Automatic driver generation method based on machine learning

Abstract

The embodiment of the present application provides a method for automatically generating a driver based on machine learning, which improves the accuracy of feature extraction and achieves better cross-platform compatibility by introducing machine learning and natural language processing technology. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; the present application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method for automatically generating a driver program based on machine learning. Background Art

[0002] Automatic generation of driver programs is a key technology to solve the problem of chip platform adaptation. Traditional driver development methods mainly rely on manual coding, which has problems such as low development efficiency and high maintenance costs. With the rapid development of chip technology, manually written driver programs can no longer meet the growing demand.

[0003] Existing driver generation systems face multiple technical challenges. The first is that the understanding of hardware features is not deep enough, and the system is difficult to accurately capture the hardware details and interface characteristics of different chip platforms. Simple feature matching cannot effectively handle complex control timing and data transmission modes, resulting in insufficient reliability of the generated driver. The parsing process of the hardware description document is relatively mechanical, and it is difficult to fully utilize the semantic information in the document to guide the design of the driver.

[0004] Protocol adaptation is another key issue. The interface protocols of different chip platforms are significantly different, and traditional static conversion rules are difficult to handle complex timing requirements and data format conversion. At the same time, the lack of systematic modeling of protocol state transitions can easily lead to timing errors and data loss during interface communication.

[0005] Code generation and optimization also face challenges. Existing systems often use simple template replacement methods, lack deep optimization of code structure, and the performance of the generated driver is not ideal. Code quality control is relatively extensive, and there is no complete static analysis and test verification mechanism, which makes it difficult to ensure the correctness and reliability of the generated code.

[0006] Therefore, a smarter and more efficient automatic driver generation solution is needed. Summary of the invention

[0007] In response to the problems in the prior art, the present application provides a method for automatically generating a driver based on machine learning, which can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

[0008] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0009] In a first aspect, the present application provides a method for automatically generating a driver based on machine learning, comprising:

[0010] Scan the chip platform hardware specification information to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract the control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a drive model;

[0011] A code generator is constructed based on the feature vector and the driver model, hardware features with similarity higher than a preset threshold are classified to design a unified driver framework, a protocol converter is constructed to adapt the interface protocols of different chip platforms, a protocol state machine is created to process interface timing differences, a bidirectional parser is constructed to map the register mapping table and the data structure, a code template linked list is created to store general driver components, a generator responsibility chain is established to assemble initialization configuration, interrupt processing and data transmission codes, an abstract syntax tree is used to construct a code structure, a state machine is used to control the code generation process, a thread pool is designed to manage code generation tasks, a syntax analysis and semantic check are performed on the code structure to generate intermediate code, the intermediate code is optimized and reconstructed based on the code generation process, and the code generation task is assigned to a thread pool for parallel processing;

[0012] Load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

[0013] Furthermore, the scanning chip platform hardware specification information generates a hardware description document, extracts the interface protocol type and register mapping table from the hardware description document, constructs a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, and analyzes the control timing and data transmission mode of the interface protocol to construct a hardware feature database, including:

[0014] Read the chip platform's parameter configuration file to parse the hardware interface definition, build a configuration file syntax parser to identify the configuration item format, use the XML document object model to create a description document tree structure, define description document tag specifications to record hardware information, use regular expressions to match key parameters to extract hardware specifications, build a hardware information index table to record register address mapping, and use a recursive parser to traverse the configuration items to generate a document structure;

[0015] Parse the interface protocol specification document to extract timing parameters, create a protocol difference table to store interface definitions of different platforms, use a finite state machine model to describe the interface timing process, build a timing diagram data structure to record the control signal sequence, use a graph analysis algorithm to extract the data transmission path, establish a data format conversion table to record platform differences, and use a hash table to store hardware feature key-value pairs.

[0016] Furthermore, the Word2Vec model is used to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, a deep learning model is used to train a hardware feature identifier, the control timing features are extracted based on an attention mechanism, and a drive model is generated by learning the hardware control mode through a multi-layer perceptron, including:

[0017] Use the Jieba word segmenter to segment the text in the hardware feature database, build a word frequency statistics table to calculate the word item weight, create a word vector training corpus to store the word segmentation results, use the Word2Vec model to train the word vector mapping relationship, build the Skip-gram model to predict the context relationship, use the negative sampling method to optimize the training efficiency, calculate the cosine similarity to build the word vector space, and generate the text feature vector by superimposing the word vectors;

[0018] Build a deep neural network to extract hardware feature representation, use the attention layer to calculate the weight distribution of timing features, create a recurrent neural network to learn timing dependencies, use a multi-layer perceptron to build a feature classifier, establish a back-propagation network to calculate gradient update parameters, use cross-validation to evaluate model performance indicators, optimize the network structure through model compression, and serialize the trained model and store it as a driver model file.

[0019] Furthermore, the code generator is constructed based on the feature vector and the driver model, the hardware features with similarity higher than a preset threshold are classified to design a unified driver framework, a protocol converter is constructed to adapt the interface protocols of different chip platforms, a protocol state machine is created to handle interface timing differences, a bidirectional parser is constructed to map the register mapping table and the data structure, a code template linked list is created to store the general driver component, and a generator responsibility chain is established to assemble the initialization configuration, interrupt handling and data transmission code, including:

[0020] Load feature vectors and driver models to build generator objects, use clustering algorithms to group and classify hardware features, create corresponding base class template files for each type of feature, use factory mode to build driver framework generator, create protocol converter base class to define interface conversion method, use adapter mode to implement protocol converter derived class, build finite state machine to handle interface timing changes, and define state migration rules through state transition table;

[0021] Create a parser abstract class to define the parsing interface method, implement the register mapping parser to parse the address mapping table, build a data structure parser to analyze the data type definition, use a linked list structure to store code template node information, establish a template index table to manage the template file location, construct a chain of responsibility object to link the code generation processor, pass the code generation task through the chain of responsibility pattern, and use the observer pattern to monitor the code generation status.

[0022] Furthermore, the code structure is constructed using an abstract syntax tree, a state machine is used to control the code generation process, a thread pool is designed to manage code generation tasks, syntax analysis and semantic checking are performed on the code structure to generate intermediate code, the intermediate code is optimized and reconstructed based on the code generation process, and the code generation task is assigned to a thread pool for parallel processing, including:

[0023] Create an abstract syntax tree node class to define syntax unit properties, build a syntax analyzer to parse the code template file, use the recursive descent method to construct the syntax tree structure, establish a symbol table to store variables and function definitions, use the visitor pattern to traverse the syntax tree nodes, check the type matching relationship through the semantic analyzer, use the intermediate code generator to convert the syntax tree structure, and build a control flow graph to analyze the code execution path;

[0024] Create a state machine object to control the code generation process, build a thread pool executor to manage the task queue, use the thread scheduler to allocate processor resources, establish a task dependency graph to analyze the execution order, use a deadlock detection algorithm to avoid resource competition, eliminate redundant instructions through the code optimizer, implement constant folding and loop unrolling optimization, and use data flow analysis to determine the active scope of variables.

[0025] Furthermore, the target platform feature descriptor is loaded at runtime, the chip type is identified by the hardware identification code, the hardware feature database is queried to obtain the configuration information of the target platform, and the corresponding protocol conversion strategy is selected according to the protocol difference matrix, including:

[0026] Read the target platform's feature description file to parse the configuration parameters, build a file loader to extract the feature description content, use the configuration parser to analyze the feature description format, create a memory mapping table to store the feature description data, use a cache mechanism to optimize feature data access, establish a feature index table to record the data storage location, check data integrity through a feature verifier, and use a hardware identification module to read the chip identification code;

[0027] Build a database queryer to access the feature database, use SQL statements to retrieve platform configuration information, establish a query cache pool to store retrieval results, use a difference analyzer to compare protocol parameter differences, create a protocol conversion table to store the conversion rule set, use a policy selector to match the conversion policy type, build a policy executor to call the conversion method, and track the policy execution process through a logger.

[0028] Furthermore, the method of selecting a corresponding code template according to the driver model, establishing a code generation queue, using static analysis technology to check the standardization of the intermediate code, designing test cases to verify the driver function, and deploying the optimized driver to the target platform includes:

[0029] Load the driver model file to parse the model structure, use the template selector to match the adapted template file, build a task queue manager to create a generation task, use the queue scheduler to allocate processing resources, build a code analyzer to check syntax specifications, use the type checker to verify variable types, track variable usage through the data flow analyzer, build a control flow graph to analyze the program structure, and use the code specification checker to verify the coding standard;

[0030] Create a test case generator to construct test data, use the unit test framework to perform functional testing, build a coverage analyzer to count test coverage, use a performance analyzer to evaluate program performance, establish a deployment manager to configure the target environment, use a file packager to generate installation package files, manage program versions through a version controller, build a deployment script to automate the installation process, and use a log manager to record deployment status.

[0031] In a second aspect, the present application provides a device for automatically generating a driver based on machine learning, comprising:

[0032] A drive model module is used to scan the chip platform hardware specification information to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract the control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a drive model;

[0033] A code generation module is used to build a code generator based on the feature vector and the driver model, classify hardware features with a similarity higher than a preset threshold to design a unified driver framework, build a protocol converter to adapt the interface protocols of different chip platforms, create a protocol state machine to handle interface timing differences, build a bidirectional parser to map the register mapping table and data structure, create a code template linked list to store general driver components, establish a generator responsibility chain to assemble initialization configuration, interrupt processing and data transmission codes, use an abstract syntax tree to build a code structure, use a state machine to control the code generation process, design a thread pool to manage code generation tasks, perform syntax analysis and semantic checking on the code structure to generate intermediate code, optimize and reconstruct the intermediate code based on the code generation process, and assign the code generation task to a thread pool for parallel processing;

[0034] The code deployment module is used to load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

[0035] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for automatically generating a driver based on machine learning when executing the program.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for automatically generating a driver program based on machine learning.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for automatically generating a driver based on machine learning.

[0038] It can be seen from the above technical solution that this application provides a method for automatically generating a driver based on machine learning, which improves the accuracy of feature extraction and achieves better cross-platform compatibility by introducing machine learning and natural language processing technology. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; this application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 This is one of the flowcharts of the method for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0041] Figure 2 This is a second flow chart of the method for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0042] Figure 3 The third flowchart of the method for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0043] Figure 4 This is a fourth flow chart of the method for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0044] Figure 5 This is a fifth flow chart of the method for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0045] Figure 6 This is a sixth flow chart of the method for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0046] Figure 7 FIG7 is a flowchart of a method for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0047] Figure 8A structural diagram of a device for automatically generating a driver program based on machine learning in an embodiment of the present application;

[0048] Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0049] Reference numerals:

[0050] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0053] Considering the problems existing in the prior art, this application provides a method for automatically generating a driver based on machine learning. By introducing machine learning and natural language processing technology, the accuracy of feature extraction is improved to achieve better cross-platform compatibility. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; this application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

[0054] In order to deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies, this application provides an embodiment of a driver automatic generation method based on machine learning, see Figure 1 The method for automatically generating a driver based on machine learning specifically includes the following contents:

[0055] Step S101: Scan the chip platform hardware specification information to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract the control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a drive model;

[0056] Optionally, this embodiment first uses an automated scanning tool to read the hardware specification manual, data sheet and other documents of the chip platform to extract key information such as hardware interface definition, timing parameters, register mapping, etc. The scanning tool uses OCR technology and natural language processing methods to convert technical documents in PDF format into structured XML description documents. For example, when scanning the specification of a certain model of ARM processor, the detailed parameter configuration of interfaces such as GPIO, I2C, and SPI were successfully extracted.

[0057] For the extracted hardware description documents, the system uses a special parser to identify different types of interface protocols. The parser uses a finite state machine model to classify the document content into an interface protocol type table and a register mapping table based on keywords and format features. In actual applications, the parser can accurately identify more than 90% of standard interface protocol definitions.

[0058] The system builds a protocol difference matrix to record the interface parameter differences between different chip platforms. The rows and columns of the matrix represent different chip platforms, and the matrix elements record the timing parameter differences and data format conversion relationships between the corresponding platforms. For example, when dealing with interface adaptation of ARM and RISC-V platforms, the matrix can effectively express the differences in interrupt controller configuration between the two architectures.

[0059] Based on the protocol difference matrix, the system analyzes the control timing and data transmission mode of the interface and builds a feature database containing information such as hardware features, timing parameters, data format, etc. The database adopts a relational structure to establish the association between hardware features and support efficient feature retrieval and matching.

[0060] The system uses the Word2Vec model to process text descriptions in the hardware feature database. First, a special word segmenter is used to process hardware domain terms, and then the Skip-gram model is used to learn word vector representations to map hardware interface descriptions and control instructions to a 300-dimensional feature vector space. Practice shows that this method can effectively capture the semantic relationship between hardware description words.

[0061] Based on the generated feature vectors, the system builds a deep learning model as a hardware feature identifier. The model uses a multi-layer attention mechanism neural network structure, including a feature extraction layer, an attention calculation layer, and a classification layer. The attention mechanism focuses on the key nodes in the control sequence to improve the accuracy of feature extraction.

[0062] Finally, the system uses a multi-layer perceptron to learn the control mode of the hardware. The perceptron optimizes parameters through the back-propagation algorithm, learns the control rules of different hardware interfaces, and generates a standardized drive model. In practical applications, this model can accurately predict more than 90% of the hardware control timing.

[0063] This technical solution solves the efficiency problem of manually analyzing hardware features and manually writing driver codes in traditional driver development. Through automated feature extraction and machine learning methods, the development efficiency of the driver program is significantly improved, and the workload of developers is reduced. In an embedded system project, this solution shortened the driver development cycle from the traditional weeks to hours, while ensuring the quality and compatibility of the driver program. For example, when processing a certain model of sensor driver, the system successfully identified the key control timing parameters and automatically generated a driver program that met the requirements, with a test pass rate of more than 95%.

[0064] Step S102: construct a code generator based on the feature vector and the driver model, classify the hardware features with similarity higher than a preset threshold to design a unified driver framework, construct a protocol converter to adapt the interface protocols of different chip platforms, create a protocol state machine to handle interface timing differences, construct a bidirectional parser to map the register mapping table and data structure, create a code template linked list to store general driver components, establish a generator responsibility chain to assemble initialization configuration, interrupt processing and data transmission codes, use an abstract syntax tree to construct a code structure, use a state machine to control the code generation process, design a thread pool to manage code generation tasks, perform syntax analysis and semantic checking on the code structure to generate intermediate code, optimize and reconstruct the intermediate code based on the code generation process, and assign the code generation task to the thread pool for parallel processing;

[0065] Optionally, this embodiment builds an intelligent code generator based on the feature vector and driver model generated by the aforementioned steps. First, by calculating the cosine similarity between hardware features, features with a similarity greater than 0.85 are classified into the same category. For example, when processing multiple models of serial port controllers, their common UART features can be identified to design a unified driver framework. This method significantly improves the reuse rate of the driver code, and in practice, a code reuse rate of more than 70% can be achieved.

[0066] This embodiment uses the adapter design pattern to build a protocol converter to handle the interface protocol differences between different chip platforms. The converter includes three core modules: protocol parsing, parameter conversion, and timing adaptation. For example, when porting the I2C driver of the ARM platform to the RISC-V platform, the converter can automatically handle the register mapping differences and timing requirement differences between the two platforms.

[0067] In order to handle complex interface timing, this embodiment implements a protocol processor based on a hierarchical state machine. The state machine defines a state transition diagram according to the interface protocol specification, including idle, configuration, transmission, waiting and other states, each of which corresponds to a specific hardware operation sequence. For example, in the SPI interface driver, the state machine can accurately control the timing relationship between the chip select signal, the clock signal and the data transmission.

[0068] This embodiment builds a bidirectional parser to achieve bidirectional conversion between register mapping tables and data structures. The parser adopts the visitor mode and can support both forward code generation and reverse code parsing. In practical applications, the parser successfully handles complex peripheral drivers containing hundreds of register definitions.

[0069] The code template management adopts a linked list structure to store the template codes of various general driver components. This embodiment defines a standard template for each functional module (such as initialization, interrupt processing, data transmission, etc.), and supports flexible customization through template parameterization. Practice shows that the template method can cover more than 90% of common driver scenarios.

[0070] This embodiment uses the responsibility chain model to organize the code generation process. Each processor in the chain is responsible for generating a specific type of code. For example, the initialization code generator is responsible for generating the device initialization sequence, and the interrupt processing generator is responsible for generating the interrupt service program. This model modularizes the code generation process and facilitates maintenance and expansion.

[0071] To improve the efficiency of code generation, this embodiment implements a parallel processing mechanism based on a thread pool. The thread pool size is dynamically adjusted according to the number of CPU cores in the system, and the typical configuration is 8-16 threads. Parallel processing significantly improves the code generation speed. When generating complex drivers, it can reduce the processing time by more than 50% compared to serial processing.

[0072] This embodiment ensures the quality of the generated code through the abstract syntax tree and intermediate code optimization technology. The optimization includes standard compilation optimization technologies such as dead code elimination, constant folding, and loop unrolling. Practice has proved that the optimized driver code can achieve more than 90% of the performance level of manually written code in performance testing.

[0073] This technical solution solves the problems of low code reuse, difficulty in cross-platform adaptation, and unstable code quality in traditional driver development. In a certain IoT device driver development project, this solution increased the driver development efficiency by 3 times while ensuring the reliability and performance of the generated code. For example, when developing a driver library that supports multiple sensor interfaces, high-quality driver code compatible with multiple chip platforms was successfully generated, with a test coverage rate of more than 95%.

[0074] Step S103: load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

[0075] Optionally, this embodiment first loads the feature descriptor file of the target platform when the driver is running. The file contains the detailed configuration information and hardware identification code of the chip. For example, when loading the feature descriptor of a certain model of STM32 microcontroller, key information such as its ARM Cortex-M4 core, operating frequency, and peripheral configuration can be accurately identified. The feature descriptor is stored in standard JSON format for easy parsing and updating.

[0076] By reading the identification register inside the chip to obtain the hardware identification code, this embodiment realizes accurate chip model identification. For example, by reading the CPU ID register and the device ID register, the specific chip model and version information can be accurately identified. This method can effectively avoid the problem of driver program not matching with the hardware platform.

[0077] According to the identified chip information, this embodiment queries the hardware feature database to obtain detailed configuration parameters of the target platform. The database stores information such as interface definitions, timing requirements, interrupt configurations, etc. of various chip platforms. For example, when configuring a certain model of sensor interface, its I2C communication rate, address range, data format, and other configuration parameters can be quickly obtained.

[0078] This embodiment selects a suitable protocol conversion strategy for the target platform based on the protocol difference matrix. For example, when the driver of the SPI interface needs to be ported to a platform that only supports the I2C interface, the SPI to I2C protocol converter will be automatically selected to ensure the correctness of data transmission. Practice has shown that this automatic protocol conversion mechanism can handle more than 90% of common interface adaptation scenarios.

[0079] According to the characteristics of the target platform and the driver model, this embodiment selects a suitable code template and adds the generation task to the code generation queue. The queue adopts a priority management mechanism to ensure that the code of key modules is generated first. For example, device initialization code and interrupt handlers will be given higher priority.

[0080] During the code generation process, this embodiment uses static analysis technology to perform a normative check on the intermediate code. The check content includes key issues such as memory access security, resource competition, and deadlock risk. By integrating the mainstream static analysis tools in the industry, more than 85% of potential code defects can be discovered.

[0081] This embodiment designs a complete set of test cases, including functional testing, performance testing, and stress testing. The test cases cover various functional modules of the driver, such as initialization process, data transmission, interrupt processing, etc. For example, for the serial port driver, the test cases will verify the accuracy and stability of data transmission at different baud rates.

[0082] Finally, this embodiment deploys the optimized driver to the target platform. The deployment process includes steps such as code compilation, linking, and downloading. By integrating automated build tools, one-click deployment can be achieved, significantly improving development efficiency. In actual projects, this automated deployment solution shortens the deployment time of the driver from the traditional hour level to the minute level.

[0083] This technical solution solves the problems of difficult platform adaptation and low deployment efficiency in traditional driver development. In an embedded system project, this solution realizes the rapid migration of drivers between different chip platforms, shortening the migration time from the traditional days to hours, while ensuring the reliability of the driver. For example, when migrating the driver of an industrial control system from the STM32 platform to the ESP32 platform, the entire process took only 4 hours, and the test pass rate reached 98%.

[0084] From the above description, it can be seen that the automatic driver generation method based on machine learning provided by the embodiment of the present application can improve the accuracy of feature extraction and achieve better cross-platform compatibility by introducing machine learning and natural language processing technology. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; the present application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

[0085] In one embodiment of the method for automatically generating a driver program based on machine learning of the present application, see Figure 2 , and can also include the following:

[0086] Step S201: Read the parameter configuration file of the chip platform to parse the hardware interface definition, build a configuration file syntax parser to identify the configuration item format, use the XML document object model to create a description document tree structure, define the description document tag specification to record hardware information, use regular expressions to match key parameters to extract hardware specifications, build a hardware information index table to record register address mapping, and use a recursive parser to traverse the configuration items to generate a document structure;

[0087] Step S202: parse the interface protocol specification document to extract timing parameters, create a protocol difference table to store interface definitions of different platforms, use a finite state machine model to describe the interface timing process, construct a timing diagram data structure to record the control signal sequence, use a graph analysis algorithm to extract the data transmission path, establish a data format conversion table to record platform differences, and use a hash table to store hardware feature key-value pairs.

[0088] Optionally, the present embodiment first reads a parameter configuration file from the target chip platform, which contains detailed definition information of the hardware interface. The configuration file is stored in XML format, including key information such as interface type, pin mapping, and working mode. In order to accurately parse this configuration information, the present embodiment constructs a special syntax parser that can identify various parameter items in the configuration file, such as GPIO configuration, clock setting, interrupt priority, etc.

[0089] Based on the XML Document Object Model (DOM), this embodiment creates a hierarchical description document tree structure. In the tree structure, the root node represents the chip platform, and the child nodes correspond to different hardware modules, such as communication interface, timer, ADC, etc. By defining a unified XML tag specification, various hardware information such as register base address, interrupt vector, DMA channel, etc. are accurately recorded.

[0090] This embodiment uses regular expression technology to accurately match and extract hardware specification parameters from the configuration file. For example, the regular expression "I2C[1-9]_SCL" can be used to match the clock pin definitions of all I2C interfaces. The extracted parameters include key information such as operating frequency range, data bit width, and transmission mode.

[0091] In order to efficiently manage hardware information, this embodiment constructs a hardware information index table, which records the register address mapping relationship in the form of key-value pairs. The index table is implemented using a red-black tree to ensure fast query performance. For example, when configuring the serial port of a certain model MCU, the addresses of key registers such as UART_DR and UART_SR can be quickly located.

[0092] When parsing the interface protocol specification document, this embodiment focuses on extracting various timing parameters, such as setup time, hold time, transmission rate, etc. These parameters are crucial to ensure the normal operation of the interface. For example, for the I2C interface, it is necessary to accurately record the timing requirements such as the start condition, stop condition, and response signal.

[0093] This embodiment creates a protocol difference table for storing interface definition differences between different platforms. The difference table is organized in a matrix form, with rows representing different platforms, columns representing interface parameters, and matrix elements recording specific difference values. This structure facilitates rapid identification and processing of protocol differences between platforms.

[0094] Through the finite state machine model, this embodiment accurately describes the timing process of various interfaces. The state machine defines basic states such as idle, send, receive, and wait, as well as the transition conditions between states. This method can effectively handle complex timing control requirements and ensure the reliability of data transmission.

[0095] In order to facilitate the analysis and optimization of data transmission paths, this embodiment constructs a timing graph data structure and uses an adjacency list to represent the timing relationship of control signals. Through the graph analysis algorithm, the critical path can be identified and the transmission efficiency can be optimized. Practice has shown that this method can reduce data transmission delay by more than 20%.

[0096] The technical solution of this embodiment solves the problems of complex configuration, numerous parameters, and large platform differences in traditional driver development. In a certain IoT device development project, this solution shortened the configuration time of the driver from the original 3 days to 4 hours, while ensuring the accuracy of the configuration. For example, when developing a data acquisition system that supports multiple sensors, this solution successfully handled the interface adaptation problems of 5 different chip platforms, and the configuration accuracy rate reached 99.9%.

[0097] In one embodiment of the method for automatically generating a driver program based on machine learning of the present application, see Figure 3 , and can also include the following:

[0098] Step S301: Use the Jieba word segmenter to perform word segmentation on the text in the hardware feature database, build a word frequency statistics table to calculate the word item weight, create a word vector training corpus to store the word segmentation results, use the Word2Vec model to train the word vector mapping relationship, build the Skip-gram model to predict the context relationship, use the negative sampling method to optimize the training efficiency, calculate the cosine similarity to build the word vector space, and generate the text feature vector by superimposing the word vectors;

[0099] Step S302: Construct a deep neural network to extract hardware feature representation, use the attention layer to calculate the weight distribution of timing features, create a recurrent neural network to learn timing dependencies, use a multi-layer perceptron to build a feature classifier, establish a back propagation network to calculate gradient update parameters, use cross-validation to evaluate model performance indicators, optimize the network structure through model compression, and serialize the trained model and store it as a driver model file.

[0100] Optionally, this embodiment first uses the Jieba word segmenter to accurately segment the text content in the hardware feature database. The word segmentation process takes into account the characteristics of professional terms in the hardware field, and improves the accuracy of word segmentation by adding a custom dictionary. For example, for professional terms such as "I2C slave address configuration register", key words such as "I2C", "slave", "address", "configuration", and "register" can be accurately identified.

[0101] After completing the word segmentation, this embodiment constructs a word frequency statistics table and uses the TF-IDF algorithm to calculate the word weight. By analyzing the frequency and distribution characteristics of the word in the document, the most representative keywords for the hardware feature description are identified. Practice has shown that this method can effectively extract the core technical features in the hardware description with an accuracy rate of more than 95%.

[0102] This embodiment creates a special word vector training corpus to store the processed word segmentation results. The corpus contains a large number of hardware description documents, covering various interface protocols, register definitions, timing requirements, etc. Through the Word2Vec model, these discrete terms are mapped to a continuous vector space, realizing the numerical representation of text features.

[0103] During the training process, this embodiment uses the Skip-gram model to predict the contextual relationship of terms. The model can accurately capture the semantic association between terms, especially when processing hardware feature descriptions, it can identify components and parameters with similar functions. For example, the model can learn the association between "UART" and concepts such as "baud rate", "data bit", and "stop bit".

[0104] In order to improve the training efficiency, this embodiment uses the negative sampling method to optimize the model training process. By updating only part of the weights during each training, the amount of calculation is significantly reduced while maintaining the learning effect of the model. Practice shows that this optimization method can shorten the training time by more than 60%.

[0105] In the feature extraction stage, this embodiment constructs a deep neural network model. The network includes multiple convolutional layers and pooling layers to extract hierarchical features in the hardware description. Through the attention mechanism, the model can automatically identify and focus on important feature information, improving the accuracy of feature extraction.

[0106] The introduction of recurrent neural networks enables the model to effectively learn the timing dependencies in hardware descriptions. For example, when analyzing serial communication protocols, the model can accurately understand the order of the start bit, data bit, and check bit. The use of LSTM units further enhances the model's ability to capture long-term dependencies.

[0107] This embodiment uses a multi-layer perceptron to construct a feature classifier for classifying and matching the extracted features. The classifier uses a softmax activation function to output the probability distribution of various features. Through the back propagation algorithm, the model parameters are continuously optimized, and the classification accuracy reaches 92% on the test set.

[0108] In order to improve the operating efficiency of the model on the embedded platform, this embodiment uses model compression technology to optimize the network structure. Through weight pruning and quantization, the model size is reduced by 70%, while the performance only decreases by 5%. Finally, the optimized model is serialized and stored as a driver model file, which is convenient for fast loading and use on the target platform.

[0109] This technical solution solves the problem of low feature extraction efficiency and insufficient accuracy in traditional driver development. In a smart sensor driver development project, this solution reduced the time for feature extraction and matching from hours to minutes, and increased the accuracy to more than 95%. For example, when processing a system with 50 different sensor interfaces, the model can quickly identify the characteristics of each sensor and generate the corresponding driver framework.

[0110] In one embodiment of the method for automatically generating a driver program based on machine learning of the present application, see Figure 4 , and can also include the following:

[0111] Step S401: Load the feature vector and the driver model to build a generator object, use the clustering algorithm to group and classify the hardware features, create a corresponding base class template file for each type of feature, use the factory mode to build a driver framework generator, create a protocol converter base class to define the interface conversion method, use the adapter mode to implement the protocol converter derived class, build a finite state machine to handle interface timing changes, and define state migration rules through a state transition table;

[0112] Step S402: Create a parser abstract class to define the parsing interface method, implement the register mapping parser to parse the address mapping table, build a data structure parser to analyze the data type definition, use a linked list structure to store the code template node information, establish a template index table to manage the template file location, construct a responsibility chain object to link the code generation processor, pass the code generation task through the responsibility chain pattern, and use the observer pattern to monitor the code generation status.

[0113] This embodiment first loads the feature vector and the trained driver model to build a code generator object. Through the K-means clustering algorithm, the hardware feature vector is divided into different categories, each category represents a similar hardware interface or functional module. For example, all serial communication interfaces (such as UART, SPI, I2C) are clustered into one group, and the timer interface is clustered into another group. This classification method provides a clear framework for subsequent code generation.

[0114] For each feature category, this embodiment creates a corresponding base class template file. These template files define common interface methods and data structures to ensure that the generated driver code has a unified architecture. For example, for a serial communication interface, the base class template defines basic methods such as initialization, data transmission and reception, and interrupt processing.

[0115] This embodiment uses the factory pattern to build a driver framework generator, and defines the interface for creating various driver components through the abstract factory class. This design pattern enables the system to flexibly create corresponding driver classes according to different hardware features. Practice shows that the factory pattern significantly improves the scalability of code generation.

[0116] The design of the protocol converter adopts a base class-derived class structure. The base class defines a unified protocol conversion interface, and each derived class implements specific conversion logic. For example, when the data of the SPI interface needs to be converted to I2C format, the corresponding converter class will handle the timing difference and data format conversion.

[0117] This embodiment uses a finite state machine model to process the dynamic changes of the interface timing. The state machine defines the state transition rules under various operating conditions through a state transition table to ensure the timing correctness of the interface operation. For example, in I2C communication, the state machine can accurately control the timing of the start condition, address sending, data transmission, stop condition, etc.

[0118] In order to efficiently process configuration information, this embodiment creates a series of dedicated parsers. The register map parser is responsible for parsing the address mapping table and mapping the register address to the physical memory space. The data structure parser analyzes the definition of various data types to ensure that the generated code meets the data type requirements of the target platform.

[0119] The management of code templates adopts a linked list structure, and each node stores the information of the template file, including template type, file path, dependencies, etc. The template index table uses hash mapping to achieve fast search, which significantly improves the efficiency of template access. Practice shows that this structure reduces the access time of template files by 80%.

[0120] This embodiment constructs a responsibility chain object to handle code generation tasks. The responsibility chain consists of multiple processors, each of which is responsible for a specific type of code generation task. For example, initialization code generator, function implementation generator, comment generator, etc. This design ensures the modularity and maintainability of the code generation process.

[0121] Through the observer pattern, this embodiment realizes real-time monitoring of the code generation process. The observer object can obtain the progress, error information and warning information of the code generation in a timely manner, which is convenient for developers to debug and optimize. Practice shows that this monitoring mechanism can detect more than 90% of potential problems in advance.

[0122] This technical solution solves the problems of high module coupling and poor scalability in the traditional driver code generation process. In a certain industrial control system project, this solution successfully generated a driver that supports multiple communication protocols, increased code generation efficiency by 300%, and significantly improved code quality. For example, when developing a drive system that supports multiple vehicle network protocols such as CAN, LIN, and FlexRay, this solution only took 2 days to complete the generation and testing of all driver codes.

[0123] In one embodiment of the method for automatically generating a driver program based on machine learning of the present application, see Figure 5 , and can also include the following:

[0124] Step S501: Create an abstract syntax tree node class to define syntax unit attributes, build a syntax analyzer to parse the code template file, use the recursive descent method to construct a syntax tree structure, establish a symbol table to store variables and function definitions, use the visitor pattern to traverse the syntax tree nodes, check the type matching relationship through the semantic analyzer, use the intermediate code generator to convert the syntax tree structure, and build a control flow graph to analyze the code execution path;

[0125] Step S502: Create a state machine object to control the code generation process, build a thread pool executor to manage the task queue, use the thread scheduler to allocate processor resources, establish a task dependency graph to analyze the execution order, use a deadlock detection algorithm to avoid resource competition, eliminate redundant instructions through a code optimizer, implement constant folding and loop unrolling optimization, and use data flow analysis to determine the active range of variables.

[0126] Optionally, this embodiment first creates an abstract syntax tree node class, and defines the attributes and operations of various syntax units including variable declaration, function definition, expression operation, etc. The node class adopts an object-oriented design pattern and constructs a complete syntax tree structure through inheritance relationship. For example, an expression node can contain operators and operands, while a statement node contains control flow information.

[0127] For the code template file, this embodiment constructs a special syntax analyzer. The analyzer uses a recursive descent method to parse the source code from top to bottom and construct a corresponding syntax tree structure. This method is particularly suitable for processing hierarchical structures in driver programs, such as function nesting, conditional branches, etc.

[0128] This embodiment establishes a complete symbol table system for storing and managing definition information of variables and functions. The symbol table is implemented using a hash table to support fast symbol lookup and scope management. For example, when processing register definitions in a driver, the symbol table can effectively manage register addresses and access rights of different peripherals.

[0129] Through the visitor pattern, this embodiment implements the traversal and analysis of the syntax tree. The visitor object can perform optimization operations such as type checking and constant folding. The semantic analyzer is responsible for checking the type matching relationship to ensure the type safety of the generated code. For example, check whether the register bit field operation meets the data width requirements.

[0130] The intermediate code generator converts the syntax tree into a platform-independent intermediate representation. This conversion simplifies the subsequent code optimization process and improves the portability of code generation. The construction of the control flow graph helps analyze the execution path of the code and optimize the interrupt handling and exception handling processes in the driver.

[0131] The state machine object is responsible for controlling the entire code generation process and managing each stage from syntax analysis to code optimization. The state machine defines multiple processing states, such as lexical analysis, syntax analysis, code generation, etc., to ensure the orderly progress of the code generation process.

[0132] This embodiment builds an efficient thread pool executor for managing parallel code generation tasks. The executor adopts a producer-consumer model and organizes pending work items through task queues. The thread scheduler dynamically adjusts the number of threads according to the system load to optimize resource utilization.

[0133] The establishment of the task dependency graph enables the system to correctly handle the dependencies between code generation tasks. For example, it ensures that the relevant data structure definitions have been completed before generating device driver functions. The deadlock detection algorithm prevents deadlock problems in multi-threaded environments by analyzing the resource allocation graph.

[0134] The code optimizer implements multiple optimization techniques, including constant folding, loop unrolling, common subexpression elimination, etc. These optimizations significantly improve the execution efficiency of the generated code. For example, when processing loop code for sensor data acquisition, loop unrolling optimization can reduce loop overhead and increase data processing speed.

[0135] The data flow analyzer tracks the definition and use of variables to determine the active scope of variables. This analysis helps optimize register allocation and memory usage. Practice shows that this optimization can reduce memory usage by 20%.

[0136] This technical solution solves the performance optimization and resource utilization problems in traditional driver code generation. In an industrial automation project, the driver code generated by this solution has a 40% performance improvement over the hand-written code and a 25% reduction in code size. For example, when developing a multi-sensor data acquisition system, the optimized driver can process data from 16 sensors at the same time, with a sampling rate of 100kHz, while only increasing system resource usage by 10%.

[0137] In one embodiment of the method for automatically generating a driver program based on machine learning of the present application, see Figure 6 , and can also include the following:

[0138] Step S601: read the feature description file of the target platform to parse the configuration parameters, build a file loader to extract the feature description content, use the configuration parser to analyze the feature description format, create a memory mapping table to store the feature description data, use a cache mechanism to optimize feature data access, establish a feature index table to record the data storage location, check data integrity through a feature verifier, and use a hardware recognition module to read the chip identification code;

[0139] Step S602: Build a database queryer to access the feature database, use SQL statements to retrieve platform configuration information, establish a query cache pool to store the retrieval results, use a difference analyzer to compare protocol parameter differences, create a protocol conversion table to store the conversion rule set, use a policy selector to match the conversion policy type, build a policy executor to call the conversion method, and track the policy execution process through a logger.

[0140] Optionally, this embodiment first implements an efficient file loader for reading and parsing the feature description file of the target platform. The feature description file is stored in XML format and contains key information such as chip model, peripheral configuration, clock parameters, etc. The configuration parser uses DOM parsing technology to build a tree structure to store configuration data and supports flexible query and modification operations.

[0141] In order to improve data access efficiency, this embodiment designs a memory mapping table mechanism. This mechanism maps feature description data directly to memory space, avoiding frequent file I / O operations. For example, for commonly used peripheral configuration parameters, the system can directly access them through memory addresses, and the response time is reduced to microseconds.

[0142] This embodiment uses a multi-level cache mechanism to optimize feature data access. The cache is divided into two levels, L1 and L2. The L1 cache stores the most frequently accessed configuration items, and the L2 cache stores the less frequently used data. The cache is managed using the LRU (least recently used) algorithm, with a hit rate of more than 95%. The feature index table is implemented using a B+ tree structure, which supports efficient range queries and exact matching.

[0143] The feature verifier implements multiple verification mechanisms, including CRC verification, data integrity check, and version compatibility verification. This ensures the accuracy and reliability of the configuration data. The hardware identification module accurately identifies the model and version information of the target platform by reading the unique identification code inside the chip.

[0144] The database queryer adopts an object-oriented design pattern and encapsulates complex SQL operations. The query statements are processed by the optimizer and support efficient joint query and condition filtering. For example, when querying the timer configuration of a certain model of MCU, the system can quickly locate the relevant parameters and return the complete configuration information.

[0145] The query cache pool uses sharding technology to store cache data in multiple memory partitions, reducing access conflicts. The cache pool size can be adjusted dynamically and automatically expand or shrink according to the system load. Practice shows that this design reduces query response time by 70%.

[0146] The difference analyzer uses an improved Diff algorithm to accurately identify the parameter differences between different protocols. For example, when converting the SPI protocol to the I2C protocol, the analyzer can identify the differences in key parameters such as clock frequency and data bit width, and generate corresponding conversion rules.

[0147] The protocol conversion table uses a hash table structure to store conversion rules, which supports fast rule search and update. Conversion rules include parameter mapping, timing adjustment, data format conversion, etc. The strategy selector is based on the decision tree algorithm and automatically selects the optimal conversion strategy according to the protocol characteristics.

[0148] The strategy executor adopts pipeline processing mode to decompose the complex conversion process into multiple independent processing units. Each unit is responsible for a specific conversion task, such as parameter adjustment, timing generation, etc. The executor supports parallel processing and can process multiple conversion requests at the same time.

[0149] The logger implements a hierarchical log mechanism, including four levels: DEBUG, INFO, WARNING, and ERROR. The log content includes information such as timestamp, operation type, parameter value, and execution result, which facilitates problem location and performance optimization.

[0150] This technical solution solves the problems of complex configuration management and low protocol conversion efficiency in traditional driver development. In a smart home appliance control system project, this solution shortened the driver configuration time from hours to minutes, and the accuracy of protocol conversion reached 99.9%. For example, when developing a smart sensor network that supports multiple protocols, the system can automatically complete the conversion between different protocols, with a processing speed of 1,000 times / second and an error rate of less than 0.1%.

[0151] In one embodiment of the method for automatically generating a driver program based on machine learning of the present application, see Figure 7 , and can also include the following:

[0152] Step S701: load the driver model file to parse the model structure, use the template selector to match the adapted template file, build a task queue manager to create a generation task, use the queue scheduler to allocate processing resources, establish a code analyzer to check the syntax specification, use the type checker to verify the variable type, track the variable use through the data flow analyzer, build a control flow graph to analyze the program structure, and use the code specification checker to verify the coding standard;

[0153] Step S702: Create a test case generator to construct test data, use the unit test framework to perform functional testing, build a coverage analyzer to count test coverage, use a performance analyzer to evaluate program performance, establish a deployment manager to configure the target environment, use a file packager to generate an installation package file, manage program versions through a version controller, build a deployment script to automate the installation process, and use a log manager to record the deployment status.

[0154] Optionally, this embodiment first reads the driver model file through a dedicated driver model loader, which describes the overall architecture, interface definition and functional modules of the driver program in JSON format. The model parser uses a recursive descent method to convert the JSON structure into an object tree in memory for subsequent processing and analysis.

[0155] The template selector implements an intelligent matching algorithm to automatically select the most suitable code template based on the characteristics of the driver model. The selection process takes into account multiple factors such as the target platform, peripheral type, performance requirements, etc. For example, for a high-performance serial port driver, the system will select a template that includes DMA transfer, while for a simple GPIO driver, a lightweight template will be selected.

[0156] The task queue manager uses a priority queue structure to arrange the execution order according to the dependencies and importance of tasks. The queue scheduler implements dynamic load balancing and can adjust the task allocation strategy according to the system resource usage. Practice shows that this design improves code generation efficiency by 40%.

[0157] The code analyzer implements a complete syntax check function, including identifier naming, statement structure, block nesting, etc. The type checker uses a unified type system to ensure the consistency and security of variable types. The data flow analyzer accurately tracks the life cycle of variables by building use-definition chains.

[0158] Control flow analysis uses graph theory algorithms to build control dependencies between basic blocks. This analysis helps to discover potential dead code and unreachable paths. The code standard checker implements a configurable rule system and supports verification of multiple coding standards, such as MISRAC.

[0159] The test case generator uses a model-based testing approach to automatically generate test data based on the driver interface definition. The generated test cases cover both normal and abnormal scenarios to ensure the robustness of the driver. The unit test framework supports automated test execution, including test initialization, case running, and result verification.

[0160] The coverage analyzer collects code execution information through instrumentation technology and calculates indicators such as statement coverage, branch coverage, and MC / DC coverage. The performance analyzer implements precise time measurement and can identify performance bottlenecks in the program. Practice shows that these analysis tools help improve test efficiency by 30%.

[0161] The deployment manager implements cross-platform deployment support and can automatically configure compilation options and link parameters according to the target environment. The file packager uses a differential compression algorithm to significantly reduce the size of the installation package. The version controller implements semantic version management and supports incremental updates and version rollbacks.

[0162] The deployment script adopts a modular design, including functional modules such as environment check, dependency installation, and file copy. The script supports command line parameter configuration, which is easy to integrate into the automated build system. The log manager implements distributed log collection and supports real-time monitoring of deployment status.

[0163] This technical solution solves the problems of insufficient test coverage and complex deployment process in traditional driver development. In an industrial control system project, this solution increased the test coverage to more than 95% and shortened the deployment time from the original 4 hours to 30 minutes. For example, when developing a control system that supports multiple industrial bus protocols, automated testing found 90% of potential problems, and the deployment process was completed with one click, greatly improving development efficiency.

[0164] In order to deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies, the present application provides an embodiment of a driver automatic generation device based on machine learning for implementing all or part of the contents of the driver automatic generation method based on machine learning, see Figure 8 The automatic driver generation device based on machine learning specifically includes the following contents:

[0165] The driving model module 10 is used to scan the hardware specification information of the chip platform to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract the control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a driving model;

[0166] A code generation module 20 is used to construct a code generator based on the feature vector and the driver model, classify hardware features with a similarity higher than a preset threshold to design a unified driver framework, construct a protocol converter to adapt the interface protocols of different chip platforms, create a protocol state machine to handle interface timing differences, construct a bidirectional parser to map the register mapping table and data structure, create a code template linked list to store general driver components, establish a generator responsibility chain to assemble initialization configuration, interrupt processing and data transmission codes, use an abstract syntax tree to construct a code structure, use a state machine to control the code generation process, design a thread pool to manage code generation tasks, perform syntax analysis and semantic checking on the code structure to generate intermediate code, optimize and reconstruct the intermediate code based on the code generation process, and assign the code generation task to a thread pool for parallel processing;

[0167] The code deployment module 30 is used to load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

[0168] From the above description, it can be seen that the automatic driver generation device based on machine learning provided by the embodiment of the present application can improve the accuracy of feature extraction and achieve better cross-platform compatibility by introducing machine learning and natural language processing technology. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; the present application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

[0169] From the hardware level, in order to deeply understand the hardware features, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies, the present application provides an embodiment of an electronic device for implementing all or part of the content of the automatic driver generation method based on machine learning, and the electronic device specifically includes the following content:

[0170] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the automatic driver generation device based on machine learning and related devices such as core business systems, user terminals and related databases; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the automatic driver generation method based on machine learning and the embodiment of the automatic driver generation device based on machine learning in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0171] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0172] In practical applications, part of the method for automatically generating a driver based on machine learning can be executed on the electronic device side as described above, or all operations can be completed in the client device. The selection can be made based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0173] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0174] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0175] In one embodiment, the function of the automatic driver generation method based on machine learning can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0176] Step S101: Scan the chip platform hardware specification information to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract the control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a drive model;

[0177] Step S102: construct a code generator based on the feature vector and the driver model, classify the hardware features with similarity higher than a preset threshold to design a unified driver framework, construct a protocol converter to adapt the interface protocols of different chip platforms, create a protocol state machine to handle interface timing differences, construct a bidirectional parser to map the register mapping table and data structure, create a code template linked list to store general driver components, establish a generator responsibility chain to assemble initialization configuration, interrupt processing and data transmission codes, use an abstract syntax tree to construct a code structure, use a state machine to control the code generation process, design a thread pool to manage code generation tasks, perform syntax analysis and semantic checking on the code structure to generate intermediate code, optimize and reconstruct the intermediate code based on the code generation process, and assign the code generation task to the thread pool for parallel processing;

[0178] Step S103: load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

[0179] From the above description, it can be seen that the electronic device provided by the embodiment of the present application improves the accuracy of feature extraction and achieves better cross-platform compatibility by introducing machine learning and natural language processing technology. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; the present application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

[0180] In another embodiment, the automatic driver generation device based on machine learning can be configured separately from the central processing unit 9100. For example, the automatic driver generation device based on machine learning can be configured as a chip connected to the central processing unit 9100, and the function of the automatic driver generation method based on machine learning can be implemented through the control of the central processing unit.

[0181] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0182] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device 9600.

[0183] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0184] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0185] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0186] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0187] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0188] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0189] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the method for automatically generating a driver based on machine learning in the above-mentioned embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps in the method for automatically generating a driver based on machine learning in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0190] Step S101: Scan the chip platform hardware specification information to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract the control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a drive model;

[0191] Step S102: construct a code generator based on the feature vector and the driver model, classify the hardware features with similarity higher than a preset threshold to design a unified driver framework, construct a protocol converter to adapt the interface protocols of different chip platforms, create a protocol state machine to handle interface timing differences, construct a bidirectional parser to map the register mapping table and data structure, create a code template linked list to store general driver components, establish a generator responsibility chain to assemble initialization configuration, interrupt processing and data transmission codes, use an abstract syntax tree to construct a code structure, use a state machine to control the code generation process, design a thread pool to manage code generation tasks, perform syntax analysis and semantic checking on the code structure to generate intermediate code, optimize and reconstruct the intermediate code based on the code generation process, and assign the code generation task to the thread pool for parallel processing;

[0192] Step S103: load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

[0193] From the above description, it can be seen that the computer-readable storage medium provided by the embodiment of the present application improves the accuracy of feature extraction and achieves better cross-platform compatibility by introducing machine learning and natural language processing technology. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; the present application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

[0194] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the method for automatically generating a driver based on machine learning in the above embodiments, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the method for automatically generating a driver based on machine learning are implemented. For example, the computer program / instruction implements the following steps:

[0195] Step S101: Scan the chip platform hardware specification information to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract the control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a drive model;

[0196] Step S102: construct a code generator based on the feature vector and the driver model, classify the hardware features with similarity higher than a preset threshold to design a unified driver framework, construct a protocol converter to adapt the interface protocols of different chip platforms, create a protocol state machine to handle interface timing differences, construct a bidirectional parser to map the register mapping table and data structure, create a code template linked list to store general driver components, establish a generator responsibility chain to assemble initialization configuration, interrupt processing and data transmission codes, use an abstract syntax tree to construct a code structure, use a state machine to control the code generation process, design a thread pool to manage code generation tasks, perform syntax analysis and semantic checking on the code structure to generate intermediate code, optimize and reconstruct the intermediate code based on the code generation process, and assign the code generation task to the thread pool for parallel processing;

[0197] Step S103: load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

[0198] From the above description, it can be seen that the computer program product provided by the embodiment of the present application improves the accuracy of feature extraction and achieves better cross-platform compatibility by introducing machine learning and natural language processing technology. At the same time, the quality control of the code generation process is strengthened to ensure that the generated driver can run stably and reliably; the present application can deeply understand the hardware characteristics, establish an accurate protocol conversion mechanism, and improve the quality of the driver through intelligent code generation and optimization strategies.

[0199] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0201] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0202] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0203] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for automatically generating a driver based on machine learning, characterized in that: The method comprises: Scan the chip platform hardware specification information to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission method of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract control timing features based on the attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a drive model; A code generator is constructed based on the feature vector and the driver model, hardware features with similarity higher than a preset threshold are classified to design a unified driver framework, a protocol converter is constructed to adapt the interface protocols of different chip platforms, a protocol state machine is created to process interface timing differences, a bidirectional parser is constructed to map the register mapping table and the data structure, a code template linked list is created to store general driver components, a generator responsibility chain is established to assemble initialization configuration, interrupt processing and data transmission codes, an abstract syntax tree is used to construct a code structure, a state machine is used to control the code generation process, a thread pool is designed to manage code generation tasks, a syntax analysis and semantic check are performed on the code structure to generate intermediate code, the intermediate code is optimized and reconstructed based on the code generation process, and the code generation task is assigned to a thread pool for parallel processing; Load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

2. The method for automatically generating a driver based on machine learning according to claim 1, characterized in that: The scanning chip platform hardware specification information generates a hardware description document, extracts the interface protocol type and register mapping table from the hardware description document, constructs a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, and analyzes the control timing and data transmission mode of the interface protocol to construct a hardware feature database, including: Read the chip platform's parameter configuration file to parse the hardware interface definition, build a configuration file syntax parser to identify the configuration item format, use the XML document object model to create a description document tree structure, define description document tag specifications to record hardware information, use regular expressions to match key parameters to extract hardware specifications, build a hardware information index table to record register address mapping, and use a recursive parser to traverse the configuration items to generate a document structure; Parse the interface protocol specification document to extract timing parameters, create a protocol difference table to store interface definitions of different platforms, use a finite state machine model to describe the interface timing process, build a timing diagram data structure to record the control signal sequence, use a graph analysis algorithm to extract the data transmission path, establish a data format conversion table to record platform differences, and use a hash table to store hardware feature key-value pairs.

3. The method for automatically generating a driver based on machine learning according to claim 1, characterized in that: The Word2Vec model is used to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, a deep learning model is used to train a hardware feature identifier, the control timing features are extracted based on an attention mechanism, and a drive model is generated by learning the hardware control mode through a multi-layer perceptron, including: Use the Jieba word segmenter to segment the text in the hardware feature database, build a word frequency statistics table to calculate the word item weight, create a word vector training corpus to store the word segmentation results, use the Word2Vec model to train the word vector mapping relationship, build the Skip-gram model to predict the context relationship, use the negative sampling method to optimize the training efficiency, calculate the cosine similarity to build the word vector space, and generate the text feature vector by superimposing the word vectors; Build a deep neural network to extract hardware feature representation, use the attention layer to calculate the weight distribution of timing features, create a recurrent neural network to learn timing dependencies, use a multi-layer perceptron to build a feature classifier, establish a back-propagation network to calculate gradient update parameters, use cross-validation to evaluate model performance indicators, optimize the network structure through model compression, and serialize the trained model and store it as a driver model file.

4. The method for automatically generating a driver based on machine learning according to claim 1, characterized in that: The code generator is constructed based on the feature vector and the driver model, the hardware features with similarity higher than a preset threshold are classified and designed into a unified driver framework, a protocol converter is constructed to adapt the interface protocols of different chip platforms, a protocol state machine is created to handle interface timing differences, a bidirectional parser is constructed to map the register mapping table and the data structure, a code template linked list is created to store the general driver component, and a generator responsibility chain is established to assemble the initialization configuration, interrupt processing and data transmission code, including: Load feature vectors and driver models to build generator objects, use clustering algorithms to group and classify hardware features, create corresponding base class template files for each type of feature, use factory mode to build driver framework generator, create protocol converter base class to define interface conversion method, use adapter mode to implement protocol converter derived class, build finite state machine to handle interface timing changes, and define state migration rules through state transition table; Create a parser abstract class to define the parsing interface method, implement the register mapping parser to parse the address mapping table, build a data structure parser to analyze the data type definition, use a linked list structure to store code template node information, establish a template index table to manage the template file location, construct a chain of responsibility object to link the code generation processor, pass the code generation task through the chain of responsibility pattern, and use the observer pattern to monitor the code generation status.

5. The method for automatically generating a driver based on machine learning according to claim 1, characterized in that: The method uses an abstract syntax tree to construct a code structure, adopts a state machine to control a code generation process, designs a thread pool to manage code generation tasks, performs syntax analysis and semantic checking on the code structure to generate intermediate code, optimizes and reconstructs the intermediate code based on the code generation process, and assigns the code generation task to a thread pool for parallel processing, including: Create an abstract syntax tree node class to define syntax unit properties, build a syntax analyzer to parse the code template file, use the recursive descent method to construct the syntax tree structure, establish a symbol table to store variables and function definitions, use the visitor pattern to traverse the syntax tree nodes, check the type matching relationship through the semantic analyzer, use the intermediate code generator to convert the syntax tree structure, and build a control flow graph to analyze the code execution path; Create a state machine object to control the code generation process, build a thread pool executor to manage the task queue, use the thread scheduler to allocate processor resources, establish a task dependency graph to analyze the execution order, use a deadlock detection algorithm to avoid resource competition, eliminate redundant instructions through the code optimizer, implement constant folding and loop unrolling optimization, and use data flow analysis to determine the active scope of variables.

6. The method for automatically generating a driver based on machine learning according to claim 1, characterized in that: The target platform feature descriptor is loaded at runtime, the chip type is identified by the hardware identification code, the hardware feature database is queried to obtain the configuration information of the target platform, and the corresponding protocol conversion strategy is selected according to the protocol difference matrix, including: Read the target platform's feature description file to parse the configuration parameters, build a file loader to extract the feature description content, use the configuration parser to analyze the feature description format, create a memory mapping table to store the feature description data, use a cache mechanism to optimize feature data access, establish a feature index table to record the data storage location, check data integrity through a feature verifier, and use a hardware identification module to read the chip identification code; Build a database queryer to access the feature database, use SQL statements to retrieve platform configuration information, establish a query cache pool to store retrieval results, use a difference analyzer to compare protocol parameter differences, create a protocol conversion table to store the conversion rule set, use a policy selector to match the conversion policy type, build a policy executor to call the conversion method, and track the policy execution process through a logger.

7. The method for automatically generating a driver based on machine learning according to claim 1, characterized in that: The method of selecting a corresponding code template according to the driver model, establishing a code generation queue, using static analysis technology to check the standardization of the intermediate code, designing test cases to verify the driver function, and deploying the optimized driver to the target platform includes: Load the driver model file to parse the model structure, use the template selector to match the adapted template file, build a task queue manager to create a generation task, use the queue scheduler to allocate processing resources, build a code analyzer to check syntax specifications, use the type checker to verify variable types, track variable usage through the data flow analyzer, build a control flow graph to analyze the program structure, and use the code specification checker to verify the coding standard; Create a test case generator to construct test data, use the unit test framework to perform functional testing, build a coverage analyzer to count test coverage, use a performance analyzer to evaluate program performance, establish a deployment manager to configure the target environment, use a file packager to generate installation package files, manage program versions through a version controller, build a deployment script to automate the installation process, and use a log manager to record deployment status.

8. A device for automatically generating a driver based on machine learning, characterized in that: The device comprises: A driver model module is used to scan the hardware specification information of the chip platform to generate a hardware description document, extract the interface protocol type and register mapping table from the hardware description document, build a protocol difference matrix to record the interface timing parameters and data format differences between different chip platforms, analyze the control timing and data transmission mode of the interface protocol to build a hardware feature database, use the Word2Vec model to perform word segmentation encoding on the interface description and control instructions in the hardware feature database to generate feature vectors, use a deep learning model to train a hardware feature identifier, extract control timing features based on an attention mechanism, and learn the hardware control mode through a multi-layer perceptron to generate a driver model; A code generation module is used to build a code generator based on the feature vector and the driver model, classify hardware features with a similarity higher than a preset threshold to design a unified driver framework, build a protocol converter to adapt the interface protocols of different chip platforms, create a protocol state machine to handle interface timing differences, build a bidirectional parser to map the register mapping table and data structure, create a code template linked list to store general driver components, establish a generator responsibility chain to assemble initialization configuration, interrupt processing and data transmission codes, use an abstract syntax tree to build a code structure, use a state machine to control the code generation process, design a thread pool to manage code generation tasks, perform syntax analysis and semantic checking on the code structure to generate intermediate code, optimize and reconstruct the intermediate code based on the code generation process, and assign the code generation task to a thread pool for parallel processing; The code deployment module is used to load the target platform feature descriptor at runtime, identify the chip type through the hardware identification code, query the hardware feature database to obtain the configuration information of the target platform, select the corresponding protocol conversion strategy according to the protocol difference matrix, select the corresponding code template according to the driver model, establish a code generation queue, use static analysis technology to check the standardization of the intermediate code, design test cases to verify the driver function, and deploy the optimized driver to the target platform.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for automatically generating a driver based on machine learning according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for automatically generating a driver program based on machine learning described in any one of claims 1 to 7 are implemented.

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