Lean efficiency learning working method and system based on mind map

Abstract

The invention discloses a mind map-based lean efficiency learning working method, which comprises the following steps of: obtaining request information input by a user, and determining a corresponding mind map branch in a mind map according to the request information; on the basis of a preset mind map template, a sub mind map is obtained according to the mind map branches, and the sub mind map comprises all the mind map branches; determining the association degree between the request information and keywords in the mind map branches, displaying the mind map branches according to the association degree, and outputting sub mind maps after the mind map branches are displayed. Knowledge is simplified through the mind map, the self-learning ability is improved, and the learning efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mind mapping, and in particular to a lean performance learning working method and system based on mind mapping. Background Art

[0002] Mind mapping is a graphic tool for expressing radial divergent thinking in an image-based manner. It uses lines, symbols, words and images to transform a long list of boring information into a visual, easy-to-remember, structured diagram. People use it in learning, life, work and other fields to improve learning efficiency and innovative thinking ability.

[0003] After the power plant is put into operation steadily, the maintenance team members usually learn the paper or electronic manuals by themselves. When there are problems with the equipment, most of the maintenance team members are directly led by the master to the site for maintenance. They often need to act separately during on-site operations, resulting in many on-site work experiences that cannot be shared. As a result, problems often occur, such as (1) the accident prediction is not targeted enough. (2) Individuals are in charge of different equipment, and the problems that may be encountered on duty cover a wide range, and there are certain blind spots for solving them. (3) After the relay protection test and fault elimination, it is necessary to summarize and summarize in time to form a shared knowledge source. (4) The equipment manuals are complicated, and it is time-consuming and laborious to study from scratch. At present, the maintenance team is facing the difficulties of increasing the number of management units, shortage of personnel, aging equipment, and frequent replacement of network-related equipment. Therefore, it is urgent to improve the skill level and self-learning ability of operation and maintenance personnel. Therefore, a lean efficiency learning work method based on mind mapping is proposed to simplify knowledge through mind mapping, improve self-learning ability, and improve learning efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a lean efficiency learning working method based on mind mapping, so as to solve the problems that accident anticipation is not targeted when team members are learning; individuals are in charge of different equipment, and the problems that may be encountered on duty cover a wide range and there are certain blind spots for solving; the equipment manuals are complicated and it is time-consuming and laborious to study from scratch.

[0005] The present invention provides a lean performance learning working method based on mind mapping, comprising:

[0006] Obtaining request information input by a user, and determining a corresponding mind map branch in the mind map according to the request information;

[0007] Based on a preset mind map template, a sub-mind map is obtained according to the mind map branches, wherein the sub-mind map includes all the mind map branches;

[0008] Determine the relevance between the request information and the keywords in the mind map branch, display the mind map branch according to the relevance, and output a sub-mind map after displaying the mind map branch.

[0009] In some embodiments of the present application, before obtaining the request information input by the user, the process further includes:

[0010] Analyze the mind map to obtain all mind map branches of the mind map, wherein the mind map branches include first-level branches, second-level branches, third-level branches, ..., N-level branches, wherein the first-level branches have the highest level;

[0011] The mind map branch is provided with a branch theme and a number of keywords, and the keywords are associated with detailed learning content related to the keywords and the mind map branch.

[0012] In some embodiments of the present application, determining a corresponding mind map branch in the mind map according to the request information includes:

[0013] The request information is the request information that the user plans to learn work knowledge;

[0014] Analyze and identify the request information to determine first semantic information of the request information;

[0015] Based on the keywords in the mind map branch, the mind map branch is determined according to the first semantic information.

[0016] In some embodiments of the present application, based on the keywords in the mind map branch, determining the mind map branch according to the first semantic information includes:

[0017] If the first semantic information is consistent with the keyword, determining that the mind map branch corresponding to the keyword is the mind map branch requested by the user;

[0018] If the first semantic information is inconsistent with the keyword, determining similar semantics or related semantics of the first semantic information, judging whether the similar semantics or related semantics are consistent with the keyword, and if they are consistent, determining that the mind map branch corresponding to the keyword is the mind map branch requested by the user;

[0019] If the first semantic information is inconsistent with the keyword, the detailed learning content associated with the keyword is analyzed to determine the second semantic information of the detailed learning content. If the first semantic information is consistent with the second semantic information, the mind map branch corresponding to the keyword is determined to be the mind map branch requested by the user.

[0020] In some embodiments of the present application, based on a preset mind map template, a sub-mind map is obtained according to the mind map branch, including:

[0021] According to a preset mind map association table, the content of the mind map branch is filled into the position corresponding to the mind map template to obtain a sub-mind map;

[0022] Wherein, if the mind map branch has a next-level mind map branch, the content of the next-level mind map branch is filled into the position corresponding to the mind map template, and the next-level mind map branch is hidden;

[0023] If the mind map branch has a previous mind map branch, the branch topic of the previous mind map branch is filled into the corresponding position of the mind map template, but the keyword of the previous mind map branch is not filled.

[0024] In some embodiments of the present application, determining the relevance between the request information and the keywords in the mind map branch, and displaying the mind map branch according to the relevance includes:

[0025] Determine the relevance between the first semantic information of the request information and the keywords in the mind map branches according to a semantic relevance algorithm;

[0026] Determining the total relevance between the request information and the mind map branch according to the relevance;

[0027] The mind map branches are displayed based on the total relevance.

[0028] In some embodiments of the present application, the total correlation degree is calculated as follows:

[0029]

[0030] Among them, R z is the total relevance, n is the number of keywords in the mind map branch, q k is the weight of the kth keyword, R k is the correlation between the first semantic information and the kth keyword.

[0031] In some embodiments of the present application, displaying the mind map branches based on the total relevance includes:

[0032] If the total relevance is greater than a preset relevance, the content of the mind map branch is displayed;

[0033] If the total correlation degree is greater than a preset correlation degree, the content of the mind map branch is hidden, but when the user requests to view the content of the mind map branch, the content of the mind map branch is displayed.

[0034] The present invention also discloses a lean efficiency learning work system based on mind mapping, comprising:

[0035] An acquisition module, wherein the acquisition module is used to acquire request information input by a user;

[0036] A first determination module, the determination module is used to determine a corresponding mind map branch in the mind map according to the request information, and obtain a sub-mind map according to the mind map branch based on a preset mind map template, wherein the sub-mind map includes all the mind map branches;

[0037] a second determination module, the second determination module being used to determine the relevance between the request information and the keywords in the mind map branch;

[0038] An output module is used to display the mind map branches according to the association degree, and output a sub-mind map after displaying the mind map branches.

[0039] In some embodiments of the present application, an analysis module is used to analyze a mind map to obtain all mind map branches of the mind map, wherein the mind map branches include first-level branches, second-level branches, third-level branches..., N-level branches, among which the first-level branches have the highest level; the mind map branches are provided with branch themes and keywords, and the keywords are associated with detailed learning content related to the keywords and the mind map branches.

[0040] The present invention discloses a lean efficiency learning working method based on mind mapping, comprising: obtaining request information input by a user, and determining a corresponding mind map branch in a mind map according to the request information; obtaining a sub-mind map according to the mind map branch based on a preset mind map template, wherein the sub-mind map includes all the mind map branches; determining the correlation between the request information and a keyword in the mind map branch, displaying the mind map branch according to the correlation, and outputting the sub-mind map after displaying the mind map branch.

[0041] The present invention generates relevant mind maps, simplifies knowledge, uses mind maps as bridges, speeds up the efficiency of information acquisition, realizes knowledge sharing, enhances self-learning ability, and improves learning efficiency. At the same time, it is beneficial to improve the skill level of team members, facilitates the cultivation of divergent thinking of team members, forms a complete knowledge system, and improves the ability to deal with accidents in an emergency.

[0042] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1A flowchart of a lean performance learning work system method based on a mind map of the present invention;

[0044] Figure 2 The present invention is a functional block diagram of a lean performance learning work system based on mind mapping. DETAILED DESCRIPTION

[0045] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0046] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same general meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0047] It should be noted that the terms used here are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof, without excluding other elements or objects. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The orientation or position relationship indicated by terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom" and the like is based on the orientation or position relationship shown in the drawings. It is only a relationship word determined for the convenience of describing the structural relationship of each component or element of the present invention, not specifically referring to any component or element in the invention, and cannot be understood as a limitation on the invention. Terms such as "fixed connection", "connected", "connected" and the like should be understood in a broad sense, indicating that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. For relevant scientific research or technical personnel in this field, the specific meanings of the above terms in the present invention can be determined according to specific circumstances, and they should not be understood as limitations on the present invention.

[0048] Example

[0049] The present invention provides a lean performance learning working method based on mind mapping, such as Figure 1 As shown, including:

[0050] S1, obtaining the request information input by the user.

[0051] S2: Determine a corresponding mind map branch in the mind map according to the request information.

[0052] S3, based on a preset mind map template, obtaining a sub-mind map according to the mind map branches, wherein the sub-mind map includes all the mind map branches.

[0053] S4, determining the relevance between the request information and the keywords in the mind map branch, displaying the mind map branch according to the relevance, and outputting a sub-mind map after displaying the mind map branch.

[0054] In some embodiments of the present application, before obtaining the request information input by the user, the process further includes:

[0055] The mind map is analyzed to obtain all mind map branches of the mind map, wherein the mind map branches include primary branches, secondary branches, tertiary branches, ..., N-level branches, wherein the primary branches have the highest level.

[0056] The mind map branch is provided with a branch theme and a number of keywords, and the keywords are associated with detailed learning content related to the keywords and the mind map branch.

[0057] In this embodiment, the mind map branches include multiple levels of branches, and the content of each level of branches is progressive and the content is continuously refined. For example, the first-level branch can be set to the equipment type, the second-level branch can be set to the main body or structure, and the third-level branch can be set to possible problems, solutions, management teams, etc.

[0058] In some embodiments of the present application, a method for determining a mind map branch is disclosed, and determining the corresponding mind map branch in the mind map according to the request information includes:

[0059] The request information is request information that the user plans to learn work knowledge.

[0060] The request information is analyzed and identified to determine first semantic information of the request information.

[0061] Based on the keywords in the mind map branch, the mind map branch is determined according to the first semantic information.

[0062] In this embodiment, when the user plans to learn a certain aspect of work knowledge, since the mind map contains all knowledge, the mind map needs to be filtered to filter out the knowledge the user wants to find, so the mind map is filtered and identified according to the user's request information.

[0063] In some embodiments of the present application, based on the keywords in the mind map branch, determining the mind map branch according to the first semantic information includes:

[0064] If the first semantic information is consistent with the keyword, it is determined that the mind map branch corresponding to the keyword is the mind map branch requested by the user.

[0065] If the first semantic information is inconsistent with the keyword, similar semantics or related semantics of the first semantic information are determined, and it is judged whether the similar semantics or related semantics are consistent with the keyword. If they are consistent, the mind map branch corresponding to the keyword is determined to be the mind map branch requested by the user.

[0066] If the first semantic information is inconsistent with the keyword, the detailed learning content associated with the keyword is analyzed to determine the second semantic information of the detailed learning content. If the first semantic information is consistent with the second semantic information, the mind map branch corresponding to the keyword is determined to be the mind map branch requested by the user.

[0067] In some embodiments of the present application, based on a preset mind map template, a sub-mind map is obtained according to the mind map branch, including:

[0068] According to the preset mind map association table, the content of the mind map branch is filled into the position corresponding to the mind map template to obtain a sub-mind map.

[0069] Among them, if the mind map branch has a next-level mind map branch, the content of the next-level mind map branch is filled into the position corresponding to the mind map template, and the next-level mind map branch is hidden.

[0070] If the mind map branch has a previous mind map branch, the branch topic of the previous mind map branch is filled into the corresponding position of the mind map template, but the keyword of the previous mind map branch is not filled.

[0071] In some embodiments of the present application, determining the relevance between the request information and the keywords in the mind map branch, and displaying the mind map branch according to the relevance includes:

[0072] According to a semantic relevance algorithm, the relevance between the first semantic information of the request information and the keywords in the mind map branches is determined.

[0073] The total relevance between the request information and the mind map branch is determined according to the relevance.

[0074] The mind map branches are displayed based on the total relevance.

[0075] In some embodiments of the present application, the total correlation degree is calculated as follows:

[0076] ;

[0078] Among them, R z is the total relevance, n is the number of keywords in the mind map branch, q k is the weight of the kth keyword, R k is the correlation between the first semantic information and the kth keyword.

[0079] In some embodiments of the present application, displaying the mind map branches based on the total relevance includes:

[0080] If the total correlation is greater than the preset correlation, the content of the mind map branch is displayed.

[0081] If the total correlation degree is greater than a preset correlation degree, the content of the mind map branch is hidden, but when the user requests to view the content of the mind map branch, the content of the mind map branch is displayed.

[0082] The present invention also discloses a lean efficiency learning work system based on mind mapping, such as Figure 2 As shown, including:

[0083] The acquisition module is used to acquire request information input by the user.

[0084] A first determination module is used to determine a corresponding mind map branch in the mind map according to the request information, and obtain a sub-mind map according to the mind map branch based on a preset mind map template, wherein the sub-mind map includes all the mind map branches.

[0085] The second determining module is used to determine the relevance between the request information and the keywords in the mind map branch.

[0086] An output module is used to display the mind map branches according to the association degree, and output a sub-mind map after displaying the mind map branches.

[0087] In some embodiments of the present application, an analysis module is used to analyze a mind map to obtain all mind map branches of the mind map, wherein the mind map branches include first-level branches, second-level branches, third-level branches..., N-level branches, among which the first-level branches have the highest level; the mind map branches are provided with branch themes and keywords, and the keywords are associated with detailed learning content related to the keywords and the mind map branches.

[0088] The present invention is based on existing document tools, uses hierarchical folding text to organize content, and uses a more efficient way and clearer structure to record notes, manage tasks and make plans. It can be used in the fields of instruction simplification, accident prediction analysis and technical training. Operation and maintenance personnel learn through the streamlined mind map. It is easy to use, cost-effective, easy to get started quickly, and use the mind map bridge to speed up the efficiency of information acquisition, thereby working and learning efficiently.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

[0090] The system provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations of the present invention.

[0091] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

Claims

1. A lean performance learning working method based on mind mapping, characterized in that: include: Obtaining request information input by a user, and determining a corresponding mind map branch in the mind map according to the request information; Based on a preset mind map template, a sub-mind map is obtained according to the mind map branches, wherein the sub-mind map includes all the mind map branches; Determine the relevance between the request information and the keywords in the mind map branch, display the mind map branch according to the relevance, and output a sub-mind map after displaying the mind map branch.

2. According to the mind map-based lean performance learning working method of claim 1, it is characterized in that: Before obtaining the request information entered by the user, it also includes: Analyze the mind map to obtain all mind map branches of the mind map, wherein the mind map branches include first-level branches, second-level branches, third-level branches, ..., N-level branches, wherein the first-level branches have the highest level; The mind map branch is provided with a branch theme and a number of keywords, and the keywords are associated with detailed learning content related to the keywords and the mind map branch.

3. According to the mind map-based lean performance learning working method of claim 2, it is characterized in that: Determining a corresponding mind map branch in the mind map according to the request information includes: The request information is the request information that the user plans to learn work knowledge; Analyze and identify the request information to determine first semantic information of the request information; Based on the keywords in the mind map branch, the mind map branch is determined according to the first semantic information.

4. According to the mind map-based lean performance learning working method of claim 3, it is characterized in that: Based on the keywords in the mind map branch, determining the mind map branch according to the first semantic information includes: If the first semantic information is consistent with the keyword, determining that the mind map branch corresponding to the keyword is the mind map branch requested by the user; If the first semantic information is inconsistent with the keyword, determining similar semantics or related semantics of the first semantic information, judging whether the similar semantics or related semantics are consistent with the keyword, and if they are consistent, determining that the mind map branch corresponding to the keyword is the mind map branch requested by the user; If the first semantic information is inconsistent with the keyword, the detailed learning content associated with the keyword is analyzed to determine the second semantic information of the detailed learning content. If the first semantic information is consistent with the second semantic information, the mind map branch corresponding to the keyword is determined to be the mind map branch requested by the user.

5. According to the mind map-based lean performance learning working method of claim 2, it is characterized in that: Based on the preset mind map template, a sub-mind map is obtained according to the mind map branch, including: According to a preset mind map association table, the content of the mind map branch is filled into the position corresponding to the mind map template to obtain a sub-mind map; Wherein, if the mind map branch has a next-level mind map branch, the content of the next-level mind map branch is filled into the position corresponding to the mind map template, and the next-level mind map branch is hidden; If the mind map branch has a previous mind map branch, the branch topic of the previous mind map branch is filled into the corresponding position of the mind map template, but the keyword of the previous mind map branch is not filled.

6. The lean efficiency learning working method based on mind mapping according to claim 3 is characterized in that: Determining the relevance between the request information and the keywords in the mind map branch, and displaying the mind map branch according to the relevance, including: Determine the relevance between the first semantic information of the request information and the keywords in the mind map branches according to a semantic relevance algorithm; Determining the total relevance between the request information and the mind map branch according to the relevance; The mind map branches are displayed based on the total relevance.

7. The lean performance learning working method based on mind mapping according to claim 6 is characterized in that: The calculation method of the total correlation is: Among them, R z is the total relevance, n is the number of keywords in the mind map branch, q k is the weight of the kth keyword, R k is the correlation between the first semantic information and the kth keyword.

8. The lean efficiency learning working method based on mind mapping according to claim 7 is characterized in that: Displaying the mind map branches based on the total relevance includes: If the total relevance is greater than a preset relevance, the content of the mind map branch is displayed; If the total correlation degree is greater than a preset correlation degree, the content of the mind map branch is hidden, but when the user requests to view the content of the mind map branch, the content of the mind map branch is displayed.

9. A lean efficiency learning work system based on mind mapping, characterized in that: include: An acquisition module, wherein the acquisition module is used to acquire request information input by a user; A first determination module, the determination module is used to determine a corresponding mind map branch in the mind map according to the request information, and obtain a sub-mind map according to the mind map branch based on a preset mind map template, wherein the sub-mind map includes all the mind map branches; A second determination module, the second determination module is used to determine the relevance between the request information and the keywords in the mind map branch; An output module is used to display the mind map branches according to the association degree, and output a sub-mind map after displaying the mind map branches.

10. The lean efficiency learning work system based on mind mapping according to claim 9 is characterized in that: An analysis module is used to analyze the mind map to obtain all mind map branches of the mind map, wherein the mind map branches include first-level branches, second-level branches, third-level branches..., N-level branches, among which the first-level branches have the highest level; the mind map branches are provided with branch themes and keywords, and the keywords are associated with detailed learning content related to the keywords and the mind map branches.

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