CN114282140A - Digital product library system - Google Patents
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- CN114282140A CN114282140A CN202111400275.0A CN202111400275A CN114282140A CN 114282140 A CN114282140 A CN 114282140A CN 202111400275 A CN202111400275 A CN 202111400275A CN 114282140 A CN114282140 A CN 114282140A
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Abstract
本发明公开了一种数字产品库系统，包括：功能目录单元，用于展示数字产品的属性，数字产品的属性包括业分类、系统名称、功能视图、架构视图、功能点击量和系统帮助文档中的一种或多种；系统地图单元，用于根据输入的数字产品的功能说明判断该数字产品与现有的数字产品功能的重复度；功能地图单元，用于对所有数字产品的功能统一进行聚类分析，获得各类型功能的分布和活跃度；功能超市单元，用于以功能为颗粒度对数字产品进行展示，反馈各数字产品对应的功能链接页面。本发明解决了数字产品的重复建设问题，消除数字产品库系统中使用率低的、无用的或者冗余的数字产品，同时辅助数字产品项目申报审核，为数字产品项目申报瘦身，节省资源开销。
The invention discloses a digital product library system, comprising: a function catalog unit for displaying the attributes of digital products. One or more of the digital products; the system map unit, which is used to judge the duplication of the functions of the digital product and the existing digital products according to the function description of the input digital product; the function map unit is used to uniformly carry out the functions of all digital products. Cluster analysis is used to obtain the distribution and activity of various types of functions; the functional supermarket unit is used to display digital products with the function as the granularity, and feedback the function link page corresponding to each digital product. The invention solves the problem of repeated construction of digital products, eliminates low-usage, useless or redundant digital products in the digital product library system, assists the application and review of digital product projects, reduces the size of the application for digital product projects, and saves resource overhead.
Description
Technical Field
The invention relates to the technical field of informatization systems, in particular to a digital product library system.
Background
With the advance of digitization and informatization, a plurality of digital product systems are built by some companies, and the problems of similar functions, low utilization rate and the like exist in part of the digital product systems, so that the waste of resources is caused.
Disclosure of Invention
It is an object of the present invention to overcome one or more of the deficiencies of the prior art and to provide a digital product library system.
The purpose of the invention is realized by the following technical scheme: a digital product library system comprising:
the system comprises a function catalog unit, a function display unit and a function display unit, wherein the function catalog unit is used for displaying the attributes of the digital product, and the attributes of the digital product comprise one or more of industry classification, system name, function view, architecture view, function click quantity and system help document;
the system map unit is used for judging the repetition degree of the digital product and the functions of the existing digital product according to the input function description of the digital product;
the function map unit is used for carrying out cluster analysis on the functions of all the digital products uniformly to obtain the distribution and the activity of each type of function;
and the function supermarket unit is used for displaying the digital products by taking the functions as granularity and feeding back function link pages corresponding to the digital products.
Preferably, the function directory unit comprises one or more of the following modules:
the functional view module is used for displaying a grading graph of the digital products and displaying click rate of each grade of digital products;
the architecture view module is used for displaying the architecture view of each digital product;
the function access module is used for authenticating the URL of the function point when the employee accesses the function point in the digital product, and skipping to the function point of the digital product if the authentication is successful;
and the function point utilization rate module is used for acquiring the utilization rate of the function points of each digital product.
Preferably, the hierarchical diagram of the digital product is used for expanding the functions of the digital product step by step until the table is single-level.
Preferably, the system map unit comprises a similarity analysis model;
the similarity analysis model is obtained by utilizing a cosine similarity algorithm and a semantic similarity algorithm for training based on the functional description of the existing digital product.
Preferably, the cosine similarity algorithm includes:
dividing two complete sentences to be subjected to similarity calculation into two independent word sets by using a word segmentation algorithm;
calculating a union of the two word sets;
respectively calculating the word frequency of the two word sets, and vectorizing the word frequency;
and substituting the vectorized word frequency into a vector calculation model to obtain the text similarity.
Preferably, the function map unit includes a system evaluation model, and the system evaluation model is constructed by a process including:
obtaining a standard decision matrix by a vector programming method;
forming a weighted normative matrix by using the normative decision matrix;
and generating a system evaluation model according to the weighting standard matrix.
Preferably, in the functional supermarket unit, when the functional link page is clicked, if the operator has the corresponding authority, the corresponding function is used, and if the operator does not have the corresponding authority, an authority opening and approval link is entered.
The invention has the beneficial effects that:
(1) the invention realizes the timely update of the digital products of the information system by the full record coverage of the digital products of the information system and the construction of a digital product library system of the information system;
(2) the invention solves the problem of repeated construction of digital products, eliminates useless or redundant digital products with low utilization rate in a digital product library system, simultaneously assists in declaration and verification of digital product projects, declares the digital product projects to be slim and saves resource overhead;
(3) the invention provides a digital product level search engine, which is used for displaying each system by taking a digital product as granularity, feeding back each system and a related digital product link page, clicking to obtain a related digital product, and entering a permission opening and approval link if no corresponding permission exists.
Drawings
FIG. 1 is a block diagram of one component of a digital product library system;
FIG. 2 is a diagram of a business architecture of a digital product library system;
FIG. 3 is a schematic diagram of a workflow of the function access module;
FIG. 4 is a schematic illustration of the distribution and liveness monitoring of various types of functions;
fig. 5 is a schematic diagram of authentication of a user in a functional supermarket unit.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1 to 5, the present embodiment provides a digital product library system:
as shown in fig. 1 and 2, a digital product library system includes a function catalog unit, a system map unit, a function map unit, and a function supermarket unit.
The function catalog unit is used for displaying the attributes of the digital product, and the attributes of the digital product comprise one or more of industry classification, system name, function view, architecture view, function click volume and system help document.
In some embodiments, the functional catalog unit includes one or more of a functional view module, an architecture view module, a functional access module, and a functional point usage module.
The functional view module is used for displaying a grading graph of the digital products and displaying click rate of each grade of digital products; wherein the hierarchical diagram of the digital product is used for expanding the functions of the digital product stage by stage until the table is single-stage.
The architecture view module is used for displaying architecture views of digital products, wherein the architecture views comprise contents such as server number, deployment, IP addresses and deployment modes.
The function access module is used for authenticating the URL of the function point when the staff accesses the function point in the digital product, and skipping to the function point of the digital product if the authentication is successful. As shown in fig. 3, when the user accesses the system function in the digital product library, the function URL address jumps to the system function corresponding to the function point through the unified authority authentication; if the unified authority authentication is successful, the user can access the authority, and if the authentication is unsuccessful, the user cannot access the function; if the user wants to access the function, the user needs to enter a function product library (CBD) to apply for the permission.
The function point utilization rate module is used for acquiring the utilization rate of each digital product function point, and the acquisition method of the utilization rate of each digital product function point comprises the following steps: and acquiring the utilization rate of each digital product function point through a Nanrui application system monitoring tool.
And the system map unit is used for judging the repetition degree of the digital product and the existing digital product according to the input function description of the digital product. For example, a function overlap ratio system map is generated by training a clustering algorithm through the description of the functions of the existing system, when the function specification of the newly-built digital product is imported into the digital product library, the overlap condition of the system and the functions of other systems is calculated according to semantics based on the existing algorithm model, the functions which are repeatedly built and have low utilization rate are obtained according to the evaluation model of the existing system, and reference suggestions are provided. In some embodiments, the data model is retrained again by performing manual second proofreading, and finally a more accurate repeated function analysis conclusion is obtained by the data model.
In some embodiments, the system map unit comprises a similarity analysis model; the similarity analysis model is obtained by utilizing a cosine similarity algorithm and a semantic similarity algorithm for training based on the functional description of the existing digital product.
Description of cosine similarity algorithm: cosine similarity is to measure the difference between two individuals through the cosine value of an included angle between two vectors in a vector space; the cosine similarity is characterized in that the cosine value is close to 1, and the included angle is close to 0, which indicates that the two vectors are more similar. For example, if document a is set as a (x ' 1, x ' 2, x ' 3, x ' 4, x ' 5.) by phrase and document B is set as B (y1, y2, y3, y4, y5..) by phrase, the similarity between document a and document B is:
in the formula, a is a digital product function description document word; x': are the numerical values of the words in different dimensions; i (1,2,3, 4): is dimension; b, comparing the document words y of the document: comparing the dimension values of the document words; i (1,2,3, 4): is a dimension.
Generally, the cosine similarity algorithm comprises: dividing two complete sentences to be subjected to similarity calculation into two independent word sets by using a word segmentation algorithm; calculating a union (word package) of two word sets; respectively calculating the word frequency of the two word sets, and vectorizing the word frequency; and substituting the vectorized word frequency into a vector calculation model to obtain the text similarity.
Semantic similarity algorithm specification: expressing the Query and the Title as low latitude semantic vectors by DNN (deep neural network) through phrase vectors of a text (Query) and an original text (Title), calculating the distance between the two semantic vectors through cosine distance (cosine distance), and finally training a semantic similarity model. The semantic similarity model can be used for predicting the semantic similarity of two sentences and obtaining the low latitude semantic vector expression of a certain sentence.
DSSM (deep semantic matching model) is divided into three layers from top to bottom: input layer, presentation layer, matching layer.
An input layer: the sentence is mapped into a vector space and input into the DNN.
Presentation layer: a BOW (Bag of words) mode is adopted; that is, the position information of the word vector is discarded, and the words in the whole sentence are put in a bag without any sequence.
Using Wi to represent the weight matrix of the ith layer, and bi to represent the bias term of the ith layer, then the first hidden layer vector l1(300 dimensions), the ith hidden layer vector li (300 dimensions), and the output vector y (128 dimensions) can be represented as:
l1＝W1x
li＝f(Wili-1+bi)，i＝2，...，N-1
y＝f(WNlN-1+bN)
wherein x is a weight value.
With tanh as the activation function for hidden and output layers:
in the formula, e is a natural constant and has a value of 2.71828183.
Finally outputting a 128-dimensional low latitude semantic vector.
Matching layer: semantic similarity of Query and Doc can be represented by cosine distance of the two semantic vectors (128 dimensions):
in the formula, T: a power variable; YD: the language of the word in the digital product related document; YQ: is the semantic meaning of a word in a dictionary repository.
The semantic similarity between the Query and the positive sample Doc can be converted into a posterior probability through a softmax function:
where γ is the smoothing factor of softmax, D+Is a positive sample under Query, D-Is a negative sample (taking random negative sampling) under Query, and D is the entire sample space under Query.
In the training phase, the loss function is minimized through maximum likelihood estimation
The residuals will propagate back in the DNN of the presentation layer, eventually converging the model by Stochastic Gradient Descent (SGD), resulting in the parameters { Wi, bi } for each network layer.
The function map unit is used for carrying out cluster analysis on the functions of all digital products in a unified mode to obtain the distribution and the activity of various types of functions. For example, as shown in fig. 4, the functions of all digital products are uniformly clustered to obtain the function distribution and activity of each type; monitoring the service conditions of all functions of the system, such as click rate, user IP, retention time and other data; according to the activity of two levels of the system and the function and an intelligent algorithm model, function inspection is developed, useless and repeated functions are screened, a system evaluation model is established, and the health state of the system is comprehensively evaluated.
The construction process of the system evaluation model comprises the following steps: obtaining a standard decision matrix by using a vector programming method, and setting a decision matrix x of a multi-attribute decision problem as (x)ij)mxnThe normalized decision matrix Y ═ xij)mxnThen, then
Forming a weighted normative matrix x ═ z using normative decision matricesij)mxnLet the weight vector given by the decision maker for each attribute be w ═ (w)1，w2，...,wn)tThen, then
zij＝wj·xij，i＝1，2，…，m，j＝1，2，…，n；
Generating a system evaluation model according to the weighting standard matrix, and setting the j-th attribute value of the positive rational solution Z as
Negative ideal solution z0，
And solving and constructing a system evaluation model according to formula operation.
The function supermarket unit is used for displaying the digital products by taking functions as granularity and feeding back function link pages corresponding to the digital products.
As shown in fig. 5, when the function supermarket unit clicks the function link page, if the operator has the corresponding authority, the corresponding function is used, and if the operator does not have the corresponding authority, the authority is opened and the approval link is entered.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020156688A1 (en) * | 2001-02-21 | 2002-10-24 | Michel Horn | Global electronic commerce system |
| CN112667736A (en) * | 2020-12-23 | 2021-04-16 | 山东鲁能软件技术有限公司 | Digital operation management system |
| CN112905685A (en) * | 2021-03-12 | 2021-06-04 | 国网安徽省电力有限公司 | Framework management and control system and equipment for informatization construction |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020156688A1 (en) * | 2001-02-21 | 2002-10-24 | Michel Horn | Global electronic commerce system |
| CN112667736A (en) * | 2020-12-23 | 2021-04-16 | 山东鲁能软件技术有限公司 | Digital operation management system |
| CN112905685A (en) * | 2021-03-12 | 2021-06-04 | 国网安徽省电力有限公司 | Framework management and control system and equipment for informatization construction |
Non-Patent Citations (1)
| Title |
|---|
| 陈刚: ""标准资源数字内容运营平台建设研究"", 《中国标准化》, no. 9, 5 September 2019 (2019-09-05), pages 138 - 142 * |
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