Training examination method and system based on different levels

Abstract

The invention discloses a training assessment method and system based on different levels, and the method comprises the steps: carrying out the classification of the training assessment of traffic police based on different dimensions, and obtaining a classification result; personalized training plans are customized for different types of traffic police officers according to classification results; constructing a multi-dimensional assessment system according to the training plan, and assessing the to-be-assessed traffic police through the multi-dimensional assessment system to obtain an assessment result; and constructing a recurrent neural network model, inputting the training data and the assessment data into the recurrent neural network model, analyzing the learning progress and the assessment result, and automatically adjusting the training content and the assessment standard. According to the invention, customized training and assessment schemes can be provided for traffic polices of different levels, the professional ability and work performance of the traffic polices are effectively improved, and a more accurate talent training strategy is provided for a traffic management department.

Description

Technical Field

[0001] The present invention belongs to the field of traffic management training and assessment, and in particular relates to a training and assessment method and system based on different job levels. Background Art

[0002] As the executor of traffic management, traffic police need to have corresponding legal knowledge, traffic command ability and emergency response ability. However, the current training and assessment system for traffic police mostly adopts unified training materials and assessment standards, ignoring the differences in duties and abilities of traffic police of different ranks, resulting in unsatisfactory training results and inaccurate assessment. Therefore, the existing training and assessment system lacks differentiated training for ranks, and the unified training content cannot meet the actual needs of traffic police of different ranks; the assessment method is single, lacking the assessment of the actual operation ability and on-site response ability of traffic police; ignoring continuous education, the existing system fails to provide continuous career development and learning opportunities for traffic police; insufficient data utilization, training and assessment data are not effectively used, and cannot guide the optimization of training content and improvement of assessment methods. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a training assessment method and system based on different job levels, including:

[0004] Classify the training and assessment of traffic police personnel based on different dimensions and obtain classification results;

[0005] Customizing individualized training plans for different categories of traffic police officers based on the classification results;

[0006] Construct a multi-dimensional assessment system according to the training plan, assess the traffic police personnel to be assessed through the multi-dimensional assessment system, and obtain the assessment results;

[0007] Construct a recurrent neural network model, input training data and assessment data into the recurrent neural network model, analyze the learning progress and the assessment results, and automatically adjust the training content and assessment standards.

[0008] Preferably, the process of classifying the training and assessment of traffic police personnel based on different dimensions and obtaining the classification results includes:

[0009] Traffic police personnel who are to be trained and assessed are classified according to their length of service, positions, nature, performance, responsibilities and ranks to obtain classification results.

[0010] Preferably, the process of building a multi-dimensional assessment system based on the training plan includes:

[0011] Combining theoretical knowledge, practical skills and work performance, a targeted, systematic and customized practical training course system is constructed according to the training plan.

[0012] Preferably, the practical training course system includes but is not limited to the study of laws and regulations, basic requirements for on-duty law enforcement, standardized law enforcement procedures, flexible law enforcement, reasoning law enforcement, must-learn for auxiliary police, key points for investigating and handling key violations, police team operations, use of on-duty law enforcement equipment, response and disposal of obstructions to the performance of duties, on-duty law enforcement scene safety protection, and guidance and response to public opinion involving the police.

[0013] Preferably, the training data and assessment data include training participation, assessment results, trainee feedback, behavioral data, and work performance;

[0014] The training participation rate is used to record the frequency, duration and completion of the traffic police's training participation;

[0015] The assessment results are used to store the scores, rankings and feedback of each assessment;

[0016] The trainee feedback is used to collect traffic police's evaluation of the training content, methods and effects;

[0017] The behavioral data is used to analyze the behavioral patterns of traffic police in simulation training and practical assessment;

[0018] The job performance is used to track the work performance of traffic police, including enforcement accuracy, efficiency and public satisfaction.

[0019] Preferably, the process of inputting the training data and the assessment data into the recurrent neural network model, analyzing the learning progress and the assessment results, and automatically adjusting the training content and the assessment standards includes:

[0020] The training data and assessment data are subjected to feature extraction by combining a recurrent neural network model with a principal component analysis method, and the extracted data features are subjected to a multi-dimensional analysis to identify training needs, effect evaluation, and behavior patterns;

[0021] Intelligently and automatically optimize personalized training plans, dynamic assessment standards, and teaching method improvements based on the training needs, effect evaluation, and behavior patterns;

[0022] Through adaptive learning technology, we provide instant feedback during the training process and recommend relevant additional resources and advanced courses based on learning progress and interests;

[0023] As traffic regulations are updated, the training content is adjusted in a timely manner through continuous iteration, and the training and assessment methods are updated in combination with the latest traffic management and law enforcement technologies.

[0024] The present invention also provides a training assessment system based on different job levels, including:

[0025] The classification module is used to classify the training and assessment of traffic police personnel based on different dimensions and obtain classification results;

[0026] A training plan generation module, connected to the category classification module, is used to customize personalized training plans for different categories of traffic police personnel according to the classification results;

[0027] An assessment system construction module, connected to the training plan generation module, is used to construct a multi-dimensional assessment system according to the training plan, and to assess the traffic police personnel to be assessed through the multi-dimensional assessment system to obtain the assessment results;

[0028] The data analysis and optimization module is connected to the assessment system construction module, and is used to construct a recurrent neural network model, input the training data and the assessment data into the recurrent neural network model, analyze the learning progress and the assessment results, and automatically adjust the training content and assessment standards.

[0029] Preferably, the dimensions include length of service, position, nature, performance and responsibilities, and rank.

[0030] Preferably, the multi-dimensional assessment system includes but is not limited to the study of laws and regulations, basic requirements for on-duty law enforcement, standardized law enforcement procedures, flexible law enforcement, reasoning law enforcement, must-learn for auxiliary police, key points for investigating and handling key violations, police team operations, use of on-duty law enforcement equipment, response and disposal of obstructions to the performance of duties, safety protection at the on-duty law enforcement scene, and guidance and response to public opinion involving the police.

[0031] Preferably, the training data and assessment data include training participation, assessment results, trainee feedback, behavioral data, and work performance;

[0032] The training participation rate is used to record the frequency, duration and completion of the traffic police's training participation;

[0033] The assessment results are used to store the scores, rankings and feedback of each assessment;

[0034] The trainee feedback is used to collect traffic police's evaluation of the training content, methods and effects;

[0035] The behavioral data is used to analyze the behavioral patterns of traffic police in simulation training and practical assessment;

[0036] The job performance is used to track the work performance of traffic police, including enforcement accuracy, efficiency and public satisfaction.

[0037] Compared with the prior art, the present invention has the following advantages and technical effects:

[0038] The present invention can provide customized training and assessment plans for traffic police of different ranks, effectively improve the professional ability and work performance of traffic police, and at the same time provide traffic management departments with more accurate talent training strategies.

[0039] The present invention can meet the actual needs of traffic police at different ranks through personalized training plans, thereby improving the pertinence and effectiveness of training.

[0040] The present invention can comprehensively evaluate the ability and performance of traffic police through a multi-dimensional assessment system, enhance the comprehensiveness and fairness of the assessment, provide a platform for continuous learning and development for traffic police, and promote the professional growth of traffic police.

[0041] The present invention improves the efficiency of training and assessment through intelligent data analysis, and achieves a more reasonable allocation of educational resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0047] Embodiment 1

[0048] like Figure 1 As shown, this embodiment provides a training assessment method based on different job levels, including:

[0049] Classify the training and assessment of traffic police personnel based on different dimensions and obtain classification results;

[0050] Based on the classification results, customized training plans are provided for different categories of traffic police officers;

[0051] Construct a multi-dimensional assessment system according to the training plan, and assess the traffic police officers through the multi-dimensional assessment system to obtain the assessment results;

[0052] Build a recurrent neural network model, input training data and assessment data into the recurrent neural network model, analyze the learning progress and assessment results, and automatically adjust the training content and assessment standards.

[0053] Furthermore, the training and assessment of traffic police personnel are classified based on different dimensions, and the process of obtaining the classification results includes:

[0054] Traffic police officers who are undergoing training and assessment are classified according to their length of service, positions, nature, performance, responsibilities and ranks, and the classification results are obtained. They include junior, intermediate or senior ranks, among which junior-level traffic police officers focus on traffic laws and basic command skills training; intermediate-level traffic police officers receive training on traffic flow organization and accident handling; and senior-level traffic police officers receive training on traffic planning and team management. The assessment includes theoretical tests, simulated command, on-site practical operations and work performance evaluation, and the assessment focuses on different ranks.

[0055] Furthermore, this embodiment uses advanced data analysis technology and machine learning algorithms to achieve a more accurate analysis and determination of traffic police ranks. Specifically, it includes:

[0056] Job grade analysis technology: Classification algorithms and regression analysis are used to process and analyze data such as the traffic police's years of service, performance, and responsibilities. Through these algorithms, the system can identify the key characteristics of different job grades and assign traffic police officers to corresponding job grade classifications accordingly.

[0057] Personalized rank promotion path: Based on the rank analysis results, a personalized rank promotion path is provided for each traffic police officer, including recommended training, necessary skill improvement, and possible position adjustment to help traffic police officers meet promotion conditions.

[0058] Continuous rank monitoring and adjustment: Continuously monitor the work performance and career development of traffic police to ensure the accuracy and timeliness of rank assignments. If the work performance or responsibilities of traffic police change, their rank classification will be adjusted in a timely manner.

[0059] Deep learning applications for job level analysis: Using deep learning techniques to process more complex data patterns to improve the accuracy of job level analysis. This includes using recurrent neural networks and long short-term memory networks to identify potential job level promotion trends and predict future job level changes.

[0060] Data fusion and rank analysis: By integrating data from different sources, such as traffic management records, law enforcement efficiency, and public feedback, to obtain a comprehensive rank analysis perspective, data fusion technology will help the system more accurately evaluate the overall performance of traffic police.

[0061] User interface and rank analysis: By providing an intuitive user interface, traffic police and managers can easily access rank analysis results. The interface will display rank classification, promotion path and personalized suggestions, while providing a feedback mechanism for further learning and optimization of the system.

[0062] The present invention can provide a more accurate and personalized career development platform for traffic police, and at the same time provide a more scientific rank management and human resource planning tool for traffic management departments.

[0063] Furthermore, the process of building a multi-dimensional assessment system based on the training plan includes:

[0064] Combining theoretical knowledge, practical skills and work performance, a targeted, systematic and customized practical training course system is constructed according to the training plan.

[0065] Furthermore, the practical training course system includes but is not limited to the study of laws and regulations, basic requirements for on-duty law enforcement, standardized law enforcement procedures, flexible law enforcement, reasoning law enforcement, must-learn for auxiliary police, key points for investigating and handling key violations, police team operations, use of on-duty law enforcement equipment, response and disposal of obstructions to the performance of duties, on-duty law enforcement scene safety protection, and guidance and response to public opinion involving the police.

[0066] Furthermore, training data and assessment data include training participation, assessment results, trainee feedback, behavioral data, and work performance;

[0067] Among them, training participation is used to record the frequency, duration and completion of traffic police training;

[0068] Assessment results are used to store the scores, rankings, and feedback of each assessment;

[0069] Trainee feedback is used to collect traffic police’s evaluation of the training content, methods and effectiveness;

[0070] Behavioral data is used to analyze the behavior patterns of traffic police in simulation training and practical assessment;

[0071] Job performance is used to track traffic police performance, including enforcement accuracy, efficiency, and public satisfaction.

[0072] Furthermore, the process of inputting the training data and assessment data into the recurrent neural network model, analyzing the learning progress and assessment results, and automatically adjusting the training content and assessment standards includes:

[0073] The training data and assessment data are extracted by combining the recurrent neural network model with the principal component analysis method, and the extracted data features are analyzed in multiple dimensions to identify training needs, effect evaluation, and behavior patterns.

[0074] Intelligently and automatically optimize personalized training plans, dynamic assessment standards, and teaching method improvements based on training needs, effect evaluation, and behavior patterns;

[0075] Through adaptive learning technology, we provide instant feedback during the training process to help traffic police understand their progress and shortcomings in a timely manner. We recommend additional resources and advanced courses based on their learning progress and interests;

[0076] As traffic regulations are updated, the training content is adjusted in a timely manner through continuous iteration, and the training and assessment methods are updated in combination with the latest traffic management and law enforcement technologies.

[0077] Among them, training needs are used to determine the training needs of traffic police based on their assessment scores and work performance.

[0078] Effectiveness evaluation is used to assess the effectiveness of training content and methods and identify strengths and weaknesses.

[0079] Behavioral models are used to analyze traffic police’s behavioral data to understand their reactions and decision-making processes in specific situations.

[0080] Personalized training programs are used to adjust the training content and progress according to the individual needs and performance of traffic police officers.

[0081] Dynamic assessment standards are used to update assessment standards to better reflect actual work needs and job grade characteristics.

[0082] Teaching Method Improvement is used to improve teaching methods and tools based on learner feedback and behavior analysis.

[0083] This embodiment will be able to provide traffic police with a continuously evolving, highly personalized training and assessment environment, thereby continuously improving their professional capabilities and work performance.

[0084] Embodiment 2

[0085] like Figure 2 As shown, this embodiment provides a training assessment system based on different job levels, including:

[0086] The classification module is used to classify the training and assessment of traffic police personnel based on different dimensions and obtain classification results;

[0087] The training plan generation module is connected with the category classification module and is used to customize personalized training plans for different categories of traffic police officers according to the classification results;

[0088] The assessment system construction module is connected with the training plan generation module and is used to construct a multi-dimensional assessment system according to the training plan, and to assess the traffic police personnel to be assessed through the multi-dimensional assessment system to obtain the assessment results;

[0089] The data analysis and optimization module is connected to the assessment system construction module and is used to construct a recurrent neural network model, input training data and assessment data into the recurrent neural network model, analyze learning progress and assessment results, and automatically adjust training content and assessment standards.

[0090] Furthermore, the dimensions include police length of service, position, nature, performance and responsibilities, and rank. The ranks include junior, intermediate or senior ranks, among which junior-level traffic police focus on traffic regulations and basic command skills training; intermediate-level traffic police receive traffic flow organization and accident handling training; and senior-level traffic police receive traffic planning and team management training. The assessment includes theoretical tests, simulated command, on-site practice and work performance evaluation, and the assessment focuses on different levels.

[0091] Furthermore, by combining theoretical knowledge, practical skills and work performance, a targeted, systematic and customized practical training course system is constructed according to the training plan.

[0092] The multi-dimensional assessment system includes but is not limited to the study of laws and regulations, basic requirements for on-duty law enforcement, standardized law enforcement procedures, flexible law enforcement, reasoning law enforcement, what auxiliary police officers must learn, key points for investigating and handling key violations, police team operations, use of on-duty law enforcement equipment, response and disposal of obstructions to the performance of duties, safety protection at on-duty law enforcement sites, and guidance and response to public opinion involving the police.

[0093] Furthermore, training data and assessment data include training participation, assessment results, trainee feedback, behavioral data, and work performance;

[0094] Among them, training participation is used to record the frequency, duration and completion of traffic police training;

[0095] Assessment results are used to store the scores, rankings, and feedback of each assessment;

[0096] Trainee feedback is used to collect traffic police’s evaluation of the training content, methods and effectiveness;

[0097] Behavioral data is used to analyze the behavior patterns of traffic police in simulation training and practical assessment;

[0098] Job performance is used to track traffic police performance, including enforcement accuracy, efficiency, and public satisfaction.

[0099] The training data and assessment data are extracted by combining the recurrent neural network model with the principal component analysis method, and the extracted data features are analyzed in multiple dimensions to identify training needs, effect evaluation, and behavior patterns.

[0100] Intelligently and automatically optimize personalized training plans, dynamic assessment standards, and teaching method improvements based on training needs, effect evaluation, and behavior patterns;

[0101] Through adaptive learning technology, we provide instant feedback during the training process to help traffic police understand their progress and shortcomings in a timely manner. We recommend additional resources and advanced courses based on their learning progress and interests;

[0102] As traffic regulations are updated, the training content is adjusted in a timely manner through continuous iteration, and the training and assessment methods are updated in combination with the latest traffic management and law enforcement technologies.

[0103] Among them, training needs are used to determine the training needs of traffic police based on their assessment scores and work performance.

[0104] Effectiveness evaluation is used to assess the effectiveness of training content and methods and identify strengths and weaknesses.

[0105] Behavioral models are used to analyze traffic police’s behavioral data to understand their reactions and decision-making processes in specific situations.

[0106] Personalized training programs are used to adjust the training content and progress according to the individual needs and performance of traffic police officers.

[0107] Dynamic assessment standards are used to update assessment standards to better reflect actual work needs and job grade characteristics.

[0108] Teaching Method Improvement is used to improve teaching methods and tools based on learner feedback and behavior analysis.

[0109] The present invention can provide customized training and assessment plans for traffic police of different ranks, effectively improve the professional ability and work performance of traffic police, and at the same time provide traffic management departments with more accurate talent training strategies.

[0110] The present invention can meet the actual needs of traffic police at different ranks through personalized training plans, thereby improving the pertinence and effectiveness of training.

[0111] The present invention can comprehensively evaluate the ability and performance of traffic police through a multi-dimensional assessment system, enhance the comprehensiveness and fairness of the assessment, provide a platform for continuous learning and development for traffic police, and promote the professional growth of traffic police.

[0112] The present invention improves the efficiency of training and assessment through intelligent data analysis, and achieves a more reasonable allocation of educational resources.

[0113] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A training and assessment method based on different job levels, characterized in that: include: Classify the training and assessment of traffic police personnel based on different dimensions and obtain classification results; Customizing individualized training plans for different categories of traffic police officers based on the classification results; Construct a multi-dimensional assessment system according to the training plan, assess the traffic police personnel to be assessed through the multi-dimensional assessment system, and obtain the assessment results; Construct a recurrent neural network model, input training data and assessment data into the recurrent neural network model, analyze the learning progress and the assessment results, and automatically adjust the training content and assessment standards.

2. The training and assessment method based on different job levels according to claim 1 is characterized in that: The process of classifying the training and assessment of traffic police personnel based on different dimensions and obtaining the classification results includes: Traffic police personnel who are to be trained and assessed are classified according to their length of service, positions, nature, performance, responsibilities and ranks to obtain classification results.

3. The training and assessment method based on different job levels according to claim 1 is characterized in that: The process of building a multi-dimensional assessment system based on the training plan includes: Combining theoretical knowledge, practical skills and work performance, a targeted, systematic and customized practical training course system is constructed according to the training plan.

4. The training and assessment method based on different job levels according to claim 3 is characterized in that: The practical training course system includes but is not limited to the study of laws and regulations, basic requirements for on-duty law enforcement, standardized law enforcement procedures, flexible law enforcement, reasoning law enforcement, must-learn for auxiliary police, key points for investigating and handling key violations, police team operations, use of on-duty law enforcement equipment, response and disposal of obstructions in the performance of duties, on-duty law enforcement scene safety protection, and guidance and response to public opinion involving the police.

5. The training and assessment method based on different job levels according to claim 1 is characterized in that: The training data and assessment data include training participation, assessment results, trainee feedback, behavioral data, and work performance; The training participation rate is used to record the frequency, duration and completion of the traffic police's training participation; The assessment results are used to store the scores, rankings and feedback of each assessment; The trainee feedback is used to collect traffic police's evaluation of the training content, methods and effects; The behavioral data is used to analyze the behavioral patterns of traffic police in simulation training and practical assessment; The job performance is used to track the work performance of traffic police, including enforcement accuracy, efficiency and public satisfaction.

6. The training and assessment method based on different job levels according to claim 1 is characterized in that: The process of inputting the training data and the assessment data into the recurrent neural network model, analyzing the learning progress and the assessment results, and automatically adjusting the training content and the assessment standards includes: The training data and assessment data are subjected to feature extraction by combining a recurrent neural network model with a principal component analysis method, and the extracted data features are subjected to a multi-dimensional analysis to identify training needs, effect evaluation, and behavior patterns; Intelligently and automatically optimize personalized training plans, dynamic assessment standards, and teaching method improvements based on the training needs, effect evaluation, and behavior patterns; Through adaptive learning technology, we provide instant feedback during the training process and recommend relevant additional resources and advanced courses based on learning progress and interests; As traffic regulations are updated, the training content is adjusted in a timely manner through continuous iteration, and the training and assessment methods are updated in combination with the latest traffic management and law enforcement technologies.

7. A training and assessment system based on different job levels, characterized in that: The classification module is used to classify the training and assessment of traffic police personnel based on different dimensions and obtain classification results; A training plan generation module, connected to the category classification module, is used to customize personalized training plans for different categories of traffic police personnel according to the classification results; An assessment system construction module, connected to the training plan generation module, is used to construct a multi-dimensional assessment system according to the training plan, and to assess the traffic police personnel to be assessed through the multi-dimensional assessment system to obtain the assessment results; The data analysis and optimization module is connected to the assessment system construction module, and is used to construct a recurrent neural network model, input the training data and the assessment data into the recurrent neural network model, analyze the learning progress and the assessment results, and automatically adjust the training content and assessment standards.

8. The training and assessment system based on different job levels according to claim 7 is characterized in that: The dimensions include length of service, position, nature, performance and responsibilities, and rank.

9. The training and assessment system based on different job levels according to claim 7 is characterized in that: The multi-dimensional assessment system includes but is not limited to the study of laws and regulations, basic requirements for on-duty law enforcement, standardized law enforcement procedures, flexible law enforcement, reasoning law enforcement, what auxiliary police officers must learn, key points for investigating and handling key violations, police team operations, use of on-duty law enforcement equipment, response and disposal of obstructions to the performance of duties, safety protection at on-duty law enforcement sites, and guidance and response to public opinion involving the police.

10. The training and assessment system based on different job levels according to claim 7 is characterized in that: The training data and assessment data include training participation, assessment results, trainee feedback, behavioral data, and work performance; The training participation rate is used to record the frequency, duration and completion of the traffic police's training participation; The assessment results are used to store the scores, rankings and feedback of each assessment; The trainee feedback is used to collect traffic police's evaluation of the training content, methods and effects; The behavioral data is used to analyze the behavioral patterns of traffic police in simulation training and practical assessment; The job performance is used to track the work performance of traffic police, including enforcement accuracy, efficiency and public satisfaction.

Images