Video training method and system in offline environment

Abstract

The video training method in the offline environment comprises the steps that after login information of a student end is received, whether the login information meets a preset requirement or not is verified; when the login information meets a preset requirement, creating a session for the student terminal, and distributing a session identifier; different access permissions are distributed to the student side; customizing a learning plan for the student end, and querying a training progress; recommending corresponding training content and answer point information; in a networking state, pre-downloading training content and answer point information to local storage; when detecting that the network is disconnected, automatically switching to an offline mode, and playing the pre-downloaded training content and answer point information; when the training content and the answer point information are played, the answer result and the offline training progress are stored in the ship end; when the network is recovered, synchronously storing the offline training progress and the answering result at the shore end based on the session identifier; and identifying knowledge weak points and learning preferences, and re-customizing a learning plan. The method has the effect of greatly expanding the training time and range.

Description

Technical Field

[0001] The present application relates to the technical field of information technology, and in particular to a video training method and system in an offline environment. Background Art

[0002] Currently, crew training usually relies on the Internet, which involves watching training videos online, completing online questions and recording learning progress. However, this Internet-dependent training model encounters challenges when crew members are sailing on the ocean, because during this period, they often cannot access the network, which can easily lead to interruptions in training activities and failure to proceed normally, resulting in poor training results. Therefore, it needs to be improved. Summary of the invention

[0003] In order to solve the above problems, the present application provides a video training method and system in an offline environment.

[0004] The first object of the invention of this application is achieved through the following technical solutions:

[0005] A video training method in an offline environment, characterized by comprising:

[0006] After receiving the login information from the student, verify whether the login information meets the preset requirements;

[0007] When the login information meets the preset requirements, a session is created for the student and a session identifier is assigned;

[0008] Based on the login information of the student terminal, different access rights are assigned to the student terminal;

[0009] Based on access rights, customize learning plans for students and check training progress;

[0010] Recommend corresponding training content and answer point information based on training progress and learning plan;

[0011] When connected to the Internet, pre-download training content and answer point information to local storage;

[0012] When a network disconnection is detected, it automatically switches to offline mode to play the pre-downloaded training content and answer point information;

[0013] When the training content and answer point information are played, the answer results and offline training progress are stored on the ship;

[0014] When the network is restored, the offline training progress and answer results are synchronously stored on the shore side based on the session identifier;

[0015] Based on preset algorithms, it identifies knowledge weaknesses and learning preferences and re-customizes learning plans.

[0016] In a preferred embodiment, the step of allocating different access rights to the student terminal based on the login information of the student terminal comprises the steps of:

[0017] Extract keywords based on the student's login information, including user name and department;

[0018] Match keywords with preset permission rules;

[0019] Based on the matching results, corresponding access rights are assigned to the student end;

[0020] The access rights include, from low to high, training rights, training management rights, and system management rights, and the corresponding roles are, respectively, trainer, training manager, and system manager.

[0021] In a preferred embodiment, the steps of customizing a learning plan for a trainee based on access rights and querying training progress include the following steps:

[0022] Generate a preliminary learning plan for trainers;

[0023] Match the training needs of trainers from the preset offline database;

[0024] Based on training needs, training managers adjust the preliminary learning plan, and system managers review and finalize the final learning plan;

[0025] The learning plan includes training courses and learning schedule.

[0026] In a preferred embodiment, the step of pre-downloading the training content and the answer point information to local storage in a networked state includes the following steps:

[0027] When connected to the Internet, pre-download training content and related interactive questions to local storage;

[0028] The interactive questions include multiple choice questions, fill-in-the-blank questions, and true-or-false questions;

[0029] A mapping point is set for each interactive question, and the mapping point corresponds to a specific time of the training content and is stored locally.

[0030] In a preferred embodiment, when the network is detected to be disconnected, the system automatically switches to offline mode, plays the pre-downloaded training content and answer point information, and then:

[0031] Based on the playback progress and the preset mapping point, when the mapping point is reached, the playback of the training content is paused and an interactive question associated with the mapping point is popped up;

[0032] The preset mapping points include the time points of key knowledge points, the time points at which skill demonstrations end, and the time points at which theoretical explanations end;

[0033] When the trainer answers the interactive questions correctly, the following actions are performed:

[0034] Continue playing the training content to the next mapping point;

[0035] When a trainer answers an interactive question incorrectly, the following actions are performed:

[0036] Provide immediate feedback and explain the correct answer;

[0037] Jump to a review session and revisit the relevant knowledge points;

[0038] Repeat the content before the current mapping point, study again and answer interactive questions;

[0039] When a trainee answers interactive questions incorrectly for multiple times in a row, feedback is provided to the training manager and system manager to customize the learning plan.

[0040] In a preferred embodiment, the step of identifying knowledge weaknesses and learning preferences based on a preset algorithm and re-customizing the learning plan includes the following steps:

[0041] Based on the preset algorithm, extract the answer result information and the time information of watching the training content from the offline training progress and answer results;

[0042] Analyze answer result information and time spent watching training content to identify knowledge weaknesses and learning preferences;

[0043] The answer result information includes the accuracy rate and the answer speed;

[0044] The time information of watching the training content includes the watching time and the number of pauses;

[0045] The weak points of knowledge include questions with a correctness rate lower than a first preset threshold and questions with a speed of answering questions lower than a second preset threshold;

[0046] The learning preferences include questions where the viewing time is greater than a third preset threshold, and questions where the number of pauses is greater than a fourth preset threshold;

[0047] Based on learning preferences and knowledge weaknesses, feedback is provided to training managers and system managers to re-customize the learning plan.

[0048] The above-mentioned second objective of the present application is achieved through the following technical solutions:

[0049] A video training system in an offline environment, comprising:

[0050] Receiving module: after receiving the login information from the student, verify whether the login information meets the preset requirements;

[0051] Create session module: When the login information meets the preset requirements, a session is created for the student and a session identifier is assigned;

[0052] Allocation module: assign different access rights to students based on their login information;

[0053] Planning module: Based on access rights, customize learning plans for students and check training progress;

[0054] Recommendation module: Based on the training progress and learning plan, recommend corresponding training content and answer point information;

[0055] Pre-download module: when connected to the Internet, pre-download training content and answer point information to local storage;

[0056] Offline playback module: When the network is disconnected, it automatically switches to offline mode to play the pre-downloaded training content and answer point information;

[0057] Offline storage module: When the training content and answer point information are played, the answer results and offline training progress are stored on the ship;

[0058] Online storage module: When the network is restored, the offline training progress and answer results are synchronously stored on the shore side based on the session identifier;

[0059] Identification module: Based on preset algorithms, it identifies knowledge weaknesses and learning preferences and re-customizes the learning plan.

[0060] The third objective of the present application is achieved through the following technical solutions:

[0061] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned video training method in an offline environment are implemented.

[0062] The fourth objective of the present application is achieved through the following technical solutions:

[0063] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned video training method in an offline environment are implemented.

[0064] In summary, the present application includes at least one of the following beneficial technical effects:

[0065] 1. This application ensures the security of the system and the legitimacy of the user's identity by verifying the student's login information and creating a session for it. This step not only prevents unauthorized access, but also provides protection for basic user management. Secondly, by assigning different access rights to students, the system achieves refined management of resources, allowing each student to access training content that is consistent with their responsibilities, thereby improving the relevance and practicality of the training.

[0066] 2. This application provides students with a personalized learning path through customized learning plans and query training progress, which not only improves learning efficiency but also enhances students' learning motivation. Recommending corresponding training content and answer point information further ensures that students can conduct targeted learning based on their weaknesses.

[0067] 3. The pre-download function of this application enables students to continue learning when the network is unstable or unavailable, greatly improving the flexibility and continuity of training. The ability to automatically switch to offline mode ensures that training will not be interrupted due to network problems, which is particularly important for crew training at sea or in other remote environments.

[0068] 4. When the training is completed, the answer results and offline training progress are stored on the ship and synchronized to the shore when the network is restored. This mechanism ensures the integrity and real-time nature of the data, allowing training managers to understand the trainees' learning status and progress in a timely manner.

[0069] 5. Based on the preset algorithm, the learning plan is re-customized by identifying knowledge weaknesses and learning preferences. This intelligent feedback and adjustment mechanism can help students master knowledge points more effectively and improve the overall effect of training. This ability to dynamically adjust the learning plan not only improves the level of personalization of training, but also provides data support for the continuous improvement of training content and methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a flowchart of an implementation of a video training method embodiment in an offline environment of the present application;

[0071] Figure 2 This is a flowchart for implementing step S30 in an embodiment of a video training method in an offline environment of the present application;

[0072] Figure 3 This is a first implementation flow chart of step S40 in an embodiment of a video training method in an offline environment of the present application;

[0073] Figure 4 This is a flowchart for implementing step S60 in an embodiment of a video training method in an offline environment of the present application;

[0074] Figure 5 This is a flowchart of an implementation after step S70 in an embodiment of a video training method in an offline environment of the present application;

[0075] Figure 6 This is a flowchart for implementing step S100 in an embodiment of a video training method in an offline environment of the present application;

[0076] Figure 7 This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0077] The following is combined with Figure 1-7 This application is described in further detail.

[0078] In one embodiment, if Figure 1 As shown, the present application discloses a video training method in an offline environment, which specifically includes the following:

[0079] S10: After receiving the login information of the student terminal, verify whether the login information meets the preset requirements;

[0080] In this embodiment, after receiving the login information from the student, a series of verification steps will be performed to ensure that the information meets the preset security and format requirements.

[0081] S20: When the login information meets the preset requirements, a session is created for the student terminal and a session identifier is assigned;

[0082] In this embodiment, once the student's login information is verified (as described in step S10), the following operations will be performed to establish a session for the student: Create a new session for the student, which is a server-side process used to track and identify the student's active status and operations. As the session is created, a unique session identifier (usually a randomly generated string) will be assigned to the session. This session identifier will be used in all subsequent interactions to identify a specific student session.

[0083] S30: allocating different access rights to the student terminal based on the student terminal's login information;

[0084] In this embodiment, different access rights are assigned according to the student's login information.

[0085] In an offline environment, the allocation of access rights also determines which resources trainees can access without an internet connection. This is critical for crew members working at sea or in other remote locations where internet connections may be unstable or unavailable. In this way, the S30 step not only ensures the security of training content, but also ensures that trainees have access to learning resources that match their responsibilities and training needs even in an offline environment.

[0086] S40: Based on access rights, customize learning plans for students and query training progress;

[0087] In this embodiment, the core of step S40 is to use the trainee's access rights to tailor a learning plan for him / her and track his / her training progress, which is particularly important in an offline environment. The following is a specific explanation:

[0088] In the offline environment of crew training, due to the instability of network connection, the trainees’ learning plan needs to be customized in advance according to their access rights to ensure that even when offline, the trainees can learn according to the established training path.

[0089] Based on the access rights assigned in step S30, a personalized learning plan is developed for the trainee in combination with the trainee's position, responsibilities, training history and future training needs. At the same time, the trainee's current training progress will be queried, including completed courses, ongoing courses and courses that have not yet started. This information is critical to ensure that trainees do not repeat what they have already mastered or skip necessary training steps. The customized learning plan will guide trainees on what content they should learn offline, as well as the order and pace of learning. This helps trainees maximize their learning effects with limited resources. In an offline environment, this customized learning plan can also help trainees use their time effectively and ensure that they can receive uninterrupted training and education during offshore operations. Through step S40, the crew training system can provide trainees with a structured and personalized learning experience in an offline environment, thereby ensuring that the crew's professional skills and knowledge level can meet the requirements of offshore work.

[0090] S50: Based on the training progress and learning plan, recommend corresponding training content and answer point information;

[0091] In this embodiment, the purpose of step S50 is to recommend the most appropriate training content and answer point information to the trainee according to the trainee's training progress and learning plan, so as to ensure that the trainee can receive efficient and relevant training even in an offline state. The following is a detailed explanation:

[0092] In an offline environment, it is particularly important to recommend appropriate training content to trainees in advance because it is impossible to obtain content from the server in real time. The system will determine the courses and exercises that trainees need to learn next based on the customized learning plan and the queried training progress in step S40. The recommended training content may include video tutorials, interactive simulations, case studies, etc., which are carefully selected according to the trainees' learning goals and actual work needs. The answer point information refers to the questions or knowledge points related to the training content, which helps trainees consolidate their learning results and verify their knowledge through tests. In the recommendation process, the system will consider the trainees' learning speed, comprehension ability and previous answering performance to ensure that the recommended content is neither too simple nor too difficult, but just meets the trainees' learning needs. In this way, even in an offline state, trainees can follow a continuous and orderly learning path for training, and will not interrupt the learning process or receive inappropriate training content due to network problems. Step S50 plays a bridging role in the offline environment of crew training. It ensures that trainees can still receive targeted training without network connection, thereby improving the efficiency and effectiveness of training.

[0093] S60: In an online state, pre-download training content and answer point information to local storage;

[0094] In this embodiment, step S60 is a key preparation step to ensure that the crew can continue to receive training in an offline state. The following is a detailed explanation:

[0095] When connected to the Internet, the system will pre-download the training content and answer point information required by the trainees according to their learning plan and progress to local storage. This is done to ensure that once the network is disconnected, the trainees can still access these training resources. The pre-downloaded content may include teaching videos and documents, which are carefully selected according to the trainees' learning needs to ensure the continuity and effectiveness of the training. By pre-downloading these resources when connected to the Internet, the system provides a "buffer" for the trainees so that they can continue to learn even in unstable network environments such as at sea or in remote areas. Local storage can be a server, personal computer or other storage device on board, as long as it ensures that the trainees can access these resources when offline. The importance of the S60 step is that it provides the crew with a reliable offline learning solution, which is essential to ensure the continuous updating of the crew's professional knowledge and skills, especially when performing long-term offshore missions. Through the S60 step, the crew training system can maintain the continuity of training in an offline environment, ensuring that the crew can receive the necessary education and training in any situation.

[0096] S70: When the network is disconnected, the system automatically switches to offline mode and plays the pre-downloaded training content and answer point information;

[0097] In this embodiment, step S70 is designed to ensure that crew training can continue seamlessly when network connection is unavailable. The following is a detailed explanation:

[0098] During crew training, network connection may be interrupted for a variety of reasons (such as maritime communication restrictions, equipment failure, etc.). Step S70 automatically detects the network status, and once the network is disconnected, the system will immediately switch to offline mode. After switching to offline mode, the system will start playing the training content and answer point information pre-downloaded to local storage. These contents are pre-downloaded in step S60 to ensure that trainees can access the training materials even when offline. The activation of offline mode ensures that crew members will not interrupt their learning due to network problems, which is crucial to maintaining the continuity and integrity of training, especially in professional fields that require continuous learning. In offline mode, trainees can continue to watch teaching videos and read training documents, and these activities will not be affected by the network status. The design of step S70 reflects the consideration of the special environment of crew training, ensuring that training activities can be carried out smoothly under various network conditions, thereby ensuring the professional skills and knowledge level of crew members. Through step S70, the crew training system can adapt to the offline environment and provide crew members with a stable and reliable learning platform, which is of great significance to improving the overall quality of crew members and their ability to respond to emergencies.

[0099] S80: When the training content and answer point information are played, the answer results and offline training progress are stored on the ship;

[0100] In this embodiment, step S80 is to ensure that the learning achievements and progress of the students are properly recorded and saved in an offline state. The following is a detailed explanation:

[0101] In the offline environment of crew training, due to the instability or complete disconnection of the network, trainees cannot upload their learning data to the central server in real time. Therefore, the S80 step ensures that these valuable data will not be lost due to network problems by saving the trainees' answer results and training progress after completing the training in the local system on the ship.

[0102] This step is critical for tracking students’ learning history, evaluating learning outcomes, and synchronizing data when the network is reconnected in the future. It allows training administrators or students themselves to review and analyze their performance during offline learning after the network is restored.

[0103] By storing this data onboard, the system is also able to provide trainees with a continuous learning experience, so that even if users complete training content offline, their efforts and achievements can be recognized.

[0104] The S80 steps take into account the special needs of crew training, ensuring that crew members can maintain continuous improvement of their professional knowledge and skills even when offline, which is essential for safety and efficiency at sea.

[0105] In general, the S80 procedure provides crew members with a reliable learning record and progress management mechanism in an offline environment, ensuring the continuity and effectiveness of training.

[0106] S90: When the network is restored, the offline training progress and the answer results are synchronously stored on the shore side based on the session identifier;

[0107] In this embodiment, step S90 ensures that after the network is restored, the learning activities and achievements completed by the trainees in the offline mode can be accurately recorded and updated to the central database or the shore server. The following is a detailed explanation:

[0108] During crew training, due to the particularity of offshore operations, network connections may be frequently interrupted or unavailable for a long time. Step S90 allows the system to synchronize the learning progress and answer results completed by the trainees in an offline state to the shore server when the network is reconnected.

[0109] By using session identifiers, the system can accurately match offline learning data with the corresponding student accounts to ensure the integrity and accuracy of the data, which is crucial for maintaining students' learning records and conducting subsequent learning analysis.

[0110] The synchronization process enables training managers to monitor the learning status of trainees in real time, evaluate the training effect, and adjust the training plan as needed. At the same time, trainees can also receive the latest learning materials and guidance.

[0111] The S90 step also means that even in an offline environment, students' learning activities will not be wasted, and their efforts and achievements can be formally recorded and recognized after the network is restored.

[0112] In the offline environment of crew training, the S90 steps are the bridge connecting offline learning and online management. It ensures the continuity and efficiency of the training system, while also providing trainees with a more flexible and reliable learning experience.

[0113] S100: Based on preset algorithms, it identifies knowledge weaknesses and learning preferences and re-customizes learning plans.

[0114] In this embodiment, step S100 is a key step in the entire training process, which uses data analysis to optimize the crew's learning experience and improve the training effect. The following is a detailed explanation:

[0115] In an offline environment, the crew's learning activities and answer results are recorded and synchronized to the shore server in step S90. Step S100 analyzes this data and uses a preset algorithm to identify the crew's knowledge weaknesses and learning preferences. Knowledge weaknesses are identified by analyzing the types and frequency of errors made by the crew in the process of answering questions, so that additional training and review materials can be provided to help the crew strengthen their learning in these areas. The identification of learning preferences takes into account factors such as the crew's learning habits, interests, and learning efficiency, and the system adjusts the learning plan accordingly to make it more suitable for the crew's personal needs. In the offline environment of crew training, the importance of step S100 is that it ensures that the training content can be personalized according to the crew's learning situation even when the network is limited, thereby improving the relevance and effectiveness of the training. By re-customizing the learning plan, the system can help the crew master the necessary knowledge and skills more efficiently, which is of great significance to ensure the safety of offshore operations and improve the professional quality of the crew. In summary, the S100 steps act as a bridge in the offline environment of crew training, converting crew learning data into actual training actions, ensuring the maximum utilization of training resources and meeting the crew's personal development needs.

[0116] Figure 2 , step S30, comprising the steps of:

[0117] S301: extracting keywords based on the login information of the student terminal, the keywords including user name and department;

[0118] S302: Match the keyword with the preset permission rules;

[0119] S303: Based on the matching result, corresponding access rights are allocated to the student terminal;

[0120] S304: The access rights include, from low to high, training rights, training management rights, and system management rights, and the corresponding roles are, respectively, trainer, training manager, and system manager.

[0121] In this embodiment, these steps together constitute a permission allocation mechanism for determining the access level of the trainee in the training system based on the trainee's login information. The following is a detailed explanation:

[0122] In step S301, the system extracts key information from the student's login information, such as user name and department. This information is usually related to the student's identity and position in the organization, and is the basis for determining their access rights.

[0123] In step S302, the extracted keywords are matched with the permission rules preset by the system. These rules define the correspondence between different user names or departments and specific access rights. For example, all members of a department may be granted a specific permission level.

[0124] In step S303, according to the matching results of keywords and permission rules, the system will assign corresponding access rights to the students. This process is automated, ensuring the consistency and accuracy of permission allocation.

[0125] Step S304 details the hierarchy of access rights, from low to high, including training rights, training management rights, and system management rights. These rights correspond to different roles. Trainers can usually only access training content, training managers can manage training materials and progress, and system managers have the highest rights and can manage system settings and user rights.

[0126] This permission allocation mechanism ensures that trainees can only access authorized content and functions in the training system, thereby protecting the security of the system and improving the efficiency of training management. In this way, organizations can customize the training experience of trainees according to their roles and responsibilities, ensuring the rational allocation and utilization of resources.

[0127] Figure 3 , step S40, comprising the steps of:

[0128] S401: Generate a preliminary learning plan for the trainer;

[0129] S402: Matching the training needs of the trainees from a preset offline database;

[0130] S403: Based on the training needs, the training manager adjusts the preliminary learning plan, and the system manager reviews and determines the final learning plan;

[0131] S404: The learning plan includes training courses and a learning schedule.

[0132] In this embodiment, these steps describe the entire process of creating and customizing a learning plan for a trainee, which is particularly suitable for training management in an offline environment. The following is a detailed explanation:

[0133] In step S401, the system automatically generates a preliminary learning plan for each trainee, which may be based on a common training template or the trainee's default requirements.

[0134] In step S402, the system retrieves and matches the specific training needs of the trainees from a preset offline database. The offline database contains information such as training courses, skill requirements, and job standards, which are used to determine which specific training content the trainees need.

[0135] In step S403, the training manager will adjust the preliminary learning plan based on the training needs extracted from the offline database. This may include adding or deleting certain courses, adjusting the learning sequence, etc. The adjusted learning plan is then reviewed by the system administrator to ensure that the plan meets the organizational standards and training objectives, and then the final learning plan is determined.

[0136] Step S404 defines the content of the learning plan, including specific training courses and learning schedules. These courses and schedules are customized according to the actual needs and work arrangements of the trainees, aiming to provide a structured and orderly learning path.

[0137] This learning plan formulation process ensures that even in an offline environment, trainees can obtain learning content that meets their personal and job needs. Through the collaboration of training managers and system managers, the efficiency and practicality of the learning plan can be ensured, thereby improving the training effect.

[0138] Figure 4 , step S60, comprising the steps of:

[0139] S601: When connected to the Internet, pre-download training content and related interactive questions to local storage;

[0140] S602: The interactive questions include multiple choice questions, fill-in-the-blank questions, and true-or-false questions;

[0141] S603: A mapping point is set for each interactive question, wherein the mapping point corresponds to a specific time of the training content and is stored locally.

[0142] In this embodiment, these steps describe how to prepare for offline training while connected to the Internet, ensuring that trainees can learn effectively even when they are not connected to the Internet. The following is a detailed explanation:

[0143] In step S601, the system takes advantage of the network connection to pre-download the training content and its related interactive questions to the local storage device. This is to ensure that the trainees can still access these resources in an offline state and will not interrupt their learning due to network problems.

[0144] Step S602 defines the types of interactive questions, including multiple choice questions, fill-in-the-blank questions, and true-or-false questions. These questions are designed to increase the interactivity and participation of the training and help trainees better understand and master the training content.

[0145] In step S603, the system sets a mapping point for each interactive question, and this mapping point corresponds to the specific time of the training content. For example, if an interactive question is asked at 5 minutes into the training video, the mapping point will correspond to the 5-minute timestamp of the video. Such a mapping relationship is stored locally so that when the training content is played offline, the system can trigger the interactive question at the right time.

[0146] This method of pre-downloading and mapping points ensures the availability and interactivity of training content in an offline environment, allowing trainees to conduct effective self-study and assessment even when the network is unavailable. This method is particularly suitable for crew training at sea or other remote locations where stable network connections may not be guaranteed.

[0147] Figure 5 , step S70, and subsequent steps:

[0148] S71: based on the playback progress and the preset mapping point, when the mapping point is reached, the playback of the training content is paused, and an interactive question associated with the mapping point is popped up;

[0149] S72: The preset mapping points include the time points of key knowledge points, the time points at which skill demonstration ends, and the time points at which theoretical explanation ends;

[0150] S73: When the trainee answers the interactive question correctly, the following actions are performed:

[0151] S74: Continue playing the training content to the next mapping point;

[0152] S75: When a trainer answers an interactive question incorrectly, the following actions are performed:

[0153] S76: Provide immediate feedback and explain the correct answer;

[0154] S77: Jump to a review session and re-explain the relevant knowledge points;

[0155] S78: Repeat the content before the current mapping point, study again and answer interactive questions;

[0156] S79: When a trainee answers interactive questions incorrectly for multiple times in a row, feedback is provided to the training manager and system manager to customize the learning plan.

[0157] In this embodiment, these steps constitute an interactive learning feedback mechanism, which is designed to improve training effectiveness and trainee participation. The following is a detailed explanation:

[0158] In step S71, the system pauses the playback when reaching a key time point according to the playback progress of the training content and the preset mapping point, and pops up relevant interactive questions. This design can ensure that trainees can be consolidated and tested on key knowledge points in a timely manner.

[0159] Step S72 describes the types of mapping points, including the end time points of key knowledge points, skill demonstrations, and theoretical explanations. These time points are set based on the importance and complexity of the training content.

[0160] Steps S73 to S78 describe different feedback mechanisms after students answer interactive questions:

[0161] When the trainee answers correctly (S73), the system will continue to play the training content to the next mapping point (S74).

[0162] When the learner answers incorrectly (S75), the system will provide immediate feedback and explanation of the correct answer (S76), and then jump to the review session (S77), or repeat the content before the current mapping point, allowing the learner to study and answer questions again (S78).

[0163] Step S79 provides an additional support mechanism. When a trainee gives incorrect answers multiple times in a row, the system will provide feedback to the training administrator and system administrator so that they can intervene and re-customize the learning plan to better meet the trainee's learning needs.

[0164] This interactive learning feedback mechanism not only enhances the interactivity and effectiveness of learning, but also dynamically adjusts the learning plan based on the performance of trainees, thereby providing a more personalized and effective training experience.

[0165] Figure 6 , step S100, comprising the steps of:

[0166] SA1: Based on the preset algorithm, extract the answer result information and the time information of watching the training content from the offline training progress and answer results;

[0167] SA2: Analyze the answer result information and the time information of watching the training content to identify knowledge weaknesses and learning preferences;

[0168] SA3: The answer result information includes the accuracy rate and answer speed;

[0169] SA4: The time information of watching the training content includes the watching time and the number of pauses;

[0170] SA5: The knowledge weaknesses include questions with a correctness rate lower than a first preset threshold and questions with a speed of answering questions lower than a second preset threshold;

[0171] SA6: The learning preference includes the problem that the viewing time is greater than the third preset threshold, and the problem that the number of pauses is greater than the fourth preset threshold;

[0172] SA7: Based on learning preferences and knowledge weaknesses, provide feedback to training managers and system managers to re-customize the learning plan.

[0173] In this embodiment, steps SA1 to SA7 describe an automated data analysis process that aims to optimize and personalize the training plan by analyzing the crew's offline training data. The following is a detailed explanation of this process:

[0174] SA1: The system uses a preset algorithm to extract key data from the crew's training progress and answer results completed offline. This data includes the crew's answer results (such as correct or not, answer time) and their behavior data for watching training videos (such as viewing time, number of pauses). SA2: The system then conducts in-depth analysis on these extracted data in order to identify the crew's knowledge weaknesses and learning preferences during the learning process. Knowledge weaknesses refer to the crew's lack of mastery of specific knowledge points, while learning preferences reveal the crew's interest in certain content or learning habits. SA3: The answer result information specifically includes the accuracy rate and answer speed. The accuracy rate refers to the percentage of questions answered correctly by the crew, and the answer speed refers to the time it takes the crew to answer questions. SA4: The time information for watching training content involves viewing time and number of pauses. Viewing time records the total time the crew watches each training video, while the number of pauses reflects the frequency of the crew's pauses during the viewing process. SA5: The identification of knowledge weaknesses is based on the comparison of the crew's performance on specific questions with the preset threshold. If the crew's correct rate on a certain question is lower than the first preset threshold, or the answering speed is slower than the second preset threshold, then this question is considered a knowledge weakness. SA6: Learning preferences are identified by comparing the crew's viewing time and number of pauses for a specific question with the third and fourth preset thresholds. When the crew's viewing time for a certain question is greater than the third preset threshold, or the number of pauses is greater than the fourth preset threshold, this may indicate that the crew has a higher interest in the question or needs more time to understand. SA7: Finally, the system feeds back the analyzed learning preferences and knowledge weaknesses to training managers and system managers. These managers can use this information to re-customize the learning plan to ensure that the training content is more in line with the personal needs of the crew, thereby improving the relevance and effectiveness of the training. This data analysis process provides a more personalized and efficient learning experience for the crew, helping to improve the overall quality of training and the professional skills of the crew.

[0175] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0176] In one embodiment, a video training system in an offline environment is provided, and the video training system in an offline environment corresponds to the video training method in an offline environment in the above embodiment. The video training system in an offline environment includes:

[0177] Receiving module: after receiving the login information from the student, verify whether the login information meets the preset requirements;

[0178] Create session module: When the login information meets the preset requirements, a session is created for the student and a session identifier is assigned;

[0179] Allocation module: assign different access rights to students based on their login information;

[0180] Planning module: Based on access rights, customize learning plans for students and check training progress;

[0181] Recommendation module: Based on the training progress and learning plan, recommend corresponding training content and answer point information;

[0182] Pre-download module: when connected to the Internet, pre-download training content and answer point information to local storage;

[0183] Offline playback module: When the network is disconnected, it automatically switches to offline mode to play the pre-downloaded training content and answer point information;

[0184] Offline storage module: When the training content and answer point information are played, the answer results and offline training progress are stored on the ship;

[0185] Online storage module: When the network is restored, the offline training progress and answer results are synchronously stored on the shore side based on the session identifier;

[0186] Identification module: Based on preset algorithms, it identifies knowledge weaknesses and learning preferences and re-customizes the learning plan.

[0187] Optionally, also include:

[0188] Extract keyword module: extract keywords based on the student's login information, including user name and department;

[0189] The first matching module: matches the keywords with the preset permission rules;

[0190] Access rights allocation module: Based on the matching results, the corresponding access rights are allocated to the students.

[0191] Access permission module: The access permissions include training permission, training management permission, and system management permission from low to high, and the corresponding roles are trainer, training manager, and system manager.

[0192] Optionally, also include:

[0193] Preliminary learning plan module: generates a preliminary learning plan for trainers;

[0194] The second matching module: matches the training needs of trainees from the preset offline database;

[0195] Final learning plan module: Based on training needs, training managers adjust the preliminary learning plan, and system managers review and determine the final learning plan;

[0196] Study plan module: The study plan includes training courses and study schedule.

[0197] Optionally, also include:

[0198] Pre-download content module: when connected to the Internet, pre-download training content and related interactive questions to local storage;

[0199] Interactive question module: The interactive questions include multiple choice questions, fill-in-the-blank questions, and true-or-false questions;

[0200] Mapping point setting module: a mapping point is set for each interactive question. The mapping point corresponds to the specific time of the training content and is stored locally.

[0201] Optionally, also include:

[0202] Pause module: based on the playback progress and the preset mapping point, when the mapping point is reached, the playback of the training content is paused and the interactive questions associated with the mapping point are popped up;

[0203] Preset mapping point module: The preset mapping points include the time points of key knowledge points, the time points when skill demonstration ends, and the time points when theoretical explanation ends;

[0204] Correct Answer Module: When trainees answer the interactive questions correctly, the following actions are performed:

[0205] Continue playing module: continue playing the training content to the next mapping point;

[0206] Wrong Answer Module: When a trainer answers an interactive question incorrectly, the following actions are performed:

[0207] Explanation module: provides immediate feedback and explains the correct answer;

[0208] Jump module: Jump to a review session and re-explain the relevant knowledge points;

[0209] Repeat module: repeat the content before the current mapping point, study again and answer interactive questions;

[0210] Re-customize the module: When the trainee answers the interactive questions incorrectly for multiple times in a row, feedback is given to the training manager and system manager to re-customize the learning plan.

[0211] Optionally, also include:

[0212] Information extraction module: Based on the preset algorithm, it extracts the answer result information and the time information of watching the training content from the offline training progress and answer results;

[0213] Analysis module: Analyze the answer result information and the time information of watching training content to identify knowledge weaknesses and learning preferences;

[0214] Answer result information module: the answer result information includes the accuracy rate and answer speed;

[0215] Time information module: the time information of watching the training content includes the watching time and the number of pauses;

[0216] Knowledge point weakness module: the knowledge weakness includes questions with a correct rate lower than a first preset threshold and questions with a speed of answering lower than a second preset threshold;

[0217] Learning preference module: the learning preference includes the problem of viewing time being longer than a third preset threshold, the problem of the number of pauses being longer than a fourth preset threshold;

[0218] Feedback module: Based on learning preferences and knowledge weaknesses, feedback is provided to training managers and system managers to re-customize learning plans.

[0219] For the specific definition of a video training system in an offline environment, please refer to the definition of a video training method in an offline environment above, which will not be repeated here. Each module in the above-mentioned video training system in an offline environment can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0220] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store answer results. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it is a video training method in an offline environment.

[0221] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a video training method in an offline environment is implemented.

[0222] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a video training method in an offline environment is implemented.

[0223] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0224] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0225] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A video training method in an offline environment, characterized in that: include: After receiving the login information from the student, verify whether the login information meets the preset requirements; When the login information meets the preset requirements, a session is created for the student and a session identifier is assigned; Based on the login information of the student terminal, different access rights are assigned to the student terminal; Based on access rights, customize learning plans for students and check training progress; Recommend corresponding training content and answer point information based on training progress and learning plan; When connected to the Internet, pre-download training content and answer point information to local storage; When a network disconnection is detected, it automatically switches to offline mode to play the pre-downloaded training content and answer point information; When the training content and answer point information are played, the answer results and offline training progress are stored on the ship; When the network is restored, the offline training progress and answer results are synchronously stored on the shore side based on the session identifier; Based on preset algorithms, it identifies knowledge weaknesses and learning preferences and re-customizes learning plans.

2. The video training method in an offline environment according to claim 1, characterized in that: The step of allocating different access rights to the student terminal based on the login information of the student terminal comprises the steps of: Extract keywords based on the student's login information, including user name and department; Match keywords with preset permission rules; Based on the matching results, corresponding access rights are assigned to the student end; The access rights include, from low to high, training rights, training management rights, and system management rights, and the corresponding roles are, respectively, trainer, training manager, and system manager.

3. The video training method in an offline environment according to claim 1, characterized in that: The steps of customizing a learning plan for a trainee based on access rights and querying training progress include the following steps: Generate a preliminary learning plan for trainers; Match the training needs of trainers from the preset offline database; Based on training needs, training managers adjust the preliminary learning plan, and system managers review and finalize the final learning plan; The learning plan includes training courses and learning schedule.

4. The video training method in an offline environment according to claim 1, characterized in that: The step of pre-downloading the training content and the answer point information to local storage in the online state includes the following steps: When connected to the Internet, pre-download training content and related interactive questions to local storage; The interactive questions include multiple choice questions, fill-in-the-blank questions, and true-or-false questions; A mapping point is set for each interactive question, and the mapping point corresponds to a specific time of the training content and is stored locally.

5. The video training method in an offline environment according to claim 1, characterized in that: When the network is disconnected, the system automatically switches to offline mode and plays the pre-downloaded training content and answer point information. Then, the following steps are performed: Based on the playback progress and the preset mapping point, when the mapping point is reached, the playback of the training content is paused and an interactive question associated with the mapping point is popped up; The preset mapping points include the time points of key knowledge points, the time points at which skill demonstrations end, and the time points at which theoretical explanations end; When the trainer answers the interactive questions correctly, the following actions are performed: Continue playing the training content to the next mapping point; When a trainer answers an interactive question incorrectly, the following actions are performed: Provide immediate feedback and explain the correct answer; Jump to a review session and revisit the relevant knowledge points; Repeat the content before the current mapping point, study again and answer interactive questions; When a trainee answers interactive questions incorrectly for multiple times in a row, feedback is provided to the training manager and system manager to customize the learning plan.

6. The video training method in an offline environment according to claim 1, characterized in that: The steps of identifying knowledge weaknesses and learning preferences based on a preset algorithm and re-customizing the learning plan include the following steps: Based on the preset algorithm, extract the answer result information and the time information of watching the training content from the offline training progress and answer results; Analyze answer result information and time spent watching training content to identify knowledge weaknesses and learning preferences; The answer result information includes the accuracy rate and the answer speed; The time information of watching the training content includes the watching time and the number of pauses; The weak points of knowledge include questions with a correctness rate lower than a first preset threshold and questions with a speed of answering questions lower than a second preset threshold; The learning preferences include questions where the viewing time is greater than a third preset threshold, and questions where the number of pauses is greater than a fourth preset threshold; Based on learning preferences and knowledge weaknesses, feedback is provided to training managers and system managers to re-customize the learning plan.

7. A video training system in an offline environment, characterized in that: include: Receiving module: after receiving the login information from the student, verify whether the login information meets the preset requirements; Create session module: When the login information meets the preset requirements, a session is created for the student and a session identifier is assigned; Allocation module: assign different access rights to students based on their login information; Planning module: Based on access rights, customize learning plans for students and check training progress; Recommendation module: Based on the training progress and learning plan, recommend corresponding training content and answer point information; Pre-download module: when connected to the Internet, pre-download training content and answer point information to local storage; Offline playback module: When the network is disconnected, it automatically switches to offline mode to play the pre-downloaded training content and answer point information; Offline storage module: When the training content and answer point information are played, the answer results and offline training progress are stored on the ship; Online storage module: When the network is restored, the offline training progress and answer results are synchronously stored on the shore side based on the session identifier; Identification module: Based on preset algorithms, it identifies knowledge weaknesses and learning preferences and re-customizes the learning plan.

8. A computer 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 computer program, the steps of the video training method in an offline environment as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the video training method in an offline environment as claimed in any one of claims 1 to 6 are implemented.

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