Intelligent acquisition method for software file directory of motor vehicle inspection and testing equipment

Abstract

The present application relates to a method for intelligently acquiring the software file directory of motor vehicle inspection and testing equipment. The method includes: using natural language processing technology based on deep learning to perform fine-grained semantic analysis on the relevant basic information of motor vehicle inspection and testing equipment, and digging out the deep contextual meaning therein, so as to intelligently identify the manufacturer to which the motor vehicle inspection and testing equipment belongs, and then intelligently crawl the software files of the equipment from the official website of the manufacturer, and generate a directory for the software file through the AIGC model, so as to realize the intelligent capture of the software file directory of motor vehicle inspection and testing equipment. In this way, the acquisition efficiency and accuracy of the software file directory of motor vehicle inspection and testing equipment can be significantly improved, the workload of maintenance personnel can be reduced, and the timeliness and completeness of software file updates can be guaranteed.

Description

Technical Field

[0001] The present application relates to the field of motor vehicle inspection technology, and in particular, to a method for intelligently acquiring a software file directory of motor vehicle inspection and testing equipment. Background Art

[0002] With the rapid growth of the number of motor vehicles and increasingly stringent traffic management, motor vehicle inspection and testing has become an important part of ensuring vehicle safety, reducing the risk of traffic accidents, and maintaining road traffic order. As a key tool for performing this task, the performance and accuracy of motor vehicle inspection and testing equipment are directly related to the credibility and legal effect of the inspection results. Therefore, the maintenance and software update of motor vehicle inspection and testing equipment are particularly important.

[0003] The software files of motor vehicle inspection and testing equipment are computer programs and related documents used to control the normal operation of the equipment, realize the detection function, process the detection data, and manage and maintain the equipment. With the continuous advancement of technology, software files are becoming more and more complex, and the hierarchical structure and content of the file directory are numerous, which brings considerable challenges to the equipment maintenance personnel.

[0004] Traditionally, the software file directory of motor vehicle inspection and testing equipment is mainly obtained by manual operation, which involves manually visiting the official website of each equipment manufacturer, manually searching and downloading the required software files, and obtaining a complete software directory list. However, this method is not only inefficient, but also prone to errors, especially when facing equipment from different manufacturers and models, the search and organization of information becomes particularly cumbersome. In addition, with the frequent updates of equipment software, the timeliness of manually entering the software file directory also faces huge challenges.

[0005] Therefore, a method for intelligently acquiring the software file directory of motor vehicle inspection and testing equipment is desired. Summary of the invention

[0006] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] In a first aspect, the present application provides a method for intelligently acquiring a software file directory of a motor vehicle inspection and testing equipment, the method comprising:

[0008] Collect basic information related to motor vehicle inspection and testing equipment;

[0009] Based on the basic information related to the motor vehicle inspection and testing equipment, identifying the manufacturer of the motor vehicle inspection and testing equipment;

[0010] Obtaining software file webpage content from the manufacturer's official website, and parsing the software file webpage content to obtain the software file;

[0011] Input the software file into a directory intelligent generator based on the AIGC model to obtain a software file directory of the motor vehicle inspection and testing equipment;

[0012] Wherein, based on the basic information related to the motor vehicle inspection and testing equipment, identifying the manufacturer of the motor vehicle inspection and testing equipment includes:

[0013] Performing word-granularity-based context semantic coding on the basic information related to the motor vehicle inspection and testing equipment to obtain a sequence of basic information word-granularity context semantic coding feature vectors;

[0014] Performing semantic association optimization on the sequence of the basic information word granularity context semantic coding feature vectors to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors;

[0015] The sequence of context semantic encoding feature vectors is optimized based on the basic information word granularity to determine the category of the manufacturer.

[0016] Optionally, the basic information related to the motor vehicle inspection and testing equipment is subjected to word-granularity-based contextual semantic encoding to obtain a sequence of basic information word-granularity contextual semantic encoding feature vectors, including: performing word segmentation processing on the basic information related to the motor vehicle inspection and testing equipment to obtain a sequence of basic information description words; using a word semantic embedding matrix to perform semantic embedding encoding on each basic information description word in the sequence of basic information description words to obtain a sequence of basic information description word semantic embedding encoding vectors; inputting the sequence of basic information description word semantic embedding encoding vectors into a basic information semantic encoder based on a converter structure to obtain a sequence of basic information word granularity contextual semantic encoding feature vectors.

[0017] Optionally, the sequence of basic information word granularity context semantic coding feature vectors is semantically optimized to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors, including: inputting the sequence of basic information word granularity context semantic coding feature vectors into a feature semantic association optimization network based on a semantic measure of sequence endogenous correlation to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors.

[0018] Optionally, the sequence of basic information word granularity context semantic encoding feature vectors is input into a feature semantic association optimization network based on a sequence endogenous correlation semantic metric to obtain a sequence of basic information word granularity optimized context semantic encoding feature vectors, including: calculating a semantic association score vector between any two basic information word granularity context semantic encoding feature vectors in the sequence of basic information word granularity context semantic encoding feature vectors to obtain a sequence of basic information word granularity semantic association score vectors; based on the sequence of basic information word granularity semantic association score vectors, determining a sequence endogenous correlation global representation vector; based on the sequence endogenous correlation global representation vector vector, calculate the associated optimization factor of each basic information word granularity context semantic coding feature vector in the sequence of basic information word granularity context semantic coding feature vectors to obtain a sequence of associated optimization factors; input the sequence of associated optimization factors into the Softmax activation function to obtain a sequence of associated optimization weight factors; use each associated optimization weight factor in the sequence of associated optimization weight factors as a weight, and weight each basic information word granularity context semantic coding feature vector in the sequence of basic information word granularity context semantic coding feature vectors respectively to obtain a sequence of basic information word granularity optimization context semantic coding feature vectors.

[0019] Optionally, the semantic association score vector between any two basic information word granularity context semantic coding feature vectors in the sequence of basic information word granularity context semantic coding feature vectors is calculated to obtain a sequence of basic information word granularity semantic association score vectors, including: cascading any two basic information word granularity context semantic coding feature vectors in the sequence of basic information word granularity context semantic coding feature vectors and multiplying them by a weight coefficient matrix, and then dot-adding them with a bias vector to obtain the basic information word granularity semantic association score vector.

[0020] Optionally, based on the sequence of the basic information word granularity semantic association score vectors, determining the sequence intrinsic correlation global representation vector includes: calculating the mean vector of the sequence of the basic information word granularity semantic association score vectors to obtain the sequence intrinsic correlation global representation vector.

[0021] Optionally, based on the global representation vector of intrinsic correlation of the sequence, the association optimization factor of each basic information word granularity context semantic coding feature vector in the sequence of basic information word granularity context semantic coding feature vector is calculated to obtain a sequence of association optimization factors, including: multiplying the basic information word granularity context semantic coding feature vector and the global representation vector of intrinsic correlation of the sequence by different weight coefficient vectors and then performing addition operation to obtain a semantic association coefficient; adding a bias parameter to the semantic association coefficient and then passing it through a sigmoid activation function to obtain the association optimization factor.

[0022] Optionally, based on the sequence of context semantic coding feature vectors optimized at the basic information word granularity, the category of the manufacturer is determined, including: calculating the global mean vector of the sequence of context semantic coding feature vectors optimized at the basic information word granularity to obtain a basic information semantic condensed representation vector; inputting the basic information semantic condensed representation vector into a classifier-based manufacturer identifier to obtain an identification result, wherein the identification is used to represent the category label of the manufacturer.

[0023] Optionally, the basic information semantic condensed representation vector is input into a classifier-based manufacturer identifier to obtain a recognition result, and the recognition is used to represent the category label of the manufacturer, including: using multiple fully connected layers of the classifier-based manufacturer identifier to fully connect encode the basic information semantic condensed representation vector to obtain an encoded classification feature vector; passing the encoded classification feature vector through the Softmax classification function of the classifier-based manufacturer identifier to obtain the recognition result.

[0024] The above technical solution is adopted to perform fine-grained semantic analysis on the relevant basic information of motor vehicle inspection and testing equipment by using natural language processing technology based on deep learning, and to dig out the deep contextual meaning, so as to intelligently identify the manufacturer of the motor vehicle inspection and testing equipment, and then intelligently crawl the software files of the equipment from the official website of the manufacturer, and generate a directory for the software files through the AIGC model, so as to realize the intelligent capture of the software file directory of the motor vehicle inspection and testing equipment. In this way, the acquisition efficiency and accuracy of the software file directory of the motor vehicle inspection and testing equipment can be significantly improved, the workload of maintenance personnel can be reduced, and the timeliness and completeness of software file updates can be guaranteed.

[0025] Other features and advantages of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features, advantages and aspects of the embodiments of the present application 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 the originals and elements are not necessarily drawn to scale. In the drawings:

[0027] Figure 1 The present invention is a flowchart of a method for intelligently acquiring a software file directory of a motor vehicle inspection and testing equipment according to an exemplary embodiment.

[0028] Figure 2 It is a block diagram of a system for intelligently acquiring the software file directory of motor vehicle inspection and testing equipment according to an exemplary embodiment.

[0029] Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment.

[0030] Figure 4 It is an application scenario diagram of a method for intelligently acquiring the software file directory of motor vehicle inspection and testing equipment according to an exemplary embodiment. DETAILED DESCRIPTION

[0031] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0032] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0033] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0034] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0035] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0036] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0037] In response to the above technical problems, the technical concept of this application is to use natural language processing technology based on deep learning to perform fine-grained semantic analysis on the relevant basic information of motor vehicle inspection and testing equipment, and to dig out the deep contextual meaning therein, so as to intelligently identify the manufacturer to which the motor vehicle inspection and testing equipment belongs, and then intelligently crawl the software files of the equipment from the official website of the manufacturer, and generate a directory for the software files through the AIGC model, so as to realize the intelligent capture of the software file directory of the motor vehicle inspection and testing equipment. In this way, the acquisition efficiency and accuracy of the software file directory of the motor vehicle inspection and testing equipment can be significantly improved, the workload of maintenance personnel can be reduced, and the timeliness and completeness of software file updates can be guaranteed.

[0038] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0039] Figure 1 is a flow chart showing a method for intelligently acquiring a software file directory of a motor vehicle inspection and testing device according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0040] Step S101, collecting basic information related to motor vehicle inspection and testing equipment;

[0041] Step S102: based on the basic information related to the motor vehicle inspection and testing equipment, identifying the manufacturer of the motor vehicle inspection and testing equipment;

[0042] Step S103: Obtaining the software file webpage content from the manufacturer's official website, and parsing the software file webpage content to obtain the software file;

[0043] Step S104, inputting the software file into the directory intelligent generator based on the AIGC model to obtain the software file directory of the motor vehicle inspection and testing equipment;

[0044] Wherein, step S102, based on the basic information related to the motor vehicle inspection and testing equipment, identifying the manufacturer of the motor vehicle inspection and testing equipment, includes:

[0045] Step S1021, performing word granularity-based context semantic coding on the basic information related to the motor vehicle inspection and testing equipment to obtain a sequence of basic information word granularity context semantic coding feature vectors;

[0046] Step S1022, performing semantic association optimization on the sequence of the basic information word granularity context semantic coding feature vectors to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors;

[0047] Step S1023: Optimize the sequence of context semantic coding feature vectors based on the basic information word granularity to determine the category of the manufacturer.

[0048] Based on this, in the technical solution of this application, first, basic information related to motor vehicle inspection and testing equipment is collected. The basic information related to motor vehicle inspection and testing equipment includes but is not limited to the type, function, software version and any specific technical specifications or configuration details of the equipment, which is the key to locating the correct manufacturer and corresponding software resources.

[0049] In order to achieve fine-grained semantic analysis of the basic information related to the motor vehicle inspection and testing equipment, so as to accurately identify the manufacturer to which the motor vehicle inspection and testing equipment belongs, thereby obtaining the corresponding software file directory, in the technical solution of the present application, first, the basic information related to the motor vehicle inspection and testing equipment is segmented, and the long text data is split into independent vocabulary units to obtain a sequence of basic information description words, so as to facilitate a more detailed semantic understanding of each basic information description word.

[0050] In one embodiment of the present application, the basic information related to the motor vehicle inspection and testing equipment is subjected to word-granularity-based contextual semantic encoding to obtain a sequence of basic information word-granularity contextual semantic encoding feature vectors, including: performing word segmentation processing on the basic information related to the motor vehicle inspection and testing equipment to obtain a sequence of basic information description words; using a word semantic embedding matrix to perform semantic embedding encoding on each basic information description word in the sequence of basic information description words to obtain a sequence of basic information description word semantic embedding encoding vectors; inputting the sequence of basic information description word semantic embedding encoding vectors into a basic information semantic encoder based on a converter structure to obtain a sequence of basic information word granularity contextual semantic encoding feature vectors.

[0051] Next, the word semantic embedding matrix is ​​used to perform semantic embedding encoding on each basic information description word in the sequence of the basic information description words, the basic meaning of each basic information description word is captured, and it is mapped from a discrete symbol space to a continuous high-dimensional semantic space, generating a sequence of semantic embedding encoding vectors of the basic information description words, and through the mutual position relationship of each basic information description word in the high-dimensional semantic space, the relevance between their semantics is reflected. In an embodiment of the present application, the word semantic embedding matrix is ​​obtained by a pre-trained word vector model Word2Vec.

[0052] Then, in order to further capture the semantic dependency between words and achieve more accurate semantic analysis, this application uses a basic information semantic encoder based on a converter structure to perform contextual semantic association encoding on the sequence of the basic information description word semantic embedding encoding vectors. The self-attention mechanism of the converter structure is used to dynamically focus on words at different positions in the sequence, learn the contextual dependency between words, and adjust the encoding representation of each word according to the contextual information. The contextual information is incorporated into the semantic feature representation of each word, thereby effectively improving the semantic understanding ability of text information and generating a sequence of basic information word granular contextual semantic encoding feature vectors.

[0053] Secondly, in order to further improve the ability to mine the deep semantics of the basic information related to the motor vehicle inspection and testing equipment and ensure the accuracy of manufacturer identification, this application introduces a feature semantic association optimization network based on the sequence of the basic information word granularity context semantic encoding feature vectors, which measures the semantic association of the sequence endogenous correlation. By deeply analyzing the semantic association strength between the words in the sequence of the basic information word granularity context semantic encoding feature vectors, the features are screened and optimized to make them more focused on the key information that plays a decisive role in manufacturer identification. Specifically, the feature semantic association optimization network first quantifies the semantic similarity or difference between any two basic information word granularity context semantic encoding feature vectors by calculating the semantic association between them, so as to understand the semantic structure within the sequence and generate a set of semantic association score vectors. Then, the aggregated information of the set of semantic association score vectors is used as the overall context information to calculate the association optimization factor of each basic information word granularity context semantic encoding feature vector, and quantitatively describe its consistency or deviation with the overall semantic structure of the sequence, so as to serve as a guide for feature association optimization. Furthermore, after the associated optimization factor is normalized, it is used as a weight coefficient to perform weighted optimization on the original sequence of the basic information word granularity context semantic coding feature vectors to reduce the interference of irrelevant semantic information and enhance the contribution of key features, thereby obtaining a sequence of basic information word granularity optimized context semantic coding feature vectors.

[0054] In one embodiment of the present application, a sequence of basic information word granularity context semantic coding feature vectors is semantically optimized to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors, including: inputting the sequence of basic information word granularity context semantic coding feature vectors into a feature semantic association optimization network based on a semantic measure of endogenous correlation of the sequence to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors.

[0055] Furthermore, in one embodiment of the present application, the sequence of the basic information word granularity context semantic encoding feature vectors is input into a feature semantic association optimization network based on a semantic measure of sequence endogenous correlation to obtain a sequence of the basic information word granularity optimized context semantic encoding feature vectors, including: calculating a semantic association score vector between any two basic information word granularity context semantic encoding feature vectors in the sequence of the basic information word granularity context semantic encoding feature vectors to obtain a sequence of basic information word granularity semantic association score vectors; based on the sequence of the basic information word granularity semantic association score vectors, determining a global representation vector of sequence endogenous correlation; based on the sequence endogenous correlation The invention relates to a global representation vector of the basic information word granularity context semantic coding feature vector, calculates the association optimization factor of each basic information word granularity context semantic coding feature vector in the sequence of the basic information word granularity context semantic coding feature vector to obtain a sequence of association optimization factors; inputs the sequence of association optimization factors into a Softmax activation function to obtain a sequence of association optimization weight factors; uses each association optimization weight factor in the sequence of association optimization weight factors as a weight, and weights each basic information word granularity context semantic coding feature vector in the sequence of the basic information word granularity context semantic coding feature vector to obtain a sequence of the basic information word granularity optimization context semantic coding feature vector.

[0056] Furthermore, in one embodiment of the present application, the semantic association score vector between any two basic information word granularity context semantic encoding feature vectors in the sequence of the basic information word granularity context semantic encoding feature vectors is calculated to obtain a sequence of basic information word granularity semantic association score vectors, including: cascading any two basic information word granularity context semantic encoding feature vectors in the sequence of the basic information word granularity context semantic encoding feature vectors and multiplying them by a weight coefficient matrix, and then dot-adding them with a bias vector to obtain the basic information word granularity semantic association score vector.

[0057] Furthermore, in one embodiment of the present application, based on the sequence of the basic information word granularity semantic association score vectors, a global representation vector of the sequence intrinsic correlation is determined, including: calculating the mean vector of the sequence of the basic information word granularity semantic association score vectors to obtain the global representation vector of the sequence intrinsic correlation.

[0058] Furthermore, in one embodiment of the present application, based on the global representation vector of intrinsic correlation of the sequence, the association optimization factor of each basic information word granularity context semantic coding feature vector in the sequence of basic information word granularity context semantic coding feature vector is calculated to obtain a sequence of association optimization factors, including: multiplying the basic information word granularity context semantic coding feature vector and the global representation vector of intrinsic correlation of the sequence by different weight coefficient vectors and then performing addition operation to obtain a semantic association coefficient; adding a bias parameter to the semantic association coefficient and then passing it through a sigmoid activation function to obtain the association optimization factor.

[0059] Specifically, the sequence of the basic information word granularity context semantic coding feature vectors is processed by the following semantic feature association optimization formula to obtain the sequence of the basic information word granularity optimized context semantic coding feature vectors, wherein the semantic feature association optimization formula is:

[0060]

[0061] in, represents a sequence of granular context semantic encoding feature vectors of the basic information word, , , , and Respectively represent the first, second, and third in the sequence of the basic information word granular context semantic encoding feature vector , and The basic information word granularity context semantic encoding feature vector, The value of is the number of feature vectors in the sequence of the basic information word granular context semantic encoding feature vectors, Indicates cascade operation, is the weight coefficient matrix, is the bias vector, is the first in the sequence of the basic information word granular semantic association score vector Basic information word granularity semantic association score vector, The value of is the number of vectors in the sequence of the basic information word granularity semantic association score vectors, is the global representation vector of the endogenous correlation of the sequence, and Represent different weight coefficient vectors, is the bias parameter, is the sigmoid activation function, is the normalized exponential function, For the The associated optimization factor, Indicates The context semantic encoding feature vector is optimized at the granularity of basic information words.

[0062] Then, the global semantic information of the sequence of the basic information word granularity optimized context semantic encoding feature vectors is further integrated. In the technical solution of the present application, by calculating the global mean vector of the sequence of the basic information word granularity optimized context semantic encoding feature vectors, the redundant information in the sequence is effectively removed while retaining the key semantic features, so as to achieve the information integration and high concentration of the basic information at the semantic level, simplify it into a more compact vector representation, and thus obtain the basic information semantic condensed representation vector.

[0063] In one embodiment of the present application, the category of the manufacturer is determined based on the sequence of context semantic coding feature vectors optimized at the basic information word granularity, including: calculating the global mean vector of the sequence of context semantic coding feature vectors optimized at the basic information word granularity to obtain a basic information semantic condensed representation vector; inputting the basic information semantic condensed representation vector into a classifier-based manufacturer identifier to obtain an identification result, wherein the identification is used to represent the category label of the manufacturer.

[0064] Next, the semantic condensed representation vector of the basic information is input into the classifier-based manufacturer identifier to obtain an identification result, and the identification is used to represent the category label of the manufacturer. In the technical solution of the present application, the classifier-based manufacturer identifier uses a multi-layer perceptron (MLP) as the basic structure of the classifier, and trains the model by using a large amount of labeled training data to establish a mapping relationship between the basic information related to the motor vehicle inspection and testing equipment and the manufacturer category. In actual applications, the classifier-based manufacturer identifier uses the mapping relationship learned during the training process to classify and map the input semantic condensed representation vector of the basic information, so as to accurately identify the manufacturer to which the motor vehicle inspection and testing equipment belongs.

[0065] In one embodiment of the present application, the basic information semantic condensation representation vector is input into a classifier-based manufacturer identifier to obtain a recognition result, and the recognition is used to represent the category label of the manufacturer, including: using multiple fully connected layers of the classifier-based manufacturer identifier to fully connect encode the basic information semantic condensation representation vector to obtain an encoded classification feature vector; passing the encoded classification feature vector through the Softmax classification function of the classifier-based manufacturer identifier to obtain the recognition result.

[0066] Furthermore, based on the identified manufacturer category label, the official website of the corresponding manufacturer is automatically accessed, and the intelligent crawler technology (Web Crawler) is used to efficiently traverse each page of the official website. By analyzing the structure and content of the web page, the software files related to the motor vehicle inspection and testing equipment are identified and downloaded.

[0067] After the download is completed, in order to further improve the usability and readability of the software file directory, the directory of the software file is generated using the directory intelligent generator based on the AIGC model. The AIGC model can automatically generate a structured and formatted motor vehicle inspection and testing equipment software file directory by analyzing the file naming rules, content structure and other information, so as to facilitate users to quickly find and use it.

[0068] Preferably, inputting the basic information semantic condensed representation vector into a classifier-based vendor identifier to obtain an identification result comprises:

[0069] Subtracting the zero norm of the basic information semantic condensation representation vector from the length of the basic information semantic condensation representation vector to obtain a basic information semantic condensation isolated representation value;

[0070] Calculate a power function with each eigenvalue of the basic information semantic condensation representation vector as the base, the difference of the basic information semantic condensation isolated representation value minus one as the exponent, and multiply it by the logarithm of the basic information semantic condensation isolated representation value plus one and the basic information semantic condensation isolated representation value with base 2 as the product to obtain the basic information semantic condensation leading value;

[0071] After multiplying each eigenvalue of the basic information semantic condensation representation vector by the difference of the basic information semantic condensation isolated representation value minus one and dividing by the basic information semantic condensation isolated representation value, an exponential function with a natural constant as the base is calculated to obtain a basic information semantic condensation bias value;

[0072] adding the basic information semantic concentration leading value to the basic information semantic concentration bias value to obtain each eigenvalue of the optimized basic information semantic concentration representation vector; and

[0073] The optimized basic information semantic condensed representation vector is input into a classifier-based vendor identifier to obtain an identification result.

[0074] Specifically, the basic information semantic condensation represents a vector, for example, Each eigenvalue of ,in The optimization expression is:

[0075]

[0076] in, The first eigenvalues, Indicates the length of the semantic condensed representation vector of the basic information, represents the zero norm of a vector, Indicates that the basic information is semantically condensed and isolated to represent the value, represents the logarithm operation with base 2, represents the exponential operation with base e, It represents the power function of calculating the eigenvalues ​​of each position in the eigenvector with the difference of the basic information semantic condensed isolated representation value minus one as the exponent, The first feature values.

[0077] Here, each basic information word granularity context semantic encoding feature vector in the sequence of basic information word granularity context semantic encoding feature vectors respectively represents the text context semantic association features of each word determined based on word segmentation of basic information related to motor vehicle inspection and testing equipment. When the sequence of basic information word granularity context semantic encoding feature vectors is input into a feature semantic association optimization network based on the semantic measurement of endogenous correlation of the sequence, the feature semantic association optimization network based on the semantic measurement of endogenous correlation of the sequence will optimize the significance association based on the internal correlation semantic measurement of the sequence of basic information word granularity context semantic encoding feature vectors to obtain the sequence of basic information word granularity optimized context semantic encoding feature vectors. Although this can improve the significance of the local neighborhood semantic association of the sequence of basic information word granularity optimized context semantic encoding feature vectors as a feature distribution, it will also cause the full text domain semantic aggregation deviation of the sequence of basic information word granularity optimized context semantic encoding feature vectors as a feature distribution as a whole. Therefore, it is expected to further improve the full text domain semantic aggregation regression comprehensibility of the basic information semantic condensed representation vector, thereby improving the accuracy of the recognition result obtained by inputting it into the classifier-based manufacturer identifier.

[0078] Therefore, for the high-dimensional feature manifold of the basic information semantic condensed representation vector, the eigenvalues ​​of the feature set are used as the vector field representation of the aggregation dimension, and the value of the derivative of the vector field of the basic information semantic condensed representation vector at the isolated zero position is used as the order information to fix the local position of the eigenvalues ​​of its feature set, and a bias for the reversibility of the feature regression distribution of the basic information semantic condensed representation vector is added as a reward to achieve the mapping target tracking of the regression distribution of the basic information semantic condensed representation vector for the eigenvalue position, so that the feature set of the basic information semantic condensed representation vector can perceive the mapping migration to the aggregate distribution, thereby improving the accuracy of the recognition result obtained by inputting the basic information semantic condensed representation vector into the classifier-based manufacturer identifier by improving the full-text domain semantic aggregation regression comprehensibility of the basic information semantic condensed representation vector.

[0079] In summary, the above scheme uses natural language processing technology based on deep learning to perform fine-grained semantic analysis on the relevant basic information of motor vehicle inspection and testing equipment, and dig out the deep contextual meaning, so as to intelligently identify the manufacturer of the motor vehicle inspection and testing equipment, and then intelligently crawl the software files of the equipment from the manufacturer's official website, and generate a directory for the software file through the AIGC model, so as to realize the intelligent capture of the software file directory of the motor vehicle inspection and testing equipment. In this way, the acquisition efficiency and accuracy of the software file directory of the motor vehicle inspection and testing equipment can be significantly improved, the workload of maintenance personnel can be reduced, and the timeliness and completeness of software file updates can be guaranteed.

[0080] Figure 2 The following is a block diagram of a system for intelligently acquiring the software file directory of a motor vehicle inspection and testing device according to an exemplary embodiment. Figure 2 As shown, the system 200 includes:

[0081] Basic information collection module 201, used to collect basic information related to motor vehicle inspection and testing equipment;

[0082] A motor vehicle inspection and testing equipment manufacturer identification module 202, for identifying the manufacturer of the motor vehicle inspection and testing equipment based on the basic information related to the motor vehicle inspection and testing equipment;

[0083] The software file webpage content acquisition module 203 is used to acquire the software file webpage content from the manufacturer's official website and parse the software file webpage content to obtain the software file;

[0084] A motor vehicle inspection and testing equipment software file directory generation module 204, used for inputting the software file into a directory intelligent generator based on an AIGC model to obtain a motor vehicle inspection and testing equipment software file directory;

[0085] The motor vehicle inspection and testing equipment manufacturer identification module 202 includes:

[0086] A context semantic coding unit 2021, used for performing word granularity-based context semantic coding on the basic information related to the motor vehicle inspection and testing equipment to obtain a sequence of basic information word granularity context semantic coding feature vectors;

[0087] The semantic association optimization unit 2022 is used to perform semantic association optimization on the sequence of the basic information word granularity context semantic coding feature vectors to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors;

[0088] The manufacturer category determination unit 2023 is used to determine the manufacturer category based on the sequence of context semantic coding feature vectors optimized at the basic information word granularity.

[0089] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 600 suitable for implementing the embodiment of the present application. The terminal device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0090] like Figure 3 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0091] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0092] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present application are executed.

[0093] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0094] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0095] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0096] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0097] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0098] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the test parameter acquisition module can also be described as a "module for acquiring device test parameters corresponding to the target device".

[0099] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0100] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] Figure 4 This is an application scenario diagram of a method for intelligently acquiring a software file directory of a motor vehicle inspection and testing device according to an exemplary embodiment. Figure 4 As shown, in this application scenario, first, basic information related to the motor vehicle inspection and testing equipment is collected (for example, Figure 4 Then, the basic information obtained is input into a server that is equipped with an intelligent acquisition algorithm for the software file directory of the motor vehicle inspection and testing equipment (for example, Figure 4 In S) shown in , the server is capable of processing the basic information based on an intelligent acquisition algorithm for the software file directory of the motor vehicle inspection and testing equipment to obtain the software file directory of the motor vehicle inspection and testing equipment.

[0102] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0103] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0104] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely exemplary forms of implementation. With respect to the apparatus in the above-mentioned embodiments, the specific manner in which each module performs the operation has been described in detail in the embodiments related to the method, and will not be described in detail here.

Claims

1. A method for intelligently acquiring the software file directory of motor vehicle inspection and testing equipment, It is characterized in that include: Collect basic information related to motor vehicle inspection and testing equipment; Based on the basic information related to the motor vehicle inspection and testing equipment, identifying the manufacturer of the motor vehicle inspection and testing equipment; Obtaining software file webpage content from the manufacturer's official website, and parsing the software file webpage content to obtain the software file; Input the software file into a directory intelligent generator based on the AIGC model to obtain a software file directory of the motor vehicle inspection and testing equipment; Wherein, based on the basic information related to the motor vehicle inspection and testing equipment, identifying the manufacturer of the motor vehicle inspection and testing equipment includes: Performing word-granularity-based context semantic coding on the basic information related to the motor vehicle inspection and testing equipment to obtain a sequence of basic information word-granularity context semantic coding feature vectors; Performing semantic association optimization on the sequence of the basic information word granularity context semantic coding feature vectors to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors; Optimizing the sequence of context semantic encoding feature vectors based on the basic information word granularity to determine the category of the manufacturer; The basic information related to the motor vehicle inspection and testing equipment is subjected to word granularity-based context semantic coding to obtain a sequence of basic information word granularity context semantic coding feature vectors, including: Performing word segmentation processing on the basic information related to the motor vehicle inspection and testing equipment to obtain a sequence of basic information description words; Using a word semantic embedding matrix, semantic embedding coding is performed on each basic information description word in the sequence of basic information description words to obtain a sequence of basic information description word semantic embedding coding vectors; Inputting the sequence of semantic embedding encoding vectors of the basic information description words into a basic information semantic encoder based on a transformer structure to obtain a sequence of granular context semantic encoding feature vectors of the basic information words; The process of performing semantic association optimization on the sequence of the basic information word granularity context semantic coding feature vectors to obtain a sequence of basic information word granularity optimized context semantic coding feature vectors includes: Inputting the sequence of the basic information word granularity context semantic encoding feature vectors into a feature semantic association optimization network based on sequence endogenous correlation semantic measurement to obtain the sequence of the basic information word granularity optimized context semantic encoding feature vectors; Inputting the sequence of the basic information word granular context semantic encoding feature vectors into a feature semantic association optimization network based on sequence endogenous correlation semantic measurement to obtain the sequence of the basic information word granularity optimized context semantic encoding feature vectors, including: Calculating the semantic association score vector between any two basic information word granularity context semantic encoding feature vectors in the sequence of the basic information word granularity context semantic encoding feature vectors to obtain a sequence of basic information word granularity semantic association score vectors; Determine a sequence endogenous correlation global representation vector based on the sequence of the basic information word granular semantic association score vectors; Based on the sequence endogenous correlation global representation vector, calculating the association optimization factor of each basic information word granular context semantic encoding feature vector in the sequence of basic information word granular context semantic encoding feature vectors to obtain a sequence of association optimization factors; Inputting the sequence of association optimization factors into a Softmax activation function to obtain a sequence of association optimization weight factors; Using each of the associated optimization weight factors in the sequence of associated optimization weight factors as a weight, weighting each of the basic information word granularity context semantic coding feature vectors in the sequence of the basic information word granularity context semantic coding feature vectors to obtain the sequence of the basic information word granularity optimized context semantic coding feature vectors; Calculating the semantic association score vector between any two basic information word granularity context semantic encoding feature vectors in the sequence of the basic information word granularity context semantic encoding feature vectors to obtain a sequence of basic information word granularity semantic association score vectors, including: Any two basic information word granularity context semantic encoding feature vectors in the sequence of the basic information word granularity context semantic encoding feature vectors are cascaded and multiplied by a weight coefficient matrix, and then dotted with the bias vector to obtain the basic information word granularity semantic association score vector; Based on the sequence of the basic information word granular semantic association score vectors, determining a sequence endogenous correlation global representation vector includes: Calculating the mean vector of the sequence of the basic information word granular semantic association score vectors to obtain the global representation vector of the sequence endogenous correlation; Based on the sequence endogenous correlation global representation vector, calculating the association optimization factor of each basic information word granular context semantic encoding feature vector in the sequence of basic information word granular context semantic encoding feature vectors to obtain a sequence of association optimization factors, including: The basic information word granular context semantic encoding feature vector and the sequence endogenous correlation global representation vector are multiplied by different weight coefficient vectors respectively and then added to obtain a semantic correlation coefficient; The semantic association coefficient is added with a bias parameter and then passed through a sigmoid activation function to obtain the association optimization factor.

2. The method for intelligently acquiring the software file directory of the motor vehicle inspection and testing equipment according to claim 1, It is characterized in that Optimizing the sequence of context semantic encoding feature vectors based on the basic information word granularity to determine the category of the manufacturer includes: Calculating the global mean vector of the sequence of the basic information word granularity optimized context semantic encoding feature vectors to obtain a basic information semantic condensed representation vector; The semantic condensed representation vector of the basic information is input into a classifier-based vendor identifier to obtain a recognition result, wherein the recognition is used to represent a category label of the vendor.

3. The method for intelligently acquiring the software file directory of the motor vehicle inspection and testing equipment according to claim 2, It is characterized in that The basic information semantic condensation representation vector is input into a classifier-based vendor identifier to obtain a recognition result, wherein the recognition is used to represent the category label of the vendor, including: Using multiple fully connected layers of the classifier-based vendor identifier to perform fully connected encoding on the basic information semantic condensation representation vector to obtain an encoded classification feature vector; The encoded classification feature vector is passed through the Softmax classification function of the classifier-based manufacturer identifier to obtain the identification result.

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