CN112330342A - Method and system for optimally matching enterprise name and system user name - Google Patents
Method and system for optimally matching enterprise name and system user name Download PDFInfo
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Abstract
The invention relates to a technology for associating internal data with external data, in particular to a method and a system for optimally matching an enterprise name and a system user name, wherein the method comprises the following steps: accurately matching the enterprise name with the system user name based on the strong logic relationship; calculating the similarity between the enterprise name and the user name based on the Jacard coefficient, and comparing the similarity with a threshold value for primary judgment; and performing regular analysis on the enterprise address corresponding to the determined enterprise name and the corresponding system user address based on the primary determination result, confirming matching if the enterprise address and the system user address have the same county (region) or the same town (street), and outputting user number information of the corresponding user. The invention combines the methods of accurate matching of user names, similarity calculation and address verification to assist the matching and association of enterprise names in external data and system user names in internal data, and establishes a data sharing fusion association relationship bridge.
Description
Technical Field
The invention relates to a technology for associating internal data with external data, in particular to a method and a system for optimally matching an enterprise name and a system user name.
Background
In the current big data era, the essence of data fusion is to perform collision association and clue analysis on complex mass data with multiple sources, multiple dimensions and multiple forms, seek and explore the value of data, further extract an optimized management mode and a technical route, so as to explore the value of extracting low-value density data and improve the availability of the data. Therefore, power grid enterprises need to use big data technology and specific service scenes for reference on the basis of data assets and the correlation capacity with external data, and play a great role in data mining.
When a new customer applies for electricity, the new customer needs to fill in information such as a user name, an electricity utilization address, a communication address, a name of a person in charge, a telephone number, a loading capacity and the like, then a business worker inputs a new user in a marketing system, and the marketing system automatically generates unique user number information for the new customer.
In the face of various conventional and emergent events, a plurality of government public authority units require power utilization data to be shared by power grid enterprises, and in the face of the requirements of the government units, the power grid enterprises have problems while responding positively. The first issue is faced with a body list provided by government bodies that need to obtain power data, which is related to matching with the customers in the power system.
In summary, the accurate matching and correlation analysis of the enterprise name based on the subject list and the user name in the power marketing system are the mainstream means for fusing the power system and the external data at present, but the method has certain problems.
The main disadvantage of the accurate matching correlation of the enterprise name and the user name in the power marketing system is that the user name in the marketing system is influenced by manual filling of a client, so that the enterprise user name in the marketing system has the problems of few words, wrong words or non-living users according to living user installation, and the user name is called as a legal name, so that the accurate matching method of the user name has certain influence on the accuracy.
Jaccard index, also known as Jaccard similarity coefficient (Jaccard similarity coefficient), is used to compare similarity and difference between finite sample sets. The larger the Jaccard similarity coefficient value, the higher the sample similarity. The proportion of the number of intersection elements of the two sets A and B in the A, B union is called the Jacard similarity coefficient of the two sets and is represented by the symbol J (A, B). The Jacard similarity coefficient is an index for measuring the similarity of two sets.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method and a system for optimizing and matching an enterprise name and a system user name, which are combined with a method for accurately matching the user name, calculating similarity and verifying an address to assist the matching and the association of the enterprise name in external data and the system user name in internal data and establish a data sharing and fusing association relationship bridge.
The invention relates to a method for optimizing and matching an enterprise name and a system user name, which comprises the following steps:
(1) accurately matching the enterprise name with the system user name based on the strong logic relationship, outputting the unique user number information of the corresponding user if the matching is successful, and otherwise, turning to the step (2);
(2) calculating the similarity between the enterprise name and the user name based on the Jacard similarity coefficient, and comparing the similarity with a threshold value for primary judgment; if the similarity is larger than the threshold value in the result of the first judgment, the step (4) is carried out, otherwise, the step (3) is carried out;
(3) matching the enterprise legal name corresponding to the enterprise name with the system user name, and if the matching is successful, turning to the step (4);
(4) comparing the enterprise address corresponding to the enterprise name with the corresponding system user address by using a regular analysis method, confirming matching if the address is consistent, and outputting the user number information of the corresponding user.
The invention relates to a system for optimizing and matching enterprise names and system user names, which comprises:
the data receiving module is used for receiving enterprise information provided from the outside; the enterprise information comprises an enterprise name, an enterprise legal person name, an enterprise address and an enterprise unified social credit code;
the data matching module is used for performing association matching on the received enterprise information and the user information in the system database; the user information comprises a user name and a user address;
the data statistics and display module is used for summarizing the matching results of the enterprise names and the user names in the system database and performing visual display;
and the data export module is used for exporting the matching result.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention combines the methods of accurate matching of user names, similarity calculation and address verification, assists the matching and association of enterprise names in external data and system user names in internal data, establishes a data sharing and fusing association relationship bridge, enables government departments or other external subjects to be associated with client basic files in internal data (such as an electric power marketing system) through an enterprise name fuzzy matching technology, further realizes the fusion and sharing of internal power grid data and external data such as government affairs, operators, water conservancy, natural gas and the like, and supports the digital service and digital enabling work of power grid companies.
2. According to the invention, through upgrading the matching algorithm, the overall matching degree can be optimized and improved at the later stage, so that the correlation matching degree of external data and internal power grid data is improved, and the problem that the matching degree is low and the matching can be realized only by the consistency of names in the existing method is solved.
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Fig. 1 is a general flowchart of a method for optimally matching enterprise names with users of an electric power system according to an embodiment of the present invention;
FIG. 2 is a detailed flow chart for calculating the similarity between two names based on Jaccard;
fig. 3 is a block diagram of a matching system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. Embodiments of the present invention are not limited thereto.
Examples
In the embodiment, the enterprise name text is sequentially subjected to accurate matching and fuzzy matching, and the matching association of the enterprise name in the external data and the user name in the internal data is assisted. Fig. 1 shows an implementation flow of a method for optimally matching an enterprise name with a user name in an electric power marketing system according to an embodiment of the present invention, which is described in detail below.
And S101, receiving external enterprise information provided by a government organization, and storing the external enterprise information as text information.
In this embodiment, the business information provided by the government entity includes information such as "business name", "business corporate name", "business address", and "unified business credit code".
And S102, matching the enterprise name information in the text information with the user name information in the power consumption client table in the power marketing system one by using an accurate matching method, outputting unique user number information of a corresponding user if matching is successful, and otherwise, turning to S103.
The accurate matching method is realized by adopting strong logical association of database languages, namely that the enterprise name is equal to the user name, and the enterprise name and the user name are completely the same.
Step S103, after word segmentation processing is carried out on the enterprise name in the received text information, Jaccard similarity coefficient between the enterprise name and the user name in the database is calculated, if the similarity coefficient (namely, similarity) is larger than a preset threshold value, matching association is carried out, and the step S105 is carried out, otherwise, the step S104 is carried out.
The Jacard similarity coefficient, also called Jacard similarity, Jaccard similarity, is used to compare similarity and difference between finite sample sets. The larger the value of the similarity coefficient, the higher the sample similarity. In fact, it is very simple to calculate, i.e. the value obtained by dividing the intersection of two samples by the union of two samples, the result is 1 when the two samples are identical, and the result is 0 when the two samples are completely different. And calculating the quotient of the intersection of the two samples and the union of the two samples, namely the Jacard similarity coefficient of the samples. The calculation formula of the method is as follows:
wherein | A ≦ B | is the intersection of sample A and sample B, and | A ≦ B | is the union of sample A and sample B.
In the present embodiment, the Jaccard similarity coefficient between the "business name" and the "user name" is calculated according to the flow shown in fig. 2. For example, there are business names: guangdong province, Chuang A Co., Ltd, user name: the method comprises the following steps that Guangdong Chuang A limited company selects an enterprise name and a user name as characteristic words, calculates TF participle matrix vectors of the characteristic words, namely [ 'wide', 'east', 'province', 'creation', 'A', 'with', 'limited', 'public', 'department' ], and converts the TF participle matrix vectors into word frequency vectors of [ [1,1,1,1,1,1,1,1,1], [1,1,0,1,1,1,1,1,1, 1,1] ]; then, calculating P: the TF word segmentation matrix vector A of the enterprise name and the TF word segmentation matrix vector B of the user name are the number of dimensions of 1, Q: the number of the dimensionalities of the participle matrix vector A is 1, the participle matrix vector B is 0, R: the number of dimensions of the participle matrix vector A is 0 and the participle matrix vector B is 1. The summation P + Q + R can be understood here as the number of elements of the union of the participle matrix vector a and the participle matrix vector B, whereas P is the number of elements of the intersection of the participle matrix vector a and the participle matrix vector B. The Jaccard similarity factor can therefore be expressed as:
therefore, in the present embodiment, the Jaccard similarity between the business name "guangdong province company a limited" and the user name "guangdong company a limited" is 0.89.
Step S104, matching the enterprise legal name corresponding to the received enterprise name with the user name in the database, and if the matching is successful, turning to step S105 to compare the addresses; otherwise, the process proceeds to step S106.
And S105, respectively extracting corresponding province, city, county (district) and town (street) information from the address corresponding to the enterprise name and the user address in the database by using a regular expression method, comparing the addresses by using a regular analysis method, outputting user number information of the corresponding user if the county (district) or town (street) addresses are consistent, and otherwise, turning to S106.
And S106, performing manual matching according to the enterprise name to complete the association matching of the data item.
As shown in fig. 3, based on the same inventive concept, the present embodiment also provides a system for optimally matching the enterprise name and the system user name. The matching system operates in a big data platform of a power grid company, and specifically comprises the following steps:
a data receiving module 21, configured to receive externally provided enterprise information;
the data matching module 22 is used for performing association matching on the received enterprise information and the user information in the system database; the enterprise information comprises an enterprise name, an enterprise legal person name, an enterprise address and an enterprise unified social credit code; the user information comprises a user name and a user address;
the data statistics and display module 23 is configured to summarize matching results between enterprise names and user names in the power system database and perform visual display;
and a data export module 24, configured to export the matching result.
The data receiving module 21 includes: the data import sub-module 211 is configured to import enterprise information that needs to be matched and is provided by a government entity into a system database (e.g., a power grid company big data platform); and the data storage submodule 212 is used for storing the imported enterprise information and the profile information of the system user (such as the profile information of the power customer of the power grid system).
The data matching module 22 includes: the accurate matching sub-module 221 is used for accurate matching and screening enterprises which can be matched directly through strong logic association; a module matching sub-module 222 for calculating the Jaccard similarity between the enterprise name and the user name; and the address verification submodule 223 verifies the corresponding relation between the external enterprise address with the Jaccard similarity reaching the set threshold and the power grid system user address.
The data statistics and presentation module 23 includes: a summary counting module 231 for calculating the number of each matched enterprise, the number of unmatched enterprises, etc.; and the visualization submodule 232 is used for visualizing the analysis result, so that business personnel can conveniently check and analyze the analysis result.
The data export module 24 includes: the electric power data association submodule 241 is used for associating externally required data (such as user electricity consumption required by government units) according to the user number corresponding to the user name matched with the enterprise name; and the data export sub-module 242 is used for exporting the associated power data from the power big data platform, and the aspect data providing department delivers the power data to a government entity for use.
The embodiment of the invention combines and optimizes various matching methods such as accurate matching, fuzzy matching and the like, calculates the similarity between the enterprise name and the user name by using the Jacard method, greatly increases the matching rate of the enterprise name and the power user name, and releases a large amount of manual matching workload. The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. The method for optimizing and matching the enterprise name and the system user name is characterized by comprising the following steps of:
(1) accurately matching the enterprise name with the system user name based on the strong logic relationship, outputting the unique user number information of the corresponding user if the matching is successful, and otherwise, turning to the step (2);
(2) calculating the similarity between the enterprise name and the user name based on the Jacard similarity coefficient, and comparing the similarity with a threshold value for primary judgment; if the similarity is larger than the threshold value in the result of the first judgment, the step (4) is carried out, otherwise, the step (3) is carried out;
(3) matching the enterprise legal name corresponding to the enterprise name with the system user name, and if the matching is successful, turning to the step (4);
(4) comparing the enterprise address corresponding to the enterprise name with the corresponding system user address by using a regular analysis method, confirming matching if the address is consistent, and outputting the user number information of the corresponding user.
2. The method of claim 1, wherein the accurately matching the business name with the system username based on the strong logical relationship in step (1) comprises the business name being identical to the system username.
3. The method of claim 1, wherein calculating the similarity of the business name and the user name based on the Jacard similarity factor and comparing the similarity to a threshold for a determination in step (2) comprises: dividing words of the enterprise name and the user name in the database, and merging the samples after word division to construct a TF matrix; and constructing a Jacard similarity calculator based on the TF matrix, and calculating the similarity between the enterprise name and the user name.
4. The method of claim 3, wherein the step of constructing the Jacard similarity calculator comprises: taking the intersection of the TF word segmentation matrix vector of the enterprise name and the TF word segmentation matrix vector of the user name as a molecule; taking a union of TF word segmentation matrix vectors of enterprise names and TF word segmentation matrix vectors of user names as denominators; taking a threshold MI as a filtering standard of the similarity;
and when the primary judgment is carried out, calculating the similarity of the samples based on the constructed Jacard similarity calculator as a result of the primary judgment.
5. The method of claim 4, wherein the Jackdad similarity is calculated by:
a represents a TF word segmentation matrix vector of an enterprise name, B represents a TF word segmentation matrix vector of a user name, P represents the number of dimensions that the word segmentation matrix vector A and the word segmentation matrix vector B are both 1, Q represents the number of dimensions that the word segmentation matrix vector A is 1 and the word segmentation matrix vector B is 0, and R represents the number of dimensions that the word segmentation matrix vector A is 0 and the word segmentation matrix vector B is 1.
6. The method of claim 1, wherein the process of confirming the matching of step (4) specifically comprises: the enterprise address and the user address are split and extracted according to administrative levels of provinces, cities, counties and towns through a regular analysis method, then the contents of the same level are compared pairwise, and matching items with consistent county-level or township-level comparison results are confirmed as final matching results.
7. The method of claim 1, wherein the business name in step (1) is obtained from externally provided business information; the enterprise information includes an enterprise name, an enterprise corporate name, an enterprise address, and an enterprise unified social credit code.
8. The system that enterprise name and system user name optimization match, its characterized in that includes:
the data receiving module is used for receiving enterprise information provided from the outside; the enterprise information comprises an enterprise name, an enterprise legal person name, an enterprise address and an enterprise unified social credit code;
the data matching module is used for performing association matching on the received enterprise information and the user information in the system database; the user information comprises a user name and a user address;
the data statistics and display module is used for summarizing the matching results of the enterprise names and the user names in the system database and performing visual display;
and the data export module is used for exporting the matching result.
9. The system of claim 8, wherein the data receiving module comprises: the data import submodule is used for importing the provided enterprise information needing to be matched into a system database; and the data storage submodule is used for storing the imported enterprise information and the archive information of the system user.
10. The system of claim 8, wherein the data matching module comprises: the accurate matching sub-module is used for accurately matching and screening enterprises which are matched directly through strong logic association; the module matching submodule is used for calculating the Jacard similarity between the enterprise name and the user name; the address verification submodule is used for verifying the corresponding relation between the external enterprise address and the system user address, wherein the Jacard similarity reaches a set threshold;
the data statistics and display module comprises: the collecting and counting module is used for calculating the number of the matched enterprises and the number of the unmatched enterprises; the visualization submodule is used for visualizing the analysis result;
the data export module comprises: the electric power data association submodule is used for associating external required data according to a user number corresponding to the user name matched with the enterprise name; and the data export submodule is used for exporting the associated data from the system database.
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