CN115859911B - Automatic label generation evolution method and device adapting to dynamic change of data - Google Patents

Automatic label generation evolution method and device adapting to dynamic change of data Download PDF

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CN115859911B
CN115859911B CN202310038824.7A CN202310038824A CN115859911B CN 115859911 B CN115859911 B CN 115859911B CN 202310038824 A CN202310038824 A CN 202310038824A CN 115859911 B CN115859911 B CN 115859911B
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matrix
dynamic
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static
label
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CN115859911A (en
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姜磊
杜双育
朱振航
郑午
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Brilliant Data Analytics Inc
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention relates to artificial intelligence technology, and discloses an automatic label generation evolution method adapting to dynamic data change, which comprises the following steps: acquiring static data, calculating the data intensity of the static data, determining the static data with the intensity greater than a preset intensity as key data, and integrating the key data into a static label; acquiring real-time data of a dynamic webpage by using a preset webpage data grabbing tool, determining an API interface of the dynamic webpage and a preset cloud server by using a classification decision tree, and returning the real-time data to the cloud server through the API interface to obtain a real-time data document; extracting ternary information in the real-time data document by using an event extraction model and integrating the ternary information into a dynamic tag; and splicing the static label and the dynamic label into a fusion label by using a vector splicing technology, and determining the fusion label as an automatic generation label. The invention also provides an evolution device for automatically generating the label under the condition of adapting to the dynamic change of the data. The invention can automatically generate the label under the dynamic change of the data.

Description

Automatic label generation evolution method and device adapting to dynamic change of data
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an automatic label generation evolution method and device suitable for dynamic data change.
Background
With the rapid development of the big data age, replacing data or documents with tags can simplify the operation of people on a large amount of similar data. In order to simplify the cumbersome process of generating tags, a method is needed that can automatically generate a representation as the data changes dynamically.
Most of the existing label generation methods analyze the existing data documents to generate corresponding document labels, for example, when a certain user uses a certain push software, the user needs to use for a period of time to generate preference labels for the user according to the historical data of the user, and the preference data of the user cannot be analyzed in real time. In practical application, dynamic data are difficult to operate, different data documents are complex in operation process, and the situation that labels are generated inaccurately or cannot be generated easily occurs.
Disclosure of Invention
The invention provides an evolution method and device for automatically generating labels under the condition of adapting to dynamic data change, and mainly aims to solve the problem of how to generate labels under the condition of dynamic data change.
In order to achieve the above object, the present invention provides a method for automatically generating and evolving a label adapted to dynamic data changes, including:
acquiring preset static data, calculating the data intensity of the static data, determining the static data with the intensity greater than the preset intensity as key data, and integrating the key data into a static label;
acquiring a preset dynamic webpage, acquiring real-time data of the dynamic webpage by using a preset webpage data grabbing tool, determining an API (application program interface) between the dynamic webpage and a preset cloud server by using a preset classification decision tree, and returning the real-time data to the cloud server through the API to obtain a real-time data document;
extracting ternary information in the real-time data document by using a preset event extraction model, and integrating the ternary information into a dynamic tag;
and splicing the static label and the dynamic label into a fusion label by using a vector splicing technology, and determining the fusion label as an automatic generation label.
Optionally, the calculating the data intensity of the static data includes:
calculating the data intensity of the static data by using the following intensity calculation formula:
Figure SMS_1
wherein the said
Figure SMS_2
Is->
Figure SMS_6
Static data->
Figure SMS_8
Data intensity of>
Figure SMS_4
For the total number of all static data, +.>
Figure SMS_5
Is->
Figure SMS_7
Data frequency of static data,/->
Figure SMS_9
Is->
Figure SMS_3
Total number of occurrences of static data.
Optionally, the integrating the key data into a static tag includes:
vector encoding is carried out on the key data one by one to obtain key vectors;
acquiring the maximum length in the key vector as a standard length;
extending the key vectors to the standard length by using preset vector parameters to obtain standard vectors;
and carrying out column dimension combination on the standard vectors to obtain a combination result, and determining the combination result as a static label.
Optionally, the determining, by using a preset classification decision tree, the API interface between the dynamic web page and a preset cloud server includes:
acquiring a webpage interface of the dynamic webpage;
determining the API interface of the webpage interface and a preset cloud server by using the following classification decision tree function:
Figure SMS_10
wherein ,
Figure SMS_11
for the output value of said classification decision tree function, < >>
Figure SMS_12
For the parameters of the classification decision tree function,
Figure SMS_13
an input value for the classification decision tree function;
taking the webpage interface as a classification decision tree function input value, and calculating and outputting an API interface of a cloud server corresponding to the webpage interface through the classification decision tree function;
when the input value is smaller than the parameters of the classification decision tree function, the output label is
Figure SMS_14
I.e. API interface of cloud server corresponding to the web page interface->
Figure SMS_15
；
When the input value is larger than the parameter of the classification decision tree function, the output label is
Figure SMS_16
I.e. API interface of cloud server corresponding to the web page interface->
Figure SMS_17
；
When the input value is equal to the parameter of the classification decision tree function, the output label is
Figure SMS_18
I.e. API interface of cloud server corresponding to the web page interface->
Figure SMS_19
。
Optionally, the returning the real-time data to the cloud server through the API interface includes:
creating a real-time database exclusive to the real-time data in the cloud server;
docking a webpage interface of the dynamic webpage with the API interface to form a data transmission channel;
and uploading the real-time data to a real-time database, namely a cloud server, by utilizing the data transmission channel.
Optionally, the extracting ternary information in the real-time data document by using a preset event extraction model includes:
paragraph clauses are carried out on the real-time data document, and document clauses are obtained;
mapping the document clauses one by one into dimensions conforming to ternary labels by using a mapping function preset in an event extraction model;
and outputting the ternary label corresponding to the document clause at a model output layer, and determining that the ternary label is ternary information.
Optionally, the splicing the static tag and the dynamic tag into a fusion tag by using a vector splicing technology includes:
converting the static label into a static matrix;
converting the dynamic label into a dynamic matrix;
and calculating a product matrix of the static matrix and the dynamic matrix, and determining the product matrix as a fusion tag.
Optionally, the calculating the product matrix of the static matrix and the dynamic matrix includes:
respectively obtaining the matrix row number and the matrix column number of the static matrix and the dynamic matrix;
the maximum value of the matrix row number and the matrix column number is the standard row number and the standard column number;
expanding the matrix row number and the matrix column number of the static matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard static matrix;
expanding the matrix row number and the matrix column number of the dynamic matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard dynamic matrix;
and calculating the matrix product of the standard static matrix and the standard dynamic matrix by using a preset matrix product formula.
Optionally, the calculating the matrix product of the standard static matrix and the standard dynamic matrix by using a preset matrix product formula includes:
calculating the matrix product of the standard static matrix and the standard dynamic matrix by using the following matrix product formula:
Figure SMS_20
wherein ,
Figure SMS_21
is a product matrix>
Figure SMS_22
For the standard static matrix to be described,
Figure SMS_23
for the standard dynamic matrix,/a>
Figure SMS_24
For matrix parameters within said product matrix, < >>
Figure SMS_25
For matrix parameters within said standard static matrix, < > j->
Figure SMS_26
And the matrix parameters in the standard dynamic matrix.
In order to solve the above problems, the present invention further provides an automatic tag generation evolution device adapted to dynamic data change, where the device includes:
and acquiring a static data module: acquiring preset static data, calculating the data intensity of the static data, determining the static data with the intensity greater than the preset intensity as key data, and integrating the key data into a static label;
and a dynamic data acquisition module: acquiring a preset dynamic webpage, acquiring real-time data of the dynamic webpage by using a preset webpage data grabbing tool, determining an API (application program interface) between the dynamic webpage and a preset cloud server by using a preset classification decision tree, and returning the real-time data to the cloud server through the API to obtain a real-time data document;
generating a dynamic label module: extracting ternary information in the real-time data document by using a preset event extraction model, and integrating the ternary information into a dynamic tag;
and (3) performing a label fusion module: and splicing the static label and the dynamic label into a fusion label by using a vector splicing technology, and determining the fusion label as an automatic generation label.
According to the embodiment of the invention, the static label corresponding to the static webpage is obtained and used as a supplement to be fused with the dynamic data, so that accurate label information is generated; acquiring real-time data of the dynamic webpage by using a preset webpage data grabbing tool, extracting ternary information in the real-time data document by using a preset event extraction model, and integrating the ternary information into a dynamic tag; the static label and the dynamic label are spliced into a fusion label by using a vector splicing technology, the fusion label is determined to be an automatic generation label, the label characteristics of the static label and the dynamic label can be reserved by a maximum program, and the obtained automatic generation label is more accurate, is simple to operate and is easy to realize; the matrix fusion process is intuitively expressed through a formula through a matrix product formula, so that the calculation is convenient to understand, the formula using process is concise, errors are not easy to occur due to simple method, and fusion labels can be accurately acquired. Therefore, the automatic label generation evolution method and device adapting to the dynamic change of data can solve the problem of lower accuracy of the short-circuit impedance tester.
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Fig. 1 is a schematic flow chart of a method for automatically generating and evolving a label under adaptive data dynamic change according to an embodiment of the present invention;
FIG. 2 is a flow chart of generating a static tag according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of extracting ternary information according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of an evolution device for automatically generating labels under adaptive data dynamic change according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an automatic label generation evolution method suitable for dynamic data change. The execution subject of the automatic tag generation evolution method under the dynamic change of the adaptive data comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the automatic tag generation evolution method under the dynamic change of the adaptive data can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of an evolution method for automatically generating a tag adapted to dynamic data change according to an embodiment of the present invention is shown. In this embodiment, the automatic tag generation evolution method under the dynamic change of the adaptive data includes:
s1, acquiring preset static data, calculating the data intensity of the static data, determining the static data with the intensity greater than the preset intensity as key data, and integrating the key data into a static tag;
because webpage data generally contains static data and dynamic data, information provided by the dynamic data is limited and incomplete, the static data is required to be obtained as supplement and fused with the dynamic data, so that accurate tag information is generated.
In the embodiment of the invention, the static data comprises information which is stored in a computer hard disk and does not change along with the running of the program. Such as the name of an entity, employee information, system parameters, etc.
In an embodiment of the present invention, the calculating the data intensity of the static data includes:
calculating the data intensity of the static data by using the following intensity calculation formula:
Figure SMS_27
wherein the said
Figure SMS_28
Is->
Figure SMS_31
Static data->
Figure SMS_33
Data intensity of>
Figure SMS_30
For the total number of all static data, +.>
Figure SMS_32
Is->
Figure SMS_34
Data frequency of static data,/->
Figure SMS_35
Is->
Figure SMS_29
Number of static statesBased on the total number of occurrences.
The data intensity calculated by the intensity calculation formula is accurate and efficient, the data intensity of all static data can be rapidly obtained in a short time, the calculation efficiency is high, and the data intensity can be visually represented by numbers so as to be convenient for subsequent comparison.
In an embodiment of the present invention, referring to fig. 2, the integrating the key data into a static tag includes:
s21, carrying out vector coding on the key data one by one to obtain a key vector;
s22, acquiring the maximum length in the key vector as a standard length;
s23, extending the key vectors to the standard length by using preset vector parameters to obtain standard vectors;
s24, carrying out column dimension combination on the standard vectors to obtain a combination result, and determining the combination result as a static label.
In detail, since the vectors with different lengths cannot be combined in column dimension, all the key vectors to be combined need to be unified in vector length, so that the subsequent combination operation can be facilitated, errors are not easy to occur, and the format is more standard and complete.
Further, the key vectors are all extended to a standard length by using a preset vector parameter, for example: key vector a: 11, 36, 22], key vector B: 14, 25, 31, 27], the standard length is 5, and the vector of the key vector a may be extended by using a predetermined vector parameter (e.g. 0) until the vector length of the key vector a and the vector length of the key vector B are both 5, so as to obtain an extended key vector a: [11, 36, 22,0,0], the elongated key vector B [14, 25, 31, 27,0].
In detail, the data characteristics of the static data can be reserved as far as possible by integrating higher data intensity in all the static data into the static label, the formed static label is more accurate, and the accuracy, the strictness and the high efficiency of the steps are ensured.
S2, acquiring a preset dynamic webpage, acquiring real-time data of the dynamic webpage by using a preset webpage data grabbing tool, determining an API (application program interface) between the dynamic webpage and a preset cloud server by using a preset classification decision tree, and returning the real-time data to the cloud server through the API to obtain a real-time data document;
in the embodiment of the invention, the webpage data grabbing tool is simple to operate and good in universality, can automatically grab data information on a webpage, has a powerful acquisition function, and can randomly convert a data format. In the embodiment of the invention, the Web page data grabbing tools include but are not limited to report.
In the embodiment of the present invention, the determining the API interface between the dynamic web page and the preset cloud server by using the preset classification decision tree includes:
acquiring a webpage interface of the dynamic webpage;
determining the API interface of the webpage interface and a preset cloud server by using the following classification decision tree function:
Figure SMS_36
wherein ,
Figure SMS_37
for the output value of said classification decision tree function, < >>
Figure SMS_38
For the parameters of the classification decision tree function,
Figure SMS_39
an input value for the classification decision tree function;
taking the webpage interface as a classification decision tree function input value, and calculating and outputting an API interface of a cloud server corresponding to the webpage interface through the classification decision tree function;
when the input value is smaller than the parameters of the classification decision tree function, the output label is
Figure SMS_40
I.e. API interface of cloud server corresponding to the web page interface->
Figure SMS_41
；
When the input value is larger than the parameter of the classification decision tree function, the output label is
Figure SMS_42
I.e. API interface of cloud server corresponding to the web page interface->
Figure SMS_43
；
When the input value is equal to the parameter of the classification decision tree function, the output label is
Figure SMS_44
I.e. API interface of cloud server corresponding to the web page interface->
Figure SMS_45
。
In detail, the API interface of the server corresponding to the webpage interface of the dynamic webpage can be accurately identified by using the classification decision tree function, so that efficient determination and accurate transmission can be realized, the classification efficiency is high, the accuracy is high, the cost can be saved, and the low-cost and high-efficiency data transmission can be realized.
In the embodiment of the present invention, the returning the real-time data to the cloud server through the API interface includes:
creating a real-time database exclusive to the real-time data in the cloud server;
docking a webpage interface of the dynamic webpage with the API interface to form a data transmission channel;
and uploading the real-time data to a real-time database, namely a cloud server, by utilizing the data transmission channel.
In detail, because the cloud service contains a large amount of data of different types, the real-time data and the mass data are integrated into a whole once uploaded and are difficult to call and recognize, a database needs to be independently created in the cloud server so as to be convenient for storing the real-time data, and data confusion is avoided.
S3, extracting ternary information in the real-time data document by using a preset event extraction model, and integrating the ternary information into a dynamic tag;
in the embodiment of the invention, the ternary information is composed of three pieces of information, namely an event name, an occurrence time and an event type, for example, an example sentence is a small red, a user can walk on a weekend, and the ternary information extracted according to the requirement is { "shopping", "weekend", "entertainment" }.
In the embodiment of the present invention, referring to fig. 3, the extracting ternary information in the real-time data document by using a preset event extraction model includes:
s31, paragraph clauses are carried out on the real-time data document, and document clauses are obtained;
s32, mapping the document clauses one by one into dimensions conforming to the ternary labels by using a mapping function preset in an event extraction model;
s33, outputting the ternary label corresponding to the document clause at a model output layer, and determining that the ternary label is ternary information.
In detail, mapping the document clauses one by one to conform to the dimensions of the ternary labels by using a mapping function preset in the event extraction model, wherein the mapping function comprises a GaussianRadial Basis Function function, a Gaussian function and the like in a MATLAB library.
For example, the document clause is a point in a two-dimensional plane, and the ternary label is a point in a three-dimensional plane, two-dimensional coordinates of the point in the two-dimensional plane may be calculated by using a mapping function to convert the two-dimensional coordinates into three-dimensional coordinates, and the calculated three-dimensional coordinates are used to map the point to a pre-built three-dimensional space, so that the document clause is mapped to a dimension conforming to the ternary label.
In the embodiment of the invention, the output value of the model output layer can be calculated by using a preset activation function, and the output value larger than a preset threshold value is determined as the ternary label, wherein the activation function comprises but is not limited to a sigmoid activation function, a tanh activation function and a relu activation function.
In the embodiment of the present invention, the step of integrating the ternary information into a dynamic tag is similar to the step of integrating the key data into a static tag, and will not be described herein.
And S4, splicing the static label and the dynamic label into a fusion label by using a vector splicing technology, and determining the fusion label as an automatic generation label.
The fusion tag can keep the tag characteristics of the static tag and the dynamic tag in the maximum program, so that the obtained automatic generation tag is more accurate, the operation is simple, and the method is easy to realize. Therefore, the static label and the dynamic label are fused, and the optimal realization method of automatic label generation under the dynamic change of data is realized.
In the embodiment of the present invention, the splicing the static tag and the dynamic tag into the fusion tag by using the vector splicing technology includes:
converting the static label into a static matrix;
converting the dynamic label into a dynamic matrix;
and calculating a product matrix of the static matrix and the dynamic matrix, and determining the product matrix as a fusion tag.
In an embodiment of the present invention, the calculating the product matrix of the static matrix and the dynamic matrix includes:
respectively obtaining the matrix row number and the matrix column number of the static matrix and the dynamic matrix;
the maximum value of the matrix row number and the matrix column number is the standard row number and the standard column number;
expanding the matrix row number and the matrix column number of the static matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard static matrix;
expanding the matrix row number and the matrix column number of the dynamic matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard dynamic matrix;
and calculating the matrix product of the standard static matrix and the standard dynamic matrix by using a preset matrix product formula.
Specifically, the process of expanding the matrix rows and the matrix columns of the static matrix to the standard rows and the standard columns by using the preset matrix parameters and extending the key vectors to the standard length by using the preset vector parameters is similar to the process of obtaining the standard vectors, and will not be described herein.
Furthermore, because the matrix sizes are different, fusion calculation cannot be performed, unified planning is required to be performed on the matrix sizes of the static matrix and the dynamic matrix, and subsequent fusion calculation is facilitated.
In detail, the calculating the matrix product of the standard static matrix and the standard dynamic matrix by using a preset matrix product formula includes:
calculating the matrix product of the standard static matrix and the standard dynamic matrix by using the following matrix product formula:
Figure SMS_46
wherein ,
Figure SMS_47
is a product matrix>
Figure SMS_48
For the standard static matrix to be described,
Figure SMS_49
for the standard dynamic matrix,/a>
Figure SMS_50
For matrix parameters within said product matrix, < >>
Figure SMS_51
For matrix parameters within said standard static matrix, < > j->
Figure SMS_52
Is the standardMatrix parameters within the dynamic matrix.
Specifically, the matrix fusion process is intuitively expressed through a formula through a matrix product formula, so that the calculation is convenient to understand, the formula process is simple, errors are not easy to occur, and the fusion label can be accurately acquired.
Fig. 4 is a functional block diagram of an automatic tag generation evolution device adapted to dynamic data change according to an embodiment of the present invention.
The automatic label generation evolution device 100 adapting to the dynamic change of data can be installed in electronic equipment. Depending on the implementation function, the automatic tag generation evolution device 100 adapted to the dynamic change of data may include a static data acquisition module 101, a dynamic data acquisition module 102, a dynamic tag generation module, and a tag fusion module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the static data acquisition module 101: acquiring preset static data, calculating the data intensity of the static data, determining the static data with the intensity greater than the preset intensity as key data, and integrating the key data into a static label;
the acquire dynamic data module 102: acquiring a preset dynamic webpage, acquiring real-time data of the dynamic webpage by using a preset webpage data grabbing tool, determining an API (application program interface) between the dynamic webpage and a preset cloud server by using a preset classification decision tree, and returning the real-time data to the cloud server through the API to obtain a real-time data document;
the dynamic tag generation module 103: extracting ternary information in the real-time data document by using a preset event extraction model, and integrating the ternary information into a dynamic tag;
the tag fusion module 104: and splicing the static label and the dynamic label into a fusion label by using a vector splicing technology, and determining the fusion label as an automatic generation label.
In detail, each module in the automatic tag generation evolution device 100 under the dynamic change of adaptive data in the embodiment of the present invention adopts the same technical means as the automatic tag generation evolution method under the dynamic change of adaptive data described in fig. 1 to 3, and can generate the same technical effects, which are not described herein.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims (7)

1. An evolution method for automatically generating labels under the condition of adapting to dynamic data change, which is characterized by comprising the following steps:
acquiring preset static data, calculating the data intensity of the static data, determining the static data with the intensity greater than the preset intensity as key data, and integrating the key data into a static label;
acquiring a preset dynamic webpage, acquiring real-time data of the dynamic webpage by using a preset webpage data grabbing tool, determining an API (application program interface) between the dynamic webpage and a preset cloud server by using a preset classification decision tree, and returning the real-time data to the cloud server through the API to obtain a real-time data document;
extracting ternary information in the real-time data document by using a preset event extraction model, and integrating the ternary information into a dynamic tag;
splicing the static tag and the dynamic tag into a fusion tag by using a vector splicing technology, and determining the fusion tag as an automatic generation tag;
the method for splicing the static label and the dynamic label into a fusion label by using a vector splicing technology comprises the following steps:
converting the static label into a static matrix;
converting the dynamic label into a dynamic matrix;
calculating a product matrix of the static matrix and the dynamic matrix, and determining the product matrix as a fusion tag;
the calculating the product matrix of the static matrix and the dynamic matrix comprises:
respectively obtaining the matrix row number and the matrix column number of the static matrix and the dynamic matrix;
setting the maximum value of the matrix row number and the matrix column number as a standard row number and a standard column number;
expanding the matrix row number and the matrix column number of the static matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard static matrix;
expanding the matrix row number and the matrix column number of the dynamic matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard dynamic matrix;
and calculating the matrix product of the standard static matrix and the standard dynamic matrix by using a preset matrix product formula.
2. The automatic label generation evolution method according to claim 1, wherein the integrating the key data into a static label comprises:
vector encoding is carried out on the key data one by one to obtain key vectors;
acquiring the maximum length in the key vector as a standard length;
extending the key vectors to the standard length by using preset vector parameters to obtain standard vectors;
and carrying out column dimension combination on the standard vectors to obtain a combination result, and determining the combination result as a static label.
3. The method for automatically generating and evolving a tag adapted to dynamic data changes according to claim 1, wherein determining the API interface between the dynamic web page and a preset cloud server by using a preset classification decision tree comprises:
acquiring a webpage interface of the dynamic webpage;
determining the API interface of the webpage interface and a preset cloud server by using the following classification decision tree function:
Figure QLYQS_1
wherein ,
Figure QLYQS_2
for the output value of said classification decision tree function, < >>
Figure QLYQS_3
For parameters of said classification decision tree function, +.>
Figure QLYQS_4
An input value for the classification decision tree function;
taking the webpage interface as a classification decision tree function input value, and calculating and outputting an API interface of a cloud server corresponding to the webpage interface through the classification decision tree function;
when the input value is smaller than the parameters of the classification decision tree function, the output label is
Figure QLYQS_5
I.e. API interface of cloud server corresponding to the web page interface->
Figure QLYQS_6
；
When the input value is larger than the parameter of the classification decision tree function, the output label is
Figure QLYQS_7
I.e. API interface of cloud server corresponding to the web page interface->
Figure QLYQS_8
；
When the input value is equal to a parameter of the classification decision tree functionThe output label is
Figure QLYQS_9
I.e. API interface of cloud server corresponding to the web page interface->
Figure QLYQS_10
。
4. The automatic tag generation evolution method according to claim 1, wherein the returning the real-time data to the cloud server through the API interface comprises:
creating a real-time database exclusive to the real-time data in the cloud server;
docking a webpage interface of the dynamic webpage with the API interface to form a data transmission channel;
and uploading the real-time data to a real-time database, namely a cloud server, by utilizing the data transmission channel.
5. The automatic tag generation evolution method according to claim 1, wherein the extracting ternary information in the real-time data document by using a preset event extraction model comprises:
paragraph clauses are carried out on the real-time data document, and document clauses are obtained;
mapping the document clauses one by one into dimensions conforming to ternary labels by using a mapping function preset in an event extraction model;
and outputting the ternary label corresponding to the document clause at a model output layer, and determining that the ternary label is ternary information.
6. The automatic label generation evolution method according to claim 1, wherein the calculating the matrix product of the standard static matrix and the standard dynamic matrix by using a preset matrix product formula comprises:
calculating the matrix product of the standard static matrix and the standard dynamic matrix by using the following matrix product formula:
Figure QLYQS_11
wherein ,
Figure QLYQS_12
is a product matrix>
Figure QLYQS_13
For the standard static matrix to be described,
Figure QLYQS_14
for the standard dynamic matrix,/a>
Figure QLYQS_15
For matrix parameters within said product matrix, < >>
Figure QLYQS_16
For matrix parameters within said standard static matrix, < > j->
Figure QLYQS_17
And the matrix parameters in the standard dynamic matrix.
7. An automatic tag generation evolution device adapted to dynamic data change, wherein the device comprises:
and acquiring a static data module: acquiring preset static data, calculating the data intensity of the static data, determining the static data with the intensity greater than the preset intensity as key data, and integrating the key data into a static label;
and a dynamic data acquisition module: acquiring a preset dynamic webpage, acquiring real-time data of the dynamic webpage by using a preset webpage data grabbing tool, determining an API (application program interface) between the dynamic webpage and a preset cloud server by using a preset classification decision tree, and returning the real-time data to the cloud server through the API to obtain a real-time data document;
generating a dynamic label module: extracting ternary information in the real-time data document by using a preset event extraction model, and integrating the ternary information into a dynamic tag;
and (3) performing a label fusion module: splicing the static tag and the dynamic tag into a fusion tag by using a vector splicing technology, and determining the fusion tag as an automatic generation tag;
the method for splicing the static label and the dynamic label into a fusion label by using a vector splicing technology comprises the following steps:
converting the static label into a static matrix;
converting the dynamic label into a dynamic matrix;
calculating a product matrix of the static matrix and the dynamic matrix, and determining the product matrix as a fusion tag;
the calculating the product matrix of the static matrix and the dynamic matrix comprises:
respectively obtaining the matrix row number and the matrix column number of the static matrix and the dynamic matrix;
setting the maximum value of the matrix row number and the matrix column number as a standard row number and a standard column number;
expanding the matrix row number and the matrix column number of the static matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard static matrix;
expanding the matrix row number and the matrix column number of the dynamic matrix to the standard row number and the standard column number by using preset matrix parameters to obtain a standard dynamic matrix;
and calculating the matrix product of the standard static matrix and the standard dynamic matrix by using a preset matrix product formula.
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