CN111222040A - Scheme self-matching processing method and system based on training requirement - Google Patents
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Abstract
The invention discloses a scheme self-matching processing method and a 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 other words in the database corresponding to one or more keywords 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, of which the correlation coefficients with any one of the one or more keywords are higher than the preset level requirement, in the first map; matching in a preset scheme library according to one or more keywords and a plurality of associated vocabularies and a preset rule to obtain one or more schemes with a matching value higher than a preset threshold value; calculating the correlation degree of each of one or more schemes according to a preset rule, and selecting the scheme with the maximum correlation degree as an optimal recommendation scheme; the method overcomes the defect of manual intervention in the prior art, and is beneficial to the perfection and popularization of 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 and development of the internet, the network classroom and similar products are gradually normalized, more people pay attention to the internet to acquire knowledge and the original knowledge; and the requirement is that the training content or scheme on the network is accurate and direct, and the inquiry requirement problem can be accurately trained and matched. Most of the existing online training systems analyze based on keywords of problems, or access manual course consultation and then purchase courses, for consumers or enterprises, there is no way to directly select targeted courses or training contents, manual intervention is more, the objectivity of manual course introduction is poor, and accurate training matching cannot be realized on the problems of inquirers.
Disclosure of Invention
In order to solve the problem that the prior art in the background art cannot realize accurate training matching for training demanders, the invention provides a scheme self-matching processing method and system based on training requirements, wherein the method and system calculate correlation coefficients of a plurality of keywords in advance through Pearson correlation coefficients, obtain other words with higher correlation coefficients according to keywords required by user training, and further obtain a training scheme with the highest correlation as an optimal recommendation according to the keywords and the other words; the scheme self-matching processing method based on the training requirement comprises the following steps:
receiving one or more keywords input by a user;
extracting the correlation coefficient of other words in the database corresponding to the one or more keywords 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, of which the correlation coefficients with any one of the one or more keywords are higher than the preset level requirement, in the first map;
matching in a preset scheme library according to the one or more keywords and the plurality of associated vocabularies and a preset rule to obtain one or more schemes with a matching value higher than a preset threshold value;
and according to a preset rule, calculating the correlation degree of each of the one or more schemes, and selecting the scheme with the maximum correlation degree as a preferred recommendation scheme.
Further, the receiving one or more keywords entered by the user includes:
one or more keywords entered by a user for training requirements and one or more keywords obtained by testing enterprise requirements are received.
Further, before extracting the correlation coefficient of the one or more keywords corresponding to other vocabularies 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 levels of every two vocabularies according to the correlation coefficients through a preset level classification rule;
storing the inter-vocabulary correlation coefficients and the classification levels in the database.
Further, the calculating the relevancy of each of the one or more schemes according to a preset rule includes:
acquiring keywords and associated vocabularies contained in each of the one or more schemes;
acquiring the occurrence frequency of each keyword and each associated vocabulary in the scheme 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 occurrences of each of the keywords and associated vocabularies in the schema repository to the total number of occurrences of all the vocabularies in the schema repository.
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 again by the user;
generating a second map according to the new keywords, the keywords input for the first time and the plurality of associated words in the first map;
matching and recovering one or more schemes in a preset scheme library according to a preset rule;
and calculating the correlation degree of each of the one or more schemes, and selecting the scheme with the maximum correlation degree as a preferred recommendation scheme.
The scheme self-matching processing system based on the training requirement comprises:
the system comprises a user input unit, a display unit and a display unit, wherein the user input unit is used for receiving one or more keywords input by a user;
the map generation unit is used for extracting the correlation coefficients of other vocabularies in the database corresponding to the one or more keywords from the database to generate a first map;
a scheme obtaining unit, configured to obtain, according to a classification level based on a correlation coefficient in the first graph, a plurality of associated words in the first graph, for which the correlation coefficient with any of the one or more keywords is higher than a preset level requirement;
the scheme acquisition unit is used for matching in a preset scheme library according to the one or more keywords and the plurality of associated vocabularies and a preset rule to acquire one or more schemes with a matching value higher than a preset threshold value;
a relevancy calculation unit for calculating a relevancy 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 maximum correlation degree as the preferred recommendation scheme.
Further, the user entry unit is used for receiving one or more keywords which are entered by the user and aim at the training requirement and one or more keywords which are obtained by testing the enterprise requirement.
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 calculating 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 levels of every two vocabularies according to the correlation coefficients through a preset level classification rule;
the correlation coefficient calculation unit is used for storing the correlation coefficient among the vocabularies and the classification level in the database.
Further, the relevancy calculation unit is configured to obtain keywords and associated vocabularies included in each of the one or more schemes;
the relevancy calculation unit is used for acquiring the times of occurrence 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 occurrences of each of the keywords and associated vocabularies in the schema repository to the total number of occurrences of all the vocabularies in the schema repository.
Further, the user entry unit is used for receiving one or more new keywords input again by the user;
the map generation unit is used for generating a second map according to the new keywords, the keywords input for the first time and the plurality of associated words in the first map;
the scheme acquisition unit is used for matching and reacquiring one or more schemes in a preset scheme library according to a preset rule;
the relevancy calculation unit is used for calculating the relevancy of each of the one or more schemes;
the preferred recommendation output unit is used for selecting the scheme with the maximum correlation degree as the preferred recommendation scheme.
The invention has the beneficial effects that: the technical scheme of the invention provides a scheme self-matching processing method and system based on training requirements, wherein the method and system calculate correlation coefficients of a plurality of keywords in advance through Pearson correlation coefficients, obtain other words with higher correlation coefficients according to keywords required by user training, and further obtain a training scheme with highest correlation as an optimal recommendation according to the keywords and the other words; the method and the system realize accurate self-matching of the corresponding training scheme according to the training requirement, overcome the defect of manual intervention in the prior art, and are beneficial to the perfection and popularization of on-line self-service training.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow chart of a method for training requirement based self-matching of protocols in accordance with an embodiment of the present invention;
fig. 2 is a block diagram of a training requirement based scenario self-matching processing system according to 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 embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present 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, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those 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 method for training requirement based self-matching of protocols in accordance with an embodiment of the present invention; as shown in fig. 1, the method includes:
the method comprises the steps of receiving one or more keywords for training requirements input by a user, and obtaining a preferred recommendation scheme through a scheme self-matching processing method;
the keyword entered by the user may be one keyword, for example, "big data" or a plurality of keywords "big data", "HIVE";
confirming the required training content by testing the enterprise itself for the enterprise user, generating keywords according to the training content, and also being the one or more keywords input in step 110;
the correlation coefficient is obtained by pre-calculation, specifically:
one or more keywords entered by a user for training requirements and one or more keywords obtained by testing enterprise requirements are received.
Further, before extracting the correlation coefficient of the one or more keywords corresponding to other vocabularies in the database, the method further includes:
extracting a plurality of vocabularies of each scheme in the scheme library; i.e. to the possible extracted vocabulary contained in all the solutions in the solution library;
calculating the correlation coefficient of each vocabulary relative to other vocabularies according to a Pearson correlation coefficient algorithm;
determining classification levels of every two vocabularies according to the correlation coefficients through a preset level classification rule;
the preset grade classification rule is obtained by classifying the correlation coefficient into a plurality of grade sections, and when the correlation coefficient of two vocabularies falls into a certain correlation coefficient grade section, the correlation coefficient of the two vocabularies is the classification grade.
Storing the inter-vocabulary correlation coefficients and the classification levels in the database.
for example, if the correlation coefficient is 0.8 as the preset level requirement, the associated vocabulary above 0.8 and the keyword are the vocabulary to be extracted.
the matching can be performed by selecting one or more schemes comprising the one or more keywords and all the vocabularies of the plurality of associated vocabularies as matching schemes; one or more schemes comprising the one or more keywords and the vocabulary with the preset proportion of the plurality of associated vocabularies can also be selected as matching schemes;
and 150, calculating the correlation degree of each of the one or more schemes according to a preset rule, and selecting the scheme with the maximum correlation degree as a preferred recommendation scheme.
Specifically, the method for calculating the correlation includes:
acquiring keywords and associated vocabularies contained in each of the one or more schemes;
acquiring the occurrence frequency of each keyword and each associated vocabulary in the scheme 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 occurrences of each of the keywords and associated vocabularies in the schema repository to the total number of occurrences of all the vocabularies in the schema repository.
Further, after selecting the scheme with the largest degree of correlation as the preferred recommendation scheme, the method further includes, if the user is not satisfied with the preferred recommendation scheme, when inputting a new keyword again:
receiving one or more new keywords input again by the user;
generating a second map according to the new keywords, the keywords input for the first time and the plurality of associated words in the first map;
matching and recovering one or more schemes in a preset scheme library according to a preset rule;
and calculating the correlation degree of each of the one or more schemes, and selecting the scheme with the maximum correlation degree as a preferred recommendation scheme.
Fig. 2 is a block diagram of a training requirement based scenario self-matching processing system according to an embodiment of the present invention. As shown in fig. 2, the system includes:
a user entry unit 210, wherein the user entry unit 210 is configured to receive one or more keywords entered by a user;
further, the user entry unit 210 is configured to receive one or more keywords entered by the user for the training requirement and one or more keywords obtained by testing the enterprise requirement.
The map generating unit 220 is configured to extract correlation coefficients of other vocabularies in the database corresponding to the one or more keywords from the database, and generate a first map;
a scheme obtaining unit 230, wherein the scheme 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, of which the correlation coefficient with any one of the one or more keywords is higher than a preset level requirement;
the scheme obtaining unit 230 is configured to perform matching in a preset scheme library according to a preset rule according to the one or more keywords and the plurality of associated vocabularies, and obtain one or more schemes with a matching value higher than a preset threshold;
a relevancy calculation unit 240, wherein the relevancy calculation unit 240 is configured to calculate a relevancy of each of the one or more schemes according to a preset rule;
further, the relevancy calculation unit 240 is configured to obtain keywords and associated vocabularies included in each of the one or more schemes;
the relevancy calculation unit 240 is configured to obtain the number of times that each of the keywords and the associated vocabularies 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 occurrences of each of the keywords and associated vocabularies in the schema repository to the total number of occurrences of all the vocabularies in the schema repository.
A preferred recommendation output unit 250, where the preferred recommendation output unit 250 is configured to select a scheme with the largest correlation as the 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 calculating 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 levels of every two vocabularies according to the correlation coefficients through a preset level classification rule;
the correlation coefficient calculation unit is used for storing the correlation coefficient among the vocabularies and the classification level in the database.
Further, the user entry unit 210 is configured to receive one or more new keywords input again by the user;
the map generation unit 220 is configured to generate a second map according to the new keyword, the first input keyword, and the plurality of associated words in the first map;
the scheme acquiring unit 230 is configured to match and retrieve one or more schemes in a preset scheme library according to a preset rule;
the relevancy calculation unit 240 is configured to calculate a relevancy of each of the one or more schemes;
the preferred recommendation output unit 250 is configured to select a scheme with the largest correlation as a preferred recommendation scheme.
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 device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. 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. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements 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 only for distinguishing between steps and is not intended to limit the temporal or logical relationship between steps, which includes all possible scenarios unless the context clearly dictates otherwise.
Moreover, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, 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 can be used in any combination.
Various component embodiments of the 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 apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or 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 directed to embodiments of the present disclosure, and it is noted that numerous improvements, modifications, and variations may be made by those skilled in the art without departing from the spirit of the disclosure, and that such improvements, modifications, and variations are considered to be within the scope of the present disclosure.
Claims (10)
1. A scheme self-matching processing method based on training requirements is characterized by comprising the following steps:
receiving one or more keywords input by a user;
extracting the correlation coefficient of other words in the database corresponding to the one or more keywords 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, of which the correlation coefficients with any one of the one or more keywords are higher than the preset level requirement, in the first map;
matching in a preset scheme library according to the one or more keywords and the plurality of associated vocabularies and a preset rule to obtain one or more schemes with a matching value higher than a preset threshold value;
and according to a preset rule, calculating the correlation degree of each of the one or more schemes, and selecting the scheme with the maximum correlation degree as a preferred recommendation scheme.
2. The method of claim 1, wherein receiving one or more keywords entered by a user comprises:
one or more keywords entered by a 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 coefficient of the one or more keywords corresponding to other vocabularies 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 levels of every two vocabularies according to the correlation coefficients through a preset level classification rule;
storing the inter-vocabulary correlation coefficients and the classification levels in the database.
4. The method of claim 1, wherein said calculating the degree of correlation for each of the one or more solutions according to a predetermined rule comprises:
acquiring keywords and associated vocabularies contained in each of the one or more schemes;
acquiring the occurrence frequency of each keyword and each associated vocabulary in the scheme 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 occurrences of each of the keywords and associated vocabularies in the schema repository to the total number of occurrences of all the vocabularies in the schema repository.
5. The method of claim 1, wherein after selecting the scheme with the largest correlation as the preferred recommendation scheme, the method further comprises:
receiving one or more new keywords input again by the user;
generating a second map according to the new keywords, the keywords input for the first time and the plurality of associated words in the first map;
matching and recovering one or more schemes in a preset scheme library according to a preset rule;
and calculating the correlation degree of each of the one or more schemes, and selecting the scheme with the maximum correlation degree as a preferred recommendation scheme.
6. A program self-matching processing system based on training needs, the system comprising:
the system comprises a user input unit, a display unit and a display unit, wherein the user input unit is used for receiving one or more keywords input by a user;
the map generation unit is used for extracting the correlation coefficients of other vocabularies in the database corresponding to the one or more keywords from the database to generate a first map;
a scheme obtaining unit, configured to obtain, according to a classification level based on a correlation coefficient in the first graph, a plurality of associated words in the first graph, for which the correlation coefficient with any of the one or more keywords is higher than a preset level requirement;
the scheme acquisition unit is used for matching in a preset scheme library according to the one or more keywords and the plurality of associated vocabularies and a preset rule to acquire one or more schemes with a matching value higher than a preset threshold value;
a relevancy calculation unit for calculating a relevancy 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 maximum correlation degree as the preferred recommendation scheme.
7. The system of claim 6, wherein:
the user entry unit is used for receiving one or more keywords which are entered by a user and aim at the training requirement and one or more keywords which are obtained by testing the enterprise requirement.
8. The system of claim 6, wherein: 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 calculating 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 levels of every two vocabularies according to the correlation coefficients through a preset level classification rule;
the correlation coefficient calculation unit is used for storing the correlation coefficient among the vocabularies and the classification level in the database.
9. The system of claim 6, wherein:
the relevancy calculation unit is used for acquiring keywords and associated vocabularies contained in each of the one or more schemes;
the relevancy calculation unit is used for acquiring the times of occurrence 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 occurrences of each of the keywords and associated vocabularies in the schema repository to the total number of occurrences of all the vocabularies in the schema repository.
10. The system of claim 6, wherein:
the user input unit is used for receiving one or more new keywords input again by the user;
the map generation unit is used for generating a second map according to the new keywords, the keywords input for the first time and the plurality of associated words in the first map;
the scheme acquisition unit is used for matching and reacquiring one or more schemes in a preset scheme library according to a preset rule;
the relevancy calculation unit is used for calculating the relevancy of each of the one or more schemes;
the preferred recommendation output unit is used for selecting the scheme with the maximum correlation degree as the preferred recommendation scheme.
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