Signal processing method and device for application layer model

Abstract

The invention provides a signal processing method and device for an application layer model, and the method comprises the steps: carrying out the splitting of a processing rule according to a character string before all to-be-matched signals for a target model are matched with standard signal names, such as a standard signal short name and a standard signal full name, from various signal library files, each to-be-matched signal and each standard signal short name are split into corresponding character string arrays which are composed of one or more combinations of capital letters, lowercase letters and numbers in the signal names, interference characters in the signal names are eliminated, and the matching accuracy is improved based on the character string arrays of different signal types. And the target similarity between different to-be-matched signals and each standard signal name is obtained, so that the target standard signal name associated with each to-be-matched signal can be quickly and accurately determined, and the signal interaction requirement for the target model is reliably supported and met.

Description

Technical Field

[0001] The present application relates to the field of automotive electronics technology, and more specifically to a signal processing method and device for an application layer model. Background Art

[0002] In the development of automotive electronics application layer software, factors such as differences in signal naming habits among different software engineers and different signal naming standards among different OEMs often lead to inconsistencies in signal names between application layer models and various signal library files. Currently, software engineers need to spend a lot of time and energy manually identifying the port names of application layer models, analyzing the corresponding signal names in the signal library files, and then manually establishing an association between the two to assist the application layer model in correctly parsing and using the signals defined in the signal library files to meet signal interaction requirements.

[0003] It can be seen that the above-mentioned manual matching and modification of the port signals of the application layer model with the standard signals in the signal library is very time-consuming and labor-intensive, especially when encountering large-scale complex models. Because there are many signals defined by the input and output ports, the workload of software engineers increases. In addition, the signal matching operation needs to be re-performed due to the replacement of software engineers or model reuse, which greatly reduces the efficiency and reliability of software development. Summary of the Invention

[0004] In view of the above problems, this application provides the following solutions:

[0005] In a first aspect, the present application proposes a signal processing method of an application layer model, the method comprising:

[0006] Acquire each to-be-matched signal for the target model and standard signal names from various signal library files; the to-be-matched signal represents the port name of the target model, and the standard signal name includes a standard signal short name and a standard signal full name;

[0007] Splitting each of the to-be-matched signals and each of the standard signal short names according to a string splitting processing rule to obtain a first string array corresponding to the to-be-matched signals and a second string array corresponding to the standard signal short names; each array element contained in the first string array and the second string array is composed of one or more combinations of uppercase letters, lowercase letters, and numbers;

[0008] Based on each of the first character string arrays or each of the second character string arrays, obtaining target similarities between different signals to be matched and each of the standard signal names;

[0009] Based on the target similarity, a target standard signal name associated with each of the to-be-matched signals is determined from each of the standard signal names.

[0010] A second aspect of the present application provides a signal processing device for an application layer model, the device comprising:

[0011] A signal name acquisition module is used to obtain each to-be-matched signal for the target model and standard signal names from various signal library files; the to-be-matched signal represents the port name of the target model, and the standard signal name includes a standard signal short name and a standard signal full name;

[0012] a signal name splitting module, configured to split each of the to-be-matched signals and each of the standard signal short names according to a string splitting processing rule, to obtain a first string array corresponding to the to-be-matched signals and a second string array corresponding to the standard signal short names; wherein each array element contained in the first string array and the second string array is composed of one or more combinations of uppercase letters, lowercase letters, and numbers;

[0013] a target similarity acquisition module, configured to acquire target similarities between the corresponding signal to be matched and each of the standard signal names based on each of the first character string arrays or each of the second character string arrays;

[0014] The association processing module is configured to determine, based on the target similarity, from the various standard signal names, target standard signal names associated with the various signals to be matched.

[0015] It can be seen that in the signal processing method of the application layer model proposed in this application, before matching the obtained signals to be matched for the target model with the standard signal names from various signal library files, such as standard signal short names and standard signal full names, each signal to be matched and each standard signal short name will be split into corresponding string arrays according to the string splitting processing rules, which are composed of one or more combinations of uppercase letters, lowercase letters and numbers in the signal name, and the interference characters in the signal name are eliminated, which is conducive to improving the efficiency and accuracy of signal matching. In this way, based on the string arrays of different signal types, after obtaining the target similarity between different signals to be matched and each standard signal name, the target standard signal name associated with each signal to be matched can be quickly and accurately determined, and the signal interaction requirements for the target model can be reliably supported. In this way, even in the face of changes in software engineers, reuse of target models, etc., this processing method of the present application can effectively solve the technical problems of high human coupling and increased time and labor costs caused by signal naming misunderstandings and unfamiliarity with target model ports in manual processing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0017] Figure 1 A flowchart of a signal processing method of an application layer model proposed in Example 1 of the present application;

[0018] Figure 2 A schematic diagram of a structure for splitting a signal name into strings in a signal processing method of an application layer model proposed in an embodiment of the present application;

[0019] Figure 3 A flowchart of a signal processing method of an application layer model proposed in Example 2 of the present application;

[0020] Figure 4 This is a flow chart of a signal processing method of an application layer model proposed in Example 3 of the present application;

[0021] Figure 5 A schematic diagram of a signal name matching interface in a signal processing method of an application layer model proposed in an embodiment of the present application;

[0022] Figure 6 A schematic diagram of matching ambiguity setting in the signal processing method of the application layer model proposed in the embodiment of the present application;

[0023] Figure 7 A schematic diagram of a signal matching result processing interface in the signal processing method of the application layer model proposed in the embodiment of the present application;

[0024] Figure 8 This is a flow chart of a signal processing method of an application layer model proposed in Example 4 of the present application;

[0025] Figure 9 A schematic diagram of a method for obtaining and storing a signal to be matched and different types of standard signal names in a signal processing method of an application layer model proposed in an embodiment of the present application;

[0026] Figure 10 This is a flowchart of a signal processing method of an application layer model proposed in Example 5 of the present application;

[0027] Figure 11 A schematic diagram of the structure of a signal processing device of an application layer model provided in an embodiment of the present application;

[0028] Figure 12Schematic diagram of the hardware structure of an electronic device suitable for the signal processing method of the application layer model proposed in the embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation methods section of this application are only used to explain the specific embodiments of the present application and are not intended to limit this application. The embodiments of the present application are described below in conjunction with the drawings. It is known to those skilled in the art that with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0030] The terms "first", "second" etc. in the context of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable in appropriate circumstances, and this is merely a way of distinguishing the objects of identical properties when describing in the embodiments of the present application. "If" can be interpreted as "at the time of" or "when" or "in response to determining" etc., and can be understood according to the context. In addition, the terms "comprise" and "have" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0031] In order to solve the technical problems described in the background technology section, the present application embodiment proposes a signal processing method of an application layer model. The signal processing method of the application layer model of the present application embodiment is described in detail below with reference to the accompanying drawings.

[0032] Reference Figure 1 , is a flow chart of a signal processing method of an application layer model proposed in the first embodiment of the present application. The method is applicable to electronic devices, which may include but are not limited to terminal devices with data processing capabilities such as laptops, desktop computers or robots, such as Figure 1 As shown, the signal processing method of the application layer model proposed in this embodiment may include but is not limited to:

[0033] Step S11, obtaining each to-be-matched signal for the target model and the standard signal names from various signal library files; the to-be-matched signal represents the port name of the target model, and the standard signal name includes the standard signal short name and the standard signal full name;

[0034] In an embodiment of the present application, the signal to be matched is obtained by naming each port in the process of the software engineer constructing the target model. The content of the signal can be determined based on the automobile function that needs to be implemented in this construction of the target model. Usually, the software engineer uses one or more character strings such as uppercase letters, lowercase letters, numbers (this application refers to Arabic numerals) and other types of characters (such as special characters, etc.) according to his personal habits of signal naming. This application does not limit the content of each signal to be matched.

[0035] The target model refers to an application-layer model, such as a Simulink model, which is a collection of software components and modules used to implement specific automotive functions (such as vehicle control, driver assistance systems, entertainment systems, etc., which can be developed based on the vehicle's actual driving scenarios and needs). It can be based on a specific software architecture, such as the AUTOSAR (AUTomotive OpenSystem Architecture) standard, to ensure software reusability, scalability, and reliability. Therefore, the target model constructed in this application needs to rely on standard signals in a signal library that conforms to the standards for signals and interfaces defined by the software architecture (such as AUTOSAR) to implement the automotive functions it develops.

[0036] In the development environment of automotive electronic systems, signal library files are used to standardize signal definitions to ensure consistency in communication and data exchange between different electronic control units. These files typically include, but are not limited to, hardwired signal files, DBC (Database CAN (Controller Area Network)) files, and ARXML (AUTOSAR XML) files. The standard signals in these signal library files of different formats are hardware signals, DBC signals, and ARXML signals, respectively.

[0037] Hardwire signals are directly connected to chip pins and can transmit high and low voltage levels. Hardwire signals can be used for basic control functions such as the vehicle's KL15 (ignition control). DBC signals are data transmitted by CAN signals and can consist of bit streams of 0s and 1s. CAN signals can carry a large amount of data and are therefore suitable for intelligent automotive control. In practical applications, communication within a CAN network can be implemented according to the CAN bus communication protocol described in the DBC file, ensuring error-free collaborative development of node controllers across the entire CAN network. ARXML signals are a human- and machine-readable text format that uses XML (Extensible Markup Language) to describe AUTOSAR models (i.e., models built based on the AUTOSAR software architecture). These ARXML files typically contain all the information required for AUTOSAR model development, supporting complex communication requirements and dynamic configuration. Importing and exporting these ARXML signal files enables switching between different tools, such as MATLAB, Simulink, and VRTE.

[0038] For a series of standard signals described in the signal library file, standard signal names are configured to identify each standard signal, such as the standard signal short name (Signal Short Name, which can be called the standard signal abbreviation or identifier, and is the unique name to identify a specific signal) and the standard signal full name (Signal Full Name, which can also be called Signal Long Name, which usually contains information such as the physical meaning, unit and measurement range of the signal, and can be used as a comment or detailed description of the corresponding signal in the signal library file). For example, if the standard signal of the application interface for "body and comfort" is defined: AIBodyAndComfort, its standard signal short name can be: AIBC, and the standard signal full name can be: ApplicationInterfaces "Body and Comfort". This application does not elaborate on the standard signal short names and full names of a series of standard signals in various signal library files.

[0039] It should be noted that in order to facilitate the execution of subsequent processing steps, the port signal names in the target model and the standard signal names identified from various signal library files can be stored in a specified format. For example, each signal to be matched is defined as Signal, the standard signal short name is defined as Signal_Short_Name, and the standard signal full name is defined as Signal_Long_Name. A table storage method can be used to fill in these three signal types in the table header or the first row, that is, fill in the obtained signals to be matched in the column where Signal is located, fill in the standard signal short names obtained from various signal library files in the column where Signal_Short_Name is located, and fill in the standard signal full names obtained from various signal library files in the column where Signal_Long_Name is located, so that the signal names of various types can be read from the table later, and the automatic association between the signals to be matched and the standard signal names can be realized in the manner described below.

[0040] Step S12: Split each to-be-matched signal and each standard signal short name according to a string splitting processing rule to obtain a first string array of the corresponding to-be-matched signal and a second string array of the corresponding standard signal short name; each array element contained in the first string array and the second string array is composed of one or more combinations of uppercase letters, lowercase letters, and numbers;

[0041] Since signal names are usually composed of one or more characters, in order to reduce the complexity of realizing the association between the signal to be matched and the standard signal in the signal library, the present application proposes to split the character string consisting of the signal name of a certain signal type before performing the association operation. Subsequently, the array elements in the split string array can be mapped and matched with the signal name of another signal type. Compared with directly performing association analysis on all the character strings of signal names of different signal types, the present application can more accurately and reliably determine the association between signal names of different signal types.

[0042] Among them, since the length of the standard signal short name is usually less than or equal to the length of the signal to be matched, the length of the standard signal full name is usually greater than or equal to the length of the signal to be matched. In this way, after summarizing the three types of signal names of the signal to be matched, the standard signal short name and the standard signal full name, in order to realize the association between each signal to be matched and each standard signal full name, the corresponding signal to be matched can be split, that is, the character string composed of each signal to be matched is split to obtain the character string array of the corresponding signal to be matched (for the convenience of description, it can be recorded as the first character string array); and in order to realize the association between each signal to be matched and each standard signal short name, the corresponding standard signal short name can be split, that is, the character string composed of each standard signal short name is split to obtain the character string array of the corresponding standard signal short name (for the convenience of description, it can be recorded as the second character string array).

[0043] In order to reduce the interference privacy in the signal name mapping and matching process, in the above-mentioned signal name splitting process, this application can be implemented based on the pre-configured string splitting processing rules, and the string splitting processing rules can be determined based on the signal naming habits and modeling naming specifications of the developer of the target model (such as the above-mentioned software engineer). It can be seen that the string splitting processing rules can be dynamically adjusted as the software engineer's signal naming habits and / or modeling naming specifications change, and this application does not limit the content of the string splitting processing rules. It should be noted that the array elements in each string array obtained after signal name splitting will be composed of one or more combinations of uppercase letters, lowercase letters and numbers, and interference characters such as underscores in the signal name are eliminated to reduce mapping and matching time and improve signal processing efficiency.

[0044] Step S13: obtaining a target similarity between the corresponding to-be-matched signal and the standard signal name based on each first character string array or each second character string array;

[0045] During the development of the target model, each to-be-matched signal for the target model must be associated with the standard signal names in the development environment of the software architecture (such as AUTOSAR) it complies with. This is necessary to meet the signal interaction requirements between the target model at the application layer and the bus system, and to implement the vehicle control functions of the developed target model. Therefore, after processing the signal names as described in the steps above, when mapping and matching each to-be-matched signal with each standard signal short name, a similarity calculation method can be used to automatically perform a matching analysis between each to-be-matched signal and each second string array. This results in a target similarity between the to-be-matched signal and the corresponding standard signal short name, representing the degree of association between the two signal names (this can be considered as the credibility of the association between the standard signal short name and the to-be-matched signal, and also the credibility of the target model's reliance on the corresponding standard signal in the signal library to implement the corresponding function of the to-be-matched signal).

[0046] In the embodiments of the present application, the greater the target similarity, the higher the credibility of the association between the two corresponding signal names, and the more likely the corresponding standard signal is the signal that the target model truly relies on to implement the corresponding function of the to-be-matched signal. This application does not limit the similarity calculation method used in the above matching process, and one or more similarity algorithms can be used according to actual circumstances.

[0047] Step S14 : Based on the target similarity, determine the target standard signal name associated with each signal to be matched from each standard signal name.

[0048] Based on the above description of target similarity, the target similarity between each standard signal name and the same signal to be matched can be sorted, and the target standard signal name associated with the signal to be matched can be selected according to actual needs to meet the signal interaction requirements. This application does not limit the method for determining the target standard signal name associated with each signal to be matched.

[0049] In summary, during the automotive electronics application layer software development process, even if software engineers replace or reuse the target model they develop, the present application can, after obtaining the to-be-matched signal representing the port name of the target model, as well as standard signal names from various signal library files, such as standard signal short names and standard signal full names, first perform string splitting on the to-be-matched signal and the standard signal name to form a string array consisting of one or more combinations of uppercase letters, lowercase letters, and numbers, and delete other types of characters in the signal name, thereby reducing the computational complexity and interference factors of subsequent similarity calculations and eliminating the need for manual processing. Subsequently, by obtaining the target similarity between each to-be-matched signal and each standard signal name, the target standard signal name associated with each to-be-matched signal is determined. Compared to the manual processing method of manually analyzing which signal name in the target model is associated with which signal name in the signal library file, this automatic processing method of the present application greatly reduces time and labor costs, and solves the problem of reduced signal matching accuracy due to high human coupling, ensuring that the association between the to-be-matched signal and the target standard signal name can reliably meet signal interaction requirements.

[0050] In some embodiments, combined with the above description of the method for dynamically determining string splitting processing rules, the present application can analyze the modeling naming specifications and the signal naming habits formed by software engineers based on this, and the determined string splitting processing rules may include but are not limited to the following: splitting non-continuous uppercase letters and numbers in the signal name (numbers in the context of this application refer to Arabic numerals); splitting consecutive uppercase letters and non-continuous uppercase letters in the signal name; splitting consecutive lowercase letters and non-continuous uppercase letters in the signal name; merging consecutive lowercase letters and numbers in the signal name; deleting non-alphabetic and non-numeric characters in the signal name, etc., etc. At least one of the splitting methods.

[0051] For example, if the name of a signal to be split is "EPS_DrvReqWhlAng", it can be split according to the splitting methods described above, and the resulting string array can be: ['EPS', 'Drv', 'Req', 'Whl', 'Ang']; if the name of a signal to be split is: "EE!PNaa037Sig_01", after the string splitting process, the resulting string array can be: ['EEP', 'Naa', '037', 'Sig', '01']. In actual applications, the split string arrays are written into a table file, and different cells in the same row record the array elements of the same string array in sequence, such as Figure 2The string splitting processing result shown is a schematic diagram, but is not limited to this storage method. It should be understood that if the modeling naming conventions in the industry and / or the signal naming habits of software engineers change, one or more splitting methods can be adaptively adjusted, and one or more splitting methods can be added or deleted as needed. This application does not provide detailed examples one by one here.

[0052] Reference Figure 3 , which is a flow chart of a signal processing method of an application layer model proposed in Example 2 of this application, such as Figure 3 As shown, in combination with the method described above, after obtaining the first string array of each signal to be matched and the second string array of each standard signal short name, the method proposed in this embodiment may include:

[0053] Step S31, determining a plurality of signal name pairs from each to-be-matched signal, each first string array, and each second string array; a signal name pair consisting of a to-be-matched signal and a second string array of a standard signal short name, or consisting of a to-be-matched signal and a first string array of a standard signal full name;

[0054] During the software development and testing process, it is necessary to associate the signal to be matched in the target model with the standard signal name in the signal library file, and the standard signal name is divided into two categories: standard signal short name and standard signal full name. For the matching process of the signal to be matched with different types of standard signal names, the signal names that need to be split by the present application are different. As analyzed above, in the matching process of the signal to be matched with the standard signal short name, the string composed of the standard signal short name can be split to obtain the corresponding second string array, without splitting the signal to be matched. Then, the second string array and the signal to be matched constitute a signal name pair that needs to perform similarity calculation, which also represents a signal name pair composed of the standard signal short name and the signal to be matched. Similarly, in the matching process of the signal to be matched with the standard signal full name, the signal to be matched can be split to obtain a corresponding first string array, retaining the string composition of the standard signal full name, and the first string array and the standard signal full name constitute a signal name pair that needs to perform similarity calculation, which also represents a signal name pair composed of the standard signal full name and the signal to be matched.

[0055] It can be seen that due to the different types of standard signal names, the present application can constitute two major categories of signal name pairs, namely, according to the above method, one type of signal name pairs is composed of each standard signal short name and each signal to be matched, and another type of signal name pairs is composed of each standard signal full name and each signal to be matched. These two types of signal name pairs can be stored in a table format, such as storing the two types of signals in one type of signal name pair in different columns of the table. In this way, a signal name (one of which is represented by a string array) can be read directly from different columns in the table to form a signal name pair, so as to perform similarity calculation according to the method described below, thereby improving the integrity of the determined signal name pairs and avoiding affecting the accuracy of the association results due to omissions.

[0056] It should be understood that in one possible implementation, before step S12 is executed, if one type of signal name pairs consisting of each standard signal short name and each signal to be matched, and another type of signal name pairs consisting of each standard signal full name and each signal to be matched have been stored in different table files respectively, and the same type of signal names are stored in the same column / row of the table, the present application can also write the above-mentioned split string array into a new column / row in the corresponding table, and correspond to the signal name before the split (such as when a column of the table stores a type of signal name, the signal names before and after the split can be in the same row), so that when similarity calculation is required, all signal name pairs can be reliably traversed and read. In another possible implementation, the present application writes each split string array into a new table file, such as Figure 2 As shown, during the matching process between signal names of different categories, a signal name / string array of the corresponding category can be read from the corresponding table to form a corresponding signal name pair, and a subsequent similarity calculation can be performed on it. It should be noted that this application does not require that each signal name pair must be determined before performing the subsequent similarity calculation.

[0057] Step S32, performing similarity calculation on each signal name pair according to the first string matching algorithm and the second string matching algorithm, respectively, to obtain a first similarity and a second similarity for the same signal name pair;

[0058] In an embodiment of the present application, the first string matching algorithm may be a KMP (Knuth-Morris-Pratt) pattern matching algorithm, and the second string matching algorithm may be a Levenshtein distance algorithm. The KMP pattern matching algorithm can utilize feedback after a character match between two types of signal names fails, thereby reducing the number of matches between the main string and the pattern string and improving matching efficiency. For example, when the strings do not match, the sliding distance of the appropriate pattern string (i.e., the distance from the string array in which the current match fails (here, the array element) is slid to the next matching string) is utilized to slide the pattern string as far to the right as possible (for a string array, starting from the first string on the left, sliding to the right to determine the next string to match) for a longer distance before continuing to match.

[0059] The KMP pattern matching implementation process includes calculating the similarity between the signal to be matched and the second string array of the standard signal short name, as well as calculating the similarity between the first string array of the signal to be matched and the full name of the standard signal. For different signal matching processes, the main string and pattern string defined by the KMP pattern matching algorithm are different. In the matching process between the signal to be matched and the standard signal short name, the signal to be matched is defined as the main string, and the second string array corresponding to the standard signal short name (i.e., the standard signal short name after string splitting) is defined as the pattern string. In the matching process between the signal to be matched and the full name of the standard signal, the full name of the standard signal is defined as the main string, and the first string array corresponding to the signal to be matched (i.e., the signal to be matched after string splitting) is defined as the pattern string. According to the defined main string and pattern string, the KMP pattern matching algorithm is executed to match each set of main string and pattern string character by character, that is, each string (i.e., each array element) in the pattern string (i.e., string array) is matched with the main string character by character. Based on the number of successful matches of each array element in a single string array and the total number of matches, the similarity of the corresponding two types of signal names is obtained, which can be recorded as the first similarity.

[0060] The Levenshtein distance algorithm can be used to calculate the difference between two signal names (i.e., the edit distance). The algorithm can be used to determine the similarity between the two signal names by performing operations such as insertion, deletion, and substitution, by translating the minimum number of operations required to convert a character from one signal name to a character from another signal name. This similarity can be recorded as the second similarity. This implementation process is not described in detail in this application. In actual applications, the first and second similarities for each signal name pair calculated in step S32 can be stored, but is not limited to, in a separate table file or in a table file that records the corresponding signal name pairs, for subsequent access.

[0061] Step S33 : Using the matching weight parameter, weighted sum is performed on the first similarity and the second similarity of the same signal name pair to obtain the target similarity of the signal name pair.

[0062] For the two string matching algorithms proposed in this application, the accuracy and processing speed of the similarities calculated for signal name pairs often differ, and the technical effects achieved by the two algorithms are also different. For these two string matching algorithms, this application can flexibly configure appropriate matching weight parameters based on actual requirements (such as similarity calculation efficiency and accuracy). In this way, for each signal name pair's first and second similarities obtained according to the similarity calculation method described above, step S33 can be executed to obtain the target similarity for the signal name pair. Compared with directly using the average of the first and second similarities of the same signal name pair as the target similarity, the accuracy and reliability of the target similarity are improved. It should be noted that this application does not limit the method for obtaining the matching weight parameters. Multiple test samples can be pre-used, each containing the values ​​of the two similarities obtained using the method described above. After repeatedly trying different weight ratios, the accuracy of the corresponding similarities can be evaluated to determine whether the corresponding weight ratio is appropriate. The optimal weight ratio can then be determined as the matching weight parameter to implement step S33. However, this is not limited to the acquisition method described in this application. Therefore, in one possible implementation, the method for obtaining the matching weight parameter can include, but is not limited to:

[0063] Determine multiple weight ratios for the first string matching algorithm and the second string matching algorithm. For the two similarities calculated by the two string matching algorithms, different multiple weight ratios can be determined randomly from 0 to 1 or according to preset rules. Subsequently, the two similarities can be calculated using different weight ratios to determine whether the similarity results obtained are accurate, and the optimal weight parameter can be determined as the matching weight parameter for the calculation of the above-mentioned target similarity. Therefore, the present application can perform weighted calculations on the first similarity and the second similarity of the same pair of signal names to be matched based on the multiple weight ratios to obtain corresponding predicted similarities, and then perform accuracy evaluation on the predicted similarities corresponding to each of the multiple weight ratios to obtain corresponding accuracy evaluation results, thereby determining the matching weight parameter for the signal name pair from the multiple weight ratios based on the accuracy evaluation results.

[0064] Optionally, during the processing of step S33, the matching weight parameter acquisition method and the target similarity acquisition method described above can be combined to pre-train a similarity processing model, such as a machine learning model. The first and second similarities of each signal name pair are input into the similarity processing model, and the target similarity of the signal name pair is quickly and accurately obtained according to, but not limited to, the processing method described above. In one possible implementation, for the implementation method described in step S33 and the subsequent process of determining the target standard signal name for each signal to be matched, the present application can pre-train a comprehensive screening machine to implement these two processing processes, which can be a machine learning model. In this way, the first and second similarities of each signal name pair are input into the comprehensive screening machine, which can directly output the target standard signal name corresponding to each signal to be matched, or a preset number (such as 3 or 5, which can be flexibly configured or adjusted according to actual conditions and the present application does not limit the value) of subsequent standard signal names corresponding to each signal to be matched, so that the target standard signal name for each signal to be matched can be determined in combination with the implementation conditions.

[0065] In summary, the present application splits the character strings consisting of each signal to be matched and each standard signal short name respectively according to the method described above, and after obtaining the corresponding character string array, whether in the matching process of the first character string array corresponding to the signal to be matched and the full name of the standard signal (i.e., a signal name pair), or in the matching process of the second character string array corresponding to the signal to be matched and the standard signal short name (another signal name pair), two different character string matching algorithms will be used to obtain two similarities of the same signal name pair. Afterwards, the reasonable matching weights determined by considering the advantages of these two character string matching algorithms in calculating the differences between different signal names in the signal name pair are used. The parameters are re-measured and the two similarities of the same signal name pair are weighted and summed to obtain the target similarity of the signal name pair. In this way, the correlation between each signal to be matched and each standard signal name is automatically and accurately determined. This solves the problem in the above manual processing method, especially when the engineer performing the signal matching work is not the target model developer, the developer's personal signal naming habits may cause the engineer to misunderstand and be unfamiliar with the port naming of the target model, thereby increasing human coupling and time cost. This improves the efficiency and accuracy of signal matching analysis, helps to accurately and efficiently determine the target standard signal name that each signal to be matched is truly associated with, and reliably meets the signal interaction needs.

[0066] In some embodiments, to improve the accuracy and reliability of signal matching, the present application analyzes various modeling naming conventions and the typical signal naming habits of software engineers, and learns that software engineers often omit vowels from signal names. Therefore, the present application proposes optimizing the aforementioned Levenshtein distance algorithm by assigning different weights to vowels and consonants in signal names, enabling the algorithm to instruct the algorithm to assign different weights to vowels and consonants in each signal name involved in similarity calculation. Thus, during the execution of step S32, when determining the minimum edit distance between characters of two corresponding signal names (i.e., the minimum number of single-character edit operations required to convert from signal name A to signal name B) using the Levenshtein distance algorithm as the second string matching algorithm, the respective weights of vowels and consonants can be combined to improve the accuracy and reliability of the resulting minimum edit distance, thereby improving the accuracy and reliability of the second similarity calculated based on the minimum edit distance and parameters such as the length of each signal.

[0067] In some other embodiments, in order to further improve the efficiency and accuracy of similarity calculation, the present application can also remove interference characters in the signal names involved in the calculation during the above-mentioned edit distance calculation, especially interference characters in the unsplit signal names. After analyzing the signal names named according to the modeling naming convention, it can be seen that the signal name not only covers the meaning of the signal itself, but also covers the characteristic expression of the signal attributes. For example, the enumeration value will use '_e_', the integer will use '_I_', etc., and special characters (such as the underscore in this example, and other non-alphabetic and non-numeric characters such as punctuation marks) can be used as interference characters. Before performing the edit distance calculation, it is indicated to delete the interference characters in the various signal names involved in the similarity calculation, thereby reducing the unnecessary interference in the similarity calculation caused by the modeling naming convention, and improving the accuracy and reliability of the second similarity between the two types of signal names obtained.

[0068] It should be noted that the method for calculating the similarity between the two signal names contained in each signal name pair using two string matching algorithms proposed in this application includes but is not limited to the content described above. An optimized Levenshtein distance algorithm can also be used, and its optimization method can be flexibly configured according to actual needs. In addition, after determining the target similarity between each standard signal name and different signals to be matched, this application can also use, but is not limited to, a table to store the target similarity of each signal name pair, such as by configuring a target similarity list (Simi_list) for each signal to be matched to store the above-obtained target similarities for use in subsequent steps.

[0069] Optionally, the present application can also sort the target similarities of each standard signal name and the same signal to be matched by calling the sorting function sort(), and then write the sorted target similarities for the signal to be matched into the same column / row in the table to obtain a storage list of target similarities. At this time, based on the indexes of the signal to be matched and the standard signal name before the similarity calculation, an index list for querying each target similarity, i.e., an idx list, can be generated for subsequent queries. Of course, the present application can also use the signal to be matched and the standard signal name as different dimensions, and use a two-dimensional matrix to store each target similarity, etc. The present application does not limit the storage method of the target similarity.

[0070] Based on the above analysis, in one possible implementation, the standard signal name corresponding to the highest target similarity can be selected from the various target similarities for the same signal to be matched as the target standard signal name associated with the signal to be matched. If there are signal interaction requirements or other functional requirements based on the target model, the standard signal name corresponding to the highest target similarity is not necessarily the target standard signal name. In this regard, the present application proposes to first select a preset number of candidate standard signal names that may be associated with the signal to be matched based on the target similarity, and then select the target standard signal name from them in combination with the requirements under actual conditions. The implementation process can refer to but is not limited to the implementation method described below.

[0071] Reference Figure 4 , is a flow chart of a signal processing method of an application layer model proposed in Example 3 of the present application, such as Figure 4 As shown, the method for obtaining the target similarity may include but is not limited to the following steps:

[0072] Step S41: outputting a signal name matching interface; the signal name interface includes a display area for a signal to be matched and a display area for a standard signal name;

[0073] Step S42, in response to a selection operation on the signal to be matched displayed in the signal to be matched display area, determining a selected first signal to be matched;

[0074] Following the above analysis, the present application proposes to use a visual interface, combined with the target similarity determined above, to accurately select the target standard signal name associated with each signal to be matched that meets actual needs, save the correct mapping relationship between the signal in the target model determined thereby and the standard signal in the signal library file, and reliably meet the signal interaction needs. Therefore, the present application can output a signal name matching interface with different matching areas, in which different display areas are configured to display the signal names of different types of signals. In this way, the signal to be matched display area in the signal name matching interface can be used to display each signal to be matched in the target model, and the standard signal name display area can be used to display each standard signal name or the candidate standard signal name obtained by preliminary screening according to the method described below. For the latter, the display window of the standard signal name display area can be blank before performing the preliminary screening. For example Figure 5 In the left display area shown, the signal matching personnel can browse the various signals to be matched by dragging the up and down sliders of the display window, and browse the full signal names of each signal to be matched by dragging the left and right sliders of the window. Afterwards, they can select any signal to be matched as the first signal to be matched by using, but not limited to, a finger or stylus, voice command, or other input methods. At this time, the display status can also be adjusted (such as one or more combinations of background color, character color, or model number; this application does not limit the display status type) to intuitively determine whether it is the signal to be matched for this signal matching process.

[0075] In some other embodiments, according to the signal matching processing requirements, the present application can also configure the processing method for matching the standard signal name for the signal to be matched in the signal display area to be matched, and configure corresponding function buttons for different processing methods of its signal matching results, such as Figure 5 As shown, "batch matching", "loading progress" and "clear progress" are displayed. In this way, the signal matching personnel can trigger the "batch matching" function button (as implemented by the selection operation of the first signal to be matched above) and use the stored target similarity to automatically select a preset number of candidate standard signal names corresponding to each signal to be matched (or a selected signal to be matched). The names can be displayed as needed to select the target standard signal name from them in combination with the implementation requirements. The screening implementation process will not be described in detail in this embodiment.

[0076] Step S43, in response to the standard signal name matching operation, displaying a preset number of candidate standard signal names associated with the first signal to be matched in the standard signal name display area according to the target similarity between the first signal to be matched and each standard signal name;

[0077] In order to reduce the time and labor costs of manual processing, the present application can obtain the target similarities corresponding to the first signal to be matched and each standard signal name that has been stored (such as stored in DescSimi_list), and select a preset number of candidate standard signal names with higher target similarities from all target similarities for the same signal to be matched contained in DescSimi_list based on its corresponding index list, i.e., idx list. Among them, the preset number can be 3 or 5, etc. In the solution in which the screening process of the candidate standard signal names described above is implemented using a fuzzy function, the preset number represents a pre-configured matching fuzziness, which can be flexibly configured or adjusted according to actual conditions, and the present application does not impose any restrictions on its numerical value. Exemplarily, before performing signal matching, a configuration window for the matching fuzziness can be displayed in the above-mentioned signal to be matched display area, in accordance with but not limited to Figure 6 The matching fuzziness configuration method shown is to select a value in the output fuzziness selection window (which can also be directly activated by voice or specific physical buttons, etc.) as the matching fuzziness required for signal matching processing.

[0078] Step S44, in response to the selection operation of a preset number of candidate standard signal names, determining a target standard signal name associated with the first signal to be matched;

[0079] Step S45 : storing the determined association relationships between each to-be-matched signal and each target standard signal name.

[0080] Following the above analysis, the candidate standard signal names of each signal to be matched that have been screened out, including the candidate standard signal short name and the candidate standard signal full name, can also be displayed through a visual interface, that is, a candidate standard signal name write operation is performed on the selected first signal to be matched, and in response to the write operation, the various candidate standard signal names of the determined first signal to be matched are displayed through the interface. When the signal matching personnel understand the various standard signals in the signal library, they can select the target standard signal name associated with the first signal to be matched from the preset number of candidate standard signal names currently displayed based on the actual requirements such as the development requirements of the target model and the signal interaction requirements of the electronic equipment. Afterwards, the other candidate standard signal names that have not been selected for the first signal to be matched can be directly deleted. At this time, if files corresponding to these candidate standard signal names have been stored previously, they can also be deleted synchronously to release storage resources.

[0081] Among them, since the target standard signal name may be the standard signal short name in the signal library or the standard signal full name, the standard signal name display area in the signal name matching interface can be divided into the standard signal short name display area and the standard signal full name display area, such as Figure 5 As shown, the right side shows the area ( Figure 5The "short signal" in the middle display area ( Figure 5 The "long signal" in the table represents the full name of the standard signal), but the display of signal names is not limited to this format. The associations between the determined signals to be matched and the determined target standard signal names can be stored in a table format, but are not limited to this format. For example, the signal names of corresponding signal types can be recorded in cells in different columns / rows of the table, with the two associated signal types recorded in the same row / column. This allows for quick and accurate access to the required data when the table-stored data is subsequently needed.

[0082] Optionally, combine Figure 5 As shown in the schematic diagram of candidate standard signal short names, after selecting the first signal to be matched in the signal to be matched display area, the "match" function button in the standard signal short name display area can be triggered. In response to the trigger operation, i.e., the standard signal short name matching operation, the electronic device will screen a preset number of standard signal short names with higher target similarities as candidate standard signal short names based on the target similarities between the determined first signal to be matched and each standard signal short name, and display them in the display window of the standard signal short name display area, as shown in FIG. Figure 5 The three short signals with high similarity are displayed. Then, according to actual needs, you can select the target standard signal short name and write it into the signal matching result processing interface, such as Figure 7 As shown, if the first signal to be matched is VeCANR_V_VehSpdAvgDrvn, the automatic matching is as follows Figure 5 After the three candidate standard signal short names are shown, you can Figure 7 In the interface shown, trigger the "Write" function button, select the target standard signal short name from the three candidate standard signal short names, and write Figure 7 The short signal display area corresponding to the first signal to be matched in the interface shown (such as Figure 7 Display unit in the short and medium signal list), such as the target standard signal short name "VehSpdAvgDrvn".

[0083] Similarly, according to the above-described method for implementing the screening of the target standard signal short name, during the process of matching the first signal to be matched (which may be the same signal to be matched as mentioned above, or a newly selected first signal to be matched) with the standard signal full name, the electronic device responds to the standard signal full name matching operation, and displays a preset number of candidate standard signal full names associated with the first signal to be matched in the standard signal full name display area based on the target similarity between the first signal to be matched and the full names of each standard signal, so that the signal matching personnel can intuitively select the target standard signal full name from them according to actual needs. At this time, the electronic device can respond to the selection operation of the preset number of candidate standard signal full names to determine the target standard signal full name associated with the first signal to be matched, and then, Figure 7 The "Write" function button is triggered to write the full name of the target standard signal into the long signal display area corresponding to the first signal to be matched (such as Figure 7 display cells in medium and long signal lists).

[0084] In the above implementation process, if the target standard signal name for a signal to be matched is incorrect, the following can be triggered after selecting the incorrect target standard signal name: Figure 7 The "Delete" function button shown in the figure responds to the deletion operation of the target standard signal name, directly deletes the target standard signal name in the interface, and then re-determines the new target standard signal name until the target standard signal name associated with each signal to be matched is determined, which can trigger Figure 7 The "Save" function button shown stores each signal to be matched, each target standard signal short name and target standard signal full name, and corresponding association relationships, such as using a table to perform association storage.

[0085] Reference Figure 8, is a flow chart of a signal processing method for an application layer model proposed in the fourth embodiment of the present application. Combined with the signal processing method for the application layer model described above, after determining the target standard signal name associated with each signal to be matched, one or more functional operations such as port operations and bus interaction operations can be performed on the target model to meet the signal interaction requirements. Among them, the port operation refers to the processing of the input and output ports of the target model. According to actual needs, a method can be created to realize one-key conversion of port types, matching result inspection and correction and other processing functions. The bus interaction layer operation refers to the creation of a method that can realize one-key generation of a virtual bus module (i.e., Bus module) between the target model and the vehicle bus / hard wire, etc. Corresponding function buttons can be created for this purpose. The present application does not limit the content and implementation methods of these two types of operations, and they can be flexibly configured or adjusted as needed. Based on the above analysis, the embodiment of the present application only takes an optional implementation method for executing the two operations listed above as an example for illustration, but does not limit the operation content described in this embodiment. It can be increased, reduced, or modified according to actual conditions. Therefore, if Figure 8 As shown, the method provided in this embodiment may further include:

[0086] Step S81, in response to the signal matching self-check operation of the target model, determine whether each to-be-matched signal is associated with a target standard signal name; if not, proceed to step S82; if yes, proceed to step S83;

[0087] Step S82, adjusting the first display state of the determined second signal to be matched to a second display state; the second signal to be matched refers to a signal to be matched that is not associated with any standard signal name;

[0088] In one possible implementation, Figure 7In the signal matching result processing interface shown, the "Mapping Self-Check" function button is triggered. Mapping can represent the signal matching result of the target model. If the above-mentioned association relationship is stored in the form of a mapping table, the electronic device can directly perform a self-check on the mapping table in response to the signal matching self-check operation of the target module to determine whether each to-be-matched signal has an associated target standard signal name. For example, the electronic device can traverse the corresponding column storage content of the table to promptly send the missed second to-be-matched signal and determine its associated target standard signal name according to the method described above. In order to intuitively see whether the checked second to-be-matched signal is correct, the present application can also adjust the current first display state of each determined second to-be-matched signal to a second display state, so that it is different from the first display state of other to-be-matched signals associated with the target standard signal name. For example, the matching signal column stored in the mapping table can be traversed to determine whether there is a blank row. If so, the function set_param() can be called to flexibly configure the background color of the blank row to red or yellow (i.e., the second display state), but is not limited to the background color display state type.

[0089] Step S83, in response to the port type conversion operation on the target model, the first type module is replaced with the second type module;

[0090] In actual applications, the first type of modules in the target module may include Inport modules and Outport modules, and the second type of modules may include From modules and Goto modules. Among them, Inport modules and Outport modules can be used to define the input and output ports of the Simulink model, and their port names can be stored as signals to be matched, which enables the Simulink model to receive external signals (through Inport) and send signals to the outside (through Outport). From modules and Goto modules are a pair of modules used to control the flow and jump of signals in the Simulink environment. This application does not limit the functions, uses, and configuration parameters of these types of modules, which can be determined according to the circumstances.

[0091] After determining that the port type needs to be converted, that is, after determining that the module type needs to be converted, the input and output port type conversion processing in the target model can be realized by replacing the attribute value, such as converting the Inport / Outport module to the From / Goto module. For this implementation process, the present application can call a suitable function to implement it. For example, after selecting an Inport module or an Outport module in the target model that needs to convert the port type, it can be realized by selecting the corresponding signal to be matched, and by triggering Figure 7The "Port Type Conversion" function button in the interface shown calls the replacement function replace_block () to implement the port type conversion. For example, in the recently opened target model file (such as the Simulink model file), the module type (BlockType) attribute is searched layer by layer to replace the module with Inport / Outport as From / Goto.

[0092] Step S84, in response to the signal name modification operation on the target model, modifying the third signal to be matched into a target standard signal name associated with the third signal to be matched;

[0093] Optionally, after selecting the third signal to be matched whose signal name needs to be modified from the various signals to be matched defined in the target model, the following can be triggered: Figure 7 The "Signal Name Modification" function button shown responds to the signal name modification operation for the third signal to be matched. After determining the target standard signal name associated with the third signal to be matched based on the stored association relationship, the third signal to be matched is directly defined as the target standard signal name associated with the third signal to be matched, or the function set_param() is called to define the signal source corresponding to the third signal to be matched in the target model as a standard signal in the signal library. It should be noted that when using each signal to be matched and the target standard signal name associated with the signal to be matched to implement at least one port operation on the target model, in addition to the one or more combined port operation methods described in steps S81 to S84 above, other port operations can also be performed according to actual processing requirements, which will not be described in detail in this application.

[0094] Step S85 , generating a virtual bus module for the target model based on the association between each to-be-matched signal and each target standard signal name; the virtual bus module is used to implement signal interaction between the target model and the vehicle bus and hard wire.

[0095] When executing the bus interaction layer operation for the target model, in order to meet the signal interaction requirements, the position of each module in the target model can be processed to obtain the position data of the virtual bus module to be generated, and the corresponding modules of the input and output ports can be generated and configured accordingly, such as triggering Figure 7 Click the "Generate Bus" function button shown to automatically generate a virtual bus module, namely the Bus module. Configure the module ports and size parameters based on the number of signals to automatically connect different ports of the target model with the virtual bus module, thereby completing the bus interaction layer operation.

[0096] In some embodiments, during the implementation of step S85, corresponding functions can be called to execute various implementation steps until the bus interaction layer operation is completed. For example, during the acquisition of target model data, the find_system() function can be called to retrieve the paths of all modules in the current system (e.g., the most recently opened target model file) and store them in Blocks. The getSimulinkBlockHandle() function can then be called to store the handle of the module at the module path stored in Blocks (e.g., "Blocks(2:end)" represents all values ​​in Blocks except the first one) in BlocksHandle. The get() function can then be called to retrieve the location data of each module in the target model from the stored handle and store it. Based on the location data of each module, a location layout for the virtual bus module (Bus module) can be automatically generated, for example, by calling the add_block() function and assigning the module name attribute to the Bus module corresponding to each ECU (Electronic Control Unit). During this implementation, the set_param() function can be called to flexibly configure module properties to meet design requirements.

[0097] After completing the generation and configuration of the BUS module as described above, you can also generate and configure the modules corresponding to the signal input and output ports in the target model. At this time, you can still call the add_block() function to generate the From module in the target model model and store the module's properties in in. After completing the generation and configuration of the target model and its modules, you can call the add_line() function to connect the generated Bus module with the signal lines of the From module, such as connecting the Bus Creator module to the From module and the Bus Selector module to the Goto module, thereby completing the bus interaction layer operation.

[0098] It can be seen that the present application has created a method for one-click generation of any port operation such as port type conversion, signal matching result self-checking, signal name modification, etc. according to the method described above, as well as a method for one-click generation of a virtual bus module for signal interaction with the vehicle bus / hard line, such as Figure 7As shown, a function button for executing the corresponding method is configured. By triggering the corresponding function button once, the corresponding type of port operation and bus interaction layer operation is automatically executed according to the corresponding method. The generation and configuration of the corresponding module of the associated signal to be matched can also be realized according to actual needs. Based on this, the matching of the signal defined for the target module with the standard signal in the signal library and the automatic connection of the port and bus interaction layer of the target model are reliably and automatically realized to meet the signal interaction requirements, which is equivalent to the current manual processing method. The present application greatly reduces time and labor costs, and improves the signal processing efficiency and reliability for the application layer model.

[0099] Based on the signal processing method of the application layer model described in the above embodiment, in order to ensure that all types of signals that need to be matched are obtained, the present application can obtain the names of each port of the target model and store them as corresponding signals to be matched. In combination with the various modules required to build the target model described in the above embodiment, if the target model is a Simulink model, the modules required to build the target model may include From / Goto modules, Inport / Outport modules, etc. The signal names corresponding to different modules usually need to be processed differently, and the corresponding signals to be matched are stored in the corresponding files. For example, the find_system() function is called to search the properties of each target module layer by layer (whose names are usually consistent with the port names) through a regular expression search method. The name of each From module under the target model is obtained and stored in the A_Signal_In data table. For example, the names of each From module under the same path of the target model are stored in the same column of the A_Signal_In data table to facilitate subsequent queries. Similarly, in order to obtain the signal name corresponding to the Goto module, the find_system function can also be called to store the obtained name of each Goto module under the target model in the A_Signal_Out data table. Afterwards, you can call the function get_param() to automatically obtain the handle corresponding to the From / Goto module queried above and store it in the SignalHandle data table. For example, the handles found in the same column / row record of the data table, such as 196.0005, 197.0005, ..., 208.0005, 29.0005, etc., so as to call the function get(SignalHandle) to directly obtain the properties of the corresponding module based on the handles of each module stored in SignalHandle and store it in the SignalData file.

[0100] For the handles of each From / Goto module stored in SignalData, the GotoTag parameter in the module properties can be extracted from the corresponding From / Goto module, and the GotoTag parameters of the two modules can be merged to determine the signals to be matched based on the From / Goto module, such as VeCANR_v_VehclSpd, VeCANR_Pct_HVBattSOC, CoastRegActv, BrkRegActive, etc. This application does not give detailed examples one by one, and can be determined based on development requirements. For determining the signals to be matched corresponding to each From / Goto module, a Sig data table can be constructed for storage, such as writing the determined signals to be matched into the same column / row, etc., for subsequent reading of the signals to be matched, but it is not limited to this signal name storage method. It should be noted that, combined with the above description of the target model, the modules that need to be generated (to define ports) include Inport modules and Outport modules in addition to From / Goto modules. You can still obtain the name of each Inport / Outport module under the target model by calling the find_system() function and store them in the SigPath_In / SigPath_Out data table.

[0101] Preferably, the present application can pre-process the module names of the corresponding types stored in the SigPath_In / SigPath_Out data table, such as calling the erase() function to erase the model name contained in the module name stored in the data table, retaining the module name of each Inport / Outport module itself, and because it is consistent with the naming of the corresponding input and output ports, the retained module name can be used as the signal to be matched of the Inport / Outport module. At this time, an independent data table can be constructed to store the signals to be matched of each Inport / Outport module, or the signals to be matched of each Inport / Outport module can be written into the same column / row of the Sig data table for storage, so as to facilitate the subsequent direct traversal of the row / column and read all the signals to be matched for the target model.

[0102] It can be seen from this that referring to Figure 9As shown in the schematic diagram of the signal to be matched, for the input signal in the Simulink model, that is, for each signal to be matched of the target model, the two selection methods described above can be used to obtain the respective signals to be matched of different first-class modules (such as From / Goto modules) and second-class modules (such as Inport / Outport modules), that is, obtain the names of the input and output ports of the From / Goto modules and store them as signals to be matched, and obtain the names of the input and output ports of the Inport / Outport modules and store them as signals to be matched. The obtained signals to be matched are stored as the signal type of Signal, which includes but is not limited to Figure 9 List the signals to be matched in the Signal column.

[0103] In order to analyze which standard signal in the signal library file each signal to be matched is mapped to, such as Figure 9 As shown, it is necessary to first obtain the standard signal names in the various required signal library files (such as the above-mentioned hard-line signal files, DBC files, ARXML files, etc.), which can be stored in a custom format file (i.e., a data table for recording standard signal names described in the context) according to the specified format. Different format parsing can be performed for signal library files of different formats to identify each signal name therein, and the same column / row in the Hwsignal data table is stored as the standard signal name of the corresponding signal type for subsequent query. This application does not impose any restrictions on this implementation method.

[0104] In some embodiments, the present application can import external signal library files of various formats based on the various signals to be matched in the target model, and then identify the corresponding standard signal names by calling appropriate functions. For example, the uigetfile() function is called to obtain the various signal library files that need to be processed, and the corresponding signal library files are imported, and then the various signal names are read from the signal name column. Afterwards, the readtable() function is called to obtain the signal names from the various imported signal library files, such as reading the various signal names in the signal name column from the table data of the hard-wired signal file (i.e., a table file) of the standard signal. Specifically, the signal name column can be traversed through the array sequence generation method of Matlab to realize the creation of the signal array, that is, to generate the standard signal name of the corresponding signal type and store it in the Hwsignal data table, but it is not limited to this acquisition method.

[0105] During the DBC file import process, this application can be implemented by selecting a message sending table file and a message receiving table file. For example, by calling the readtable() function, the contents of each table (such as the signal names under the Name column) in the loaded sending table file DBCTx and receiving table file DBCRx are stored as TxData and RxData, respectively, for subsequent processing. Similarly, during the ARXML file (here, a table file) import process, the readtable() function can be called to read the signal names under the Signal Name column from the table data in the ARXML file. For each row of standard signal name attributes, the ECU signals are classified and stored according to whether they belong to a transmitted signal (Tx signal) or a received signal (Rx signal), thereby obtaining the corresponding standard signal names. However, this storage implementation is not limited to this method.

[0106] In summary, the present application has constructed a format parsing method that supports multiple types of external files (the various signal library files listed above), which can read the various signal names stored in the signal library file from the signal name column of different signal library files, and store the corresponding signal type (header format) in the storage area of ​​the custom format file according to the signal type, such as the signal type list defined by the short name of the standard signal and the signal type list defined by the full name of the standard signal, and store the corresponding signals to be matched determined by the naming of the input / output ports of each module based on the target module in the signal type list of the signals to be matched, such as Figure 9 The custom format signals shown include three types of signals: signals to be matched, standard short-lived signals, and standard full-name signals. For the standard short-lived signals / standard full-names generated above by calling the readtable() function, they can be stored in different data tables in correspondence with the signals to be matched. For example, in Data Table 1, one column is used to store each signal to be matched, and its header is set to Signal to indicate the signal type of the signal to be matched. Another column is used to store each standard signal short name, that is, the standard signal name belonging to the signal type of the standard signal short name. Its header can be set to Signal_Short_Name to indicate the signal type of the standard signal short name. Similarly, in Data Table 2, the column with the header Signal is used to store each signal to be matched, and the other column is used to store each standard signal full name, that is, the standard signal name belonging to the signal type of the standard signal full name. Its header can be set to Signal_Long_Name to indicate the signal type of the standard signal long name. Among them, this application can obtain the header string of each table file by calling the strcmp() function, and store it in the column where the signal type belongs in the data table for subsequent quick reading, and determine the target standard signal name associated with each signal to be matched through split matching.

[0107] It can be seen that the embodiment of the present application can perform format parsing on the target model file and various signal library files (which can be called external files) based on the custom format file, such as Figure 9 As shown, the various signals to be matched obtained based on the target model file are summarized and organized into a specified format signal name, the various standard signal short names obtained based on the various signal library files are summarized and organized into a specified format signal name, and the various standard signal full names obtained based on the various signal library files are summarized and organized into a specified format signal name, laying the foundation for subsequent signal name matching processing. Among them, as in the example above, if the target model file and the contents of each signal library file are recorded in a table format, the custom format file can be a file with a specified header format in these files, so as to realize the automatic recognition of the signal names of each of the multiple format files, and obtain the corresponding signals to be matched or the signal library standard signals, thereby improving the convenience of data import processing.

[0108] Preferably, before performing signal matching, the present application can first review the signals that need to be matched and processed to determine that the complete and correct signal names are obtained, and then perform matching processing according to the method described in the above embodiment. Among them, the review process can be implemented by the target model developer or signal matching personnel or professional reviewers, and output a data table storing various signal names. By browsing the data table content, it is determined whether there are any missing signal names that need to be matched and processed. If it is determined that there are omissions, the matching signals and standard signal names can be selectively added based on the actual situation such as the importance and implementation degree of the omitted signals. If addition is required, the array (which can be the above-mentioned signal name pair) can be appended and stored by calling the corresponding function. If addition is not required, no processing can be performed.

[0109] Based on the signal processing method of the application layer model described in the above embodiments, refer to Figure 10 As shown in the flowchart, this application follows the method described above and uses the custom format file pre-configured by this application to parse the imported target model file and signal library files of various formats and then store them in a classified manner. After reviewing the stored data, Figure 10 As shown, not only does the required fuzziness (the preset number) need to be set, but signal matching is also performed after string splitting. This reduces string processing complexity and improves signal matching efficiency and accuracy. Furthermore, by creating a Bus module, signal interaction between the vehicle bus / hardwire and the target model is reliably implemented.

[0110] The above introduces a signal processing method of an application layer model provided in an embodiment of the present application. The following will introduce a device for executing the signal processing method of the above application layer model.

[0111] Reference Figure 11 , is a structural diagram of a signal processing device of an application layer model provided in an embodiment of the present application. Figure 11 As shown, the signal processing device of the application layer model may include:

[0112] The signal name acquisition module 111 is used to acquire each to-be-matched signal for the target model and standard signal names from various signal library files; the to-be-matched signal represents the port name of the target model, and the standard signal name includes a standard signal short name and a standard signal full name;

[0113] The signal name splitting module 112 is configured to split each of the to-be-matched signals and each of the standard signal short names according to a string splitting processing rule to obtain a first string array corresponding to the to-be-matched signals and a second string array corresponding to the standard signal short names; each array element contained in the first string array and the second string array is composed of one or more combinations of uppercase letters, lowercase letters, and numbers;

[0114] A target similarity acquisition module 113 is configured to acquire a target similarity between the corresponding signal to be matched and each of the standard signal names based on each of the first character string arrays or each of the second character string arrays;

[0115] The association processing module 114 is configured to determine, based on the target similarity, from the various standard signal names, target standard signal names associated with the various signals to be matched.

[0116] Those skilled in the art will understand that the functions and technical effects of each module in the above-mentioned device embodiment, as well as the units for implementing the functions, are equivalent to the corresponding steps described in the above-mentioned method embodiment. For specific implementation details, please refer to the description of the method part and will not be repeated here.

[0117] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the signal processing method of any application layer model provided in the embodiment of the present application.

[0118] The computer-readable storage medium can be any available medium capable of storing data on a computer, or a data storage device such as a training device or data center that includes one or more available media. Available media can include magnetic media (e.g., floppy disks, hard disks, or magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0119] Reference Figure 12 , is a hardware structure diagram of an electronic device applicable to the signal processing method of the application layer model proposed in the embodiment of the present application. The electronic device may be a terminal device, such as Figure 12 As shown, the electronic device may include at least one display 121, at least one communication element 122, at least one memory 123 and at least one processor 124, wherein the at least one display 121, at least one communication element 122, at least one memory 123 and at least one processor 124 can communicate with each other via a bus.

[0120] The display screen 121 can be a touch screen or a non-touch screen, etc., and is used to display information input by the user, various menu interaction interfaces of the terminal device, file display and / or the display of any type of table file. The communication element 122 can be used to realize the communication connection between the electronic device and other devices (such as the platform device where the signal library file is located), as well as the communication connection between the components inside the electronic device to meet the corresponding communication needs. It can be seen that the communication element 122 can include a first communication element that supports wireless communication networks, such as a WIFI module, a 5G / 6G (fifth generation mobile communication network / sixth generation mobile communication network) module, a GPRS module, etc., and can also include a second communication element that supports wired communication networks, such as a USB interface, a serial / parallel port, etc., which can be determined based on the actual communication connection requirements.

[0121] The memory 123 is used to store a plurality of computer instructions, which are loaded and executed by the processor 124 to implement the various steps of the signal processing method of the application layer model proposed in the embodiment of the present application. The implementation process can refer to the description of the corresponding part of the method embodiment above. In the embodiment of the present application, the memory 123 may include at least one volatile memory (volatile memory), such as a random access memory (RAM), and may also include at least one non-volatile memory (non-volatile memory) and other storage media. The processor 124 may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), a digital signal processor (DSP), an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0122] It should be understood that Figure 12 The structure of the electronic device shown does not constitute a limitation on the electronic device in the embodiment of the present application. In actual applications, the electronic device may include Figure 12 More components as shown, or combinations of certain components, such as a touch sensing unit for sensing touch events on a touch display panel, at least one input component such as a keyboard, a mouse, a camera, a microphone; at least one output component such as a speaker, a vibration mechanism, an indicator light; an antenna, a radio frequency unit; multiple sensors; a power supply module, etc., are not listed one by one in this application.

[0123] In addition, it should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0124] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general-purpose hardware, and of course can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. That is to say, in the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part.

[0125] The various embodiments in this specification are described in a progressive or parallel manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices and electronic devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

Claims

1. A signal processing method of an application layer model, characterized in that: The method comprises: Acquire each to-be-matched signal for the target model and standard signal names from various signal library files; the to-be-matched signal represents the port name of the target model, and the standard signal name includes a standard signal short name and a standard signal full name; Splitting each of the to-be-matched signals and each of the standard signal short names according to a string splitting processing rule to obtain a first string array corresponding to the to-be-matched signals and a second string array corresponding to the standard signal short names; each array element contained in the first string array and the second string array is composed of one or more combinations of uppercase letters, lowercase letters, and numbers; Based on each of the first character string arrays or each of the second character string arrays, obtaining target similarities between different signals to be matched and each of the standard signal names; Based on the target similarity, a target standard signal name associated with each of the to-be-matched signals is determined from each of the standard signal names.

2. The method according to claim 1, characterized in that The acquiring, based on each of the first string arrays or each of the second string arrays, target name similarities between different signals to be matched and each of the standard signal names includes: Determine a plurality of signal name pairs from each of the signals to be matched, each of the first string arrays, and each of the second string arrays; the signal name pairs are composed of one of the signals to be matched and one of the second string arrays of the standard signal short names, or one of the first string arrays of the signal to be matched and one of the standard signal full names; Calculating similarity for each of the signal name pairs according to a first string matching algorithm and a second string matching algorithm, respectively, to obtain a first similarity and a second similarity for the same signal name pair; The first similarity and the second similarity of the same signal name pair are weightedly summed using a matching weight parameter to obtain a target similarity of the signal name pair.

3. The method according to claim 2, characterized in that The second string matching algorithm is a Levenshtein distance algorithm, which indicates at least one of the following steps: Assign different weights to the vowels and consonants in the names of the signals involved in the similarity calculation; Delete interfering characters in each signal name involved in similarity calculation; the interfering characters include special characters.

4. The method according to claim 2, characterized in that The process of obtaining the matching weight parameter includes: Determining a plurality of weight ratios for the first string matching algorithm and the second string matching algorithm; performing weighted calculation on the first similarity and the second similarity of the same pair of signal names to be matched according to the multiple weight ratios to obtain corresponding predicted similarities; Performing accuracy evaluation on the predicted similarities corresponding to each of the multiple weight ratios to obtain corresponding accuracy evaluation results; According to the accuracy evaluation result, a matching weight parameter for the signal name pair is determined from the multiple weight ratios.

5. The method according to any one of claims 1 to 4, characterized in that The string splitting processing rule is determined based on the signal naming conventions and modeling naming specifications of the developer of the target model; wherein the string splitting processing rule includes at least one of the following string splitting methods: Separate non-consecutive uppercase letters and numbers in signal names; Separate consecutive capital letters from non-consecutive capital letters in signal names; Separate consecutive lowercase letters from non-consecutive uppercase letters in signal names; Merge consecutive lowercase letters and numbers in signal names; Remove non-alphabetic and non-numeric characters from signal names.

6. The method according to any one of claims 1 to 4, characterized in that The determining, based on the target similarity, from the respective standard signal names, a target standard signal name associated with each of the signals to be matched, includes: Output signal name matching interface; the signal name matching interface includes a to-be-matched signal display area and a standard signal name display area; In response to a selection operation on the signal to be matched displayed in the signal to be matched display area, determining a selected first signal to be matched; In response to a standard signal name matching operation, displaying a preset number of candidate standard signal names associated with the first signal to be matched in the standard signal name display area according to the target similarity between the first signal to be matched and each of the standard signal names; In response to the selection operation of the preset number of candidate standard signal names, a target standard signal name associated with the first signal to be matched is determined.

7. The method according to any one of claims 1 to 4, characterized in that The method further comprises at least one of the following: Implementing at least one port operation on the target model by using each of the to-be-matched signals and the target standard signal name associated with the to-be-matched signal; generating a virtual bus module for the target model according to the association relationship between each of the to-be-matched signals and each of the target standard signal names; The virtual bus module is used to implement signal interaction between the target model and the vehicle bus and hard wire.

8. The method according to claim 7, characterized in that The utilizing each of the to-be-matched signals and the target standard signal name associated with the to-be-matched signal to implement at least one port operation on the target model includes at least one of the following: In response to a signal matching self-check operation on the target model, determining whether each of the to-be-matched signals is associated with a target standard signal name, and adjusting the first display state of a second to-be-matched signal to a second display state; the second to-be-matched signal is the to-be-matched signal that is not associated with any of the standard signal names; In response to a port type conversion operation on the target model, replacing a first type module in the target model with a second type module; In response to the signal name modification operation on the target model, the third signal to be matched is modified to the target standard signal name associated with the third signal to be matched; the third signal to be matched is the signal to be matched whose signal name needs to be modified.

9. The method according to any one of claims 1 to 4, characterized in that The acquisition of each to-be-matched signal for the target model and the standard signal names from various signal library files includes: Obtain the names of each port of the target model and store them as the corresponding signals to be matched; In response to the signal library file import operation for the target model, calling a function to obtain the corresponding signal library file; The signal names of the various standard signals in the signal library file are read and stored as corresponding standard signal names.

10. A signal processing device of an application layer model, characterized in that: The device comprises: A signal name acquisition module is used to obtain each to-be-matched signal for the target model and standard signal names from various signal library files; the to-be-matched signal represents the port name of the target model, and the standard signal name includes a standard signal short name and a standard signal full name; a signal name splitting module, configured to split each of the to-be-matched signals and each of the standard signal short names according to a string splitting processing rule, to obtain a first string array corresponding to the to-be-matched signals and a second string array corresponding to the standard signal short names; wherein each array element contained in the first string array and the second string array is composed of one or more combinations of uppercase letters, lowercase letters, and numbers; a target similarity acquisition module, configured to acquire target similarities between the corresponding signal to be matched and each of the standard signal names based on each of the first character string arrays or each of the second character string arrays; The association processing module is configured to determine, based on the target similarity, from the various standard signal names, target standard signal names associated with the various signals to be matched.

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