Enterprise online training examination integrated platform and method

Abstract

The present application relates to the technical field of network data processing, and particularly relates to an enterprise online training examination integrated platform and method, the method comprising: collecting training process data, wherein the training process data at least comprises a video playback track, a verification point answer record and an operation event stream; mapping the video playback track, the verification point answer record and the operation event stream to the same time axis for time sequence alignment comparison; determining that a corresponding training process is a real learning behavior when the verification point answer record respectively forms a time sequence connection with the video playback track and the operation event stream, and there is no state conflict between the video playback track and the operation event stream; and determining that a corresponding training process is an invalid learning behavior when there is a time sequence connection conflict or a state conflict based on the comparison result, and the valid learning hours are not counted and a make-up verification is triggered. The present application improves the efficiency of enterprise online training.

Description

Technical Field

[0001] This invention relates to the field of network data processing technology, and in particular to an integrated platform and method for online training and examination for enterprises. Background Technology

[0002] As enterprises advance their digital transformation, internal training and assessment are gradually shifting from offline to online. Online training and examination platforms have become common tools for enterprises to conduct new employee onboarding training, job skills training, compliance and security training, and job qualification certification assessments. Existing technologies typically integrate training courses, question banks, test paper generation, online examinations, and grade management into a single platform, achieving streamlined training management processes for course learning, online examinations, and grade archiving.

[0003] However, existing online training and examination platforms for enterprises have the following shortcomings: First, the authenticity of learning process data is not guaranteed. It is common for trainees to evade learning monitoring by means of leaving their computers running, playing at double speed, or using scripts to answer questions. When learning time is used as the basis for training assessment, false learning records render the training assessment a mere formality. Second, the test papers are disconnected from the individual's actual learning process. Test papers with fixed parameters cannot reflect the knowledge points that individual trainees have not mastered. Third, the training content is updated in a way that lags behind changes in corporate systems, processes, and business events. The entire process operates in a one-way manner and lacks a closed loop.

[0004] Chinese Patent Publication No. CN109215422A discloses a system and method for monitoring online course video learning. The system includes: a detection module for detecting whether the current course video segment has just finished playing; a push module for pushing questions related to the course video segment to the student in a pop-up window when the course video segment has just finished playing; a judgment module for judging whether the answer to the question submitted by the student is correct; and an execution module for continuing to play the next course video segment when the answer to the question is correct, and controlling the player to jump to the beginning of the current course video segment and replay the course video segment if the answer to the question is incorrect.

[0005] It is evident that existing technologies suffer from a problem: they rely solely on correct answers as the sole criterion for judgment, failing to differentiate based on the reasons for errors. This leads to a disconnect between learning intervention strategies and actual mastery, making it impossible to effectively target and precisely reinforce individual weaknesses. Summary of the Invention

[0006] To address this issue, the present invention provides an integrated online training and examination platform and method for enterprises, which overcomes the problem in existing technologies that rely solely on correct answers as the sole criterion for judgment, without differentiating based on the reasons for errors. This leads to a disconnect between learning intervention strategies and actual mastery, making it impossible to effectively target and precisely strengthen individual weaknesses.

[0007] To achieve the above objectives, the present invention provides an integrated online training and examination method for enterprises, comprising: Collect training process data, which includes video playback trajectory, verification point response records, and operation event flow; The video playback trajectory, the verification point response records, and the operation event stream are mapped to the same timeline for temporal alignment and comparison. When the verification point answer record forms a temporal sequence relationship with the video playback trajectory and the operation event stream respectively, and there is no state conflict between the video playback trajectory and the operation event stream, the corresponding training process is determined to be a real learning behavior. If there are temporal or state conflicts in the comparison results, the corresponding training process is determined to be an invalid learning behavior, not counted as valid learning hours, and a supplementary learning verification is triggered. Based on real learning behavior, the system locates incorrect answers at verification points and repeatedly watched video segments to identify weak knowledge points and generates a set of weak knowledge points. If the set of weak knowledge points exists, corresponding test questions are extracted based on the set of weak knowledge points to generate a personalized test questionnaire; if the set of weak knowledge points does not exist, a regular comprehensive questionnaire is generated. The training content is updated based on the questionnaire responses and the set of weak knowledge points. When a business change event is detected, the training course is changed and the corresponding training process data is updated.

[0008] Furthermore, the alignment comparison involves mapping the timestamp sequence of the playback trajectory, the timestamp sequence of the verification point answer record, and the timestamp sequence of the operation event stream to the same time axis.

[0009] Furthermore, if the timestamp of the verification point response record is located after the timestamp of the corresponding verification point playback position, and the timestamp of the verification point response record maintains temporal continuity with the preceding and following operation events in the operation event stream, it is determined that the verification point response record forms a temporal sequence succession.

[0010] Furthermore, the state conflict includes: The operation events corresponding to the continuous changes in the video playback trajectory are missing; The drag operation event corresponding to the change in the video playback trajectory is missing.

[0011] Furthermore, the supplementary learning verification includes: Based on the training process, a supplementary learning and verification task is generated, which includes repeatedly playing the video clips corresponding to the training process and answering verification points. When the verification point is answered correctly, the corresponding learning record and the effective learning hours are restored, and the training process data is collected again.

[0012] Furthermore, identifying weak knowledge points includes: The incorrect answers at the verification points are mapped to the corresponding knowledge points, and the repeated viewing segments of the video are mapped to the corresponding knowledge points. When the corresponding knowledge point has both a record of incorrect answers at the verification point and repeated video viewing, the corresponding knowledge point is determined to be a weak knowledge point and a set of weak knowledge points is constructed.

[0013] Furthermore, a personalized exam questionnaire is generated, including: Based on the set of weak knowledge points and the annotation information of test question knowledge points, candidate test questions corresponding to the set of weak knowledge points are obtained. The candidate questions are combined to generate the personalized examination questionnaire.

[0014] Furthermore, the updated training content includes: The responses to several knowledge points in the questionnaire were compared with the responses to several knowledge points in the verification point response records. If the verification point is answered correctly but the questionnaire is answered incorrectly, it is determined that the mastery of the corresponding knowledge point is questionable, and the corresponding knowledge point is added to the set of weak knowledge points and the corresponding learning segment is pushed to update the training content.

[0015] Furthermore, when the business change event is detected, the changed business object and the business change content corresponding to the business change event are extracted, and the changed business object and the business change content are associated with the course content of the training course; Update the course content and version of the change training course based on the aforementioned business changes.

[0016] This invention also provides an integrated online training and examination platform for enterprises, comprising: The data acquisition module is used to collect training process data, which includes at least video playback trajectory, verification point answer records, and operation event stream; The timing alignment verification module is used to map the video playback trajectory, the verification point answer records, and the operation event stream to the same time axis for timing alignment comparison, and to determine the authenticity of the training and learning behavior. Master the state recognition module, which is used to locate the wrong answer records of verification points and repeatedly watched video segments based on real learning behavior to identify weak knowledge points and generate a set of weak knowledge points; The questionnaire generation module is used to extract corresponding test questions based on the set of weak knowledge points to generate a personalized examination questionnaire, and to generate a regular comprehensive questionnaire when the set of weak knowledge points does not exist. The content update module is used to update the training and learning content based on the questionnaire response results and the set of weak knowledge points; The business change-driven module is used to identify the affected training courses and update the corresponding training process data when a business change event is detected.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: the method of this invention forms a closed loop with multi-source temporal alignment verification, weakness tracing, and dynamic updates of questionnaires and training. The authenticity determination relies only on the temporal continuity and state conflict relationship between the three types of behavioral data, without relying on external benchmarks, historical annotations, and threshold settings, thus identifying invalid learning behaviors and ensuring the credibility of learning data entering questionnaire matching analysis; training content is located by weak knowledge points to achieve personalized question generation, matching questionnaire assessment with individual real learning processes; and the dynamic updates of training content are driven by business change events, solving the problems of distorted learning records, disconnect between assessment and learning, and lagging updates of training content in existing technologies, thereby improving the efficiency of online training for enterprises.

[0018] Furthermore, the method described in this invention determines the authenticity of training by performing time-series alignment and comparison of training process data. Since the authenticity determination of existing training records largely relies on external benchmarks and historical annotations, it is difficult to accurately identify whether the actual viewing behavior and learning process of trainees are genuine and credible. Moreover, it is easily interfered with by invalid learning behaviors such as course-brushing scripts and automated learning, resulting in a serious lack of credibility in the backend statistical data. By establishing a multi-source alignment verification relationship between video playback sequence, verification response sequence, and operation events, and based on the time-series continuity and state conflict relationship between the three types of behavioral data, authenticity determination can be performed. This allows for real-time identification of invalid learning behaviors, achieving precise screening of invalid learning data and further improving the efficiency of online training for enterprises.

[0019] Furthermore, the method described in this invention identifies weak knowledge points to pinpoint training content and generates targeted questionnaires. Since the questionnaire content is often disconnected from the trainees' actual learning process, and the questions are mostly from a pre-set, fixed question bank without any correlation to the trainees' performance, the assessment results are difficult to reflect the true weaknesses. By using video clips as analysis units, marking weak segments and extracting their corresponding knowledge points as weak knowledge points, and dynamically generating personalized assessment questionnaires that match the trainees' learning trajectory, the method accurately maps the assessment content to the trainees' actual mastery, making subsequent training interventions more targeted and further improving the efficiency of online training for enterprises.

[0020] Furthermore, the method described in this invention drives the dynamic updating of training content through business change events. Since training content and assessment questionnaires are mostly statically deployed and updates rely on periodic manual revisions, the existing training system struggles to respond promptly when external policies, operating procedures, or business processes change. This results in a lag and discrepancy between assessment content and current business requirements. By identifying business change events and triggering automatic re-identification of course content associated with the change events, the method drives dynamic linkage updates of associated questionnaires and training content. This achieves real-time synchronization between the training assessment system and business status, solving the technical problem of training content lagging behind business changes and further improving the efficiency of online training for enterprises. Attached Figure Description

[0021] Figure 1 This is an overall flowchart of the integrated online training and examination method for enterprises according to an embodiment of the present invention; Figure 2 This is a logical flowchart illustrating the process of determining the authenticity of learning behavior in the integrated online training and examination method for enterprises according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the process of generating an examination questionnaire for the integrated online training and examination method for enterprises, as described in this embodiment of the invention. Figure 4 This is a block diagram of the overall structure of the integrated online training and examination platform for enterprises, according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are intended to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0025] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Please see Figure 1 The diagram shown is an overall flowchart of the integrated online training and examination method for enterprises according to an embodiment of the present invention. The method of the present invention includes: Step S1: Collect training process data, which includes at least video playback trajectory, verification point response records, and operation event flow; Step S2: Map the video playback trajectory, the verification point response record, and the operation event stream to the same time axis for time sequence alignment and comparison; Step S3: Based on the verification point answer records forming a temporal sequence with the video playback trajectory and the operation event stream respectively, and when there is no state conflict between the video playback trajectory and the operation event stream, the corresponding training process is determined to be a real learning behavior. Step S4: If there is a temporal sequence conflict or state conflict based on the comparison results, the corresponding training process is determined to be an invalid learning behavior, not counted as valid learning hours, and a supplementary learning verification is triggered. Step S5: Based on real learning behavior, locate the incorrect answer records of verification points and repeatedly watched video segments to identify weak knowledge points and generate a set of weak knowledge points; Step S6: If the set of weak knowledge points exists, extract corresponding test questions based on the set of weak knowledge points to generate a personalized test questionnaire; if the set of weak knowledge points does not exist, generate a regular comprehensive questionnaire. Step S7: Update the training content based on the questionnaire response results and the set of weak knowledge points. When a business change event is detected, determine the change of training courses and update the corresponding training process data.

[0027] Specifically, training process data refers to the data collected by the platform during the period when trainees perform learning tasks on the training platform. This data reflects the process of learning behavior and has a timestamp, and includes at least three categories: video playback trajectory, verification point answer records, and operation event flow. In this embodiment, training process data is stored in association with student identifiers, course identifiers, and chapter identifiers, and uses a unified timestamp format.

[0028] Specifically, the video playback trajectory is time-series data collected by the player's embedded points, reflecting the video playback process, including the playback progress time sequence, playback speed, and drag operation records; The playback progress timeline consists of the video playback position and its corresponding timestamp, which are periodically recorded during playback.

[0029] Specifically, the verification point is an interactive detection point set at the training video node; the verification point answer record is a record generated when the trainee submits an answer at the verification point, including the verification question identifier, the answer result, and the answer timestamp. The location of the verification point and the content of the verification question are pre-configured by the structured tags of the course content.

[0030] Specifically, the operation event flow refers to the time sequence of a series of operation events generated by the trainee's terminal during the training process, including mouse movement, keyboard input, page focus switching and screen switching events, and each operation event is timestamped.

[0031] Specifically, time-series alignment and comparison refers to the process of mapping the timestamp sequences of three types of data—video playback trajectory, verification point answer records, and operation event stream—to the same time axis, so that events occurring at the same moment in different data sources can be compared under the same time reference.

[0032] Specifically, temporal sequence refers to the sequential relationship between events from different data sources on the timeline, and the sequence of events corresponds to the actual execution order of the training process; in this invention, it specifically refers to the connection between the verification point answer records and the video playback trajectory and the operation event flow.

[0033] Specifically, genuine learning behavior refers to a training process in which, after temporal alignment and comparison, the verification point response records form a temporal sequence with the video playback trajectory and the operation event stream, and there is no state conflict between the video playback trajectory and the operation event stream; invalid learning behavior refers to a training process in which the comparison results have temporal mismatch or state conflict.

[0034] In this embodiment, the method forms a closed loop with multi-source temporal alignment verification, weakness tracing, and dynamic updates of questionnaires and training. The authenticity determination relies solely on the temporal continuity and state conflict relationships between the three types of behavioral data, without relying on external benchmarks, historical annotations, or threshold settings. This allows for the identification of invalid learning behaviors and ensures the credibility of learning data entering the questionnaire matching analysis. Training content is located based on weak knowledge points to achieve personalized question generation, matching questionnaire assessments with individuals' actual learning processes. The dynamic updates of training content are driven by business change events, solving the problems of distorted learning records, disconnect between assessment and learning, and lagging training content updates in existing technologies, thereby improving the efficiency of online training for enterprises.

[0035] Please see Figure 2As shown, it is a logical flowchart of the process of determining the authenticity of learning behavior in an embodiment of the present invention.

[0036] Specifically, the alignment comparison involves mapping the timestamp sequence of the playback trajectory, the timestamp sequence of the verification point answer record, and the timestamp sequence of the operation event stream to the same timeline.

[0037] Specifically, if the timestamp of the verification point response record is after the timestamp of the corresponding verification point playback position, and the timestamp of the verification point response record is sequentially continuous with the preceding and following operation events in the operation event stream, it is determined that the verification point response record forms a sequential succession.

[0038] Specifically, temporal sequence refers to the state in which events from different data sources are sequentially connected on the timeline, and the connection order matches the actual execution order of the training process; in this invention, it specifically refers to the connection relationship formed by the verification point answer records with the video playback trajectory and the operation event flow, respectively.

[0039] It is understandable that by uniformly mapping timestamps from different data sources to the same timeline, the time reference differences formed by independent records from different data sources can be eliminated, enabling video playback, verification point responses, and operation events to be associated in the actual order of occurrence. By judging the temporal relationship between verification point response records and corresponding video playback positions and adjacent operation events, it can be confirmed whether the verification point response occurred in the corresponding training process, thereby avoiding the need to determine the authenticity of learning behavior based solely on records from a single data source.

[0040] In this embodiment, the method determines the authenticity of training by performing time-series alignment and comparison of training process data. Since the authenticity determination of existing training records largely relies on external benchmarks and historical annotations, it is difficult to accurately identify whether the actual viewing behavior and learning process of trainees are genuine and credible. Furthermore, it is easily interfered with by invalid learning behaviors such as course-brushing scripts and automated learning, resulting in a serious lack of credibility in the background statistical data. By establishing a multi-source alignment verification relationship between video playback sequence, verification of answer sequence, and operation events, and by determining authenticity based on the time-series continuity and state conflict relationship among the three types of behavioral data, invalid learning behaviors can be identified in real time, achieving accurate screening of invalid learning data and further improving the efficiency of online training for enterprises.

[0041] Please see Figure 3 The diagram shown is a logical flowchart of the examination questionnaire generation process in the integrated online training and examination method for enterprises according to this embodiment.

[0042] Specifically, the state conflict includes: The operation events corresponding to the continuous changes in the video playback trajectory are missing; The drag operation event corresponding to the change in the video playback trajectory is missing.

[0043] Specifically, the supplementary learning verification includes: Based on the training process, a supplementary learning and verification task is generated, which includes repeatedly playing the video clips corresponding to the training process and answering verification points. When the verification point is answered correctly, the corresponding learning record and the effective learning hours are restored, and the training process data is collected again.

[0044] Specifically, identifying weak knowledge points includes: The incorrect answers at the verification points are mapped to the corresponding knowledge points, and the repeated viewing segments of the video are mapped to the corresponding knowledge points. When the corresponding knowledge point has both a record of incorrect answers at the verification point and repeated video viewing, the corresponding knowledge point is determined to be a weak knowledge point and a set of weak knowledge points is constructed.

[0045] Specifically, personalized exam questionnaires are generated, including: Based on the set of weak knowledge points and the annotation information of test question knowledge points, candidate test questions corresponding to the set of weak knowledge points are obtained. The candidate questions are combined to generate the personalized examination questionnaire.

[0046] Specifically, state conflict refers to a situation where the training process states reflected by different data sources contradict each other within the same time interval. For example, the video playback trajectory shows that the playback progress is continuously advancing, while the operation event stream does not have any operation events within the corresponding time interval.

[0047] Specifically, the weak knowledge points are those identified by combining incorrect answers to verification points with repeatedly viewed video clips. Specifically, personalized exam questionnaires are assessment questionnaires generated by extracting corresponding test questions from a question bank based on a set of weak knowledge points, while regular comprehensive questionnaires are assessment questionnaires that cover all assessment knowledge points when there is no set of weak knowledge points.

[0048] Specifically, business change events refer to structured messages that indicate changes in a company's systems, business processes, or quality and safety status, including system change events, incident record events, and quality inspection report events.

[0049] Specifically, the process of generating a standard comprehensive questionnaire involves obtaining a set of course knowledge points corresponding to the training course, matching the set of course knowledge points with the knowledge point annotation information of the test questions to obtain candidate test questions corresponding to the set of course knowledge points, and combining the candidate test questions according to the knowledge points corresponding to the candidate test questions to generate a standard comprehensive questionnaire covering the set of course knowledge points.

[0050] Specifically, the continuous change in playback progress means that the playback positions corresponding to adjacent timestamps in the video playback trajectory continuously increase in the order of video time.

[0051] Specifically, the playback progress jumps to a state where the playback positions corresponding to adjacent timestamps in the video playback trajectory are not connected in a continuous playback order, but instead change directly from one playback position to another non-continuous playback position.

[0052] Specifically, drag operation events are progress control operation events in the operation event stream used to change the video playback position, including dragging the video progress bar, sliding the playback position, or jumping the playback position.

[0053] Specifically, the relearning verification task is a learning verification task regenerated for the training process that has been judged as invalid learning behavior. It is used to re-verify the training process by rewatching the corresponding video segments and completing the answers to the associated verification points.

[0054] Specifically, a video replay segment is a segment in which the video playback trajectory re-enters the playback position corresponding to the same video content during the same training process.

[0055] Specifically, knowledge point mapping refers to mapping the incorrect answers to the verification points and the repeatedly watched segments of the video to the corresponding knowledge points based on the association information between the verification points and the knowledge points, as well as the content association information between the video playback position and the knowledge points.

[0056] Specifically, weak knowledge points refer to knowledge points that are simultaneously associated with incorrect answers to the verification points and repeated viewing segments of the video.

[0057] Specifically, the knowledge point annotation information of the test questions is used to represent the correspondence between test questions and knowledge points, including one or more knowledge point identifiers corresponding to the test questions.

[0058] Specifically, candidate questions are those questions for which the knowledge point annotation information of the question is associated with at least one knowledge point in the set of weak knowledge points.

[0059] Specifically, the course knowledge point set is the set of knowledge points contained in the course content corresponding to the training course.

[0060] Understandably, this embodiment aligns the video playback trajectory, verification point answer records, and operation event flow along a unified timeline. It then determines the authenticity of training behavior based on the temporal sequence and state consistency between different data sources, thus eliminating invalid learning behaviors that do not correspond to the actual training process. For learning behaviors deemed authentic, incorrect verification point answers and repeated video viewings are mapped to knowledge points, and weak knowledge points are identified through their common focus, avoiding relying solely on a single incorrect answer or a single repeated viewing to determine the degree of knowledge weakness. Based on this, personalized examination questionnaires or standard comprehensive questionnaires are generated depending on whether a set of weak knowledge points has been formed, ensuring that the examination content corresponds to actual training performance. When business change events occur, the affected training courses are located based on the changed content, and the course and subsequent training process data are updated to ensure that the training content corresponds to the enterprise's business status.

[0061] In this embodiment, the method identifies weak knowledge points to pinpoint training content and generates targeted questionnaires. Since the questionnaire content is often disconnected from the trainees' actual learning process, and the questions are mostly from a pre-set, fixed question bank without any correlation to the trainees' performance, the assessment results are difficult to reflect the true weaknesses. By using video clips as analysis units, marking weak segments and extracting their corresponding knowledge points as weak knowledge points, and dynamically generating personalized assessment questionnaires that match the trainees' learning trajectory, the method accurately maps the assessment content to the trainees' actual mastery, making subsequent training interventions more targeted and further improving the efficiency of online training for enterprises.

[0062] Specifically, the updated training content includes: The responses to several knowledge points in the questionnaire were compared with the responses to several knowledge points in the verification point response records. If the verification point is answered correctly but the questionnaire is answered incorrectly, it is determined that the mastery of the corresponding knowledge point is questionable, and the corresponding knowledge point is added to the set of weak knowledge points and the corresponding learning segment is pushed to update the training content.

[0063] Specifically, when the business change event is detected, the changed business object and the business change content corresponding to the business change event are extracted, and the changed business object and the business change content are associated with the course content of the training course; Update the course content and version of the change training course based on the aforementioned business changes.

[0064] Specifically, the answer status corresponding to the knowledge point refers to the answer results of the test questions in the questionnaire that are associated with the corresponding knowledge point, including whether the answer is correct or incorrect.

[0065] Specifically, doubts about mastery refer to discrepancies between the answers to verification points during training and the answers to subsequent questionnaires for the same knowledge point. This manifests as correct answers to verification points but incorrect answers to the corresponding questionnaires.

[0066] Specifically, the change of business object refers to the enterprise business entity or business process involved in the business change event, including business processes, business positions, business operation matters or quality and safety matters.

[0067] Specifically, the content of business change refers to the business rules, operating procedures, work requirements, or quality and safety requirements that have changed relative to the original business state in the business change event.

[0068] Specifically, the association refers to mapping the changed business object and the changed business content to the business processes, business matters, operational requirements, and quality and safety requirements included in the training course, in order to identify the training courses that have a content correspondence with the changed business event.

[0069] Specifically, the course version is used to characterize the status of the course content corresponding to the training course. When the course content is updated according to the business change content, a new course version is generated accordingly.

[0070] In this embodiment, the method drives the dynamic updating of training content through business change events. Since training content and assessment questionnaires are mostly statically deployed, updates rely on periodic manual revisions. When external policies, operating procedures, or business processes change, the existing training system struggles to respond promptly, resulting in a lag and deviation between assessment content and current business requirements. By identifying business change events and triggering automatic re-identification of course content associated with the change events, the method drives the dynamic linkage and updating of associated questionnaires and training content. This achieves real-time synchronization between the training assessment system and business status, solving the technical problem of training content lagging behind business changes and further improving the efficiency of online training for enterprises.

[0071] Please see Figure 4 The diagram shown is an overall structural block diagram of the integrated online training and examination platform for enterprises in this embodiment.

[0072] This embodiment provides an integrated online training and examination platform for enterprises, including: The data acquisition module is used to collect training process data, which includes at least video playback trajectory, verification point answer records, and operation event stream; The timing alignment verification module, which is connected to the data acquisition module, is used to map the video playback trajectory, the verification point answer record, and the operation event stream to the same time axis for timing alignment comparison, and to determine the authenticity of the training and learning behavior. The master state recognition module is connected to the time sequence alignment verification module. It is used to locate the verification point wrong answer records and video repeated viewing segments based on the actual learning behavior to determine weak knowledge points and generate a set of weak knowledge points. The questionnaire generation module, which is connected to the mastery status recognition module, is used to extract corresponding test questions based on the set of weak knowledge points to generate a personalized examination questionnaire, and to generate a regular comprehensive questionnaire when the set of weak knowledge points does not exist. The content update module, which is connected to the questionnaire generation module, is used to update the training and learning content based on the questionnaire response results and the set of weak knowledge points. The business change driving module is connected to the data acquisition module and the questionnaire generation module respectively, and is used to identify the affected training courses and update the corresponding training process data when a business change event is detected.

[0073] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for integrating online training and examination for enterprises, characterized in that, include: Collect training process data, which includes video playback trajectory, verification point response records, and operation event flow; The video playback trajectory, the verification point response records, and the operation event stream are mapped to the same timeline for temporal alignment and comparison. When the verification point answer record forms a temporal sequence relationship with the video playback trajectory and the operation event stream respectively, and there is no state conflict between the video playback trajectory and the operation event stream, the corresponding training process is determined to be a real learning behavior. If there are temporal or state conflicts in the comparison results, the corresponding training process is determined to be an invalid learning behavior, not counted as valid learning hours, and a supplementary learning verification is triggered. Based on real learning behavior, the system locates incorrect answers at verification points and repeatedly watched video segments to identify weak knowledge points and generates a set of weak knowledge points. If the set of weak knowledge points exists, corresponding test questions are extracted based on the set of weak knowledge points to generate a personalized test questionnaire; if the set of weak knowledge points does not exist, a regular comprehensive questionnaire is generated. The training content is updated based on the questionnaire responses and the set of weak knowledge points. When a business change event is detected, the training course is changed and the corresponding training process data is updated.

2. The integrated online training and examination method for enterprises according to claim 1, characterized in that, The alignment comparison involves mapping the timestamp sequence of the playback trajectory, the timestamp sequence of the verification point answer record, and the timestamp sequence of the operation event stream to the same time axis.

3. The integrated online training and examination method for enterprises according to claim 2, characterized in that, If the timestamp of the verification point response record is after the timestamp of the corresponding verification point playback position, and the timestamp of the verification point response record is sequentially continuous with the preceding and following operation events in the operation event stream, it is determined that the verification point response record forms a sequential succession.

4. The integrated online training and examination method for enterprises according to claim 3, characterized in that, The state conflict includes: The operation events corresponding to the continuous changes in the video playback trajectory are missing; The drag operation event corresponding to the change in the video playback trajectory is missing.

5. The integrated online training and examination method for enterprises according to claim 4, characterized in that, The supplementary learning verification includes: Based on the training process, a supplementary learning and verification task is generated, which includes repeatedly playing the video clips corresponding to the training process and answering verification points. When the verification point is answered correctly, the corresponding learning record and the effective learning hours are restored, and the training process data is collected again.

6. The integrated online training and examination method for enterprises according to claim 5, characterized in that, The identification of weak knowledge points includes: The incorrect answers at the verification points are mapped to the corresponding knowledge points, and the repeated viewing segments of the video are mapped to the corresponding knowledge points. When the corresponding knowledge point has both a record of incorrect answers at the verification point and repeated video viewing, the corresponding knowledge point is determined to be a weak knowledge point and a set of weak knowledge points is constructed.

7. The integrated online training and examination method for enterprises according to claim 6, characterized in that, Generate personalized exam questionnaires, including: Based on the set of weak knowledge points and the annotation information of test question knowledge points, candidate test questions corresponding to the set of weak knowledge points are obtained. The candidate questions are combined to generate the personalized examination questionnaire.

8. The integrated online training and examination method for enterprises according to claim 7, characterized in that, The updated training content includes: The responses to several knowledge points in the questionnaire were compared with the responses to several knowledge points in the verification point response records. If the verification point is answered correctly but the questionnaire is answered incorrectly, it is determined that the mastery of the corresponding knowledge point is questionable, and the corresponding knowledge point is added to the set of weak knowledge points and the corresponding learning segment is pushed to update the training content.

9. The integrated online training and examination method for enterprises according to claim 8, characterized in that, When the business change event is detected, the changed business object and the business change content corresponding to the business change event are extracted, and the changed business object and the business change content are associated with the course content of the training course; Update the course content and version of the change training course based on the aforementioned business changes.

10. An integrated online training and examination platform for enterprises using the integrated online training and examination method according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect training process data, which includes at least video playback trajectory, verification point answer records, and operation event stream; The timing alignment verification module is used to map the video playback trajectory, the verification point answer records, and the operation event stream to the same time axis for timing alignment comparison, and to determine the authenticity of the training and learning behavior. Master the state recognition module, which is used to locate the wrong answer records of verification points and repeatedly watched video segments based on real learning behavior to identify weak knowledge points and generate a set of weak knowledge points; The questionnaire generation module is used to extract corresponding test questions based on the set of weak knowledge points to generate a personalized examination questionnaire, and to generate a regular comprehensive questionnaire when the set of weak knowledge points does not exist. The content update module is used to update the training and learning content based on the questionnaire response results and the set of weak knowledge points; The business change-driven module is used to identify the affected training courses and update the corresponding training process data when a business change event is detected.

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