CN114546985A - Enterprise intelligent knowledge management system with learning ability - Google Patents
Enterprise intelligent knowledge management system with learning ability Download PDFInfo
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- CN114546985A CN114546985A CN202210068183.5A CN202210068183A CN114546985A CN 114546985 A CN114546985 A CN 114546985A CN 202210068183 A CN202210068183 A CN 202210068183A CN 114546985 A CN114546985 A CN 114546985A
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses an enterprise intelligent knowledge management system with learning ability, which comprises a data transmission module, a data processing system, a data receiving module and a server, and the enterprise intelligent knowledge management system with learning ability comprises the following specific operation steps: s1: each department of the enterprise uploads related data knowledge through a data transmission module; s2: the server collects the data knowledge in a classified manner through the data receiving module. According to the method and the system, when the data knowledge is uploaded by different departments of an enterprise, the data knowledge is labeled and classified, and the important degree of the knowledge can be more conveniently known when the enterprise staff retrieve the data knowledge by extracting keywords and adding the core concept and the basic concept; by establishing the language library and combining with the data knowledge, the relevance degree of the retrieved information can be greatly improved, and the upper and lower levels and the level data knowledge of the inquired information can be checked.
Description
Technical Field
The invention belongs to the technical field of knowledge management systems, and particularly relates to an enterprise intelligent knowledge management system with learning capacity.
Background
The knowledge Management system is generally referred to as KMS (knowledge Management System), is an IT system for supporting and managing the Management and implementation of organization knowledge, and is used for classifying, storing and managing a large amount of valuable schemes, plans, fruits, experiences and other knowledge in an organization by using tools such as an organization application platform, tools, software and the like, and can construct an enterprise knowledge base; the system helps to accumulate knowledge assets, promotes learning, sharing, training, recycling and innovation of knowledge, and effectively reduces organization and operation cost; the enterprise knowledge structure can be quickly analyzed, knowledge data can be classified and stored, knowledge application can be shared, and enterprise management efficiency can be improved; the enterprise can be helped to evaluate the knowledge and resource yield, the utilization rate and the growth rate; and the knowledge inquiry and calling are simpler, the knowledge achievement is fully utilized, and the working efficiency is improved.
The existing knowledge management system collects and stores data for enterprise personnel to use, when the enterprise personnel searches in the knowledge management system of the enterprise, only the related data knowledge containing search bytes is displayed generally, although the purpose of acquiring the knowledge to be understood can be achieved, much time is consumed, and contains search bytes, but has a lot of useless information knowledge, and when the staff learns the enterprise knowledge, the staff can only obtain the information through the general information, integrates the information through self understanding (learning is known only after the information is learned), has slow speed of establishing a thinking frame and long learning time, therefore, aiming at the problems, the enterprise with learning ability only can provide a knowledge management system, and has the advantages of high retrieval efficiency, good effect and short learning period.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the enterprise intelligent knowledge management system with learning capacity, and the enterprise intelligent knowledge management system has the advantages of high retrieval efficiency, good effect and short learning period.
In order to achieve the purpose, the invention provides the following technical scheme: the enterprise intelligent knowledge management system with the learning ability comprises a data transmission module, a data processing system, a data receiving module and a server, and specifically comprises the following operation steps:
s1: each department of the enterprise uploads related data knowledge through a data transmission module;
s2: the server classifies and collects the data knowledge through the data receiving module;
s3: establishing a language library matched with the type of an enterprise by cooperation of all departments;
s4: establishing a pyramid type three-dimensional model for the language library;
s5: the data processing system analyzes and detects the data knowledge received by the data receiving module;
s6: numbering data knowledge, and matching the data knowledge with a language library according to keywords;
s7: storing the matched data knowledge;
s8: the learner enters the system to learn.
Preferably, when the data knowledge is uploaded through the data module in S1, various attributes and labels are defined for the data knowledge uploaded by each department, for example: the method has the advantages that the data types, the associated skills and the like are classified, the keywords are summarized for the uploaded data knowledge, the records are convenient, the tags are added for the uploaded data knowledge, the data knowledge of different departments can be distinguished more conveniently, the retrieval of company staff is facilitated, the keywords can be summarized for the convenience of users, the data correlation is higher compared with the traditional retrieval mode, and less useless retrieved information exists.
Preferably, the summarized keywords are provided with core concepts and basic concepts, the core concepts have higher priority than the basic concepts, each keyword is classified according to the distinction between the core concepts and the basic concepts, the core concepts and the basic concepts are arranged in a three-dimensional mode according to the inclusion relationship and are linked, information related to the concepts can be displayed during search and query after the linkage, the core concepts and the basic concepts are provided for distinguishing the core content of data information, and the core concepts are more important than the basic concepts relative to the data as a whole and play a role in prompting staff.
Preferably, when the data knowledge is classified and collected by the data receiving module in S2, the data knowledge of different departments is classified, and when different data of different departments have relevance, the relevant data is linked and associated, such data can be conveniently looked up and stored in the server during query, and the data knowledge is classified and stored, so that the data knowledge can be more conveniently sorted and extracted, and meanwhile, the server has higher performance, the data knowledge can be more freely stored, and the security is better.
Preferably, the specific construction form of the pyramid type solid model in S4 is as follows: the method is characterized in that the words and phrases in the language library are sorted according to the meanings and the upper-lower level relation or the inclusion relation, the pyramid distribution form is finally established, the established pyramid distribution form is convenient for grading, summarizing and sorting the words and phrases in the language library, the data can be integrally grasped by sorting the meanings and the upper-lower level relation or the inclusion relation, other related data can be inquired while inquiring the data, the comparison and verification are convenient, the understanding effect of a user is increased, the understanding degree of the user on the data is accelerated, and meanwhile, the rough framework for helping the user to quickly understand can be realized.
Preferably, the analyzing and detecting of the data in S5 is specifically as follows: the method comprises the steps of firstly, primarily screening data of different departments, screening out repeated data or invalid data, then, integrating the data of the different departments, screening the integrated data, searching for the repeated data, if the same data is extracted, reserving one part of the data, independently reserving the part of the data in a cross-border label column, linking the part of the data with the department to which the part of the data belongs, adding a plurality of corresponding labels to the data, screening the repeated data, wherein the validity of the data can be ensured, the primarily screened data is directly deleted, the data of the different departments is screened after being integrated, and the repeated data is reserved.
Preferably, when the keyword in S6 is matched with the language library, each keyword may match multiple data, and each data may match multiple keywords, so as to increase the relevance of the data, improve the range and relevance of the retrieved data, and facilitate the comparative analysis of the data.
Preferably, in S7, the data may be stored and retrieved, the data knowledge may display corresponding tags during retrieval, and the data knowledge may also display related upper and lower level data knowledge or approximate flat level data knowledge, when data query is required, the data may be extracted from a certain data item, and corresponding multiple data may be retrieved through the keyword, and the retrieved data may have tags, so that the source of the data may be easily identified.
Preferably, in S8, when the learner learns, the server can record the learner 'S query data, query the learner' S data such as learning record, evaluation, learning exchange, notes, etc., extract repeated keywords from the learner 'S query data, generate new tags, and sort according to the occurrence frequency, wherein the keywords extracted by the server by each learner are separately stored in the learner' S account information; meanwhile, the system is combined with the user information base, the age, the gender, the working attribute and the post category of the user are intelligently collected, the tag base is enriched, the recommendation of related knowledge is carried out on a specific user, the extraction of keywords is carried out when a learner learns, the knowledge is pushed by combining the user information, the knowledge leakage can be compensated for the learner, and meanwhile, the learning depth and the learning breadth of the learner can be improved.
Preferably, in S8, after the learner learns, the system may form a knowledge map according to knowledge management set by the user, and trace to knowledge formation time and review frequency; the system can also score the importance degree according to the evaluation of the learner on the knowledge contained in the server, trace out various skills talents through the scores, gather the knowledge mastered by the learner more effectively through the generation of a knowledge map for the learner, trace out various skills talents through the scores of the knowledge evaluation, and improve the utilization rate of the talents by enterprises.
Compared with the prior art, the invention has the following beneficial effects:
according to the method and the system, when the data knowledge is uploaded by different departments of an enterprise, the data knowledge is labeled and classified, and the important degree of the knowledge can be more conveniently known when the enterprise staff retrieve the data knowledge by extracting keywords and adding the core concept and the basic concept; by establishing a language library and combining with data knowledge, the relevance degree of the retrieved information can be greatly improved, the upper and lower levels and the level data knowledge of the queried information can be checked, the overall control on the data knowledge is facilitated, and the detected data knowledge has wider relevance and less useless information; the knowledge management system has learning capacity, and can extract repeated keywords from the review, evaluation, learning communication and notes of learners to generate a new label, so that the search is more convenient; meanwhile, the information of the user can be intelligently collected by combining with a user information base, such as: age, gender, working attribute, post category, enriching a tag library, and realizing pushing corresponding knowledge to a specific user; the knowledge management system can form a knowledge map according to knowledge management set by a user, and trace to knowledge forming time and reference frequency; and various technical talents can be traced according to the knowledge evaluation of the user.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention provides a technical scheme that: the intelligent knowledge management system of enterprise that possesses learning ability, including data transmission module, data processing system, data receiving module and server, this intelligent knowledge management system of enterprise that possesses learning ability concrete operating procedure as follows:
s1: each department of the enterprise uploads related data knowledge through a data transmission module;
s2: the server classifies and collects the data knowledge through the data receiving module;
s3: establishing a language library matched with the type of an enterprise by cooperation of all departments;
s4: establishing a pyramid type three-dimensional model for the language library;
s5: the data processing system analyzes and detects the data knowledge received by the data receiving module;
s6: numbering data knowledge, and matching the data knowledge with a language library according to keywords;
s7: storing the matched data knowledge;
s8: the learner enters the system to learn.
When the data knowledge is uploaded through the data module in S1, various attributes and tags are defined for the data knowledge uploaded by each department, for example: the data types, the associated skills and the like are classified, the uploaded data knowledge is summarized with the keywords, the records are convenient, the uploaded data knowledge is added with the tags, the data knowledge of different departments can be distinguished more conveniently, the company staff can conveniently search, the keywords can be summarized and inquired conveniently by a user, and compared with a traditional search mode, the data retrieval method has the advantages that the data relevance is higher, and less useless retrieved information exists.
The core concept and the basic concept are proposed for summarized keywords, the priority of the core concept is higher than that of the basic concept, each keyword is classified according to the distinguishing of the core concept and the basic concept, the core concept and the basic concept are arranged in a three-dimensional mode according to the inclusion relation and are linked, information related to the concepts can be displayed during search and query after the links are connected, the core concept and the basic concept are proposed for distinguishing the core content of data information, the core concept is more important than the basic concept relative to the whole data, and the core concept plays a role in prompting staff.
Wherein, when carrying out categorised collection to data knowledge through the data receiving module in S2, classify the data knowledge of different departments, when the different data of different departments have the associativity, link the association to the data of association, can be convenient for look over the data of association when this type of data when inquiring, and finally save in the server, carry out categorised the save to data knowledge, can be more convenient arrange data knowledge in order, and more convenient extract data knowledge, the performance of server is higher simultaneously, store data knowledge more freely, the security is also better.
The concrete construction form of the character tower type three-dimensional model in S4 is as follows: the method is characterized in that the words and phrases in the language library are sorted according to the meaning and the upper-lower level relation or the inclusion relation, the pyramid distribution form is finally established, the established pyramid distribution form is convenient for grading, summarizing and sorting the words and phrases in the language library, the data can be conveniently and integrally grasped by sorting the meaning and the upper-lower level relation or the inclusion relation, other related data can be inquired while inquiring the data, comparison and verification are convenient, the understanding effect of a user is increased, the understanding degree of the user on the data is accelerated, and meanwhile, a rough framework for helping the user to know the data more quickly can be realized.
Wherein, the specific expression of analyzing and detecting the data in the S5 is as follows: the method comprises the steps of firstly, primarily screening data of different departments, screening repeated data or invalid data, then, integrating the data of the different departments, screening the integrated data, searching for the repeated data, if the same data is extracted, reserving one part, independently storing the part in a cross label column, linking the part of data with the department to which the part of data belongs, adding a plurality of corresponding labels to the data, screening the repeated data, ensuring the validity of the data, directly deleting the primarily screened data, screening the data of the different departments after integration, and reserving one part repeatedly, wherein the data has a plurality of labels, so that the space occupied by the data is saved, the uniqueness of the retrieval is ensured, and the repetition is prevented during the retrieval.
When the keywords in S6 are matched with the language library, each keyword may match multiple data, and each data may match multiple keywords, so as to increase the relevance of the data, improve the range and relevance of the retrieved data, and facilitate the comparative analysis of the data.
The data can be retrieved after being stored in S7, the data knowledge can display corresponding tags during retrieval, and can also be searched according to the keyword tags, and can display associated upper and lower level data knowledge or approximate flat level data knowledge.
When the learner learns in S8, the server can record the data queried by the learner, query the data of the learner, such as learning record, evaluation, learning exchange, notes and the like, extract repeated keywords from the data, generate new tags, and sort according to the occurrence frequency, wherein the keywords extracted by each learner through the server are separately stored in the account information of the learner; meanwhile, the system is combined with the user information base, the age, the sex, the work attribute and the post category of the user are intelligently collected, the tag base is enriched, the recommendation of related knowledge is carried out on the specific user, the extraction of keywords is carried out when the learner learns, the knowledge is pushed by combining the user information, the knowledge leakage can be compensated for the learner, and meanwhile, the learning depth and the learning breadth of the learner can be improved.
After the learner learns in the step S8, the system may form a knowledge map according to knowledge management set by the user, and trace to knowledge formation time and review frequency; the system can also score the importance degree according to the evaluation of the learners on the knowledge contained in the server, trace out various technical talents through the scores, gather the knowledge mastered by the learners more effectively by generating a knowledge map for the learners, trace out various technical talents through the scores of the knowledge evaluation and improve the utilization rate of the enterprises on the talents.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. Enterprise intelligent knowledge management system with learning ability comprises a data transmission module, a data processing system, a data receiving module and a server, and is characterized in that: the enterprise intelligent knowledge management system with the learning ability comprises the following specific operation steps:
s1: each department of the enterprise uploads related data knowledge through a data transmission module;
s2: the server classifies and collects the data knowledge through the data receiving module;
s3: establishing a language library matched with the type of an enterprise by cooperation of all departments;
s4: establishing a pyramid type three-dimensional model for the language library;
s5: the data processing system analyzes and detects the data knowledge received by the data receiving module;
s6: numbering data knowledge, and matching the data knowledge with a language library according to keywords;
s7: storing the matched data knowledge;
s8: the learner enters the system to learn.
2. The system of claim 1, wherein: when the data knowledge is uploaded through the data module in S1, various attributes and labels are defined for the data knowledge uploaded by each department, for example: the knowledge type, the associated skills and the like are classified, and keywords are summarized for the uploaded data knowledge, so that the recording is facilitated.
3. The system of claim 2, wherein: and providing a core concept and a basic concept for the summarized keywords, wherein the priority of the core concept is higher than that of the basic concept, classifying each keyword according to the distinction of the core concept and the basic concept, arranging the core concept and the basic concept in a three-dimensional way according to the inclusion relationship, linking, displaying information related to the concept during search and query after linking, classifying each keyword according to the distinction of the core concept and the basic concept, arranging the core concept and the basic concept in a three-dimensional way according to the inclusion relationship, linking, and displaying the information related to the concept during search and query after linking.
4. The system of claim 1, wherein: in S2, when the data knowledge is collected by classifying through the data receiving module, the data knowledge of different departments is classified, and when different data of different departments have relevance, the relevant data is linked and associated, so that the relevant data can be conveniently viewed during query, and finally stored in the server.
5. The system of claim 1, wherein: the concrete construction form of the pyramid type three-dimensional model in the S4 is as follows: and (4) sequencing the words and phrases in the language library according to the meanings and the upper-lower level relation or the inclusion relation, and finally establishing a pyramid distribution form.
6. The intelligent learning-capable enterprise knowledge management system of claim 1, wherein: the analysis and detection of the data in the step S5 are specifically as follows: the method comprises the steps of firstly, primarily screening data of different departments, screening out repeated data or invalid data, then, integrating the data of the different departments, screening the integrated data, searching for the repeated data, extracting the same data if the repeated data exists, reserving one part of the data, independently storing the part of the data in a cross-border tag column, linking the part of the data with the department to which the part of the data belongs, and adding a plurality of corresponding tags for the data.
7. The system of claim 1, wherein: when the keyword in S6 is matched with the language library, each keyword may match multiple data, and each data may match multiple keywords.
8. The system of claim 1, wherein: in S7, the data may be retrieved after being stored, and the data knowledge may display a corresponding tag during retrieval, or may be searched based on a keyword tag, and may display related upper and lower level data knowledge or approximate level data knowledge.
9. The system of claim 1, wherein: in S8, when the learner learns, the server can record the data queried by the learner, query the data of the learner, such as learning record, evaluation, learning exchange, notes, etc., and extract repeated keywords from the data, generate new tags, and sort according to the occurrence frequency, wherein the keywords extracted by the server by each learner are separately stored in the account information of the learner; meanwhile, the system is combined with a user information base, the age, the gender, the working attribute and the post category of the user are intelligently collected, a tag base is enriched, and the recommendation of related knowledge is carried out on the specific user.
10. The system of claim 1, wherein: in S8, after the learner learns, the system may form a knowledge map according to knowledge management set by the user, and trace to the knowledge formation time and the reference frequency; and scoring the importance degree according to the evaluation of the learner on the knowledge contained in the server, and tracing out various skills talents through scores.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115033693A (en) * | 2022-06-13 | 2022-09-09 | 四川数愈医疗科技有限公司 | Knowledge management method of medical cognitive intelligent scientific research platform |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115033693A (en) * | 2022-06-13 | 2022-09-09 | 四川数愈医疗科技有限公司 | Knowledge management method of medical cognitive intelligent scientific research platform |
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