CN117649319B - Intelligent teaching method, device, equipment and medium based on sea dangerous goods classification - Google Patents
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Abstract
The invention belongs to the technical field of intelligent teaching, and discloses an intelligent teaching method, device, equipment and storage medium based on sea dangerous goods classification, wherein after a user logs in, search factors input by the user on an interface are received, list data are obtained through searching, and the list data and learning options are displayed on the interface; acquiring learning data of a user, analyzing the learning data to obtain strong and weak item information of the user, and generating learning content recommendation for the user by a corresponding current recommendation algorithm; judging whether the user accepts the learning content according to the user feedback, if not, adjusting the current recommendation algorithm of the user to a corresponding target recommendation algorithm to generate new learning content, and re-judging until the user accepts, wherein when the user accepts, a learning path is generated according to the learning content and displayed on an interface, so that the recommendation algorithm for intelligently adjusting the learning content can be performed according to the personalized requirements of the student user, and a personalized learning path is generated, thereby improving the intelligent degree.
Description
Technical Field
The invention belongs to the technical field of intelligent teaching, and particularly relates to an intelligent teaching method, device, equipment and storage medium based on sea dangerous goods classification.
Background
In the current global trade setting, maritime has become the most dominant means of transporting goods between different countries and continents. Along with the transportation of a large amount of cargoes, dangerous goods management in maritime is particularly important. Improper handling and sorting of dangerous goods can lead to serious environmental pollution, property damage and even casualties. To ensure the safety of navigation, the International Maritime Organization (IMO) has formulated the international maritime Code (IMDG Code). The rules specify the requirements for sorting, packaging, labeling, transportation, etc. of various dangerous goods. However, due to the wide variety of dangerous goods and the continual renewal of new substances and technologies, it is particularly difficult for the maritime industry practitioners and students to correctly, quickly and categorize dangerous goods.
Traditional maritime dangerous goods classification teaching mainly relies on static paper teaching materials, theoretical teaching and field practice, and is not intuitive and practical. However, the update period of the paper teaching material is long, the latest regulations and technical progress may not be reflected in time, and the traditional theoretical teaching may cause the students to confuse the complicated classification rules and examples, so that the students are hard to understand deeply. While field practice is the most effective way of learning, it is not possible to provide each student with sufficient practical opportunities due to cost, safety and time constraints. These methods therefore suffer from late update, lack of interactivity, limited practical opportunities, etc.
At present, some digital teaching tools are developed, but according to research, the digital teaching tools are less applied to maritime dangerous goods classification teaching, for example, some classification teaching software provides basic knowledge and practice of product classification for students through a graphical interface and voice prompt. The software has the advantages that the learning interest and effect of students can be improved, but has the defects that the content is simpler, all dangerous goods types and rules cannot be covered, and the software cannot be adjusted according to the personalized requirements of the students, so that the intelligent degree is not enough.
Disclosure of Invention
The invention aims to provide an intelligent teaching method, device, equipment and storage medium based on marine dangerous goods classification, which can carry out intelligent adaptation adjustment according to individual demands of students so as to improve the degree of intellectualization.
The first aspect of the invention discloses an intelligent teaching method based on marine dangerous goods classification, which comprises the following steps:
After a user logs in, receiving search factors input by the user on an operation interface, wherein the search factors comprise a substance name, a danger category, definition, classification information and/or characteristic information;
retrieving list data according to the search factors, and displaying the list data and learning options on the operation interface; the list data comprises dangerous goods attributes, classification rules and specific cases;
Acquiring learning data of a user aiming at the learning options;
According to the learning data, analyzing and obtaining the information of the strength items of the user;
Generating learning content recommendation to a user by using a current recommendation algorithm corresponding to the strong and weak item information;
judging whether the user accepts the learning content according to feedback information input by the user within a preset time after the learning content is output;
if the user does not accept, the current recommendation algorithm of the user is adjusted to be a target recommendation algorithm corresponding to the feedback information, new learning content is generated according to the target recommendation algorithm and recommended to the user, and the step of judging whether the user accepts the learning content or not according to the feedback information input by the user within the preset time after the learning content is output is executed again, so that the cycle is performed until the user accepts the learning content;
and if the user accepts the learning content, generating a learning path of the user, and displaying the learning path on the operation interface.
In some embodiments, after the learning path is presented on the operator interface, the method further comprises:
Receiving a learning target input by a user aiming at the learning path, and generating a corresponding learning plan according to the learning target, wherein the learning plan comprises a plurality of learning chapters and recommended learning duration of each learning chapter;
Acquiring learning chapters completed by a user and the actual learning duration of each completed learning chapter in real time, and determining the current learning progress of the user;
And pushing reminding information periodically according to the current learning progress, wherein the reminding information is used for reminding a user whether the current learning progress accords with the learning plan.
In some embodiments, the method further comprises:
Acquiring the hall-following test results of the user aiming at each completed learning chapter in real time and accumulating the hall-following test times;
According to the hall test result and the accumulated hall test times, evaluating the grasping degree of the user on each knowledge point;
Carrying out weighted summation on the grasping sub-degree of each knowledge point to obtain the overall grasping degree of the user;
determining a user level according to the overall mastery degree, wherein the user level comprises a primary scholars, a middle level scholars or a high level scholars;
recommending learning resources with the same level as the user level to the user.
In some embodiments, after obtaining the learning chapters completed by the user and the actual learning duration of each completed learning chapter in real time, the method further includes:
Determining the accumulated learning duration of the user according to the actual learning duration of each completed learning chapter of the user;
When the accumulated learning duration reaches the appointed learning duration and the accumulated along with the hall test times reach the appointed times, a corresponding learning certificate is generated for the user.
In some embodiments, the learning options include case learning, knowledge testing, and interactive simulation; the acquiring learning data of the user for the learning options includes:
if the case learning selected by the user is detected, receiving analysis information input by the user aiming at the specific case, and outputting a correct answer and detailed analysis of the specific case;
if the fact that the user selects a knowledge test is detected, outputting a test question of a preset knowledge point, and correcting the received answer information input by the user aiming at the test question to obtain correction information;
if the user selection interaction simulation is detected, determining a target substance selected by the user on the operation interface, outputting a classification problem related to the substance attribute of the target substance, and receiving a classification answer input by the user aiming at the classification problem;
And obtaining learning data of the user for the learning options according to the analysis information, the correction information and/or the classification answers.
In some embodiments, prior to outputting the categorization problem related to the substance attribute of the target substance, the method further comprises:
Outputting the substance attribute of the target substance, and displaying the 3D model of the target substance;
Detecting an operation instruction of a user aiming at the 3D model, wherein the operation instruction comprises a drag, zoom or rotation instruction;
And controlling the 3D model to change the corresponding display position, display size or display angle according to the operation instruction.
In some embodiments, outputting a categorization problem related to a substance property of the target substance includes:
outputting a plurality of interaction scenes on the operation interface for selection by a user;
Determining a target interaction scene selected by a user;
And determining and outputting the classification problem of the target substance corresponding to the target interaction scene.
The second aspect of the invention discloses an intelligent teaching device based on marine dangerous goods classification, which comprises:
The receiving unit is used for receiving search factors input by a user on the operation interface after the user logs in, wherein the search factors comprise a substance name, a danger category, definition, classification information and/or characteristic information;
The searching unit is used for searching list data according to the searching factors and displaying the list data and learning options on the operation interface; the list data comprises dangerous goods attributes, classification rules and specific cases;
An acquisition unit, configured to acquire learning data of a user for the learning option;
the analysis unit is used for analyzing and obtaining the strength information of the user according to the learning data;
The recommending unit is used for generating learning content recommendation to the user by using a current recommending algorithm corresponding to the strong and weak item information;
the judging unit is used for judging whether the user accepts the learning content according to feedback information input by the user within a preset time after the learning content is output;
The adjusting unit is used for adjusting the current recommendation algorithm of the user to a target recommendation algorithm corresponding to the feedback information when the judging unit judges that the user is not accepted, generating new learning content recommendation to the user according to the target recommendation algorithm, triggering the judging unit to re-execute the operation of judging whether the user accepts the learning content according to the feedback information input by the user within the preset time after the learning content is output, and circulating until the user accepts the learning content;
And the planning unit is used for generating a learning path of the user according to the learning content when the judging unit judges that the user accepts, and displaying the learning path on the operation interface.
A third aspect of the invention discloses an electronic device comprising a memory storing executable program code and a processor coupled to the memory; the processor invokes the executable program code stored in the memory for executing the intelligent teaching method based on the sea-going hazard classification disclosed in the first aspect.
A fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the intelligent teaching method based on sea-going hazard classification disclosed in the first aspect.
The method has the advantages that after a user logs in, search factors input by the user on an operation interface are received, list data are obtained through search according to the search factors, and the list data and learning options are displayed on the operation interface; acquiring learning data of a user aiming at learning options, analyzing and acquiring strong and weak item information of the user, and generating learning content recommendation for the user by using a current recommendation algorithm corresponding to the strong and weak item information; and then judging whether the user accepts the learning content according to feedback information input by the user within a preset time after the learning content is output, if the user does not accept the learning content, adjusting the current recommendation algorithm of the user into a corresponding target recommendation algorithm, generating new learning content according to the target recommendation algorithm, and re-judging whether the user accepts the learning content, so that the cycle is completed until the user accepts the learning content, when the user accepts the learning content, generating a learning path of the user, and displaying the learning path on an operation interface, thereby intelligently adapting the recommendation algorithm for adjusting the learning content according to the personalized requirements of the student user to generate a personalized learning path, and further improving the intelligent degree.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles and effects of the invention.
Unless specifically stated or otherwise defined, the same reference numerals in different drawings denote the same or similar technical features, and different reference numerals may be used for the same or similar technical features.
FIG. 1 is a core software architecture of a digital teaching system specially designed for marine dangerous goods classification, disclosed in an embodiment of the present invention;
FIG. 2 is a flow chart of an intelligent teaching method based on sea hazard classification, disclosed in an embodiment of the invention;
FIG. 3 is a main interface screenshot of a user interface disclosed in an embodiment of the invention;
Fig. 4 is a schematic structural diagram of an intelligent teaching device based on sea dangerous goods classification, which is disclosed by the embodiment of the invention;
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Reference numerals illustrate:
401. a receiving unit; 402. a retrieval unit; 403. an acquisition unit; 404. an analysis unit; 405. a recommending unit; 406. a judging unit; 407. an adjusting unit; 408. a planning unit; 501. a memory; 502. a processor.
Detailed Description
Unless defined otherwise or otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In the context of a realistic scenario in connection with the technical solution of the invention, all technical and scientific terms used herein may also have meanings corresponding to the purpose of the technical solution of the invention. The terms "first" and "second" … "as used herein are used merely for distinguishing between names and not necessarily for describing a particular amount or sequence. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood that when an element is referred to as being "fixed" to another element, it can be directly fixed to the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present; when an element is referred to as being "mounted to" another element, it can be directly mounted to the other element or intervening elements may also be present. When an element is referred to as being "disposed on" another element, it can be directly on the other element or intervening elements may also be present.
As used herein, unless specifically stated or otherwise defined, "the" means that the feature or technical content mentioned or described before in the corresponding position may be the same or similar to the feature or technical content mentioned. Furthermore, the terms "comprising," "including," and "having," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses an intelligent teaching method based on sea dangerous goods classification, which can be realized through computer programming. The execution main body of the method can be electronic equipment such as a computer, a notebook computer, a tablet computer, a smart phone and the like, or an intelligent teaching device embedded in the electronic equipment and based on sea dangerous goods classification, and the invention is not limited to the above. In order that the invention may be readily understood, a more particular description of specific embodiments thereof will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
The embodiment of the invention provides a digital teaching system specially designed for classifying maritime dangerous goods, which is embedded in electronic equipment, and as shown in fig. 1, a core software architecture of the digital teaching system comprises an operation interface (namely a front end UI), a display layer, a service layer, a data layer, a database layer and an operation environment layer. Wherein,
Front end UI layer: the interface composed of the real-time data display component and the interactive simulation component is realized by using the HTML and CSS technology, so that the user friendliness and the interactivity are ensured. This is critical to the student's intuitive experience and operation during the learning process;
display layer: the module engine rendering and Ajax interaction ensure real-time transmission and display of data, so that students can immediately see the operation results of the students;
Service layer: the content management, user management, system setting and other modules provide a platform for the education workers to customize the content according to their teaching needs, while ensuring that the learning progress and performance of each student is correctly tracked and managed;
Data layer: ensuring that all data interactions, storage and retrieval are accurate and efficient, which is necessary for students to use the digital teaching system seamlessly in the class;
Database layer: storing student data, course content, test results and other information;
running environment layer: the digital teaching system may be deployed on a cloud host, a stand-alone server, or a virtual machine, depending on the IT infrastructure of the school.
The back end of the digital teaching system is tightly connected with an integrated data center. The data center adopts a Web crawler technology to grasp the latest information of International maritime dangerous goods rule (IMDG Code) from International maritime organization (International Maritime Organization, IMO) and other authoritative institutions every day, and the Web crawler automatically browses a target website to extract required data and stores the data in the data center. In addition, the instant data update can be obtained through the notification mode of the application programming interface (ApplicationProgrammingInterface, API), so that the teaching data can be automatically and dynamically updated, and the students can be ensured to obtain the latest and most accurate information. Once the Web crawler has extracted new data from the target Web site, the update module of the data center may be triggered. This update module first performs quality checks and cleans on the new data, e.g., removes null values, duplicate items, and inconsistent data; and converting the original data into a unified format, such as JSON or XML, to facilitate storage and querying; to increase query speed, indexing tools such as elastic search are used to index commonly used query fields. And then compares it to existing data to determine if there are any changes. If a change in data is detected, the module automatically updates the database and pushes the updated data to the user interface. When the user uses the digital teaching system, the latest and most authoritative classified data is always faced, so that the learning accuracy is greatly improved.
Meanwhile, the back end of the data center comprises a rule engine, and the rule engine comprises laws and regulations and industry standards of various regions or countries. When a user logs in and selects the location area, the rule engine automatically matches the corresponding categorization data and adjusts the content displayed on the user interface. For example, when the user selects "China", the rules engine automatically invokes the data of "China maritime dangerous goods rules" (CHDG Code) and displays it on the user interface. Thus, the user can grasp the classification requirements of different areas, and unnecessary misunderstanding and disputes are avoided. The rule engine is based on a decision tree algorithm, and is used for rapidly matching and providing corresponding classification data.
Specifically, the data center adopts a micro-service architecture, and each service is responsible for a single function, so that high availability and high concurrency are ensured. This design also facilitates fast iteration and expansion. Meanwhile, a load equalizer is adopted to automatically distribute user requests, so that smooth operation of the system is ensured. And the database adopts cluster configuration, so that the read-write speed is improved and a fault switching mechanism is provided. The data backup strategy comprises daily hot standby and weekly cold standby, and the integrity and reliability of the data are ensured. The data center has formal data cooperation relation with IMO and other navigation organizations, and acquires authoritative data through RESTful API interface. This API interface uses oauth2.0 for security validation. Besides regular data capture, the method also subscribes to official data update notification of IMO, and ensures data instantaneity.
As shown in fig. 2, the embodiment of the invention provides an intelligent teaching method based on sea dangerous goods classification, which comprises the following steps 210 to 280:
210. and after the user logs in, receiving a search factor input by the user on the operation interface, wherein the search factor comprises a substance name, a danger category, definition, classification information and/or characteristic information.
Among the hazard classes include, but are not limited to, flammable liquids, oxidants, toxic substances, radioactive substances, flammable solids, self-reacting substances, solid water removal substances, infectious substances, corrosive substances, and the like.
The operation interface refers to a user operation interface, such as an interface screenshot shown in fig. 3, fig. 3 is a main interface design of the user operation interface, from which a simulation module, a case library, a knowledge testing module (selected according to the classification of several dangerous goods in international commodity practice), a learning report, an administrator and other modules, and a clear query field are seen, and are used for user input and searching of substances; the substance list area displays the search results; the categorization rules display area provides the user with categorization rules and specifications related to the selected substance, labels suitable for packaging and application. In addition, the shortcut entries of the simulation module and the knowledge testing module also occupy conspicuous positions on the main interface. Preferably, the framework of the user operation interface uses a front-end framework such as act or Vue, so as to ensure the response speed and user interaction experience of the interface.
220. And retrieving list data according to the search factors, and displaying the list data and learning options on an operation interface. The list data comprises dangerous goods attributes, classification rules and specific cases.
The user can input keywords such as a substance name, a CAS number and the like, the classification result is obtained in real time, the search query result is displayed in a list form, and the user is supported to carry out quick screening and sorting according to the types, the risk levels and the like. The list shows the dangerous goods attribute, the classifying rule and the specific case of various common maritime dangerous goods. The dangerous goods attributes comprise brief descriptions and icons of each maritime dangerous goods, such as material types, dangerous levels, transportation modes, occurrence places and the like, so that users can conveniently and quickly locate the areas which are concerned or need to be enhanced. For example, flammable liquids such as gasoline, alcohols, certain perfumes, certain adhesives, etc., describe the flash point, vapor pressure, etc. characteristics of certain oils under certain conditions, and their safe storage methods in certain containers. Also for example, oxidizing agents such as hydrogen peroxide, chlorates, certain fertilizers, etc., describe reactions that may occur when certain chemicals come into contact with other substances, as well as precautions. The categorization rules detail categorization rules, packaging and labeling requirements associated with the selected materials. The content layout is clear, and the user is supported to zoom in, zoom out and scroll to view. The specific case refers to a real maritime dangerous goods classifying case, so that a user can deeply learn and apply classifying rules from a real scene.
230. Learning data of a user for learning options is acquired.
In the present invention, learning options may include case learning, knowledge testing, and interactive simulation. The acquiring learning data of the user for the learning option in step 230 may include the following steps 2301 to 2304:
2301. if the user selection case learning is detected, the analysis information input by the user aiming at the specific case is received, and the correct answer and detailed analysis of the specific case are output.
If the user selects case learning, the user reads and attempts to parse the case, and the system provides the correct answer and detailed parsing. That is, the user first attempts to complete the categorization by himself, and then looks at the answers and detailed resolution given by the system. By this "middle school" approach, the user's understanding of the categorization rules is enhanced.
After learning each case, the system will give a score and feedback based on the user's selections. If the user is confused or frequently makes mistakes in some way, the system will recommend relevant learning materials and additional exercise topics to him, ensuring his full grasp of knowledge points.
2302. If the fact that the user selects the knowledge test is detected, outputting a test question of a preset knowledge point, and correcting the received answer information input by the user aiming at the test question to obtain correction information.
If the user selects the knowledge test, the system will immediately correct and display the score after the user completes the test, so that the user can know the own performance. For questions that the user answers wrong, the system can provide detailed resolution to help the user understand the cause of the error. The system can also review knowledge points related to questions which are answered by the user, so that the user can understand each knowledge point deeply. Based on the test performance of the user, the system generates personalized learning suggestions, such as chapters needing important review, recommended learning materials and the like, and recommends the personalized learning suggestions to the user.
2303. If the user selection interaction simulation is detected, determining a target substance selected by the user on an operation interface, outputting a classification problem related to the substance attribute of the target substance, and receiving a classification answer input by the user aiming at the classification problem.
If the user selects interactive simulation, the user is guided to conduct dangerous goods classification simulation step by step through the classification problem, and feedback and advice are provided in real time. In the embodiment of the invention, the complete process of classifying and simulating the marine dangerous goods by the user can be provided. Starting from the selection of the substance, the user will be guided through critical steps of risk level determination, package selection, label printing, etc., each of which is accompanied by prompts and advice by the system, thereby ensuring the authenticity and accuracy of the simulation.
In order to provide an immersive simulation environment, users can go deep into the process of classifying dangerous goods, and challenges similar to real environments are faced. Preferably, in step 2303, the manner of outputting the classification problem related to the substance attribute of the target substance may specifically be: outputting a plurality of interaction scenes on an operation interface for selection by a user, determining a target interaction scene selected by the user, and determining and outputting classification problems related to the substance attribute of the target substance under the target interaction scene.
Wherein a plurality of interaction scenarios are used to simulate conditions that a substance may encounter during transportation. For example, a user may simulate wave impact of a substance during marine transport, or reaction in a high temperature environment. Each scenario may provide a series of categorization issues to the user, such as selecting the correct packaging material or determining the appropriate storage conditions. Additionally, by way of example, the categorization problem associated with the substance property of the target substance may be: "what changes this material will undergo at high temperatures? Or what reactions may occur if this substance is mixed with another substance? The user needs to judge and select according to own knowledge, so that the corresponding classifying answer is input. Thus, randomness can be added to the simulation process, so that each simulation has different scenes, and the learning challenge is increased. Preferably, a machine learning algorithm such as a decision tree or a neural network can be used to dynamically adjust the simulation difficulty according to the selection and input of the user.
2304. And obtaining learning data of the user aiming at the learning options according to the analysis information, the correction information and/or the classifying answers.
Further, in order to facilitate steps such as packaging and label selection, when a user selects a certain substance as a target substance, a WebGL or three.js library may be used to provide 3D simulation, so that the user can intuitively see the packaging and label effect of the substance. Thus, before outputting the classification problem related to the substance property of the target substance, such as chemical composition, physical state, and risk, is output in addition to the substance property; a 3D model of the target substance may also be presented, which may be viewed from various angles by a user through drag, zoom, and rotate operations, to more intuitively understand the characteristics of the substance. Specifically, the electronic device may detect an operation instruction of the user for the 3D model, where the operation instruction includes a drag, zoom, or rotation instruction, and control the 3D model to change a corresponding display position, display size, or display angle according to the operation instruction. The dragging, zooming and rotating instructions respectively correspond to the display position, the display size and the display angle, namely, when an operation instruction input by a user is a dragging instruction, the 3D model is controlled to change the corresponding display position; when an operation instruction input by a user is a scaling instruction, controlling the 3D model to transform a corresponding display size; when the operation instruction input by the user is a rotation instruction, the 3D model is controlled to change the corresponding display angle.
240. And analyzing and obtaining the information of the strength items of the user according to the learning data.
250. And generating learning content recommendation to the user by using a current recommendation algorithm corresponding to the strong and weak item information.
The system analyzes the strong items, weak items, etc. of the user, and then recommends courses, cases, etc. for the user based on the analysis result. Current recommendation algorithms include, but are not limited to, collaborative filtering or deep learning algorithms, among others.
260. Judging whether the user accepts the learning content according to feedback information input by the user within a preset time after the learning content is output. If the user does not accept, go to step 270 and then go back to step 260, so as to loop until the user accepts; if the user accepts, go to step 280.
The user can feed back the recommended learning content, for example, feedback information is 'too difficult' or 'too simple', which indicates that the user does not accept the current difficulty, for example, feedback information is 'known', which indicates that the user accepts the current difficulty.
270. And adjusting the current recommendation algorithm of the user into a target recommendation algorithm corresponding to the feedback information, and generating new learning content recommendation for the user according to the target recommendation algorithm.
By adjusting the recommendation algorithm according to user feedback, user requirements can be better met. Based on the new recommendation algorithm, new learning content after adjustment is generated.
280. And generating a learning path of the user according to the learning content, and displaying the learning path on the operation interface.
A personalized learning path is generated based on the learning content, and after the learning path is presented on the operation interface, the user can see the learning path customized for his/her body on the interface.
As an alternative embodiment, after the learning path is shown on the operation interface, the electronic device may further perform the following steps S11 to S13:
S11, receiving a learning target input by a user aiming at a learning path, and generating a corresponding learning plan according to the learning target, wherein the learning plan comprises a plurality of learning chapters and recommended learning duration of each learning chapter.
S12, acquiring the learning chapters completed by the user and the actual learning duration of each completed learning chapter in real time, and determining the current learning progress of the user.
And S13, periodically pushing reminding information according to the current learning progress, wherein the reminding information is used for reminding the user whether the current learning progress accords with the learning plan.
In the present invention, the user can set his own learning target, such as "grasp the classification rule of all flammable liquids within one month". Based on the learning objectives of the user, the system automatically generates a reasonable learning plan listing the chapters that must be learned and the recommended learning duration. The system will then send a reminder periodically telling the user if the learning progress is consistent with the plan and giving corresponding advice and encouragement.
By implementing steps S11 to S13, the system tracks the chapters completed by the user, the cases watched and the tests completed in real time, thereby tracking the learning progress of the user and realizing dynamic learning analysis. And the reminding information is pushed regularly, so that each link is fully learned.
The invention also provides diversified test modes, for example, each time a chapter is learned, a user can exercise the chapter, and the core knowledge points of the chapter are consolidated. In addition, in order to simulate the actual dangerous goods classification examination, the device can also perform a staged simulation test function, wherein various question types such as single selection, multiple selection, judgment, filling, simple answering and the like are included. In the learning process, the system can be used for checking the situation of the user in real time by checking the small measurement along with the hall at random. Thus, as an alternative embodiment, the electronic device may also perform the following steps S21-S25:
S21, acquiring the hall-following test results of the user aiming at each completed learning chapter in real time, and accumulating the hall-following test times.
S22, evaluating the grasping degree of the user on each knowledge point according to the hall test result and the accumulated hall test times.
S23, carrying out weighted summation on the grasping sub-degree of each knowledge point to obtain the overall grasping degree of the user.
S24, determining the user level according to the overall mastering degree.
Wherein the user level includes a primary scholars, a middle scholars, or a high scholars.
S25, recommending learning resources with the same level as the user level to the user.
Through the small test and the along-with-the-hall check, the system can evaluate the grasping degree of the user on each knowledge point and give further learning advice according to the requirement. That is, the system can automatically generate proper test questions according to the learning history and the ability of the user, so as to ensure that the difficulty level of the test is matched with the actual level of the user.
For advanced learner users, a deep challenge model is provided that includes more difficult topics and complex case analysis. Further, advanced learner users can also create and share their own cases, communicating and competing with other users. Through the test and feedback mechanism, a user can not only obtain the immediate evaluation and guidance of the system, but also conduct targeted review and improvement according to the actual situation of the user, so that the firm grasp of knowledge is ensured.
As an optional implementation manner, the accumulated learning duration of the user can be determined according to the actual learning duration of each completed learning chapter of the user; when the accumulated learning duration reaches the appointed learning duration and the accumulated along-with-hall test times reach the appointed times, a corresponding learning certificate is generated for the user. That is, when a user completes a certain number of tests and studies, the system will generate a study certificate for him, proving his grasp on the maritime hazard classification knowledge.
In order to enhance interactivity and practical application sense, the invention also provides a role playing mode. In this mode, the user may choose to play a different role, such as cargo inspector, crew, or port manager. Each character has its own unique tasks and challenges. The user may also cooperate with other online users to perform a task in concert, such as ensuring safe transport of a large cargo. Specifically, as an optional implementation manner, when detecting a role playing mode starting instruction input by a user, the electronic device may further output multiple roles for selection by the user, determine a target role selected by the user, allocate a role task corresponding to the target role to the user, output the role task, and track completion of the role task by the user. Through the rich role playing interaction simulation, a user can not only grasp the classification knowledge of dangerous goods in theory, but also exercise and perfect continuously in practical application, and high-efficiency and safe operation in a real environment is ensured.
The following is a simplified example illustrating how a user learns about the classification of maritime hazards by the present invention, comprising the following steps a-f:
a. Logging in a system: the user first logs in at the main interface. The new user needs to register and fill in the basic information first.
B. And (3) selecting a learning module: at the main interface, the user may select "simulation module", "case learning module", or "test module".
C. simulation was performed: in the simulation module, the user first selects a substance from the list of substances. The system then directs the user to complete the simulation according to the actual sorting process, such as determining risk, selecting packaging materials, printing labels, etc.
D. study case: at the case learning module, the user may select a case of interest to learn. The system will provide detailed categorization rules and resolution to aid the user in understanding.
E. testing: at the test module, the user can select different topics for testing. The system will give a score and feedback based on the user's answer.
F. viewing a learning report: after learning is completed, the user can check the learning report of the user, and know the learning progress and grasp condition of the user.
As shown in fig. 4, the embodiment of the invention discloses an intelligent teaching device based on sea dangerous goods classification, which comprises a receiving unit 401, a retrieving unit 402, an obtaining unit 403, an analyzing unit 404, a recommending unit 405, a judging unit 406, an adjusting unit 407 and a planning unit 408, wherein,
A receiving unit 401, configured to receive, after a user logs in, a search factor input by the user on an operation interface, where the search factor includes a substance name, a risk category, a definition, classification information, and/or characteristic information;
a retrieving unit 402, configured to retrieve list data according to the search factor, and display the list data and the learning options on the operation interface; the list data comprises dangerous goods attributes, classification rules and specific cases;
An acquiring unit 403, configured to acquire learning data of a user for learning options;
an analysis unit 404, configured to obtain information of the strength item of the user according to the learning data;
a recommending unit 405, configured to generate a learning content recommendation for the user according to a current recommendation algorithm corresponding to the strong and weak item information;
a judging unit 406, configured to judge whether the user accepts the learning content according to feedback information input by the user within a preset time period after the learning content is output;
An adjusting unit 407, configured to adjust, when the determining unit 406 determines that the user does not accept, the current recommendation algorithm of the user to a target recommendation algorithm corresponding to the feedback information, generate a new learning content recommendation according to the target recommendation algorithm, and trigger the determining unit 406 to re-execute an operation of determining whether the user accepts the learning content according to the feedback information input by the user within a preset time period after the learning content is output, so as to cycle until the user accepts the learning content;
And a planning unit 408, configured to generate a learning path of the user according to the learning content when the determining unit 406 determines that the user accepts, and display the learning path on the operation interface.
As an alternative embodiment, the intelligent teaching device shown in fig. 4 may further include the following units not shown:
The objective planning unit is configured to receive a learning objective input by a user for the learning path after the planning unit 408 displays the learning path on the operation interface, and generate a corresponding learning plan according to the learning objective, where the learning plan includes a plurality of learning chapters and a recommended learning duration of each learning chapter;
The progress tracking unit is used for acquiring the learning chapters completed by the user and the actual learning duration of each completed learning chapter in real time and determining the current learning progress of the user;
The reminding unit is used for regularly pushing reminding information according to the current learning progress, and the reminding information is used for reminding a user whether the current learning progress accords with the learning plan.
Further optionally, the intelligent teaching device shown in fig. 4 may further include the following units not shown:
The accumulation unit is used for acquiring the hall-following test results of the user aiming at each completed learning chapter in real time and accumulating the hall-following test times;
The evaluation unit is used for evaluating the grasping degree of the user on each knowledge point according to the hall test result and the accumulated hall test times; carrying out weighted summation on the grasping sub-degree of each knowledge point to obtain the overall grasping degree of the user;
The grading unit is used for determining a user level according to the overall mastery degree, wherein the user level comprises a primary scholars, a middle scholars or a high scholars;
correspondingly, the recommending unit 405 is further configured to recommend learning resources with a level equal to that of the user to the user.
As an optional implementation manner, the intelligent teaching device shown in fig. 4 may further include an authentication unit, not shown, configured to determine, after the progress tracking unit acquires the learning chapters completed by the user and the actual learning duration of each completed learning chapter in real time, an accumulated learning duration of the user according to the actual learning duration of each completed learning chapter of the user; when the accumulated learning duration reaches the appointed learning duration and the accumulated along-with-hall test times reach the appointed times, a corresponding learning certificate is generated for the user.
As an alternative embodiment, the learning options include case learning, knowledge testing, and interactive simulation; the acquisition unit 403 may include the following sub-units, not shown:
the case learning subunit is used for receiving analysis information input by a user aiming at a specific case when the case learning selected by the user is detected, and outputting a correct answer and detailed analysis of the specific case;
The knowledge testing subunit is used for outputting test questions of preset knowledge points when detecting that a user selects a knowledge test, and correcting the received answer information input by the user aiming at the test questions to obtain correction information;
The interactive simulation sub-unit is used for determining a target substance selected by a user on an operation interface when detecting that the user selects interactive simulation, outputting a classification problem related to the substance attribute of the target substance and receiving a classification answer input by the user aiming at the classification problem;
And the acquisition subunit is used for acquiring learning data of the user aiming at the learning options according to the analysis information, the correction information and/or the classification answers.
Further optionally, before outputting the classification problem related to the substance attribute of the target substance, the interactive simulation subunit further outputs the substance attribute of the target substance, and displays the 3D model of the target substance; detecting an operation instruction of a user aiming at the 3D model, wherein the operation instruction comprises a drag instruction, a zoom instruction or a rotation instruction; and controlling the 3D model to change the corresponding display position, display size or display angle according to the operation instruction.
Further optionally, the manner in which the interactive simulation subunit is configured to output the classification problem related to the substance attribute of the target substance is specifically:
The interactive simulation subunit is used for outputting a plurality of interactive scenes on an operation interface for selection by a user; determining a target interaction scene selected by a user; and determining and outputting the classification problem of the target substance corresponding to the target interaction scene.
As shown in fig. 5, an embodiment of the present invention discloses an electronic device comprising a memory 501 storing executable program code and a processor 502 coupled to the memory 501;
The processor 502 invokes the executable program code stored in the memory 501 to execute the intelligent teaching method based on the classification of the marine dangerous goods described in the above embodiments.
The embodiment of the invention also discloses a computer readable storage medium which stores a computer program, wherein the computer program enables a computer to execute the intelligent teaching method based on the sea dangerous goods classification described in the above embodiments.
The foregoing embodiments are provided for the purpose of exemplary reproduction and deduction of the technical solution of the present invention, and are used for fully describing the technical solution, the purpose and the effects of the present invention, and are used for enabling the public to understand the disclosure of the present invention more thoroughly and comprehensively, and are not used for limiting the protection scope of the present invention.
The above examples are also not an exhaustive list based on the invention, and there may be a number of other embodiments not listed. Any substitutions and modifications made without departing from the spirit of the invention are within the scope of the invention.
Claims (7)
1. An intelligent teaching method based on sea dangerous goods classification is characterized by comprising the following steps:
After a user logs in, receiving search factors input by the user on an operation interface, wherein the search factors comprise a substance name, a danger category, definition, classification information and/or characteristic information;
retrieving list data according to the search factors, and displaying the list data and learning options on the operation interface; the list data comprises dangerous goods attributes, classification rules and specific cases;
Acquiring learning data of a user aiming at the learning options;
According to the learning data, analyzing and obtaining the information of the strength items of the user;
Generating learning content recommendation to a user by using a current recommendation algorithm corresponding to the strong and weak item information;
judging whether the user accepts the learning content according to feedback information input by the user within a preset time after the learning content is output;
if the user does not accept, the current recommendation algorithm of the user is adjusted to be a target recommendation algorithm corresponding to the feedback information, new learning content is generated according to the target recommendation algorithm and recommended to the user, and the step of judging whether the user accepts the learning content or not according to the feedback information input by the user within the preset time after the learning content is output is executed again, so that the cycle is performed until the user accepts the learning content;
If the user accepts the learning content, generating a learning path of the user, and displaying the learning path on the operation interface;
The learning options comprise case learning, knowledge testing and interactive simulation; the acquiring learning data of the user for the learning options includes:
if the case learning selected by the user is detected, receiving analysis information input by the user aiming at the specific case, and outputting a correct answer and detailed analysis of the specific case;
if the fact that the user selects a knowledge test is detected, outputting a test question of a preset knowledge point, and correcting the received answer information input by the user aiming at the test question to obtain correction information;
if the user selection interaction simulation is detected, determining a target substance selected by the user on the operation interface, outputting a classification problem related to the substance attribute of the target substance, and receiving a classification answer input by the user aiming at the classification problem;
Obtaining learning data of the user for the learning options according to the analysis information, the correction information and/or the classification answers;
The way of outputting the classification problem related to the substance attribute of the target substance is specifically as follows: outputting a plurality of interaction scenes on the operation interface for selection by a user, determining a target interaction scene selected by the user, determining a classification problem related to the substance attribute of the target substance under the target interaction scene and outputting the classification problem; wherein, a plurality of interaction scenes are used for simulating the conditions possibly encountered by the substance in the transportation process;
Wherein prior to outputting the categorization problem related to the substance attribute of the target substance, the method further comprises: outputting a substance attribute of the target substance and displaying a 3D model of the target substance; detecting an operation instruction of a user aiming at a 3D model, wherein the operation instruction comprises a dragging, zooming or rotating instruction, and controlling the 3D model to change a corresponding display position, display size or display angle according to the operation instruction; wherein the drag, zoom, and rotate instructions correspond to a display position, a display size, and a display angle, respectively.
2. The intelligent teaching method based on marine threat item categorization of claim 1, wherein after presenting the learning path on the operation interface, the method further comprises:
Receiving a learning target input by a user aiming at the learning path, and generating a corresponding learning plan according to the learning target, wherein the learning plan comprises a plurality of learning chapters and recommended learning duration of each learning chapter;
Acquiring learning chapters completed by a user and the actual learning duration of each completed learning chapter in real time, and determining the current learning progress of the user;
And pushing reminding information periodically according to the current learning progress, wherein the reminding information is used for reminding a user whether the current learning progress accords with the learning plan.
3. The intelligent teaching method based on marine threat categorization of claim 2, wherein the method further comprises:
Acquiring the hall-following test results of the user aiming at each completed learning chapter in real time and accumulating the hall-following test times;
According to the hall test result and the accumulated hall test times, evaluating the grasping degree of the user on each knowledge point;
Carrying out weighted summation on the grasping sub-degree of each knowledge point to obtain the overall grasping degree of the user;
determining a user level according to the overall mastery degree, wherein the user level comprises a primary scholars, a middle level scholars or a high level scholars;
recommending learning resources with the same level as the user level to the user.
4. The intelligent teaching method based on marine dangerous article classification according to claim 3, wherein after obtaining the learning chapters completed by the user and the actual learning duration of each completed learning chapter in real time, the method further comprises:
Determining the accumulated learning duration of the user according to the actual learning duration of each completed learning chapter of the user;
When the accumulated learning duration reaches the appointed learning duration and the accumulated along with the hall test times reach the appointed times, a corresponding learning certificate is generated for the user.
5. Intelligent teaching device based on marine dangerous goods are categorized, a serial communication port, include:
The receiving unit is used for receiving search factors input by a user on the operation interface after the user logs in, wherein the search factors comprise a substance name, a danger category, definition, classification information and/or characteristic information;
The searching unit is used for searching list data according to the searching factors and displaying the list data and learning options on the operation interface; the list data comprises dangerous goods attributes, classification rules and specific cases;
An acquisition unit, configured to acquire learning data of a user for the learning option;
the analysis unit is used for analyzing and obtaining the strength information of the user according to the learning data;
The recommending unit is used for generating learning content recommendation to the user by using a current recommending algorithm corresponding to the strong and weak item information;
the judging unit is used for judging whether the user accepts the learning content according to feedback information input by the user within a preset time after the learning content is output;
The adjusting unit is used for adjusting the current recommendation algorithm of the user to a target recommendation algorithm corresponding to the feedback information when the judging unit judges that the user is not accepted, generating new learning content recommendation to the user according to the target recommendation algorithm, triggering the judging unit to re-execute the operation of judging whether the user accepts the learning content according to the feedback information input by the user within the preset time after the learning content is output, and circulating until the user accepts the learning content;
The planning unit is used for generating a learning path of the user according to the learning content when the judging unit judges that the user accepts, and displaying the learning path on the operation interface;
the learning options comprise case learning, knowledge testing and interactive simulation; the acquisition unit comprises the following subunits:
The case learning subunit is used for receiving analysis information input by a user aiming at a specific case when the case learning selected by the user is detected, and outputting a correct answer and detailed analysis of the specific case;
The knowledge testing subunit is used for outputting test questions of preset knowledge points when detecting that a user selects a knowledge test, and correcting the received answer information input by the user aiming at the test questions to obtain correction information;
The interactive simulation sub-unit is used for determining a target substance selected by a user on an operation interface when detecting that the user selects interactive simulation, outputting a classification problem related to the substance attribute of the target substance and receiving a classification answer input by the user aiming at the classification problem;
The acquisition subunit is used for acquiring learning data of a user aiming at learning options according to the analysis information, the correction information and/or the classification answers;
The interactive simulation subunit is used for outputting the substance attribute of the target substance and displaying the 3D model of the target substance before outputting the classification problem related to the substance attribute of the target substance; detecting an operation instruction of a user aiming at the 3D model, wherein the operation instruction comprises a drag instruction, a zoom instruction or a rotation instruction; according to the operation instruction, controlling the 3D model to change the corresponding display position, display size or display angle; wherein the drag, zoom and rotate instructions correspond to the display position, display size and display angle, respectively;
The mode of the interactive simulation subunit for outputting the classification problem related to the substance attribute of the target substance is specifically as follows: the interactive simulation subunit is used for outputting a plurality of interactive scenes on the operation interface for selection by a user; determining a target interaction scene selected by a user; and determining and outputting the classification problem of the target substance corresponding to the target interaction scene.
6. An electronic device comprising a memory storing executable program code and a processor coupled to the memory; the processor invokes the executable program code stored in the memory for performing the intelligent teaching method based on marine threat item categorization of any of claims 1 to 4.
7. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the intelligent teaching method based on marine threat categorization according to any of claims 1 to 4.
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