CN113268638B - Big data-based action library generation method and device - Google Patents
Big data-based action library generation method and device Download PDFInfo
- Publication number
- CN113268638B CN113268638B CN202110429699.3A CN202110429699A CN113268638B CN 113268638 B CN113268638 B CN 113268638B CN 202110429699 A CN202110429699 A CN 202110429699A CN 113268638 B CN113268638 B CN 113268638B
- Authority
- CN
- China
- Prior art keywords
- action
- instruction
- library
- target
- keywords
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000000875 corresponding effect Effects 0.000 claims abstract description 90
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 abstract description 8
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/90335—Query processing
- G06F16/90344—Query processing by using string matching techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Abstract
The invention relates to a big data-based action library generation method and device, which are used for acquiring action library generation instructions, acquiring a plurality of action stream information from a target database, taking each action stream information as an action speech segment, acquiring action instruction keywords corresponding to each action stream information, acquiring action types corresponding to each action instruction keyword, acquiring general logic sentences according to the action types corresponding to each action instruction keyword, and constructing an action library, wherein the action library comprises each action instruction keyword, each action speech segment and the general logic sentences, and the general logic sentences are used for executing the corresponding action speech segments. The big data-based action library generation method provided by the invention can reduce the construction difficulty of the action library, improve the construction efficiency, and obtain the corresponding action language segments and the general logic sentences according to the keywords corresponding to the action instructions when a certain action instruction is obtained later, and execute the corresponding action language segments through the general logic sentences to realize the reliable execution of the actions.
Description
Technical Field
The invention relates to a method and a device for generating an action library based on big data.
Background
The action library stores a plurality of action instructions (or keywords of the action instructions) and action segments corresponding to the action instructions (or keywords of the action instructions), when related actions are needed, corresponding action segments are obtained according to the received action instructions and the action library, and then corresponding actions can be realized by executing the corresponding action segments. Therefore, the importance of the action library is self-evident. However, the current construction mode of the action library is manual construction, so that the efficiency is low and the reliability is poor.
Disclosure of Invention
The invention provides a method and a device for generating an action library based on big data, which are used for solving the technical problem that the efficiency of the existing construction mode of the action library is low.
A big data-based action library generation method comprises the following steps:
acquiring an action library generation instruction;
according to the action library generating instruction, acquiring a plurality of action stream information from a target database;
using each action flow information as an action speech segment, and carrying out semantic mapping processing on each action flow information by combining a preset neural network to obtain action instruction keywords corresponding to each action flow information;
obtaining action types corresponding to the action instruction keywords;
acquiring general logic sentences corresponding to the action types corresponding to the action command keywords according to the action types corresponding to the action command keywords;
the method comprises the steps of constructing an action library, wherein the action library comprises action instruction keywords, action language segments corresponding to the action instruction keywords and general logic sentences corresponding to action types corresponding to the action instruction keywords, and the general logic sentences are used for executing the corresponding action language segments.
Specifically, before the generating instruction of the action library obtains the plurality of action flow information from the target database, the action library generating method further includes:
checking the validity of the action library generation instruction;
and if the action library generation instruction is a valid instruction, executing the action library generation instruction, and acquiring a plurality of action flow information from a target database.
Specifically, the action library generation instruction comprises target identity information of an operator and a target instruction character string;
correspondingly, the verification of the validity of the action library generation instruction specifically comprises the following steps:
comparing the target identity information with a preset identity database, and judging the target identity information to be effective identity information if the target identity information belongs to one of the identity information in the identity database;
comparing the target instruction character string with a preset instruction character string database, and judging the target instruction character string to be a valid instruction character string if the target instruction character string belongs to one of the instruction character strings in the instruction character string database;
and if the target identity information is effective identity information and the target instruction character string is effective instruction character string, judging that the action library generation instruction is effective instruction.
Specifically, the neural network includes an encoder for converting the respective action stream information into a corresponding intermediate semantic representation, and a decoder for converting the intermediate semantic representation into action instruction keywords corresponding to the respective action stream information.
The big data-based action library generating device comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the big data-based action library generating method when executing the computer program.
The method for generating the action library based on the big data has the technical effects that: after an action library generation instruction is acquired, a plurality of action stream information is acquired from a target database, then each action stream information is used as an action language segment, a preset neural network is combined to map each action stream information, action instruction keywords corresponding to each action stream information are obtained, after the action instruction keywords and the corresponding action language segments are obtained, the action types corresponding to each action instruction keyword are acquired firstly because the action types corresponding to each action language segment are different in general logic sentences when the action language segments are executed, then the general logic sentences corresponding to the action types corresponding to each action instruction keyword are acquired according to the action types corresponding to each action instruction keyword, finally the action library is constructed, and the action library comprises the action instruction keywords and the action language segments corresponding to each action instruction keyword and also comprises the general logic sentences corresponding to the action types corresponding to each action instruction keyword. Compared with a manual construction method, the big data-based action library generation method provided by the invention has the advantages that the construction difficulty can be reduced, the construction efficiency is improved, the reliability is higher, when a certain action instruction is obtained later, the corresponding action language segments and the general logic sentences are obtained according to the keywords corresponding to the action instruction, and the corresponding action language segments are executed through the general logic sentences, so that the reliable execution of the actions is realized.
Drawings
FIG. 1 is a flow chart of a method for generating an action library based on big data.
Detailed Description
The embodiment provides a big data based action library generating method, and a hardware execution subject of the big data based action library generating method may be a computer device, a server device, an intelligent mobile terminal, and the like. In addition, the application scene of the hardware execution main body of the big data-based action library generation method is not limited, and is set according to actual requirements.
As shown in fig. 1, the method for generating the action library based on big data comprises the following steps:
step 1: obtaining an action library generation instruction:
and acquiring an action library generating instruction, wherein the action library generating instruction is used for starting an action library generating process.
In order to improve the security of the action library generation, after the step 1, that is, before the step 2, the action library generation method based on big data further comprises:
and checking the validity of the action library generation instruction, wherein the subsequent steps can be performed only if the action library generation instruction is a valid instruction.
In this embodiment, the action library generation instruction includes target identity information of the operator and a target instruction character string. The target identity information of the operator and the target instruction string may form a data packet, or the target identity information of the operator may be connected to the target instruction string to form a data string. The target identity information may be an identity card number, an employee number, etc., and the target instruction character string is a specific character string.
Correspondingly, the verification of the validity of the action library generation instruction is specifically as follows:
the method comprises the steps of presetting an identity database and an instruction character string database, wherein the identity database comprises at least one identity information, and the identity information in the identity database is effective identity information. The instruction character string database comprises at least one instruction character string, and the instruction character strings in the instruction character string database are all valid instruction character strings.
Then comparing the target identity information with a preset identity database, and judging the target identity information to be effective identity information if the target identity information belongs to one of the identity information in the identity database; comparing the target instruction character string with a preset instruction character string database, and judging the target instruction character string to be a valid instruction character string if the target instruction character string belongs to one of the instruction character strings in the instruction character string database.
Correspondingly, if the target identity information is effective identity information and the target instruction character string is effective instruction character string, determining that the action library generation instruction is effective instruction.
Step 2: according to the action library generation instruction, a plurality of action flow information are acquired from a target database:
in this embodiment, if the action library generation instruction is a valid instruction, a plurality of (i.e., at least two) action flow information is acquired from the target database according to the action library generation instruction. The number of the action flow information acquired from the target database is set according to actual needs.
The target database stores a plurality of pieces of action flow information, and the number of pieces of action flow information is determined by the size of the required action library. The action flow information may be action segments, i.e. action program code segments corresponding to a specific course of action.
Step 3: using each action flow information as an action language segment, and combining a preset neural network to perform semantic mapping processing on each action flow information to obtain action instruction keywords corresponding to each action flow information:
and using the motion stream information as motion language segments, and combining a preset neural network to perform semantic mapping processing on the motion stream information to obtain motion instruction keywords corresponding to the motion stream information. The relation between the action instruction keywords and the action language segments is as follows: action instruction keyword→action segment, then when the action instruction keyword is known, the corresponding action segment can be obtained according to the action instruction keyword.
In this embodiment, the neural network may be a convolutional neural network, including an encoder and a decoder, where for any one action stream information, the encoder is configured to convert the action stream information into a corresponding intermediate semantic representation, and the decoder is configured to convert the intermediate semantic representation into an action instruction keyword corresponding to the action stream information. Therefore, the motion stream information is input to the encoder to be encoded, the encoder outputs the intermediate semantic representation, and then the decoder decodes the intermediate semantic representation to output the motion instruction key corresponding to the motion stream information.
Step 4: obtaining action types corresponding to the action instruction keywords:
and if the action corresponding to each action instruction has a corresponding action type, acquiring the action type corresponding to any action instruction, namely any action instruction keyword. As a specific embodiment, an action type database may be provided, where the action type database includes a plurality of action instruction keywords and action types corresponding to the action instruction keywords, and then, for any action instruction keyword, the action instruction keyword is input into the action type database to obtain a corresponding action type. And finally obtaining the action type corresponding to each action instruction keyword.
Step 5: according to the action types corresponding to the action instruction keywords, acquiring general logic sentences corresponding to the action types corresponding to the action instruction keywords:
for a certain action type, the action process corresponding to the action type has a certain similarity, and correspondingly, the execution process of each action language segment corresponding to the action type also has a great similarity. Therefore, according to the action type, the general logic statement corresponding to the action type can be obtained. As a specific embodiment, a general logic sentence database is preset, and the general logic sentence database includes a plurality of action types (including all the action types involved, for example), and general logic sentences corresponding to the action types. And then, according to the action types corresponding to the action instruction keywords, acquiring the general logic sentences corresponding to the action types corresponding to the action instruction keywords. The general logic statement is used for executing the corresponding action language segment.
Step 6: constructing an action library, wherein the action library comprises action instruction keywords, action language segments corresponding to the action instruction keywords and general logic sentences corresponding to action types corresponding to the action instruction keywords, and the general logic sentences are used for executing the corresponding action language segments:
after obtaining each action instruction keyword, an action language segment corresponding to each action instruction keyword and a general logic statement corresponding to an action type corresponding to each action instruction keyword, an action library is constructed, wherein the action library comprises each action instruction keyword, the action language segment corresponding to each action instruction keyword and the general logic statement corresponding to the action type corresponding to each action instruction keyword.
When the related actions are needed, an action instruction is received, an action instruction keyword is obtained, corresponding action language segments and general logic sentences are obtained according to the action instruction keyword and an action library, and then the corresponding action language segments are executed according to the general logic sentences.
The embodiment also provides a big data based action library generating device, which comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor realizes the big data based action library generating method when executing the computer program, and the method is not repeated because the detailed description is given above.
Claims (4)
1. The method for generating the action library based on the big data is characterized by comprising the following steps of:
acquiring an action library generation instruction;
according to the action library generating instruction, acquiring a plurality of action stream information from a target database;
using each action flow information as an action speech segment, and carrying out semantic mapping processing on each action flow information by combining a preset neural network to obtain action instruction keywords corresponding to each action flow information;
obtaining action types corresponding to the action instruction keywords;
acquiring general logic sentences corresponding to the action types corresponding to the action command keywords according to the action types corresponding to the action command keywords;
constructing an action library, wherein the action library comprises action instruction keywords, action language segments corresponding to the action instruction keywords and general logic sentences corresponding to action types corresponding to the action instruction keywords, and the general logic sentences are used for executing the corresponding action language segments;
the neural network comprises an encoder and a decoder, wherein the encoder is used for converting each piece of action stream information into a corresponding intermediate semantic representation, and the decoder is used for converting the intermediate semantic representation into action instruction keywords corresponding to each piece of action stream information;
the obtaining the action type corresponding to each action instruction keyword comprises the following steps:
setting an action type database, wherein the action type database comprises a plurality of action instruction keywords and action types corresponding to the action instruction keywords, and inputting the action instruction keywords into the action type database for any action instruction keyword to obtain corresponding action types;
before the universal logic statement corresponding to the action type corresponding to each action instruction keyword is obtained according to the action type corresponding to each action instruction keyword, the method further comprises:
a general logic statement database is preset, wherein the general logic statement database comprises a plurality of action types and general logic statements corresponding to the action types.
2. The big data based action library generating method according to claim 1, wherein before the plurality of action stream information is acquired from the target database according to the action library generating instruction, the action library generating method further comprises:
checking the validity of the action library generation instruction;
and if the action library generation instruction is a valid instruction, executing the action library generation instruction, and acquiring a plurality of action flow information from a target database.
3. The big data based action library generating method according to claim 2, wherein the action library generating instruction includes target identity information of an operator and a target instruction character string;
correspondingly, the verification of the validity of the action library generation instruction specifically comprises the following steps:
comparing the target identity information with a preset identity database, and judging the target identity information to be effective identity information if the target identity information belongs to one of the identity information in the identity database;
comparing the target instruction character string with a preset instruction character string database, and judging the target instruction character string to be a valid instruction character string if the target instruction character string belongs to one of the instruction character strings in the instruction character string database;
and if the target identity information is effective identity information and the target instruction character string is effective instruction character string, judging that the action library generation instruction is effective instruction.
4. A big data based action library generating device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the big data based action library generating method according to any of claims 1-3 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110429699.3A CN113268638B (en) | 2021-04-21 | 2021-04-21 | Big data-based action library generation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110429699.3A CN113268638B (en) | 2021-04-21 | 2021-04-21 | Big data-based action library generation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113268638A CN113268638A (en) | 2021-08-17 |
| CN113268638B true CN113268638B (en) | 2024-04-16 |
Family
ID=77229198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110429699.3A Active CN113268638B (en) | 2021-04-21 | 2021-04-21 | Big data-based action library generation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113268638B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844380A (en) * | 2015-12-04 | 2017-06-13 | 阿里巴巴集团控股有限公司 | A kind of database operation method, information processing method and related device |
| CN107832448A (en) * | 2017-11-22 | 2018-03-23 | 泰康保险集团股份有限公司 | Database operation method, device and equipment |
| CN110163131A (en) * | 2019-05-09 | 2019-08-23 | 南京邮电大学 | Mix the human action classification method of convolutional neural networks and the optimization of microhabitat grey wolf |
| CN111291674A (en) * | 2020-02-04 | 2020-06-16 | 清华珠三角研究院 | Method, system, device and medium for extracting expression and action of virtual character |
-
2021
- 2021-04-21 CN CN202110429699.3A patent/CN113268638B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844380A (en) * | 2015-12-04 | 2017-06-13 | 阿里巴巴集团控股有限公司 | A kind of database operation method, information processing method and related device |
| CN107832448A (en) * | 2017-11-22 | 2018-03-23 | 泰康保险集团股份有限公司 | Database operation method, device and equipment |
| CN110163131A (en) * | 2019-05-09 | 2019-08-23 | 南京邮电大学 | Mix the human action classification method of convolutional neural networks and the optimization of microhabitat grey wolf |
| CN111291674A (en) * | 2020-02-04 | 2020-06-16 | 清华珠三角研究院 | Method, system, device and medium for extracting expression and action of virtual character |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113268638A (en) | 2021-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109964223B (en) | Session information processing method and device, storage medium | |
| CN109858030B (en) | Two-way intent slot value cross-correlation task-based dialog understanding system and method | |
| CN108417210B (en) | Word embedding language model training method, word recognition method and system | |
| CN110704588A (en) | Multi-round dialogue semantic analysis method and system based on long-term and short-term memory network | |
| WO2021042503A1 (en) | Information classification extraction method, apparatus, computer device and storage medium | |
| KR101768509B1 (en) | On-line voice translation method and device | |
| CN109492113B (en) | Entity and relation combined extraction method for software defect knowledge | |
| CN111814466A (en) | Information extraction method based on machine reading understanding and related equipment thereof | |
| CN111739521A (en) | Electronic equipment awakening method and device, electronic equipment and storage medium | |
| CN108257593B (en) | Voice recognition method and device, electronic equipment and storage medium | |
| CN111931517A (en) | Text translation method and device, electronic equipment and storage medium | |
| CN111552799A (en) | Information processing method, information processing device, electronic equipment and storage medium | |
| WO2023201975A1 (en) | Difference description sentence generation method and apparatus, and device and medium | |
| CN110134780B (en) | Method, device, equipment and computer readable storage medium for generating document abstract | |
| CN111695338A (en) | Interview content refining method, device, equipment and medium based on artificial intelligence | |
| CN108320740B (en) | Voice recognition method and device, electronic equipment and storage medium | |
| CN112766319A (en) | Dialogue intention recognition model training method and device, computer equipment and medium | |
| CN114360502A (en) | Processing method of voice recognition model, voice recognition method and device | |
| CN114637843A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN110704597B (en) | Dialogue system reliability verification method, model generation method and device | |
| CN114861637A (en) | Method and device for generating spelling error correction model and method and device for spelling error correction | |
| CN113268638B (en) | Big data-based action library generation method and device | |
| CN112417875A (en) | Configuration information updating method and device, computer equipment and medium | |
| CN111400340A (en) | Natural language processing method and device, computer equipment and storage medium | |
| CN116881446A (en) | Semantic classification method, device, equipment and storage medium thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240319 Address after: Room 1701, 17th Floor, Building 1, Yufa Center, Henan Enterprise Union Building, No. 238 Mingli Road, Zhengdong New District, Zhengzhou City, Henan Province, 450000 Applicant after: Puhuitong Technology (Henan) Co.,Ltd. Country or region after: China Address before: No. 903, unit 2, building 8, Zhoushan new town, east of Gaoxin Longping road and south of sunshi highway, Luoyang area, China (Henan) pilot Free Trade Zone, Luoyang City, Henan Province, 471000 Applicant before: Luoyang moxiao Network Technology Co.,Ltd. Country or region before: China |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |