Traffic transportation employee education platform based on big data analysis and cloud computing

Abstract

The invention discloses a traffic transportation employee education platform based on big data analysis and cloud computing, and relates to the technical field of education platforms. The traffic transportation employee education platform based on big data analysis and cloud computing comprises a system architecture design module, a data acquisition and storage module, a data processing and analysis module, a model training and evaluation module, a user presentation layer development module, a test and optimization module and an operation and maintenance module. Through systematized architecture design, efficient collection, storage, processing and analysis of data are realized, and the data utilization efficiency and decision support capability are remarkably improved. Through application of the deep learning model, accurate pushing of education content and personalized learning path planning are ensured, and the learning experience and effect are enhanced. Meanwhile, a comprehensive test and optimization mechanism ensures the stability of the system and the user experience, and continuous operation maintenance ensures the long-term development of the platform and the timely response of the user demand.

Description

Technical Field

[0001] The present invention relates to the technical field of education platforms, and in particular to an education platform for transportation practitioners based on big data analysis and cloud computing. Background Art

[0002] The education of transportation practitioners is a vital field, which is directly related to the safety, efficiency and service quality of the transportation industry. The core goal of the education of transportation practitioners is to improve the safety awareness, professional skills and service level of practitioners, and ensure the safe and orderly conduct of transportation activities. Specific goals include: enhancing the safety awareness and sense of responsibility of practitioners, reducing the occurrence of safety accidents; improving the professional skills of practitioners, ensuring the efficiency and quality of transportation activities; improving the service level of practitioners, and meeting the public's demand for transportation services.

[0003] For example, the education platform for transportation practitioners based on big data analysis and cloud computing disclosed in publication number CN114385369A includes a user presentation layer, an application service layer, an application support layer, a core data layer, and a network foundation layer. The application server middleware is mainly responsible for completing the communication between functional modules, as well as the communication between remote functional modules and central server modules. In the analysis of big data, the education platform plans an education platform suitable for the characteristics of training through cloud computing service models and architectures, introduces key success factor methods and workflow technology in the construction of transportation training management informatization, recreates the workflow of training management informatization, realizes the informatization, scientificization and standardization of training management work, improves the work efficiency of training management, and produces positive economic and social benefits.

[0004] As shown in the above technology, the existing technology only provides a platform for online education for practitioners, but such platforms usually lack personalized learning path recommendations. Users cannot obtain customized educational resources according to their own needs, which in turn affects the learning interest of practitioners, and further affects learning efficiency, resulting in a poor user experience. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an education platform for transportation practitioners based on big data analysis and cloud computing, which solves the problems of the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a transportation practitioners education platform based on big data analysis and cloud computing, comprising:

[0007] System architecture design module, used to design and determine the layered architecture of the system, including data collection layer, data storage layer, data processing and analysis layer, application service layer and user presentation layer;

[0008] Data collection and storage module, used to realize automatic data collection and storage;

[0009] Data processing and analysis module, used for data preprocessing, feature extraction, feature engineering, and data preparation for model training;

[0010] Model training and evaluation module, which selects deep learning models for training, and evaluates and optimizes the models;

[0011] User presentation layer development module, used to design and implement user interfaces and provide user interaction functions;

[0012] Testing and optimization module, used to perform functional testing, performance testing and user experience testing on the system, and collect test data to optimize the system;

[0013] The operation and maintenance module regularly updates, optimizes and repairs the system by collecting user feedback and conducting regular inspections.

[0014] Preferably, the hierarchical architecture of the system architecture design module specifically includes:

[0015] Data collection layer: responsible for automatically collecting data from various data sources;

[0016] Data storage layer: design data storage solutions, select storage media and architecture, deploy data storage systems, and perform performance testing and optimization;

[0017] Data processing and analysis layer: responsible for data preprocessing, feature extraction and encoding, feature engineering, and providing data for model training;

[0018] Application service layer: provides business logic processing, API interface services, and supports the interaction between the front-end and data processing layers;

[0019] User presentation layer: Design and implement the user interface, provide user interaction functions, and display recommendation results and learning path information.

[0020] Preferably, the working contents of the data acquisition and storage module specifically include:

[0021] Data collection: determine the scope and source of data collection, integrate API interfaces, data capture tools or sensor devices, and write data collection scripts or programs;

[0022] Data storage: Design a data storage solution, select a database or storage system, deploy the storage system, and write a data import script to import the collected data into the storage system.

[0023] Preferably, the data processing and analysis module specifically includes:

[0024] Data preprocessing:

[0025] Data cleaning: Set the missing value threshold and remove user records whose missing values ​​exceed the missing value threshold; use the mean or median filling method to process the remaining missing values; and use the IQR method to detect and process outliers, replacing the outliers with the average of the upper and lower quartiles;

[0026] Feature extraction and encoding: Extract key features from user learning behavior data; perform one-hot encoding on categorical features; standardize numerical features, using the Z-score standardization method to ensure that all features are on the same scale;

[0027] Feature Engineering:

[0028] Feature selection: Use feature importance evaluation methods to screen out features that have a significant impact on recommendation results, remove redundant and highly correlated features, and reduce model complexity;

[0029] Feature construction: Construct new features according to business needs and construct a user-course interaction matrix as input to the deep learning model.

[0030] Preferably, the model training and evaluation module specifically includes:

[0031] Model selection: Choose to use the multi-layer perceptron MLP combined with the embedding layer as the starting point;

[0032] Model Architecture:

[0033] Embedding layer: assigns a unique embedding vector to each user and course; these embedding vectors are optimized during training to capture the underlying relationship between users and courses;

[0034] Hidden layer: After the embedding layer, add one or more fully connected layers, using ReLU or Sigmoid activation functions to learn complex nonlinear relationships;

[0035] Output layer: The output layer is a fully connected layer with the number of neurons equal to the length of the recommendation list. The softmax function is used to convert the output into a probability distribution, indicating the user's preference for each candidate course.

[0036] Parameter settings:

[0037] Embedding vector size: Set the size of the embedding vector for each user and course;

[0038] Hidden layer configuration: determine the number of hidden layers, the number of neurons in each layer, and the activation function;

[0039] Optimizer: Select an optimizer suitable for the task to be processed and set the learning rate and momentum parameters;

[0040] Loss function: Use cross entropy loss function as training target;

[0041] Model training:

[0042] Use the preprocessed data as the training set, the user-course interaction matrix as input, and the user's historical learning behavior as the label;

[0043] During the training process, the mini-batch gradient descent method is used to update the model parameters;

[0044] Use the validation set to monitor overfitting during training and adjust model parameters or use regularization and dropout techniques to prevent overfitting.

[0045] Model Evaluation:

[0046] Use the test set data to evaluate the trained model;

[0047] The evaluation indicators include accuracy, recall, F1-Score and Top-N recommendation accuracy.

[0048] Tune the model based on the evaluation results, including adjusting the model architecture, parameter settings, and feature selection until satisfactory performance is achieved.

[0049] Preferably, the step of tuning the model until satisfactory performance is achieved is performed in an iterative manner, and the judgment criteria for stopping iteration are:

[0050] Stable performance: In multiple consecutive iterations, the performance indicators of the model tend to be stable, with no obvious improvement or decline;

[0051] Reaching the preset threshold: The performance indicator of the model has reached or exceeded the preset threshold;

[0052] Overfitting judgment: If the performance of the model on the training set continues to improve, but the performance on the validation set or test set begins to decline, it means that the model may be overfitting. At this time, you should stop iterating and consider taking regularization and dropout measures to prevent overfitting;

[0053] Resource limitation: If continuing to iterate consumes too much computing resources or time and the performance improvement is not obvious, consider stopping the iteration.

[0054] Preferably, the work contents of the user presentation layer development module specifically include:

[0055] Interface design: Design a simple and intuitive user interface that supports multi-terminal access (such as PC and mobile), and determine the layout, color, and icon visual elements of the interface;

[0056] Front-end implementation: write front-end code to implement user interface rendering and interaction logic; integrate front-end frameworks and libraries to improve development efficiency;

[0057] Back-end integration: write back-end code, process user requests and call the API interface provided by the application service layer; implement user registration, login, learning path viewing, and resource browsing functions.

[0058] Preferably, the work contents of the testing and optimization module specifically include:

[0059] Functional testing: Conduct comprehensive testing on all functions of the platform; write test cases and test scripts, record test results and fix any problems found;

[0060] Performance testing: Perform stress testing and concurrent testing on the platform to evaluate its performance under high load; optimize system architecture and code based on test results;

[0061] User experience testing: Invite target users to try out the product and collect feedback; optimize interface design and interaction process based on user feedback.

[0062] Preferably, the work content of the operation and maintenance module specifically includes:

[0063] User support: Provide user manuals, online help and customer service to help users solve problems during use; and collect user feedback to optimize platform functions and user experience;

[0064] Data update: Regularly update the educational resources and learning path recommendation model in the platform; dynamically adjust the recommendation strategy according to the user's learning behavior and data changes;

[0065] System maintenance: Regularly check the stability and security of the system, fix potential vulnerabilities and problems; upgrade and update the system to adapt to new technology trends and business needs.

[0066] The present invention provides an education platform for transportation practitioners based on big data analysis and cloud computing. Compared with the prior art, it has the following beneficial effects:

[0067] 1. This transportation practitioners education platform based on big data analysis and cloud computing has achieved efficient data collection, storage, processing and analysis through systematic architecture design, significantly improving data utilization efficiency and decision-making support capabilities. The application of its deep learning model ensures the accurate push of educational content and personalized learning path planning, enhancing the learning experience and effect. At the same time, comprehensive testing and optimization mechanisms ensure the stability of the system and user experience, while continuous operation and maintenance ensure the long-term development of the platform and timely response to user needs. Overall, the platform promotes the intelligent and efficient education of transportation practitioners, and has significant industry application value and social benefits.

[0068] 2. This transportation worker education platform based on big data analysis and cloud computing has significantly improved data quality and availability through refined data processing processes and efficient storage design. In particular, the in-depth application of data preprocessing, feature extraction and encoding, and feature engineering not only optimizes the model training effect, but also enhances the personalization and accuracy of the recommendation algorithm. In addition, the modular design makes the system more flexible and scalable, and can quickly adapt to the diverse needs of transportation worker education, bringing more efficient and intelligent education solutions to the industry.

[0069] 3. The education platform for transportation practitioners based on big data analysis and cloud computing, the above-mentioned model training and evaluation module significantly improves the accuracy and personalization level of the recommendation system through sophisticated model selection and architecture design, combined with efficient parameter setting and training strategies. The iterative tuning process ensures the stable improvement of model performance while effectively preventing overfitting. These measures not only improve the intelligence of the education platform, but also provide users with more accurate and personalized learning path recommendations, thereby enhancing the user's learning experience and effect. Compared with existing technologies, it has higher practical value and market competitiveness.

[0070] 4. This transportation worker education platform based on big data analysis and cloud computing has significantly improved the user experience, system performance and business adaptability of the transportation worker education platform through comprehensive user presentation layer development, strict testing and optimization, and continuous operation and maintenance. The multi-terminal support of the interface design and the integration of the front-end framework have improved the platform's availability and development efficiency; comprehensive testing ensures the stability of functions and the efficiency of performance; and user support and data updates ensure the continuous optimization and personalized services of the platform. These measures have jointly promoted the intelligence, efficiency and user-friendliness of the education platform, which has significant advantages and competitiveness compared to existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a system principle block diagram of the present invention;

[0072] Figure 2 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] See also Figure 1-Figure 2 , the present invention provides the following four technical solutions:

[0075] The first implementation method: an education platform for transportation practitioners based on big data analysis and cloud computing, including:

[0076] System architecture design module, used to design and determine the layered architecture of the system, including data collection layer, data storage layer, data processing and analysis layer, application service layer and user presentation layer;

[0077] Data collection and storage module, used to realize automatic data collection and storage;

[0078] Data processing and analysis module, used for data preprocessing, feature extraction, feature engineering, and data preparation for model training;

[0079] Model training and evaluation module, which selects deep learning models for training, and evaluates and optimizes the models;

[0080] User presentation layer development module, used to design and implement user interfaces and provide user interaction functions;

[0081] Testing and optimization module, used to perform functional testing, performance testing and user experience testing on the system, and collect test data to optimize the system;

[0082] The operation and maintenance module regularly updates, optimizes and repairs the system by collecting user feedback and conducting regular inspections.

[0083] Through systematic architecture design, efficient data collection, storage, processing and analysis are achieved, significantly improving data utilization efficiency and decision-making support capabilities. The application of its deep learning model ensures the accurate push of educational content and personalized learning path planning, enhancing the learning experience and effect. At the same time, comprehensive testing and optimization mechanisms ensure the stability of the system and user experience, while continuous operation and maintenance ensure the long-term development of the platform and timely response to user needs. Overall, the platform promotes the intelligent and efficient education of transportation practitioners, and has significant industry application value and social benefits.

[0084] The second implementation mode is mainly different from the first implementation mode in that the hierarchical architecture of the system architecture design module specifically includes:

[0085] Data collection layer: responsible for automatically collecting data from various data sources (such as API interfaces, data crawling tools, sensor devices, etc.);

[0086] Data storage layer: design data storage solutions, select storage media and architecture, deploy data storage systems, and perform performance testing and optimization;

[0087] Data processing and analysis layer: responsible for data preprocessing, feature extraction and encoding, feature engineering, and providing data for model training;

[0088] Application service layer: provides business logic processing, API interface services, and supports the interaction between the front-end and data processing layers;

[0089] User presentation layer: Design and implement the user interface, provide user interaction functions, and display recommendation results and learning path information.

[0090] The data collection and storage module specifically includes:

[0091] Data collection: determine the scope and source of data collection, integrate API interfaces, data capture tools or sensor devices, and write data collection scripts or programs;

[0092] Data storage: Design a data storage solution, select a database or storage system (such as a relational database, NoSQL database, distributed storage, etc.), deploy the storage system, and write a data import script to import the collected data into the storage system.

[0093] The data processing and analysis module specifically includes:

[0094] Data preprocessing:

[0095] Data cleaning: Set the missing value threshold and remove user records whose missing values ​​exceed the missing value threshold; for the remaining missing values, use the mean or median filling method to process them; and use the IQR (interquartile range) method to detect and process outliers, replacing the outliers with the average of the upper and lower quartiles;

[0096] Feature extraction and encoding: Extract key features from user learning behavior data, such as learning time, learning frequency, number of completed courses, course grades, etc.; perform one-hot encoding on categorical features (such as course type, user occupation, etc.); standardize numerical features, using the Z-score standardization method to ensure that all features are on the same scale;

[0097] Feature Engineering:

[0098] Feature selection: Use feature importance evaluation methods (such as random forest feature importance) to screen out features that have a significant impact on recommendation results, remove redundant and highly correlated features, and reduce model complexity;

[0099] Feature construction: Construct new features according to business needs, such as user learning efficiency (learning time / learning outcomes), learning stability (learning progress standard deviation), etc., and construct a user-course interaction matrix as the input of the deep learning model.

[0100] Through refined data processing procedures and efficient storage design, data quality and availability have been significantly improved. In particular, the in-depth application of data preprocessing, feature extraction and encoding, and feature engineering not only optimizes the model training effect, but also enhances the personalization and accuracy of the recommendation algorithm. In addition, the modular design makes the system more flexible and scalable, and can quickly adapt to the diverse needs of transportation practitioners' education, bringing more efficient and intelligent education solutions to the industry.

[0101] The third implementation mode is mainly different from the first implementation mode in that the working contents of the model training and evaluation module specifically include:

[0102] Model selection: Choose a neural network model in deep learning, especially a sequence model or graph neural network suitable for recommendation systems (if there are complex relationships between users and courses in the data); choose to use a multi-layer perceptron (MLP) combined with an embedding layer as a starting point;

[0103] Model Architecture:

[0104] Embedding layer: assigns a unique embedding vector to each user and course; these embedding vectors are optimized during training to capture the underlying relationship between users and courses;

[0105] Hidden layers: After the embedding layer, add one or more fully connected layers (also called dense layers or hidden layers) using activation functions such as ReLU or Sigmoid to learn complex nonlinear relationships;

[0106] Output layer: The output layer is a fully connected layer with the number of neurons equal to the length of the recommendation list. The softmax function is used to convert the output into a probability distribution, indicating the user's preference for each candidate course.

[0107] Parameter settings:

[0108] Embedding vector size: Set the embedding vector size for each user and course, such as 64, 128, etc.

[0109] Hidden layer configuration: determine the number of hidden layers, the number of neurons in each layer, and the activation function;

[0110] Optimizer: Select an optimizer suitable for the task to be processed, such as Adam, RMSprop, etc., and set the learning rate and momentum parameters;

[0111] Loss function: Use the cross entropy loss function (Categorical Crossentropy) as the training target because it is suitable for multi-classification problems;

[0112] Model training:

[0113] Use the preprocessed data as the training set, the user-course interaction matrix as input, and the user's historical learning behavior as the label (or target);

[0114] During the training process, the mini-batch gradient descent method is used to update the model parameters;

[0115] Use the validation set to monitor overfitting during training and adjust model parameters or use regularization and dropout techniques to prevent overfitting.

[0116] Model Evaluation:

[0117] Use the test set data to evaluate the trained model;

[0118] The evaluation indicators include accuracy, recall, F1-Score, and Top-N recommendation accuracy. These indicators can fully reflect the performance of the model in personalized learning path recommendation.

[0119] Tune the model based on the evaluation results, including adjusting the model architecture, parameter settings, and feature selection until satisfactory performance is achieved.

[0120] The model is tuned until satisfactory performance is achieved. The tuning is performed in an iterative manner. The criteria for stopping the iteration are:

[0121] Stable performance: In multiple consecutive iterations, the performance indicators of the model (such as loss value, accuracy, etc.) tend to be stable, with no obvious improvement or decline;

[0122] Reaching the preset threshold: The performance indicator of the model has reached or exceeded the preset threshold;

[0123] Overfitting judgment: If the performance of the model on the training set continues to improve, but the performance on the validation set or test set begins to decline, it means that the model may be overfitting. At this time, you should stop iterating and consider taking regularization and dropout measures to prevent overfitting;

[0124] Resource limitation: If continuing to iterate consumes too much computing resources or time and the performance improvement is not obvious, consider stopping the iteration.

[0125] The above model training and evaluation modules significantly improve the accuracy and personalization level of the recommendation system through sophisticated model selection and architecture design, combined with efficient parameter settings and training strategies. The iterative tuning process ensures the steady improvement of model performance while effectively preventing overfitting. These measures not only improve the intelligence of the education platform, but also provide users with more accurate and personalized learning path recommendations, thereby enhancing the user's learning experience and effect. Compared with existing technologies, they have higher practical value and market competitiveness.

[0126] The fourth implementation mode is mainly different from the first implementation mode in that the work content of the user presentation layer development module specifically includes:

[0127] Interface design: Design a simple and intuitive user interface that supports multi-terminal access (such as PC and mobile), and determine the layout, color, and icon visual elements of the interface;

[0128] Front-end implementation: write front-end code to implement user interface rendering and interaction logic; integrate front-end frameworks and libraries, such as React, Vue, etc., to improve development efficiency;

[0129] Back-end integration: write back-end code, process user requests and call the API interface provided by the application service layer; implement user registration, login, learning path viewing, and resource browsing functions.

[0130] The work content of the testing and optimization module specifically includes:

[0131] Functional testing: Conduct comprehensive testing on all functions of the platform to ensure the correctness and stability of the functions; write test cases and test scripts, record test results and fix any problems found;

[0132] Performance testing: Perform stress testing and concurrent testing on the platform to evaluate its performance under high load; optimize system architecture and code based on test results to improve system performance and response speed;

[0133] User experience testing: Invite target users to try out the product and collect feedback; optimize interface design and interaction process based on user feedback to improve user experience.

[0134] The work content of the operation and maintenance module specifically includes:

[0135] User support: Provide user manuals, online help and customer service to help users solve problems during use; and collect user feedback to optimize platform functions and user experience;

[0136] Data update: Regularly update the educational resources and learning path recommendation model in the platform; dynamically adjust the recommendation strategy according to the user's learning behavior and data changes;

[0137] System maintenance: Regularly check the stability and security of the system, fix potential vulnerabilities and problems; upgrade and update the system to adapt to new technology trends and business needs.

[0138] Through comprehensive user presentation layer development, rigorous testing and optimization, and continuous operation and maintenance, the user experience, system performance, and business adaptability of the transportation practitioners education platform have been significantly improved. Multi-terminal support for interface design and integration of front-end frameworks have improved the platform's availability and development efficiency; comprehensive testing ensures functional stability and high performance; and user support and data updates ensure continuous optimization and personalized services for the platform. These measures have jointly promoted the intelligence, efficiency, and user-friendliness of the education platform, which has significant advantages and competitiveness compared to existing technologies.

[0139] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0140] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0141] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An education platform for transportation practitioners based on big data analysis and cloud computing, It is characterized in that include: System architecture design module, used to design and determine the layered architecture of the system, including data collection layer, data storage layer, data processing and analysis layer, application service layer and user presentation layer; Data collection and storage module, used to realize automatic data collection and storage; Data processing and analysis module, used for data preprocessing, feature extraction, feature engineering, and data preparation for model training; Model training and evaluation module, which selects deep learning models for training, and evaluates and optimizes the models; User presentation layer development module, used to design and implement user interfaces and provide user interaction functions; Testing and optimization module, used to perform functional testing, performance testing and user experience testing on the system, and collect test data to optimize the system; The operation and maintenance module regularly updates, optimizes and repairs the system by collecting user feedback and conducting regular inspections.

2. According to claim 1, a transportation practitioners education platform based on big data analysis and cloud computing, Features: The hierarchical architecture of the system architecture design module specifically includes: Data collection layer: responsible for automatically collecting data from various data sources; Data storage layer: design data storage solutions, select storage media and architecture, deploy data storage systems, and perform performance testing and optimization; Data processing and analysis layer: responsible for data preprocessing, feature extraction and encoding, feature engineering, and providing data for model training; Application service layer: provides business logic processing, API interface services, and supports the interaction between the front-end and data processing layers; User presentation layer: Design and implement the user interface, provide user interaction functions, and display recommendation results and learning path information.

3. According to claim 1, a transportation practitioners education platform based on big data analysis and cloud computing, Features: The data collection and storage module specifically includes: Data collection: determine the scope and source of data collection, integrate API interfaces, data capture tools or sensor devices, and write data collection scripts or programs; Data storage: Design a data storage solution, select a database or storage system, deploy the storage system, and write a data import script to import the collected data into the storage system.

4. According to claim 1, a transportation practitioners education platform based on big data analysis and cloud computing, Features: The data processing and analysis module specifically includes: Data preprocessing: Data cleaning: Set the missing value threshold and remove user records whose missing values ​​exceed the missing value threshold; use the mean or median filling method to process the remaining missing values; and use the IQR method to detect and process outliers, replacing the outliers with the average of the upper and lower quartiles; Feature extraction and encoding: Extract key features from user learning behavior data; perform one-hot encoding on categorical features; standardize numerical features, using the Z-score standardization method to ensure that all features are on the same scale; Feature Engineering: Feature selection: Use feature importance evaluation methods to screen out features that have a significant impact on recommendation results, remove redundant and highly correlated features, and reduce model complexity; Feature construction: Construct new features according to business needs and construct a user-course interaction matrix as input to the deep learning model.

5. According to claim 1, a transportation practitioners education platform based on big data analysis and cloud computing, Features: The model training and evaluation module specifically includes: Model selection: Choose to use the multi-layer perceptron MLP combined with the embedding layer as the starting point; Model Architecture: Embedding layer: assigns a unique embedding vector to each user and course; these embedding vectors are optimized during training to capture the underlying relationship between users and courses; Hidden layer: After the embedding layer, add one or more fully connected layers, using ReLU or Sigmoid activation functions to learn complex nonlinear relationships; Output layer: The output layer is a fully connected layer with the number of neurons equal to the length of the recommendation list. The softmax function is used to convert the output into a probability distribution, indicating the user's preference for each candidate course. Parameter settings: Embedding vector size: Set the size of the embedding vector for each user and course; Hidden layer configuration: determine the number of hidden layers, the number of neurons in each layer, and the activation function; Optimizer: Select an optimizer suitable for the task to be processed and set the learning rate and momentum parameters; Loss function: Use cross entropy loss function as training target; Model training: Use the preprocessed data as the training set, the user-course interaction matrix as input, and the user's historical learning behavior as the label; During the training process, the mini-batch gradient descent method is used to update the model parameters; Use the validation set to monitor overfitting during training and adjust model parameters or use regularization and dropout techniques to prevent overfitting. Model Evaluation: Use the test set data to evaluate the trained model; The evaluation indicators include accuracy, recall, F1-Score and Top-N recommendation accuracy. Tune the model based on the evaluation results, including adjusting the model architecture, parameter settings, and feature selection until satisfactory performance is achieved.

6. According to claim 5, a transportation practitioners education platform based on big data analysis and cloud computing, Features: The model is tuned until satisfactory performance is achieved. The tuning is performed in an iterative manner. The criteria for stopping the iteration are: Stable performance: In multiple consecutive iterations, the performance indicators of the model tend to be stable, with no obvious improvement or decline; Reaching the preset threshold: The performance indicator of the model has reached or exceeded the preset threshold; Overfitting judgment: If the performance of the model on the training set continues to improve, but the performance on the validation set or test set begins to decline, it means that the model may be overfitting. At this time, you should stop iterating and consider taking regularization and dropout measures to prevent overfitting; Resource limitation: If continuing to iterate consumes too much computing resources or time and the performance improvement is not obvious, consider stopping the iteration.

7. The transportation practitioners education platform based on big data analysis and cloud computing according to claim 1, Features: The work content of the user presentation layer development module specifically includes: Interface design: Design a simple and intuitive user interface that supports multi-terminal access (such as PC and mobile), and determine the layout, color, and icon visual elements of the interface; Front-end implementation: write front-end code to implement user interface rendering and interaction logic; integrate front-end frameworks and libraries to improve development efficiency; Back-end integration: write back-end code, process user requests and call the API interface provided by the application service layer; implement user registration, login, learning path viewing, and resource browsing functions.

8. The transportation practitioners education platform based on big data analysis and cloud computing according to claim 1, Features: The work content of the testing and optimization module specifically includes: Functional testing: Conduct comprehensive testing on all functions of the platform; write test cases and test scripts, record test results and fix any problems found; Performance testing: Perform stress testing and concurrent testing on the platform to evaluate its performance under high load; optimize system architecture and code based on test results; User experience testing: Invite target users to try out the product and collect feedback; optimize interface design and interaction process based on user feedback.

9. The transportation practitioners education platform based on big data analysis and cloud computing according to claim 1, Features: The work content of the operation and maintenance module specifically includes: User support: Provide user manuals, online help and customer service to help users solve problems during use; and collect user feedback to optimize platform functions and user experience; Data update: Regularly update the educational resources and learning path recommendation model in the platform; dynamically adjust the recommendation strategy according to the user's learning behavior and data changes; System maintenance: Regularly check the stability and security of the system, fix potential vulnerabilities and problems; upgrade and update the system to adapt to new technology trends and business needs.

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