A method for constructing a safety training courseware system for a production enterprise

Abstract

This invention discloses a method for constructing a safety training courseware system for manufacturing enterprises, a computer device, and a computer-readable storage medium. The method systematically builds a safety training courseware framework, reserving corresponding training courseware slots for each business activity involved in the manufacturing enterprise, thus improving the relevance and effectiveness of safety training. Simultaneously, the system can autonomously collect missing training courseware based on existing resources, and employs a series of incentive mechanisms to encourage upload nodes to specifically supplement the system with missing training courseware, ensuring the integrity of the training courseware system. Each training courseware uploaded by an upload node can be automatically stored in the optimal location, improving the standardization of training courseware storage.

Description

Technical Field

[0001] This invention relates to the field of safety training system technology, specifically to a method for constructing a safety training courseware system for manufacturing enterprises, a computer device, and a computer-readable storage medium. Background Technology

[0002] Currently, safety training in manufacturing enterprises mostly relies on manual explanations or watching documentaries together. This simplistic approach fails to provide a comprehensive understanding of potential safety hazards. While short video platforms can offer a number of safety training videos for various positions, the actual content often differs from the intended learning objectives. Summary of the Invention

[0003] To address the aforementioned technical problems, the first aspect of this invention provides a method for constructing a safety training courseware system for manufacturing enterprises. This method involves building a safety training courseware system for manufacturing enterprises that includes short safety training videos. By refining the granularity and type classification of the training courseware, the method aims to improve the relevance and effectiveness of safety training.

[0004] The present invention provides a method for constructing a safety training courseware system for manufacturing enterprises, hereinafter referred to as the construction method, which includes the following steps:

[0005] Step S1: In response to the uploaded safety training short video and its description, the uploaded safety training short video is pre-classified by extracting keywords from the description. Based on the pre-classification results, corresponding training courseware types are provided for the uploading node to select. The node that uploads the safety training short video is denoted as the uploading node. There are multiple training courseware types, all of which are configured to be pre-loaded. The pre-classification results are some or all of the training courseware types, which are used for the uploading node to select one of the pre-classification results as the training courseware type for the safety training short video.

[0006] Step S2: After selecting the training courseware type, further intelligent analysis is performed on the content of the safety training short video to obtain its intelligent analysis type. It is then determined whether the intelligent analysis type matches the training courseware type selected in Step S1. If the types do not match, the safety training short video is returned to the upload node, and Step S3 is executed. If the types match, the safety training short video and the selected training courseware type are pushed to a preset or selected subsequent node for review. Once the review is passed, Step S5 is executed. If the review fails, the safety training short video is returned to the upload node, and Step S3 is executed. At least one subsequent node is required; each subsequent node corresponds to the department head of the department where the upload node is located.

[0007] Step S3: Prompt the upload node to select the training courseware type again, and wait for the upload node to select the training courseware type again; in response to the reselected training courseware type, determine whether the intelligent analysis type matches the reselected training courseware type; if the types match, the safety training short video is streamed to the preset or selected subsequent node for review, and after the review is passed, proceed to step S5; if the types do not match, feedback the selection confirmation information of the training courseware type is sent to the upload node for confirmation, and then proceed to step S4;

[0008] Step S4: In response to the confirmation command, the safety training short video and its training courseware type are pushed to the safety review node corresponding to the upload node for initial quality review; if the initial quality review is passed, the safety training short video is transferred to a preset or selected subsequent node for review, and if the review is passed, step S5 is executed; if the initial quality review is not passed, the process returns to step S3; wherein, the safety review node is the node corresponding to the safety management position of the department where the upload node is located;

[0009] Step S5: After all subsequent nodes have passed the review, the security training video is stored in the corresponding training folder in the database and then released to the public. The database is configured to have multiple independent training folders, each corresponding to a type of training courseware, which is used to store the security training videos of that type of training courseware.

[0010] Step S6: Repeat steps S1 to S5 to build a safety training courseware system for manufacturing enterprises with short safety training videos.

[0011] In summary, the method for constructing a safety training courseware system for manufacturing enterprises provided by this invention has at least the following beneficial effects:

[0012] (1) This invention achieves full coverage of various types of safety training short videos by constructing a safety training courseware system, and at the same time achieves effective classification of training courseware.

[0013] (2) By constructing a safety training courseware framework, this invention ensures that each and every short safety training video is stored in the best position and corresponds to its description, so that when choosing to watch, the actual content of the video watched matches the content that is desired, thus ensuring the effectiveness of safety training.

[0014] (3) By continuously uploading short safety training videos at each node, this invention can enrich the safety training courseware system and provide richer courseware resources for each position. By listing as many prevention points as possible, it helps to improve the training effect for each position.

[0015] (4) This invention ensures the accuracy of the type of uploaded safety training short videos through intelligent identification and analysis, and further ensures the accuracy of the type of uploaded safety training short videos from both human and AI intelligence aspects by combining the multi-node review process with intelligent identification and analysis.

[0016] (5) The present invention manages the same type of safety training short videos under the training catalog independently, which not only helps to improve the relevance of safety training, but also, compared with the method of storing all safety training short videos directly in the same directory of the database, classifying the videos can effectively identify video resources with duplicate content, which helps to carry out subsequent deletion and other processing.

[0017] The second aspect of the present invention provides a computer device, 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, it can implement the steps of the method for constructing a safety training courseware system for manufacturing enterprises according to any one of the technical solutions of the first aspect of the present invention. Therefore, the computer device provided by the technical solution of the second aspect of the present invention has all the beneficial effects of any one of the technical solutions of the first aspect of the present invention, which will not be elaborated further here.

[0018] The third aspect of the present invention provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed, it can implement the steps of the method for constructing a safety training courseware system for manufacturing enterprises according to any one of the first aspects of the present invention. Therefore, the computer-readable storage medium provided by the third aspect of the present invention has all the beneficial effects of any one of the first aspects of the present invention, which will not be elaborated further here.

[0019] It should be noted that those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic tape storage, or any other medium that can be used to carry or store data on a computer. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method for constructing a safety training courseware system for manufacturing enterprises, as described in one embodiment of the present invention.

[0021] Figure 2 This is a partial schematic diagram of a four-level structure of a training courseware type in one embodiment of the present invention.

[0022] Figure 3 This is a flowchart illustrating a method for intelligently pushing safety training courseware to manufacturing enterprises according to one embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram of the comprehensive management process of safety training courseware in one embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the comprehensive management process of safety training courseware in another embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0026] Some embodiments of the present invention provide a comprehensive management process for safety training courseware.

[0027] The comprehensive management process for this safety training courseware includes the construction of a safety training courseware system for manufacturing enterprises and the intelligent delivery of safety training courseware to manufacturing enterprises.

[0028] like Figure 1 As shown, one embodiment of the present invention provides a method for constructing a safety training courseware system for manufacturing enterprises. The method includes the following steps:

[0029] Step S1: In response to the uploaded safety training short video and its description, the uploaded safety training short video is pre-classified by extracting keywords from the description. Based on the pre-classification results, corresponding training courseware types are provided for the uploading node to select. The node that uploads the safety training short video is denoted as the uploading node. There are multiple training courseware types, all of which are configured to be pre-loaded. The pre-classification results are some or all of the training courseware types, which are used for the uploading node to select one of the pre-classification results as the training courseware type for the safety training short video.

[0030] Step S2: After selecting the training courseware type, further intelligent analysis is performed on the content of the safety training short video (e.g., identifying audio and / or video in the training video to determine its type). The intelligent analysis type of the safety training short video is obtained, and it is determined whether the intelligent analysis type matches the training courseware type selected in Step S1. If the types do not match, the safety training short video is returned to the upload node, and Step S3 is executed. If the types match, the safety training short video and the selected training courseware type are pushed to a preset or selected subsequent node for review. Once the review is passed, the process continues. Step S5; If the review fails, the safety training short video is returned to the upload node, and step S3 is executed; wherein, the type of intelligent analysis is the same as the type of training courseware (meaning that the intelligent analysis type and the training courseware type have the same architecture, quantity, and type), and the number of subsequent nodes is at least one (in embodiments with multiple subsequent nodes, multiple subsequent nodes review the training courseware type of the safety training short video in turn, and only after the previous node passes the review will it be sent to the next node for review, and step S5 is executed only after all subsequent nodes have passed the review, any one); the subsequent node is the node corresponding to the department leader of the department where the upload node is located;

[0031] Step S3: Prompt the upload node to select a training courseware type again, and wait for the upload node (from the pre-classification results or all training courseware types) to reselect the training courseware type for the safety training short video; in response to the reselected training courseware type, determine whether the intelligent analysis type (the intelligent analysis type in step S2) matches the reselected training courseware type; if the types match, the safety training short video is transferred to a preset or selected subsequent node for review, and after the review is passed, step S5 is executed; if the types do not match, feedback of the selection confirmation information of the training courseware type is sent to the upload node for confirmation, and step S4 is executed;

[0032] Step S4: In response to the confirmation command, the safety training short video and its training courseware type are pushed to the safety review node corresponding to the upload node for initial quality review; if the initial quality review is passed, the safety training short video is transferred to a preset or selected subsequent node for review, and if the review is passed, step S5 is executed; if the initial quality review is not passed, the process returns to step S3; wherein, the safety review node is the node corresponding to the safety management position of the department where the upload node is located;

[0033] Step S5: After all subsequent nodes have passed the review, the security training video is stored in the corresponding training folder in the database and then released to the public. The database is configured to have multiple independent training folders, each corresponding to a type of training courseware, which is used to store the security training videos of that type of training courseware.

[0034] Step S6: Repeat steps S1 to S5 to build a safety training courseware system for manufacturing enterprises with short safety training videos.

[0035] It should be noted that steps S1-S5 constitute the process of uploading and intelligently classifying and storing safety training short videos. This process can be applied to both the construction and improvement of safety training courseware systems in manufacturing enterprises.

[0036] In this invention, each node (including the aforementioned upload node) can be configured as a terminal, such as a computer, laptop, or mobile phone. Specifically, it can be implemented by an operator operating the corresponding node (e.g., selection in step S1).

[0037] The training materials in this invention are short safety training videos.

[0038] In some specific embodiments, before selecting the training courseware type in step S1, the upload node adds a new training courseware type to the system through external input, and uses the newly added training courseware type as the type corresponding to the safety training short video.

[0039] For cases involving newly added training courseware types, steps S2 and S3 are skipped, and step S4 is executed directly (i.e., the intelligent review process is skipped, and manual review is initiated directly). In step S5, a training folder for the newly added training courseware type is created.

[0040] In some specific embodiments, the training courseware types are configured in a top-down four-level structure; wherein, the first-level training courseware type is the highest level, including: ideological awareness type and accident prevention capability type; the second-level training courseware type is a higher level, which is a sub-type of the first-level training courseware type; the third-level training courseware type is a lower level, which consists of the elements that cause the accidents corresponding to the first-level training courseware type; the fourth-level training courseware type is the lowest level, which is a further sub-type of the elements that cause the accidents of the third-level training courseware type; wherein, each training folder is used to store one safety training short video of the fourth-level training courseware type.

[0041] Furthermore, in cases with a four-level structure, if the selected training courseware type and the intelligent analysis type belong to different levels, then the types are considered mismatched.

[0042] In some specific embodiments, one or more intelligent analysis types are matched; if one intelligent analysis type is matched, the selected training courseware type is the same as the intelligent analysis type, then it is considered a type match; otherwise, it is considered a type mismatch; if multiple intelligent analysis types are matched, the selected training courseware type is one of the intelligent analysis types, then it is considered a type match; otherwise, it is considered a type mismatch.

[0043] In some specific embodiments, in step S2, the intelligent analysis process includes: reading a security training short video, performing image recognition and speech recognition on the content of the security training short video; and generating an intelligent analysis type for the security training short video based on the image recognition results and speech recognition results.

[0044] Furthermore, when using image recognition, each extracted video frame is matched with each keyword in the descriptive text to obtain the number of video frames corresponding to each keyword. The keyword corresponding to the video frame with the most frames is used to determine the image recognition result. When using speech recognition, each extracted audio frame is matched with each keyword in the descriptive text to obtain the number of audio frames corresponding to each keyword. The keyword corresponding to the audio with the most frames is used to determine the speech recognition result. The training courseware type corresponding to the keyword with the highest frequency in both the image recognition and speech recognition results is used as the intelligent analysis type. When the frequency of keyword occurrences in the image recognition and speech recognition results is equal, the training courseware type corresponding to the keyword in the speech recognition result is used as the intelligent analysis type.

[0045] In some specific embodiments, in step S3, if the types are inconsistent, the upload node is first prompted to select the training courseware type again, and the step of "in response to the reselected training courseware type, determining whether the intelligent analysis type is consistent with the reselected training courseware type" is executed; when the number of times the upload node is prompted to select the training courseware type again reaches the preset number, the upload node is then fed back the selection confirmation information of the training courseware type, which is then confirmed by the upload node.

[0046] In some specific embodiments, the following steps may be included before step S1: organizing employees in each position to shoot or repost short safety training videos based on the safety requirements of their positions, and then having employees upload the video courseware to the intelligent safety training system.

[0047] In some specific embodiments, the types of safety training short videos are constructed based on the production task processes and work characteristics of manufacturing enterprises, as well as the types of accident injuries. For example... Figure 2As shown, the training courseware type system includes four levels: Level 1 is the highest, including: ideological awareness and accident prevention capabilities; Level 2 is relatively high, taking Level 1 as an example of accident prevention capabilities, it is divided into twenty types according to accident injury, including crane injury prevention, vehicle injury prevention, fire prevention, and fall from height prevention; Level 3 is low, composed of elements that may lead to Level 1. Taking Level 2 as crane injury prevention, Level 3 includes: inspection points for safety devices of different types of cranes, inspection points for transmission devices of different types of cranes, inspection points for protective devices of different types of cranes, safety inspection points for different types of slings, operation points for different types of cranes, and safety precautions during the lifting of different types of loads; Level 4 is the lowest, further subdivided based on Level 3. Taking inspection points for safety devices of different types of cranes as an example, Level 4 includes inspection points for safety devices of gantry cranes, portal cranes, and bridge cranes.

[0048] (That is, the training courseware types have a four-level training catalog. The first-level training catalog includes two types of training courseware: ideological awareness and accident prevention capabilities. The second-level training catalog includes second-level training courseware types such as crane injury prevention, vehicle injury prevention, fire prevention, and fall from height prevention. The third-level training catalog includes third-level training courseware types such as inspection points for safety devices of different types of cranes, inspection points for transmission devices of different types of cranes, inspection points for protective devices of different types of cranes, safety inspection points for different types of slings, operation points for different types of cranes, and safety precautions during the lifting of different types of loads. The fourth-level training catalog includes fourth-level training courseware types such as inspection points for safety devices of gantry cranes, portal cranes, and bridge cranes. Only each fourth-level training courseware type under the fourth-level training catalog has a corresponding training folder in the database.)

[0049] In the training courseware type system, each fourth-level category has pre-set corresponding keywords. When the system performs intelligent analysis, it sets the priority of matching results based on the number of keywords matched. The more keywords matched, the higher the recommendation priority.

[0050] When using image recognition, the system preloads images of some devices, facilities, and scenes, along with their corresponding names. When the system matches an image, it directs the user to the corresponding image name. The system then matches this name with preloaded keywords. The longer a matched image appears in the video, the higher its recommendation priority. When using intelligent voice recognition for matching, the priority of the matching results is set based on the number of keywords matched. The more keywords matched, the higher the recommendation priority.

[0051] One of the higher-priority results in the intelligent analysis can be set as the intelligent analysis type.

[0052] When the image recognition result contradicts the speech recognition result, the speech recognition result takes precedence over the image recognition result.

[0053] If two or more results are to be adapted at the same time, as long as the selected training courseware type matches one of them, it is considered a type match. In the video delivery stage, when any of the two or more adapted results needs to be pushed, it can guide the uploaded video file.

[0054] In cases with multiple levels of directories, if the selected type and the analysis type are in the same level directory but not exactly the same, then the match is not considered successful.

[0055] When selecting the type of training courseware, you can choose from existing options in the system or create a new type.

[0056] If a newly created training courseware type passes the final review, the type will be retained and the training courseware system architecture will be optimized; if it fails the initial or final review, the newly created type will be deleted.

[0057] The methods for issuing the special training courseware solicitation order include the system automatically sending it periodically and the system pushing the special training courseware solicitation order suggestion to the system administrator, who will then issue it periodically or irregularly.

[0058] In summary, the method for constructing a safety training courseware system for manufacturing enterprises provided by this invention systematically builds a safety training courseware framework, reserving corresponding training courseware slots for each business activity involved in the manufacturing enterprise, thereby improving the relevance and effectiveness of safety training. Simultaneously, the system can autonomously collect missing training courseware based on the existing training courseware situation, and supplement this with a series of incentive measures to encourage upload nodes to specifically supplement the system with missing training courseware, ensuring the integrity of the training courseware system. Each training courseware uploaded by the upload nodes can be autonomously stored in the optimal location, improving the standardization of training courseware storage.

[0059] One embodiment of the present invention provides a method for intelligently pushing safety training courseware to manufacturing enterprises. For example... Figure 3 As shown, this intelligent push method includes the following steps:

[0060] Step S1: Obtain the number of safety training short videos in each training folder, and generate a corresponding number of ranking lists based on the number of second-level training courseware types under the four-level structure, using the second-level training courseware type as the basic unit. Each safety training short video under the same second-level training courseware type is configured to participate in the ranking in the ranking list of its respective second-level training courseware type. The ranking list is used to rank the safety training short videos from high to low scores.

[0061] Step S2: When the operating system date reaches the preset collection date, issue a special training courseware collection order for the training topic pre-bound to the preset date; or when receiving external instructions, send a special training courseware collection order for the specific safety accident specified by the external instructions as the training topic; wherein, the pre-bound training topics include the theme of Safety Production Month and the theme of Fire Prevention Month; each training topic is associated with at least one second-level training courseware type.

[0062] Step S3: Receive the uploaded safety training short videos, store each safety training short video in the corresponding training folder in the database, and publish it externally; wherein, the database is configured to have multiple independent training folders, each training folder corresponding to a fourth-level training courseware type, used to store safety training short videos of that training courseware type;

[0063] Step S4: When the operating system date reaches the preset training date, a preset number of security training short videos are periodically pushed to the nodes corresponding to the relevant positions according to the training topic; among them, in the first push cycle, security training short videos released after the preset collection date are pushed first; each node is bound to a position tag, which is used to indicate the position of the node and the department to which the position belongs. The relevant position is the position involving the topic training content. Each relevant position is bound to at least one fourth-level training courseware type, which is used to push only security training short videos belonging to that fourth-level training courseware type;

[0064] Step S5: After each push cycle ends, evaluate the uploaded security training videos to obtain a push index. Update the push order based on the push index of each security training video, prioritize pushing security training videos with high push indices, and stop pushing security training videos with scores below the preset threshold.

[0065] Step S6: Repeat the subsequent periodic push and push order update process until all push cycles are completed to finish the entire topic training task.

[0066] Further, in step S5, the formula for calculating the push index is: Push Index = Department to which the pushed node belongs × First preset weight + Position corresponding to the pushed node × Second preset weight + Total historical viewing time × Third preset weight + Whether it belongs to the training topic × Fourth preset weight + Whether it was published after the preset collection date × Fifth preset weight + Video quality × Sixth preset weight; Wherein, Video quality = Number of times the video is liked × Seventh preset weight + Number of times the video is disliked × Eighth preset weight + Number of times the video is clicked × Ninth preset weight + Number of times the video is favorited × Tenth preset weight + Number of times the video is shared × Eleventh preset weight + Number of times the video is viewed × Twelfth preset weight; Wherein, a video playback time of 5 minutes or more is considered as a video being viewed.

[0067] Furthermore, after step S5, "evaluate the uploaded security training short videos and obtain the push index," the following steps are also included:

[0068] Step S7: Based on the push index and preset excellent, good, and poor thresholds, the safety training short videos are divided into four levels: excellent, good, average, and poor. Among them, safety training short videos with a push index higher than the excellent threshold are considered excellent videos; safety training short videos with a push index higher than the good threshold but not higher than the excellent threshold are considered good videos; safety training short videos with a push index not higher than the poor threshold are considered poor videos; and safety training short videos with other push indices are considered average videos.

[0069] Furthermore, the following steps are included after step S7:

[0070] Step S8: Count the total number of safety training short videos in each training folder. This number only includes the number of excellent, good, and average videos, and does not include the number of poor quality videos. Determine the difference between the total number of safety training short videos and the preset threshold. If the total number of safety training short videos is less than the preset threshold, issue a video collection instruction to the relevant positions regarding the fourth-level training courseware type corresponding to that training folder.

[0071] Furthermore, the following steps are included after step S7:

[0072] Step S9: Publish the training courseware compliance rate ranking list for each training folder, and update the ranking list in real time or periodically; the formula for calculating the training courseware compliance rate is:

[0073] The pass rate = the coverage rate of key training points for the fourth-level training courseware type × the thirteenth preset weight + the number of videos × (the percentage of excellent videos × the fourteenth preset weight + the percentage of good videos × the fifteenth preset weight + the percentage of average videos × the sixteenth preset weight) × the seventeenth preset weight; where the coverage rate of key training points refers to the percentage of the number of key points involved in the content of all safety training short videos in this training folder to the total number of key points of this fourth-level training courseware type.

[0074] Furthermore, the following steps are included after step S7:

[0075] Step S10: Based on the uploaded security training short video, return points to the uploading node; these points are determined based on the number of times the video is liked, saved, and shared.

[0076] It should be noted that, regarding the intelligent push process for safety training courseware for manufacturing enterprises, after the video solicitation notice is issued and the upload node uploads the safety training short video, the uploaded safety training short video can be processed according to steps S1-S5 of the construction method of the safety training courseware system for manufacturing enterprises. Furthermore, it can be understood that this is not limited to the issuance of a video solicitation notice; the upload node can also upload safety training short videos. The system can process all uploaded safety training short videos using the above process. Figure 3 As shown.

[0077] Conversely, regarding the construction of a safety training courseware system for manufacturing enterprises, after the system is completed, videos can be regularly and targeted to various positions.

[0078] In summary, the intelligent push method for safety training courseware for manufacturing enterprises provided by this invention, based on the job identifier bound to the upload node and combined with the developed video push model, pushes high-quality training video courseware that combines job requirements and individual preferences to each upload node, thereby improving the relevance and effectiveness of safety training. Furthermore, it performs quality and effectiveness evaluation and optimization on short safety training videos, autonomously judging the quality of training courseware, prioritizing the push of excellent video courseware, and autonomously eliminating inferior video courseware. Simultaneously, the system calculates the effective inventory of training courseware based on its quality and autonomously collects missing training courseware based on the effective inventory, supplemented by a series of incentive measures, to encourage upload nodes to specifically supplement the system with missing training courseware, ensuring the integrity of the training courseware system.

[0079] The steps in the intelligent push method and the steps in the construction method of this invention can be combined in various ways. In some specific embodiments, they are respectively as follows: Figure 4 , Figure 5As shown. It is understandable that, since some steps in the construction method can serve as a process for improving or enriching the training courseware system, and the upload timing in the intelligent push method is not limited to after the video call for submissions is issued, the combination of these steps is not limited to... Figure 4 , Figure 5 The situation is shown.

[0080] In addition, the present invention also provides an incentive mechanism.

[0081] The incentive elements of the incentive mechanism include: learning incentives, sharing incentives, high-quality courseware incentives, special response incentives, and other incentives;

[0082] The learning incentives include: watching training materials for ≤5 minutes, watching training materials for 5-30 minutes, watching training materials for ≥30 minutes, and completing an exam and correcting mistakes.

[0083] Among them, viewing training materials for ≤5 minutes, viewing training materials for 5-30 minutes, and viewing training materials for ≥30 minutes are further distinguished between working hours and non-working hours;

[0084] Working hours: 8:00-12:00 and 13:00-18:00 on legal working days; Non-working hours: time outside of working hours;

[0085] The sharing incentives include: publishing original videos and publishing reposted videos;

[0086] The incentives for high-quality courseware include: original videos receiving likes, reposted videos receiving likes, original videos being played, reposted videos being played, original videos being saved, and reposted videos being saved.

[0087] The specific response incentives include: Response to the solicitation of specific training courseware.

[0088] Other incentives include: daily check-in, daily login, first login reward, and inviting users.

[0089] Incentive points = Learning incentives (reward points × weight) + Sharing incentives (reward points × weight) + High-quality courseware incentives (reward points × weight) + Specific response incentives (reward points × weight) + Other incentives (reward points × weight).

[0090] Incentive points can be used to redeem prizes, etc.

[0091] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0092] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0093] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these changes and modifications should all fall within the protection scope of the claims of the present invention.

Claims

1. A method for constructing a production manufacturing enterprise safety training courseware system, characterized in that, Includes the following steps: Step S1: In response to the uploaded safety training short video and its description, the uploaded safety training short video is pre-classified by extracting keywords from the description. Based on the pre-classification results, corresponding training courseware types are provided for the uploading node to select. The node that uploads the safety training short video is denoted as the uploading node. There are multiple training courseware types, all of which are configured to be pre-loaded. The pre-classification results are some or all of the training courseware types, which are used for the uploading node to select one of the pre-classification results as the training courseware type for the safety training short video. Step S2: After selecting the training courseware type, further intelligent analysis is performed on the content of the safety training short video to obtain its intelligent analysis type. It is then determined whether the intelligent analysis type matches the training courseware type selected in Step S1. If the types do not match, the safety training short video is returned to the upload node, and Step S3 is executed. If the types match, the safety training short video and the selected training courseware type are pushed to a preset or selected subsequent node for review. Once the review is passed, Step S5 is executed. If the review fails, the safety training short video is returned to the upload node, and Step S3 is executed. At least one subsequent node is required; each subsequent node corresponds to the department head of the department where the upload node is located. Step S3: Prompt the upload node to select the training courseware type again, and wait for the upload node to select the training courseware type again; in response to the reselected training courseware type, determine whether the intelligent analysis type matches the reselected training courseware type; if the types match, the safety training short video is streamed to the preset or selected subsequent node for review, and after the review is passed, proceed to step S5; if the types do not match, feedback the selection confirmation information of the training courseware type is sent to the upload node for confirmation, and then proceed to step S4; Step S4: In response to the confirmation command, the safety training short video and its training courseware type are pushed to the safety review node corresponding to the upload node for initial quality review; if the initial quality review is passed, the safety training short video is transferred to a preset or selected subsequent node for review, and if the review is passed, step S5 is executed; if the initial quality review is not passed, the process returns to step S3; wherein, the safety review node is the node corresponding to the safety management position of the department where the upload node is located; Step S5: After all subsequent nodes have passed the review, the security training video is stored in the corresponding training folder in the database and then released to the public. The database is configured to have multiple independent training folders, each corresponding to a type of training courseware, which is used to store the security training videos of that type of training courseware. Step S6: Repeat steps S1 to S5 to build a safety training courseware system for manufacturing enterprises with short safety training videos.

2. The method for constructing a safety training courseware system for manufacturing enterprises according to claim 1, characterized in that, In step S1, before selecting the training courseware type, the upload node adds a new training courseware type to the system through external input, and uses the newly added training courseware type as the type corresponding to the safety training short video.

3. The method for constructing a safety training courseware system for manufacturing enterprises according to claim 1, characterized in that, The training courseware types are configured in a top-down four-level structure. The first level of training courseware types is the highest level, including: ideological awareness type and accident prevention capability type; the second level of training courseware types is relatively high, and consists of sub-types of the first level training courseware types; the third level of training courseware types is relatively low, and consists of the elements that lead to the accidents corresponding to the first level training courseware types; the fourth level of training courseware types is the lowest level, and consists of a further sub-type of the elements that lead to the accidents of the third level training courseware types. Each training folder is used to store a short safety training video of the fourth-level training courseware type.

4. The method for constructing a safety training courseware system for manufacturing enterprises according to claim 3, characterized in that, In cases with a four-level structure, if the selected training courseware type and the intelligent analysis type belong to different levels, then the types are considered mismatched.

5. The method for constructing a safety training courseware system for manufacturing enterprises according to claim 1, characterized in that, One or more intelligent analysis types are matched; If only one intelligent analysis type is matched, the selected training courseware type is considered a type match if it is the same as the intelligent analysis type; otherwise, it is considered a type mismatch. If multiple intelligent analysis types are matched, and the selected training courseware type is one of the intelligent analysis types, then it is considered a type match; otherwise, it is considered a type mismatch.

6. The method for constructing a safety training courseware system for manufacturing enterprises according to claim 1, characterized in that, In step S2, the intelligent analysis process includes: reading a security training short video, performing image recognition and speech recognition on the content of the security training short video, and generating an intelligent analysis type for the security training short video based on the image recognition results and speech recognition results.

7. The method for constructing a safety training courseware system for manufacturing enterprises according to claim 6, characterized in that, When using image recognition, each extracted video frame is matched with each keyword in the descriptive text to obtain the number of frames of the video image corresponding to each keyword. The keyword corresponding to the video image with the most frames is used to determine the image recognition result. When using speech recognition, each extracted audio frame is matched with each keyword in the descriptive text to obtain the number of audio frames corresponding to each keyword. The keyword corresponding to the audio with the most frames is used to determine the speech recognition result. Among them, the training courseware type corresponding to the one with the most occurrences of keywords in the image recognition result and the speech recognition result is used as the intelligent analysis type; when the number of occurrences of keywords in the image recognition result and the speech recognition result are equal, the training courseware type corresponding to the keywords in the speech recognition result is used as the intelligent analysis type.

8. The construction method of the production manufacturing enterprise safety training courseware system according to claim 1, characterized in that, In step S3, if the types do not match, the parent node is prompted to select the training courseware type again, and the step of "judging whether the intelligent analysis type matches the training courseware type selected again in response to the selection again" is performed; when the number of times of prompting the parent node to select the training courseware type again reaches a preset number of times, the selection confirmation information of the training courseware type is fed back to the parent node, and the confirmation is performed by the parent node.

9. A readable storage medium, characterized by, The readable storage medium comprises a stored program, wherein the program can execute the construction method of the production manufacturing enterprise safety training courseware system according to any one of claims 1 to 8 when running.

10. An electronic device, comprising: A processor and a memory are included; the memory stores computer readable instructions, and the processor is used to run the computer readable instructions, wherein the computer readable instructions execute the construction method of the production manufacturing enterprise safety training courseware system according to any one of claims 1 to 8 when running.

Images