and then use a write function to write the content into the corresponding columns of the template according to the corresponding columns of each data content.

[0078] Optionally, in step S14, the spliced ​​signal data is obtained by concatenating prefix or suffix information into the signal data based on a preset naming rule, including the following execution steps:

[0079] Step S141: Determine the prefix information or suffix information based on the properties of the signal data;

[0080] Step S142: By calling the concatenation statement, the signal data is concatenated with the corresponding prefix or suffix information to obtain concatenated signal data, wherein the concatenated signal data conforms to the preset naming rules.

[0081] In this embodiment of the invention, a concatenation statement refers to a statement or function used to merge two or more strings into a new string.

[0082] Determining prefix or suffix information based on the properties of signal data can be understood as deciding on the prefix or suffix information to be added to the signal data name based on the characteristics of the signal data (such as the function, flow direction, type, etc.).

[0083] By calling the concatenation statement to concatenate the signal data with the corresponding prefix or suffix information, the concatenated signal data can be understood as combining the signal data with the aforementioned determined prefix or suffix information by calling the string concatenation function to generate concatenated signal data that conforms to the preset naming rules.

[0084] As can be seen, by implementing the above steps, the embodiments of the present invention can efficiently generate spliced ​​signal data that conforms to the naming rules, while also improving the automation level of signal processing, reducing human error, and accelerating the development process.

[0085] Figure 4This is a flowchart of a signal name automatic splicing method according to one embodiment of the present invention, such as... Figure 4 As shown, the template for signal extraction is obtained, and a script is written according to the naming rules to extract the signal data types and bus signal names from the template into an array. Based on the internally specified unified naming rules, corresponding prefix and suffix information is added. For example, the corresponding PortIn prefix is ​​added for received signals, and the corresponding PortOut suffix is ​​added for output signals. MATLAB statements are then called to concatenate the prefix, suffix information, and corresponding signal name fields according to the naming rules to form a signal name that conforms to the naming rules, and the concatenated data is written back to the corresponding column of the template.

[0086] Optionally, the method for building a vehicle signal analysis model may also include the following steps:

[0087] Step S15: Determine the template display content based on the preset format standard. The template display content includes: a custom signal name column, a bus signal name column, and a signal data type column.

[0088] Step S17: Create an initial signal analysis template based on the template display content.

[0089] In this embodiment of the invention, the template display content refers to the preset columns and content structure when the automated script is used to create the signal analysis model template.

[0090] The custom signal name column is used to store concatenated signal data after prefixing or suffixing information, which facilitates the identification of signal attributes (such as input, output, or state signals) and functions in the signal analysis model.

[0091] The bus signal name column contains the original signal names extracted directly from the network protocol file, which are used to identify the signal's identity in network communication.

[0092] The signal data type column records the data type information for each signal data, such as unsigned integer, signed integer, floating-point number, etc., and there are no restrictions here.

[0093] Determining the template display content based on preset format standards can be understood as selecting column structures and content specifications that conform to preset format standards for constructing signal analysis templates. Preset format standards ensure that signal analysis templates are consistent in format, comprehensive in information, and easy to understand.

[0094] Creating an initial signal analysis template based on template display content can be understood as using an automated script to construct the initial signal analysis template according to the determined template display content.

[0095] It can be seen that the collection and display of standardized signal data lays the foundation for the automated construction of signal analysis models, which not only improves development efficiency but also reduces maintenance costs.

[0096] Optionally, in step S16, filling the spliced ​​signal data into the initial signal parsing template to obtain the target signal parsing template includes the following execution steps:

[0097] Step S161: Determine the preset display position of the spliced ​​signal data in the initial signal parsing template based on preset logic;

[0098] Step S162: Fill the spliced ​​signal data into the initial signal analysis template according to the preset display position to obtain the target signal analysis template.

[0099] In this embodiment of the invention, the preset display position refers to the specific position in the initialized signal parsing template where the signal data will be filled.

[0100] Determining the preset display position of the spliced ​​signal data in the initial signal analysis template based on preset logic can be understood as the automated script determining the display position of the spliced ​​signal data in the initial signal analysis template according to preset logical rules.

[0101] The process of filling the spliced ​​signal data into the initial signal analysis template according to the preset display position to obtain the target signal analysis template can be understood as follows: the spliced ​​signal data is automatically filled into the initial signal analysis template according to the preset display position by an automated script to obtain the target signal analysis template.

[0102] It can be seen that obtaining the target signal analysis template through automated filling not only reduces human error, but also lowers the error rate of the signal analysis model and the complexity of subsequent debugging, saving development resources.

[0103] Optionally, in step S18, a signal analysis model is constructed according to preset construction rules and the target signal analysis template, including the following execution steps:

[0104] Step S181: Obtain the spliced ​​signal data from the target signal parsing template;

[0105] Step S182: Determine the module type of multiple modules and the preset step size between multiple modules by traversing the spliced ​​signal data;

[0106] Step S183: Determine the position parameters of multiple modules based on module type and preset step size;

[0107] Step S184: Set the signal line connection method between multiple modules according to the preset construction rules and location parameters;

[0108] Step S185: Based on the signal line connection method, multiple modules are built to obtain a signal analysis model.

[0109] In this embodiment of the invention, module type refers to the type of each component in the signal parsing model, distinguished by different signal processing functions. In the automation script, module types may include, but are not limited to: a signal receiving module: responsible for receiving raw signals from the network; a signal conversion module: converting the received signals into a format recognizable and processable by the system, potentially involving data type conversion, signal decoding, etc.; a signal processing module: performing further processing on the converted signals, such as data filtering, calculation, and status detection; and a signal output module: sending the processed signals to the target system or component, without limitation.

[0110] The preset step size refers to the fixed spacing set during the construction of the signal analysis model in order to maintain a reasonable layout between modules and a clear connection of signal lines.

[0111] Position parameters are a set of parameters that describe the specific location of the module in the signal analysis model, including the module's x-coordinate, y-coordinate, and possible rotation angles.

[0112] The signal line connection method refers to the specific way of signal transmission between modules in the signal analysis model, including the start and end points of the signal line, the type of signal line (such as data flow, control flow), the direction of signal transmission, etc., which are not restricted here.

[0113] Obtaining the spliced ​​signal data from the target signal parsing template can be understood as reading the pre-processed target signal parsing template and extracting the spliced ​​and defined signal data from it.

[0114] Determining the module types and preset step sizes between multiple modules by traversing and splicing signal data can be understood as follows: the automated script iterates through the extracted spliced ​​signal data, determining the corresponding module type based on the characteristics of each signal. Simultaneously, based on the overall layout plan of the model and the sequence of signal processing, preset step sizes are set between modules—that is, the relative distances between modules within the model—to ensure clear signal line connections and a reasonable module layout.

[0115] Determining the position parameters of multiple modules based on module type and preset step size can be understood as follows: based on the module type and preset step size determined in the previous step, the automated script can calculate the optimal position of each module in the signal analysis model, ensuring that all modules can be placed in the model in a predetermined order and layout.

[0116] Setting the signal line connection method between multiple modules according to preset setup rules and location parameters can be understood as follows: after determining the location parameters of multiple modules, the script will automatically set the signal line connection method between the modules according to the preset setup rules and location parameters, ensuring that the signal can be transmitted between the modules according to the predetermined path and logic.

[0117] Building a signal analysis model by constructing multiple modules based on signal line connection methods can be understood as automatically building a signal analysis model on a model building platform according to the determined module type, location parameters, and signal line connection methods.

[0118] As can be seen, through the above steps, the embodiments of the present invention achieve fully automated construction of signal analysis models, which not only greatly reduces the need for manual intervention and reduces development costs caused by human error, but also improves the efficiency and accuracy of signal analysis.

[0119] Figure 5 This is a flowchart of an automated signal analysis model construction method according to one embodiment of the present invention, such as... Figure 5 As shown, firstly, a complete signal template with custom names is created. The template Excel file is loaded using a read function, and the corresponding custom-named signal columns and other content are added to a defined array. Simultaneously, blank rows in the template, such as those occupied by checksums or livecounters, are filtered out, leaving only the remaining signal data. Next, MATLAB's built-in `new_system` and `open_system` statements are used to create an empty Simulink model and open it. Size arrays are defined for the signal receiving port module, signal conversion and processing module, and output interface port module, with the size of each module determined according to model building conventions. Then, based on the signal parsing module's building logic, an automated script is written to extract the required signal columns from the signal template and add them to the array. Next, a fixed step size is set between each module. A for loop iterates through all signals in the signal array, and the `add_block` function adds modules to the created model. The order of modules is defined by adding step sizes and using the `set_param` function. Finally, the top-level module path is obtained through find_system, the signal handle and port handle are obtained through get_param, and the module and signal line are associated through the Set function to realize the construction of the signal parsing module. Then, a nested for loop is used to traverse all signal data to realize the automatic construction of the parsing model for all signals.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0121] This embodiment also provides a vehicle signal analysis model building device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0122] Figure 6 This is a structural block diagram of a vehicle signal analysis model building device according to one embodiment of the present invention, such as... Figure 6 As shown, an on-board signal analysis model building device 600 is used as an example. This device includes: an acquisition module 601, used to acquire a network protocol file, wherein the network protocol file is used to record the parameters and definitions involved in vehicle network communication; an extraction module 602, used to extract signal data from the network protocol file by analyzing the network protocol file; a splicing module 603, used to splice prefix information or suffix information to the signal data according to a preset naming rule to obtain spliced ​​signal data, wherein the prefix information or suffix information is used to describe the properties of the signal data; a filling module 604, used to fill the spliced ​​signal data into an initial signal analysis template to obtain a target signal analysis template, wherein the initial signal analysis template is created based on a preset format standard; and a building module 605, used to build a signal analysis model according to preset building rules and the target signal analysis template.

[0123] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0124] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when run on a computer or processor.

[0125] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0126] Step S10: Obtain the network protocol file, wherein the network protocol file is used to record the parameters and definitions involved in vehicle network communication;

[0127] Step S12: Extract signal data from the network protocol file by analyzing the network protocol file;

[0128] Step S14: Based on the preset naming rules, prefix information or suffix information is concatenated to the signal data to obtain concatenated signal data, wherein the prefix information or suffix information is used to describe the properties of the signal data;

[0129] Step S16: Fill the spliced ​​signal data into the initial signal parsing template to obtain the target signal parsing template, wherein the initial signal parsing template is created based on a preset format standard;

[0130] Step S18: Based on the preset construction rules and target signal analysis template, a signal analysis model is constructed.

[0131] Optionally, in this embodiment, the computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0132] Embodiments of the present invention also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0133] Optionally, in this embodiment, the processor in the above-described electronic device may be configured to run a computer program to perform the following steps:

[0134] Step S10: Obtain the network protocol file, wherein the network protocol file is used to record the parameters and definitions involved in vehicle network communication;

[0135] Step S12: Extract signal data from the network protocol file by analyzing the network protocol file;

[0136] Step S14: Based on the preset naming rules, prefix information or suffix information is concatenated to the signal data to obtain concatenated signal data, wherein the prefix information or suffix information is used to describe the properties of the signal data;

[0137] Step S16: Fill the spliced ​​signal data into the initial signal parsing template to obtain the target signal parsing template, wherein the initial signal parsing template is created based on a preset format standard;

[0138] Step S18: Based on the preset construction rules and target signal analysis template, a signal analysis model is constructed.

[0139] Embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0140] Optionally, in this embodiment, the computer program in the above-described computer program product can be configured to perform the following steps when executed by a processor:

[0141] Step S10: Obtain the network protocol file, wherein the network protocol file is used to record the parameters and definitions involved in vehicle network communication;

[0142] Step S12: Extract signal data from the network protocol file by analyzing the network protocol file;

[0143] Step S14: Based on the preset naming rules, prefix information or suffix information is concatenated to the signal data to obtain concatenated signal data, wherein the prefix information or suffix information is used to describe the properties of the signal data;

[0144] Step S16: Fill the spliced ​​signal data into the initial signal parsing template to obtain the target signal parsing template, wherein the initial signal parsing template is created based on a preset format standard;

[0145] Step S18: Based on the preset construction rules and target signal analysis template, a signal analysis model is constructed.

[0146] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0147] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0148] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0149] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0153] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for building a vehicle-mounted signal analysis model, characterized in that, include: Obtain a network protocol file, wherein the network protocol file is used to record the parameters and definitions involved in vehicle network communication; By analyzing the network protocol file, signal data is extracted from the network protocol file; Prefix or suffix information is concatenated to the signal data based on a preset naming rule to obtain concatenated signal data, wherein the prefix or suffix information is used to describe the properties of the signal data; The spliced ​​signal data is filled into the initial signal parsing template to obtain the target signal parsing template, wherein the initial signal parsing template is created based on a preset format standard; A signal analysis model is constructed based on the preset construction rules and the target signal analysis template.

2. The method of claim 1, wherein, The method further includes: The network protocol file is parsed using an automated script to obtain its structure and syntax. The preset logic of the network protocol file is determined based on the structure and the syntax.

3. The method according to claim 2, characterized in that, The step of analyzing the network protocol file and extracting signal data from it includes: The field extraction protocol is determined based on the preset logic, wherein the field extraction protocol is the extraction rule corresponding to the automated script; The network protocol file is analyzed based on the field extraction protocol, and the signal data is extracted.

4. The method of claim 1, wherein, The process of concatenating prefix or suffix information into the signal data based on a preset naming rule to obtain concatenated signal data includes: The prefix information or the suffix information is determined based on the properties of the signal data; The signal data is obtained by concatenating the signal data with the corresponding prefix information or suffix information by calling the concatenation statement, wherein the concatenated signal data conforms to the preset naming rules.

5. The method of claim 1, wherein, The method further includes: The template display content is determined based on the preset format standard, wherein the template display content includes: a custom signal name column, a bus signal name column, and a signal data type column; The initial signal analysis template is created based on the content displayed in the template.

6. The method of claim 5, wherein, The step of filling the spliced ​​signal data into the initial signal parsing template to obtain the target signal parsing template includes: Based on the preset logic, the preset display position of the spliced ​​signal data in the initial signal parsing template is determined; The spliced ​​signal data is filled into the initial signal parsing template according to the preset display position to obtain the target signal parsing template.

7. The method of claim 1, wherein, The step of constructing a signal analysis model based on preset construction rules and the target signal analysis template includes: Obtain the spliced ​​signal data from the target signal parsing template; The module types of multiple modules and the preset step size between the multiple modules are determined by traversing the spliced ​​signal data. The position parameters of the plurality of modules are determined based on the module type and the preset step size; The signal line connection method between the multiple modules is set according to the preset construction rules and the location parameters; The signal analysis model is obtained by constructing the multiple modules based on the signal line connection method.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the vehicle signal analysis model building method as described in any one of claims 1 to 7 when run on a computer or processor. 9.An electronic device comprising a memory and a processor, the electronic device characterized by, The memory stores a computer program, and the processor is configured to run the computer program to execute the vehicle signal analysis model building method as described in any one of claims 1 to 7.

10. A computer program product, characterised in that, The system includes a computer program that, when executed by a processor, implements the vehicle signal analysis model construction method as described in any one of claims 1 to 7.

Images