Training system and method for field of survey design, and storage medium and device

Abstract

Provided in the present application are a training system and method for the field of survey design, and a storage medium and a device. The training system comprises: a data collection module, which is configured to acquire student data and training data in the field of survey design; a knowledge graph construction module, which is configured to construct a training knowledge graph on the basis of the training data; a profile generation module, which is configured to generate a student profile on the basis of the student data, and generate a course profile on the basis of a training course in the field of survey design; and a training plan generation module, which is configured to use an artificial intelligence model to generate a personalized training plan on the basis of the student profile, the course profile and the training knowledge graph. The training system can generate a personalized training plan and has good training effects.

Description

Training system and method, storage medium and equipment for surveying and design fields Technical Field

[0001] The present application belongs to the field of surveying and design technology, and relates to a training system and method, and in particular to a training system and method, storage medium and equipment for the field of surveying and design. Background Art

[0002] In the field of surveying and design, learning and improving professional knowledge requires deep practical experience and a solid accumulation of theoretical knowledge. However, existing talent development and training methods primarily convey knowledge through text, images, or videos, which lack intuitiveness and lead to poor learning outcomes. Furthermore, traditional learning methods fail to effectively organize and present professional and management knowledge in surveying and design, making it difficult for learners to build a complete knowledge system.

[0003] Summary of the Invention

[0004] The purpose of this application is to provide a training system and method, storage medium and equipment for the field of surveying and design, which are used to provide personalized training for students in the field of surveying and design.

[0005] In a first aspect, the present application provides a training system for the field of surveying and design, and the training system includes: a data acquisition module for acquiring student data and training data in the field of surveying and design; a knowledge graph construction module for constructing a training knowledge graph based on the training data; a portrait generation module for generating a student portrait based on the student data, and generating a course portrait based on the training courses in the field of surveying and design; a training program generation module for generating a personalized training program based on the student portrait, the course portrait and the training knowledge graph using an artificial intelligence model.

[0006] In an implementation of the first aspect, the training system further includes a data processing module, which is configured to pre-process the data acquired by the data acquisition module.

[0007] In an implementation of the first aspect, the training system further includes a visualization presentation module, which is used to visualize the student portrait, the course portrait and / or the training knowledge graph.

[0008] In an implementation of the first aspect, the training system also includes an effect evaluation module, which is used to obtain the trainee's learning situation and the trainee's competence level for his or her professional position. The training program generation module is also used to optimize the training program based on the trainee's learning situation and the competence level.

[0009] In an implementation of the first aspect, the trainee's competence level for his or her professional position is described through any one or more of the following dimensions: professional ability, execution ability, innovation ability, collaboration ability, and growth ability.

[0010] In an implementation of the first aspect, the training system further includes an online training module, which is used to train the trainees according to the training plan using virtual reality and / or augmented reality technology, and / or to provide the trainees with the latest course recommendations, general course recommendations and / or personalized course recommendations.

[0011] In an implementation of the first aspect, the training system also includes an interactive communication module and / or a training management module, wherein: the interactive communication module is used to provide an interactive communication platform for the trainees; the training management module is used to visualize the training status and / or course status, and perform course setting, professional management, project management and / or trainee management according to instructions.

[0012] In a second aspect, an embodiment of the present application provides a training method for the field of surveying and design, the training method comprising: obtaining student data and training data in the field of surveying and design; generating a training knowledge graph based on the training data; generating a student portrait based on the student data, and generating a course portrait based on the training courses in the field of surveying and design; generating a personalized training plan using an artificial intelligence model based on the student portrait, the course portrait and the training knowledge graph.

[0013] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the training method provided in the second aspect of the embodiment of the present application is implemented.

[0014] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a memory storing a computer program; and a processor communicatively connected to the memory, for executing the training method provided in the second aspect of the embodiment of the present application when the computer program is called.

[0015] An embodiment of the present application provides a training system for the field of surveying and design, which can generate personalized training plans using an artificial intelligence model based on student portraits, course portraits, and training knowledge graphs.

[0016] The training system provided in the embodiment of the present application can automatically adjust and optimize the training plan according to the student's learning situation and the level of competence in his or her professional position, thereby ensuring that the student learns at a pace that suits him or her.

[0017] The training system provided in the embodiment of the present application can provide students with an immersive learning experience through technologies such as virtual reality and augmented reality, thereby increasing the fun and participation of learning.

[0018] The training system provided in the embodiment of the present application can monitor and evaluate the students' learning situation in real time through the intelligent evaluation module, provide students with timely feedback and suggestions, and help students find problems in their learning and improve them.

[0019] The training system provided in the embodiment of the present application can support multiple devices and platforms, so that students can learn anytime and anywhere, which is conducive to improving the students' learning experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] FIG1 is a schematic diagram showing the structure of an electronic device in an embodiment of the present application.

[0021] FIG2 is a schematic diagram showing the structure of a training system provided in an embodiment of the present application.

[0022] Figures 3A and 3B show example diagrams of the knowledge graph in the embodiments of the present application.

[0023] FIG4 shows an example diagram of the visualization results of the job knowledge system in an embodiment of the present application.

[0024] FIG5 shows a radar chart of five-dimensional capabilities in an embodiment of the present application.

[0025] FIG6 shows a structural diagram of the artificial intelligence model in an embodiment of the present application.

[0026] FIG7 shows a flow chart of a training method provided in an embodiment of the present application.

[0027] Component number description

[0028] 100 electronic devices

[0029] 101, 107 processors

[0030] 102 Output Devices

[0031] 103 Input Devices

[0032] 104 memory cells

[0033] 105 Communication Interface

[0034] 106 Storage Media

[0035] 200 Training System

[0036] 210 Data Acquisition Module

[0037] 220 Knowledge Graph Building Module

[0038] 230 Portrait Generation Module

[0039] 240 Training program generation module

[0040] Steps S71 to S74 DETAILED DESCRIPTION

[0041] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0042] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0043] This embodiment of the application provides a training system for the surveying and design field. This system leverages the platform's functional advantages to digitize the training process, digitize learning, make effects measurable, visualize growth, and verify results. Furthermore, based on the three objectives of learning outcomes, business performance, and talent development, this training system constructs data insights, needs analysis, project design, indicator definition, progress monitoring, process services, effect presentation, and performance tracking, achieving excellent training results.

[0044] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings in the embodiments of the present application.

[0045] FIG1 is a schematic diagram of the structure of an electronic device 100 according to an embodiment of the present application. The training system provided in the embodiment of the present application can be applied to the electronic device 100. As shown in FIG1 , the electronic device 100 includes a processor 101 connected to one or more data storage units. The data storage unit may include a storage medium 106 and a memory unit 104. The storage medium 106 may be read-only, such as a read-only memory (ROM), or read-write, such as a hard disk or flash memory. The memory unit 104 may be a random access memory (RAM). The memory unit 104 may be integrated with the processor 101 or a separate component. The processor 101 is the control center of the electronic device 100 and is used to execute program code to implement functions corresponding to the program instructions. In some possible implementations, the processor 101 includes one or more central processing units (CPUs), such as CPU0 and CPU1 shown in FIG1 . In some possible implementations, the electronic device 100 includes more than one processor, such as processors 101 and 107 shown in FIG1 . Processors 101 and 107 may be single-core processors or multi-core processors. It should be noted that the term "processor" as used herein refers to one or more devices, circuits, and / or processing cores for processing data such as computer program instructions.

[0046] The CPU of processor 101 and / or 107 stores the executed program code in memory unit 104 or storage medium 106. In some possible implementations, the program code stored in storage medium 106 can be copied to memory unit 104 for execution by the processor. The processor can control the operation of electronic device 100 by controlling the execution of other programs, controlling communication with peripheral devices, and controlling the use of resources of electronic device 100 through the kernel.

[0047] The electronic device 100 may further include a communication interface 105 , through which the electronic device 100 may communicate with another device or system directly or through an external network.

[0048] In some possible implementations, the electronic device 100 also includes an output device 102 and an input device 103. The output device 102 is connected to the processor 101 and can display output information in one or more ways. An example of the output device 102 is a visual display device, for example, a liquid crystal display (LCD), a light-emitting diode (LED) display, a cathode ray tube (CRT) or a projector. The input device 103 is connected to the processor 101 and can receive user input in one or more ways. The example of the input device 103 includes a mouse, a keyboard, a touch screen device, a sensing device, etc.

[0049] The above components of the electronic device 100 may be connected to each other via any one or more combinations of buses such as a data bus, an address bus, a control bus, an expansion bus, and a local bus.

[0050] Electronic device 100 can be general electronic device or application specific electronic device.As a practical example, above-mentioned electronic device 100 can be storage array, application server, supercomputer, desktop computer, notebook computer, personal digital assistant (Personal Digital Assistant, PDA), mobile phone, tablet computer, wireless terminal device, telecommunication equipment or any other device with similar structure as shown in Figure 1.However, this application is not limited to any specific type of electronic device.After the program code with different functions stored in memory 104 is run by processor (processor 101 or processor 107), process is formed. When process is run, processor needs to allocate a section of memory space to each process to store the data generated during process operation.In order to facilitate data communication between each process, usually processor (processor 101 or processor 107) can divide a section of shared memory in memory, and allocate the shared memory to multiple processes that need to share data.The process in the embodiment of the present application can be a virtual machine, container and any other process with data sharing requirements.

[0051] FIG2 is a schematic diagram of the structure of a training system 200 provided in an embodiment of the present application. Some or all of the functions of the training system 200 may be implemented, for example, by the processor 101 and / or the processor 107. As shown in FIG2 , the training system 200 may include a data acquisition module 210, a knowledge graph construction module 220, a portrait generation module 230, and a training plan generation module 240.

[0052] The data collection module 210 is used to acquire student data and training data in the field of surveying and design. Student data includes, for example, basic student information and learning information. Learning information includes, for example, relevant knowledge and operational behaviors that students have browsed. Training data in the field of surveying and design includes, for example, professional knowledge, technical specifications, case studies, theoretical knowledge, and practical experience. This data can be obtained from professional books, papers, online resources, and other sources. By collecting this training data, the data collection module 210 can achieve the basic work of collecting courses, data, and knowledge.

[0053] The knowledge graph construction module 220 is used to construct a training knowledge graph based on the training data. The training knowledge graph uses a structured knowledge representation method to represent training data in the field of survey and design, thereby clearly displaying the structure and relationships of the data. The training knowledge graph consists of nodes and edges, where nodes represent entities and edges represent relationships between entities.

[0054] In some implementations, the training knowledge graph may include a surveying and design domain knowledge graph and a job-specific knowledge graph. The surveying and design domain knowledge graph describes general knowledge in the surveying and design field, while the job-specific knowledge graph describes job-specific knowledge. Figures 3A and 3B show example visualizations of the surveying and design domain knowledge graph and the job-specific knowledge graph, respectively.

[0055] In some implementations, job and professional knowledge gaps can be inferred online and highlighted in the knowledge graph according to their importance, so that students can identify knowledge gaps and conduct targeted course learning, thereby achieving accurate and personalized talent training.

[0056] The profile generation module 230 is used to generate student profiles based on student data and to generate course profiles based on surveying and design training courses. A student profile is a multi-dimensional model that describes a student's basic characteristics, behavior patterns, preferences, and other information. A course profile describes a training course, including, for example, course content, teaching methods, teaching materials, and popularity.

[0057] For example, Table 1 shows an example table of dimensions for a student profile. As shown in Table 1, a student profile can include dimensions such as employee information, employee knowledge browsing history, and employee training system user behavior. Employee information can include dimensions such as work experience, years of service, and search habits. Employee knowledge browsing history can include dimensions such as basic attributes of knowledge documents, knowledge bases to which they belong, and knowledge utilization evaluation information. Employee training system user behavior can include search terms, saved items, and recommended / rated items.

[0058] Table 1. Example table of student portrait dimensions

[0059] The training program generation module 240 is used to generate personalized training programs based on student portraits, course portraits and training knowledge graphs using artificial intelligence models.

[0060] Exemplarily, the training program generation module 240 can also generate personalized training programs based on user browsing behavior, names of knowledge courses learned, names of courses viewed, training course categories, favorite training courses, course sharing behavior, training courses of colleagues in the same department / professional field, etc.

[0061] In some implementations, the training program generation module 240 may also dynamically analyze the trainees' knowledge mastery based on their ability model, learning progress, learning status, etc., and flexibly adjust the training content and training methods of the training program.

[0062] In some implementations, the training system may also include a data processing module for preprocessing the data acquired by the data acquisition module to ensure the accuracy and completeness of the training knowledge. Furthermore, preprocessing the data facilitates the subsequent accurate delivery of specific training courses and the construction of a training knowledge graph.

[0063] For example, the data processing module can perform data cleaning, course training content organization, and removal of duplicate and irrelevant information on the data acquired by the data acquisition module.

[0064] In some implementations, the training system may also include a visualization module. The visualization module is used to visualize student portraits, course portraits, and / or training knowledge graphs. Visualization methods include, but are not limited to, charts, portraits, animations, and the like. Through reasonable visualization design, the knowledge graph combining employees, projects, and professions can be made more intuitive and easy for users to understand and use. By mapping professional knowledge in the field of surveying and design and presenting it using visualization technology, students can more intuitively understand the relationships between knowledge and improve learning efficiency.

[0065] Furthermore, the content presented in the visualization module can assist course developers in developing training courses in the field of surveying and design. These training courses include, for example, theoretical knowledge learning courses, practical skills training courses, and case analysis courses. These training courses can help trainees quickly master the knowledge and skills in the field of surveying and design for their corresponding positions.

[0066] In some implementations, the visualization module is also used to visualize the job knowledge system. Figure 4 shows an example visualization of the job knowledge system. As shown in Figure 4, based on this example, trainees can intuitively grasp the skills required for different positions, enabling them to learn more effectively.

[0067] In some implementations, the training system may further include an online training module for implementing the training program. Trainees may select different knowledge points to study based on their needs.

[0068] Furthermore, online training modules can be used to train students using virtual reality and / or augmented reality technologies, depending on the training plan. This approach can provide students with an immersive learning experience, making learning more interesting and engaging.

[0069] In some implementations, the online training module can also be used to provide students with the latest course recommendations, general course recommendations and / or personalized course recommendations, and provide supporting learning progress tracking, learning effect evaluation and other functions, so that students can better learn according to their own ability gaps.

[0070] In some implementations, the training system may further include an effectiveness evaluation module. This module is used to assess the learner's learning progress and their level of competence in their professional position. The training program generation module is further used to optimize the training program based on the learner's learning progress and level of competence, thereby helping the learner quickly improve their professional skills.

[0071] For example, the effectiveness evaluation module can understand the students' learning situation and their competence in their professional positions through examinations, project practice, feedback surveys, etc.

[0072] In the embodiment of the present application, the degree of competence of trainees in their professional positions can be described by an employee competency assessment model. Among them, the employee competency assessment model is constructed based on the nature of the industry, corporate attributes and talent characteristics. As shown in Figure 5, the employee competency assessment model may include any one or more of the following dimensions: professional ability, execution ability, innovation ability, collaboration ability, and growth ability. For example, Table 2 shows a classification table of the above five dimensions of ability. As shown in Table 2, professional ability includes mastery of technical tools, educational background, etc., execution ability includes the number of project participation, personal work quality, innovation ability includes basic requirements, academic research ability, etc., collaboration ability includes work collaboration performance, communication and coordination ability, etc., and growth ability includes self-growth drive, active talent training, etc.

[0073] Table 2. Capability classification table

[0074] In some implementations, the training system may also include an interactive communication module. This module provides a platform for students to interact and communicate, allowing them to share learning experiences, discuss issues, and help each other, thereby improving learning outcomes. By combining the online training module with the interactive communication module, learners can better grasp the knowledge and enhance training effectiveness.

[0075] In some implementations, the training system may also include a training management module. This module is used to visualize training status and / or course status and, based on instructions, to manage courses, disciplines, projects, and / or students. Through the training management module, administrators can manage training resources, view information such as training rankings for all students and course popularity, and perform course setup, discipline management, project management, and / or student management. Through efficient management within the training management module, the training process can be more organized and efficient.

[0076] In some implementations, the training plan generation module converts the student profile, course profile, and training knowledge graph into a data format supported by the AI ​​model, and then inputs this data into the AI ​​model. Based on the output of the AI ​​model, a personalized training plan is generated. Figure 6 shows an example diagram of an AI model in an embodiment of the present application, but the present application is not limited thereto.

[0077] According to the above description, the embodiment of the present application provides a training system for the field of surveying and design. The system uses artificial intelligence technology to perform personalized association algorithms based on factors such as each student's learning situation, ability level, learning style and interests, combined with their department, major and industry involved in the project, and can evaluate the students' abilities from different dimensions, thereby providing students with personalized and customized training programs. This method can improve the students' learning effect and training efficiency, and can also better meet the students' personalized learning needs. The system can be applied to the practice of application scenarios including online education, corporate training, vocational training, quality and safety, and scientific and technological innovation. Practice has proved that the system can help students in the surveying and design industry master knowledge and skills more efficiently and improve their personal competitiveness. At the same time, for enterprises in the surveying and design industry, it can also improve the quality and effect of training and effectively reduce training costs.

[0078] The present invention also provides a training method for surveying and design. FIG7 shows a flowchart of the training method provided by the present invention. As shown in FIG7, the training method includes the following steps S71 to S74.

[0079] S71, obtain student data and training data in the field of survey and design.

[0080] S72: Generate a training knowledge graph based on the training data.

[0081] S73, generating a student portrait based on the student data, and generating a course portrait based on the training courses in the field of surveying and design.

[0082] S74 uses artificial intelligence models to generate personalized training plans based on student portraits, course portraits, and training knowledge graphs.

[0083] It should be noted that the above steps S71 to S74 correspond one-to-one to the modules in the training system 200 shown in Figure 2 and are not described in detail here. In addition, the scope of protection of the training method provided by the embodiment of the present application is not limited to the order in which the steps are executed as listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is also included in the scope of protection of this application.

[0084] The training method provided in this application embodiment maps professional knowledge in the field of surveying and design and presents it using visualization technology. Combining professional job competency models with a multi-integrated algorithm linking people, courses, disciplines, and projects can improve students' learning efficiency and enhance training effectiveness.

[0085] It should be noted that the training system provided in the embodiment of the present application can implement the training method provided in the embodiment of the present application, but the implementation device of the training method provided in the embodiment of the present application includes but is not limited to the structure of the training system listed in this embodiment. All structural deformations and replacements of the existing technology made according to the principles of the present application are included in the scope of protection of the present application.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices or methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0087] The modules / units described as separate components may or may not be physically separate, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into a processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit.

[0088] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] The present application also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the training method provided by the present application. A person skilled in the art will appreciate that all or part of the steps in implementing the method of the above embodiment can be performed by instructing the processor through a program. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0090] An embodiment of the present application also provides an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. The processor is communicatively connected to the memory and executes the training method provided in the embodiment of the present application when the computer program is invoked.

[0091] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0092] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A training system for the survey and design field, characterized in that The training system includes: A data acquisition module for acquiring trainee data and training data in the field of survey and design; A knowledge graph construction module for constructing a training knowledge graph based on the training data; A portrait generation module for generating a trainee portrait based on the trainee data and generating a course portrait based on the training courses in the field of survey and design; A training plan generation module for generating a personalized training plan using an artificial intelligence model based on the trainee portrait, the course portrait, and the training knowledge graph.

2. The training system according to claim 1, characterized in that The training system further includes a data processing module for preprocessing the data acquired by the data acquisition module.

3. The training system according to claim 1, characterized in that, The training system further includes a visualization presentation module for visually presenting the trainee portrait, the course portrait, and / or the training knowledge graph.

4. The training system according to claim 1, characterized in that, The training system further includes an effect evaluation module for obtaining the learning situation of the trainee and the degree of competence of the trainee for their professional position, and the training plan generation module is further used to optimize the training plan according to the learning situation and the degree of competence of the trainee.

5. The training system according to claim 4, wherein The degree of competence of the trainee for their professional position is described by any one or more of the following dimensions: professional ability, execution ability, innovation ability, collaboration ability, growth ability.

6. The training system according to claim 1, characterized in that, The training system further includes an online training module for training the trainee using virtual reality and / or augmented reality technology according to the training plan, and / or for providing the trainee with the latest course recommendations, general course recommendations, and / or personalized course recommendations.

7. The training system according to claim 1, wherein The training system further includes an interactive communication module and / or a training management module, where: The interactive communication module is used to provide an interactive communication platform for the trainee; The training management module is used to visually present the training status and / or the course status, and perform course setting, professional management, project management, and / or trainee management according to instructions.

8. A training method for the survey and design field, characterized in that The training method includes: Acquiring trainee data and training data in the field of survey and design; Generating a training knowledge graph based on the training data; Generating a trainee portrait based on the trainee data and generating a course portrait based on the training courses in the field of survey and design; Generating a personalized training plan using an artificial intelligence model based on the trainee portrait, the course portrait, and the training knowledge graph.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the training method described in claim 9.

10. An electronic device, characterized in that, The electronic device includes: A memory storing a computer program; A processor communicatively connected to the memory and executing the training method described in claim 9 when calling the computer program.

Images