CN111222040B - Scheme self-matching processing method and system based on training requirements - Google Patents
Scheme self-matching processing method and system based on training requirements Download PDFInfo
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention discloses a scheme self-matching processing method and system based on training requirements, wherein the method comprises the following steps: receiving one or more keywords input by a user; extracting correlation coefficients of one or more keywords corresponding to other words in the database from the database to generate a first map; according to the classification level based on the correlation coefficient in the first map, acquiring a plurality of associated vocabularies with the correlation coefficient of any one of the one or more keywords in the first map higher than the preset level; according to one or more keywords and a plurality of associated words, matching is carried out in a preset scheme library according to preset rules, and one or more schemes with matching values higher than a preset threshold value are obtained; according to a preset rule, calculating the relevance of each of one or more schemes, and selecting a scheme with the largest relevance as a preferred recommendation scheme; the method solves the defect of manual intervention in the prior art, and is favorable for perfecting and popularizing online self-help training.
Description
Technical Field
The invention relates to the technical field of information, in particular to a scheme self-matching processing method and system based on training requirements.
Background
With the development of the Internet, online classrooms and similar products are being standardized gradually, more people pay attention to the Internet to acquire knowledge, and the original knowledge is paid; the method requires accurate direct connection of training contents or schemes on a network, and can accurately train and match the inquired requirement problems. Most of the current online training systems analyze based on keywords of problems, or access manual work to conduct course consultation and then purchase courses, and for consumers or enterprises, no method is available to directly select targeted courses or training contents, manual intervention is needed, manual course introduction objectivity is poor, and accurate training matching cannot be achieved on the problems of inquirers.
Disclosure of Invention
In order to solve the problem that the prior art cannot realize accurate training matching for training demanders in the background art, the invention provides a scheme self-matching processing method and system based on training requirements, wherein the method and system pre-calculate the correlation coefficients of a plurality of keywords through the pearson correlation coefficients, acquire other words with higher correlation coefficients according to the keywords of the training requirements of users, and acquire a training scheme with highest correlation degree according to the keywords and other words as a preferential recommendation; the scheme self-matching processing method based on training requirements comprises the following steps:
receiving one or more keywords input by a user;
extracting correlation coefficients of one or more keywords corresponding to other words in the database from the database, and generating a first map;
according to the classification level based on the correlation coefficient in the first map, acquiring a plurality of associated vocabularies with the correlation coefficient higher than the preset level requirement with any one of the one or more keywords in the first map;
according to the one or more keywords and the plurality of associated words, matching is carried out in a preset scheme library according to a preset rule, and one or more schemes with matching values higher than a preset threshold value are obtained;
and calculating the relevance of each of the one or more schemes according to a preset rule, and selecting the scheme with the largest relevance as a preferred recommendation scheme.
Further, the receiving the one or more keywords entered by the user includes:
one or more keywords entered by the user for training requirements and one or more keywords obtained by testing enterprise requirements are received.
Further, before extracting the correlation coefficients of the one or more keywords corresponding to other words in the database, the method further includes:
extracting a plurality of vocabularies of each scheme in the scheme library;
calculating the correlation coefficient of each vocabulary relative to other vocabularies according to a Pearson correlation coefficient algorithm;
determining classification grades of every two words according to the correlation coefficient through a preset grade classification rule;
and storing the inter-vocabulary correlation coefficient and the classification level in the database.
Further, the calculating the relevance of each of the one or more schemes according to the preset rule includes:
acquiring keywords and associated vocabularies contained in each of the one or more schemes;
acquiring the number of times that each keyword and each associated vocabulary in the scheme appear in the database history;
the correlation is calculated according to the following formula:
R＝A+B+C+…
wherein A, B, C ·· is the ratio of the number of times each of the keywords and associated vocabulary appears in the schema library to the total number of times all vocabulary appears in the schema library.
Further, after selecting the scheme with the largest correlation as the preferred recommendation scheme, the method further includes:
receiving one or more new keywords input by the user again;
generating a second map according to the new keyword, the keywords input for the first time and a plurality of associated words in the first map;
matching and recovering one or more schemes in a scheme library set in advance according to preset rules;
and calculating the relevance of each of the one or more schemes, and selecting the scheme with the largest relevance as a preferred recommendation scheme.
The scheme self-matching processing system based on training requirements comprises:
the user input unit is used for receiving one or more keywords input by a user;
the map generation unit is used for extracting correlation coefficients of other words in the database corresponding to the one or more keywords in the database to generate a first map;
the scheme acquisition unit is used for acquiring a plurality of associated vocabularies with the correlation coefficient higher than the preset level requirement in the first map according to the classification level based on the correlation coefficient in the first map;
the scheme obtaining unit is used for obtaining one or more schemes with matching values higher than a preset threshold value according to the one or more keywords and the plurality of associated vocabularies and matching in a preset scheme library according to preset rules;
a correlation calculation unit, configured to calculate a correlation of each of the one or more schemes according to a preset rule;
and the preferred recommendation output unit is used for selecting the scheme with the highest correlation degree as the preferred recommendation scheme.
Further, the user input unit is used for receiving one or more keywords input by a user and aiming at training requirements and one or more keywords obtained by testing enterprise requirements.
Further, the system includes a correlation coefficient calculation unit;
the correlation coefficient calculation unit is used for extracting a plurality of vocabularies of each scheme in the scheme library;
the correlation coefficient calculation unit is used for calculating the correlation coefficient of each vocabulary relative to other vocabularies according to a Pearson correlation coefficient algorithm;
the correlation coefficient calculation unit is used for determining classification grades of every two vocabularies according to the correlation coefficient through a preset grade classification rule;
the correlation coefficient calculation unit is used for storing the inter-vocabulary correlation coefficient and the classification level in the database.
Further, the relevance calculating unit is configured to obtain keywords and associated vocabulary included in each of the one or more schemes;
the correlation calculation unit is used for obtaining the times of each keyword and each associated vocabulary in the scheme in the database history;
the correlation calculation unit is used for calculating and obtaining the correlation according to the following formula:
R＝A+B+C+…
wherein A, B, C ·· is the ratio of the number of times each of the keywords and associated vocabulary appears in the schema library to the total number of times all vocabulary appears in the schema library.
Further, the user input unit is used for receiving one or more new keywords input by the user again;
the map generation unit is used for generating a second map according to the new keyword, the first inputted keyword and a plurality of associated words in the first map;
the scheme obtaining unit is used for matching and re-obtaining one or more schemes in a scheme library preset according to preset rules;
the correlation calculation unit is used for calculating the correlation of each of the one or more schemes;
the preferred recommendation output unit is used for selecting the scheme with the largest correlation degree as the preferred recommendation scheme.
The beneficial effects of the invention are as follows: according to the technical scheme, the scheme self-matching processing method and system based on training requirements are provided, the method and system are used for calculating the correlation coefficients of a plurality of keywords in advance through the pearson correlation coefficients, other words with higher correlation coefficients are obtained according to the keywords of the training requirements of users, and further training schemes with highest correlation degrees are obtained according to the keywords and other words to be used as preferential recommendation; according to the method and the system, accurate self-matching of the corresponding training scheme is realized according to the training requirement, the defect of manual intervention in the prior art is overcome, and the improvement and popularization of online self-help training are facilitated.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flow chart of a solution self-matching processing method based on training requirements according to an embodiment of the present invention;
FIG. 2 is a block diagram of a solution self-matching processing system based on training requirements in accordance with an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow chart of a solution self-matching processing method based on training requirements according to an embodiment of the present invention; as shown in fig. 1, the method includes:
the method comprises the steps of obtaining a preferred recommendation scheme through a processing method of self-matching of the scheme by receiving one or more keywords for training requirements entered by a user;
the key words input by the user can be one key word, for example, big data or a plurality of key words, big data and HIVE;
confirming the required training content of the enterprise by carrying out self test on the enterprise, generating keywords according to the training content, and also inputting one or more keywords in step 110;
the correlation coefficient is obtained through preliminary calculation, and is specifically:
one or more keywords entered by the user for training requirements and one or more keywords obtained by testing enterprise requirements are received.
Further, before extracting the correlation coefficients of the one or more keywords corresponding to other words in the database, the method further includes:
extracting a plurality of vocabularies of each scheme in the scheme library; namely, the vocabulary which is possibly extracted and contained in all schemes in the scheme library;
calculating the correlation coefficient of each vocabulary relative to other vocabularies according to a Pearson correlation coefficient algorithm;
determining classification grades of every two words according to the correlation coefficient through a preset grade classification rule;
the preset level classification rule is obtained by classifying the correlation numbers into a plurality of level segments, and when the correlation coefficient of two vocabularies falls into a certain correlation coefficient level segment, the correlation coefficient of the two vocabularies is the classification level.
And storing the inter-vocabulary correlation coefficient and the classification level in the database.
for example, if the correlation coefficient is 0.8 as the preset level requirement, the associated vocabulary with more than 0.8 and the keywords are the vocabulary to be extracted.
the matching can be carried out by selecting one or more schemes including the one or more keywords and all the vocabularies of the plurality of associated vocabularies as matching schemes; one or more schemes including the one or more keywords and the vocabulary of the plurality of associated vocabulary preset proportions can also be selected as matching schemes;
Specifically, the method for calculating the correlation degree comprises the following steps:
acquiring keywords and associated vocabularies contained in each of the one or more schemes;
acquiring the number of times that each keyword and each associated vocabulary in the scheme appear in the database history;
the correlation is calculated according to the following formula:
R＝A+B+C+…
wherein A, B, C ·· is the ratio of the number of times each of the keywords and associated vocabulary appears in the schema library to the total number of times all vocabulary appears in the schema library.
Further, after selecting the scheme with the highest correlation degree as the preferred recommendation scheme, the method further comprises, if the user is not satisfied with the preferred recommendation scheme, inputting a new keyword again:
receiving one or more new keywords input by the user again;
generating a second map according to the new keyword, the keywords input for the first time and a plurality of associated words in the first map;
matching and recovering one or more schemes in a scheme library set in advance according to preset rules;
and calculating the relevance of each of the one or more schemes, and selecting the scheme with the largest relevance as a preferred recommendation scheme.
FIG. 2 is a block diagram of a solution self-matching processing system based on training requirements in accordance with an embodiment of the present invention. As shown in fig. 2, the system includes:
a user input unit 210, where the user input unit 210 is configured to receive one or more keywords input by a user;
further, the user input unit 210 is configured to receive one or more keywords input by a user for training requirements and one or more keywords obtained by testing enterprise requirements.
The map generating unit 220 is configured to extract, from the database, correlation coefficients of the one or more keywords corresponding to other words in the database, and generate a first map;
a solution obtaining unit 230, where the solution obtaining unit 230 is configured to obtain, according to a classification level based on a correlation coefficient in the first graph, a plurality of associated vocabularies in the first graph, where the correlation coefficient with any one of the one or more keywords is higher than a preset level requirement;
the solution obtaining unit 230 is configured to match in a preset solution library according to the one or more keywords and the plurality of associated vocabularies and a preset rule, so as to obtain one or more solutions with matching values higher than a preset threshold;
a correlation calculation unit 240, where the correlation calculation unit 240 is configured to calculate a correlation of each of the one or more schemes according to a preset rule;
further, the relevance calculating unit 240 is configured to obtain keywords and associated vocabulary included in each of the one or more schemes;
the relevance calculating unit 240 is configured to obtain the number of times each of the keywords and the associated vocabulary in the scheme appears in the database history;
the correlation calculation unit 240 is configured to calculate and obtain a correlation according to the following formula:
R＝A+B+C+…
wherein A, B, C ·· is the ratio of the number of times each of the keywords and associated vocabulary appears in the schema library to the total number of times all vocabulary appears in the schema library.
And a preferred recommendation output unit 250, where the preferred recommendation output unit 250 is configured to select a scheme with the greatest correlation as a preferred recommendation scheme.
Further, the system includes a correlation coefficient calculation unit;
the correlation coefficient calculation unit is used for extracting a plurality of vocabularies of each scheme in the scheme library;
the correlation coefficient calculation unit is used for calculating the correlation coefficient of each vocabulary relative to other vocabularies according to a Pearson correlation coefficient algorithm;
the correlation coefficient calculation unit is used for determining classification grades of every two vocabularies according to the correlation coefficient through a preset grade classification rule;
the correlation coefficient calculation unit is used for storing the inter-vocabulary correlation coefficient and the classification level in the database.
Further, the user input unit 210 is configured to receive one or more new keywords input by the user again;
the map generating unit 220 is configured to generate a second map according to the new keyword, the first inputted keyword, and the plurality of associated vocabularies in the first map;
the solution obtaining unit 230 is configured to match and retrieve one or more solutions in a preset solution library according to a preset rule;
the correlation calculation unit 240 is configured to calculate a correlation of each of the one or more schemes;
the preferred recommendation output unit 250 is configured to select a solution with the greatest correlation as a preferred recommendation solution.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Reference to step numbers in this specification is used solely to distinguish between steps and is not intended to limit the time or logical relationship between steps, including the various possible conditions unless the context clearly indicates otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments. For example, any of the embodiments claimed in the claims may be used in any combination.
Various component embodiments of the present disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present disclosure may also be embodied as a device or apparatus program (e.g., computer program and computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present disclosure may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware.
The foregoing is merely a specific embodiment of the disclosure, and it should be noted that it will be apparent to those skilled in the art that several improvements, modifications, and variations can be made without departing from the spirit of the disclosure, and these improvements, modifications, and variations are to be considered within the scope of the present application.
Claims (8)
1. A solution self-matching processing method based on training requirements, the method comprising:
receiving one or more keywords input by a user;
extracting correlation coefficients of one or more keywords corresponding to other words in the database from the database, and generating a first map;
according to the classification level based on the correlation coefficient in the first map, acquiring a plurality of associated vocabularies with the correlation coefficient higher than the preset level requirement with any one of the one or more keywords in the first map;
according to the one or more keywords and the plurality of associated words, matching is carried out in a preset scheme library according to a preset rule, and one or more schemes with matching values higher than a preset threshold value are obtained;
calculating the relevance of each of the one or more schemes according to a preset rule, and selecting a scheme with the largest relevance as a preferred recommendation scheme;
wherein the calculating the correlation degree of each of the one or more schemes according to the preset rule includes:
acquiring keywords and associated vocabularies contained in each of the one or more schemes;
acquiring the number of times that each keyword and each associated vocabulary in the scheme appear in the database history;
the correlation is calculated according to the following formula:
R=A+B+C+…
wherein A, B, C ·· is the ratio of the number of times each of the keywords and associated vocabulary appears in the schema library to the total number of times all vocabulary appears in the schema library.
2. The method of claim 1, wherein the receiving one or more keywords entered by a user comprises:
one or more keywords entered by the user for training requirements and one or more keywords obtained by testing enterprise requirements are received.
3. The method of claim 1, wherein before extracting the correlation coefficients of the one or more keywords corresponding to other words in the database, the method further comprises:
extracting a plurality of vocabularies of each scheme in the scheme library;
calculating the correlation coefficient of each vocabulary relative to other vocabularies according to a Pearson correlation coefficient algorithm;
determining classification grades of every two words according to the correlation coefficient through a preset grade classification rule;
and storing the inter-vocabulary correlation coefficient and the classification level in the database.
4. The method according to claim 1, wherein after selecting the scheme with the greatest correlation as the preferred recommendation scheme, the method further comprises:
receiving one or more new keywords input by the user again;
generating a second map according to the new keyword, the keywords input for the first time and a plurality of associated words in the first map;
matching and recovering one or more schemes in a scheme library set in advance according to preset rules;
and calculating the relevance of each of the one or more schemes, and selecting the scheme with the largest relevance as a preferred recommendation scheme.
5. A solution self-matching processing system based on training requirements, the system comprising:
the user input unit is used for receiving one or more keywords input by a user;
the map generation unit is used for extracting correlation coefficients of other words in the database corresponding to the one or more keywords in the database to generate a first map;
the scheme acquisition unit is used for acquiring a plurality of associated vocabularies with the correlation coefficient higher than the preset level requirement in the first map according to the classification level based on the correlation coefficient in the first map;
the scheme obtaining unit is used for obtaining one or more schemes with matching values higher than a preset threshold value according to the one or more keywords and the plurality of associated vocabularies and matching in a preset scheme library according to preset rules;
a correlation calculation unit, configured to calculate a correlation of each of the one or more schemes according to a preset rule;
the optimal recommendation output unit is used for selecting a scheme with the largest correlation degree as an optimal recommendation scheme;
the relevance calculating unit is used for obtaining keywords and associated vocabularies contained in each of the one or more schemes;
the correlation calculation unit is used for obtaining the times of each keyword and each associated vocabulary in the scheme in the database history;
the correlation calculation unit is used for calculating and obtaining the correlation according to the following formula:
R=A+B+C+…
wherein A, B, C ·· is the ratio of the number of times each of the keywords and associated vocabulary appears in the schema library to the total number of times all vocabulary appears in the schema library.
6. The system according to claim 5, wherein:
the user input unit is used for receiving one or more keywords input by a user and aiming at training requirements and one or more keywords obtained by testing enterprise requirements.
7. The system according to claim 5, wherein: the system comprises a correlation coefficient calculation unit;
the correlation coefficient calculation unit is used for extracting a plurality of vocabularies of each scheme in the scheme library;
the correlation coefficient calculation unit is used for calculating the correlation coefficient of each vocabulary relative to other vocabularies according to a Pearson correlation coefficient algorithm;
the correlation coefficient calculation unit is used for determining classification grades of every two vocabularies according to the correlation coefficient through a preset grade classification rule;
the correlation coefficient calculation unit is used for storing the inter-vocabulary correlation coefficient and the classification level in the database.
8. The system according to claim 5, wherein:
the user input unit is used for receiving one or more new keywords input by the user again;
the map generation unit is used for generating a second map according to the new keyword, the first inputted keyword and a plurality of associated words in the first map;
the scheme obtaining unit is used for matching and re-obtaining one or more schemes in a scheme library preset according to preset rules;
the correlation calculation unit is used for calculating the correlation of each of the one or more schemes;
the preferred recommendation output unit is used for selecting the scheme with the largest correlation degree as the preferred recommendation scheme.
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