CN111680516A - PDM system product design requirement information semantic analysis and extraction method and system - Google Patents
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Abstract
The invention relates to the field of product data management, and provides a PDM system product design requirement information semantic analysis and extraction method which comprises the steps of obtaining multi-source data of product design requirements, inputting the obtained original data into a text analysis model, outputting preprocessed data, wherein the preprocessed data comprise a plurality of groups of text vocabulary data and N layers of data, inputting the preprocessed data into a feature extraction model, outputting feature data to construct a structured data set, entering a visual model, so that the data is visualized and the real and key semantic content of the product design requirement information is reflected, the invention also provides a system, the method and the system can enable specific designers to better and more quickly understand the text content in the new product design process, and reduce the loss of product design requirement information, thereby causing unnecessary product design change; the deviation of the character understanding and the reading and understanding time of designers are reduced, and the product design efficiency is further improved.
Description
Technical Field
The invention relates to the field of product data management, in particular to a PDM system product design requirement information semantic analysis and extraction method and system.
Background
The PDM system (Product Data Management, PDM) manages all information related to products, including part information, configuration, documents, CAD files, structures, authority information and the like, and all processes related to the products, including process definition and Management, enterprises implement Management of the PDM system, can improve production efficiency, is beneficial to managing the whole life cycle of the products, enhances efficient utilization of the documents, drawings and Data, standardizes the work flow, and has wide application in the Product Data Management process of the enterprises.
CN106462814B discloses a method of navigating and composing configured product lifecycle data, a network adapter configured to communicate with an application client; an accessible memory configured to store server data; and a processor operatively coupled to the network adapter and the accessible memory, the processor configured to: receiving a request from an application client for extended details regarding an architectural element of a model; parsing the request to identify the expanded details for a plurality of subsystems in the architecture element; identifying structure and configuration details of the model stored in server data; configuring the model according to the structure and the configuration details, wherein the model comprises a demand development phase, a functional analysis phase, and a logic modeling phase; performing a data traversal of the model to collect the expanded details for the plurality of subsystems in the architecture element; encapsulating the extended details by converting server data into an application format for the application client; and returning the encapsulated extended details to the application client, wherein the application client merges the extended details for the plurality of subsystems in the architectural element into a single view that includes the requirements development phase, the functional analysis phase, and the logic modeling phase.
In the actual operation process of the PDM system, a customer usually sets multi-directional description on functions, structures, sizes and the like of a required product in a product order, and specific requirement information generated by the customer is usually generated by persons in different fields, for example, engineers and managers refer to a certain part differently, but describe the same part; generally, a specific designer of a product needs to consume a lot of time when understanding the information of the product design requirement, and even more information of the product design requirement is lost, so that unnecessary changes of the product design are caused, and the product design efficiency is low.
Disclosure of Invention
Long-term practice finds that a large amount of time is consumed when a product specific designer reads and understands product design requirement information, and even more product design requirement information is lost, so that the problem of unnecessary product design change is caused.
In view of this, the present invention is directed to a method for analyzing and extracting product design requirement information semantics of a PDM system, so as to solve the problem that designers need to consume a lot of time when reading and understanding product design requirement information, and even cause more product design requirement information to be lost, thereby causing unnecessary product design changes, the method for analyzing and extracting product design requirement information semantics of a PDM system includes:
acquiring product design requirement information data;
inputting the acquired product design requirement information data into a text analysis model, and outputting preprocessed data, wherein the text analysis model comprises a word segmentation model and a text structure analysis model, and the word segmentation model is used for outputting a plurality of groups of text words after inputting the text data; the text structure analysis model is used for dividing the physical structure of input text data and dividing the input text data into N layers of data according to topics, wherein N is a positive integer greater than 0;
inputting the preprocessed data into a feature extraction model, and outputting feature data to construct a structured data set, wherein the feature extraction model is used for extracting keywords or feature words of text data and generating weights;
the structured data set is input into a visualization model, which is used for visualization of the structured data set.
Preferably, the word segmentation model comprises an inverse maximum matching model or a bidirectional matching model.
Preferably, the word segmentation model comprises word segmentation processing of numbers, dates, names and part of speech labels, and the plurality of groups of text words comprise word types of the numbers, the dates and the names.
Preferably, the text structure analysis model comprises the time and space physical segmentation of paragraphs, titles and sentences, and the N levels of data comprise structured annotation data of paragraphs, titles and sentences.
Preferably, the feature extraction model comprises extraction and classification of feature words, keywords and abstract data.
Preferably, the visualization model comprises an XML model.
An embodiment of the present invention also provides a system for performing the above method, the system comprising;
the acquisition unit is used for acquiring product design requirement information data;
the text analysis processing unit is used for inputting the acquired product design requirement information data into a text analysis model and outputting preprocessed data, wherein the text analysis model comprises a word segmentation model and a text structure analysis model, and the word segmentation model is used for outputting a plurality of groups of text words after the text data is input; the text structure analysis model is used for dividing the physical structure of input text data and dividing the input text data into N layers of data according to topics, wherein N is a positive integer greater than 0;
the feature extraction unit is used for inputting the preprocessed data into a feature extraction model and outputting feature data to construct a structured data set, wherein the feature extraction model is used for extracting keywords or feature words of the text data and generating weights;
and the display unit is used for inputting the structured data set into a visualization model, and the visualization model is used for visualization of the structured data set.
Preferably, the acquiring unit comprises a plurality of input modules of different types, at least a text input module and a voice input module, wherein the text input module is used for inputting text type data of the product design requirement information; the voice input module is used for voice type data input of product design requirement information.
Preferably, the acquiring unit further includes a voice recognition module for converting the voice information data of the voice input module into text data.
According to another aspect of the embodiments of the present invention, there is provided a storage medium, the storage medium including a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the above method.
According to the PDM system product design requirement information semantic analysis and extraction method provided by the embodiment of the invention, by acquiring multi-source data of product design requirements, inputting the acquired original data into a text analysis model, outputting preprocessed data, wherein the preprocessed data comprises a plurality of groups of text vocabulary data and N layers of data, inputting the preprocessed data into a feature extraction model, outputting feature data to construct a structured data set, entering a visual model, visualizing the data, reflecting the real and important semantic content of the product design requirement information, and providing a system for executing the method, the method and the system can enable specific designers to better and more quickly understand the character content in the new product design process, effectively reduce the description of the product requirements only depending on a product static requirement table, and reduce the loss of the product design requirement information, resulting in unnecessary product design changes; the deviation of the character understanding and the reading and understanding time of designers are reduced, and the product design efficiency is further improved.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.
In the drawings:
FIG. 1 is a flow chart of a PDM system product design requirement information semantic analysis extraction method according to an embodiment of the present invention;
fig. 2 is a system processing logic relationship diagram corresponding to the PDM system product design requirement information semantic analysis and extraction method according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged under appropriate circumstances in order to facilitate the description of the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The problem that the product design change is unnecessary due to the fact that a large amount of time is consumed when a product specific designer reads and understands the product design requirement information and even more product design requirement information is lost is solved. As shown in fig. 1-2, the present invention provides a method for analyzing and extracting product design requirement information semantics of a PDM system, as shown in fig. 1, a flow chart of a method for analyzing and extracting product design requirement information semantics of a PDM system according to an embodiment of the present invention, where the method for analyzing and extracting product design requirement information semantics of a PDM system includes:
step S1, acquiring product design requirement information data;
step S2, inputting the acquired product design requirement information data into a text analysis model, and outputting preprocessed data, wherein the text analysis model comprises a word segmentation model and a text structure analysis model, and the word segmentation model is used for outputting a plurality of groups of text words after inputting the text data; the text structure analysis model is used for dividing the physical structure of input text data and dividing the input text data into N layers of data according to topics, wherein N is a positive integer greater than 0;
step S3, inputting the preprocessed data into a feature extraction model, outputting feature data to construct a structured data set, wherein the feature extraction model is used for extracting keywords or feature words of text data and generating weights;
step S4, inputting the structured data set into a visualization model, the visualization model being used for visualization of the structured data set.
In the invention, the PDM system product design requirement information semantic analysis and extraction method obtains the multi-source data of the product design requirement, inputting the obtained original data into a text analysis model, outputting preprocessed data, wherein the preprocessed data comprise a plurality of groups of text vocabulary data and N layers of data, inputting the preprocessed data into a feature extraction model, outputting feature data to construct a structured data set, entering a visual model, so that the data is visualized, the real and key semantic content of the product design requirement information is reflected, the method can enable a specific designer to better and more quickly understand the text content in the new product design process, effectively reduce the description of product requirements only depending on a product static requirement table, and reduce the loss of product design requirement information, thereby causing unnecessary product design change; the deviation of the character understanding and the reading and understanding time of designers are reduced, and the product design efficiency is further improved.
In order to effectively perform word segmentation on input product design requirement information data, particularly for a Chinese text, under the preferable condition of the invention, the word segmentation model comprises a Reverse Maximum matching model or a bidirectional matching model, and under the more preferable condition, a Method used by the Reverse Maximum matching model is a Reverse Maximum matching Method (RMM), namely, the matching direction is from right to left; the Method used by the bidirectional Matching model is a Bidirectional Matching Method (BMM), and the correct word segmentation is determined by comparing the word segmentation results of a Maximum Matching Method (MM) and an RMM Method; the Matching direction of the Maximum Matching Method (MM) is from left to right.
For example, "the total mass of the product cannot exceed 200 g", after the preprocessing of the RMM model word segmentation, a plurality of sets of text vocabulary data of "product", "total mass", "cannot", "exceeding", "200 g" are generated; as another example, "the total mass of the product cannot exceed 200g, and the shell material is an aluminum alloy. The physical structure of the input text data is divided through a text structure analysis model to generate data of two levels, namely 'the total mass of a product cannot exceed 200 g', 'the shell material adopts aluminum alloy', and the data of the first level, namely 'the total mass of the product cannot exceed 200 g', is arranged before the data of the second level, namely 'the shell material adopts aluminum alloy'.
Inputting the data preprocessed in the step S2 into a feature extraction model, outputting feature data to construct a structured data set, selecting keywords or feature words of "product", "total mass", "200 g", "shell", "material", "aluminum alloy" by nouns, wherein verbs are "exceeding" and "adopting", other words are "unable", and obtaining a structured data set, for example, attributes of "product" include "total mass", "shell" and "material", and attribute value data for attribute include "200 g" and "aluminum alloy"; for example, the attribute value of "total mass" is "200 g", and the connection is defined as "cannot", "exceed"; the "material" of the "outer shell", i.e. the "outer shell" of the "product", is an "aluminium alloy". After step S3, the output data includes structured data of each attribute value of "product".
In order to better obtain the word segmentation analysis of key information such as numbers, dates, names and the like and analyze text data more efficiently, in a preferred case of the invention, the word segmentation model comprises word segmentation processing of labels of the numbers, the dates, the names and parts of speech, and a plurality of groups of text words comprise word types of the numbers, the dates and the names.
In order to better perform physical segmentation on the text structure and perform word segmentation analysis on the short text data, so as to better perform analysis from longer text data, in a preferred case of the present invention, the text structure analysis model includes time and space physical segmentation on paragraphs, titles and sentences, and the N hierarchical data includes structured labeling data of paragraphs, titles and sentences.
For simplicity and support of extracting text features in the text data in parallel, in a preferred case of the present invention, the feature extraction model in step S3 includes a textconditional Neural Networks model, that is, a TextCNN model.
In order to extract necessary features from the text data and perform necessary classification, in a preferred case of the present invention, the feature extraction model includes extraction and classification of feature words, keywords, and summary data.
In order to present the logical hierarchical relationship between the feature words and the keywords in the text data conveniently, in the preferred case of the present invention, the visualization model includes an eXtensible Markup Language, which is an eXtensible Markup Language model, which is an XML model.
In order to more vividly display the logical relationship between the text data, in a more preferred case of the present invention, the visualization model includes using a mind map model based on an XML model.
The invention also provides a system for executing the PDM system product design requirement information semantic analysis and extraction method, as shown in FIG. 2, the system comprises;
the acquisition unit is used for acquiring product design requirement information data;
the text analysis processing unit is used for inputting the acquired product design requirement information data into a text analysis model and outputting preprocessed data, wherein the text analysis model comprises a word segmentation model and a text structure analysis model, and the word segmentation model is used for outputting a plurality of groups of text words after the text data is input; the text structure analysis model is used for dividing the physical structure of input text data and dividing the input text data into N layers of data according to topics, wherein N is a positive integer greater than 0;
the feature extraction unit is used for inputting the preprocessed data into a feature extraction model and outputting feature data to construct a structured data set, wherein the feature extraction model is used for extracting keywords or feature words of the text data and generating weights;
and the display unit is used for inputting the structured data set into a visualization model, and the visualization model is used for visualization of the structured data set.
In the invention, by acquiring multi-source data required by product design, inputting the acquired original data into a text analysis model and outputting preprocessed data, wherein the preprocessed data comprises a plurality of groups of text vocabulary data and N layers of data, inputting the preprocessed data into a feature extraction model, outputting feature data to construct a structured data set, entering a visual model, the invention can make the data visualized and reflect the real and key semantic content of the product design requirement information, and the system for executing the PDM system product design requirement information semantic analysis and extraction method provided by the invention, the system can enable a specific designer to better and more quickly understand the text content in the new product design process, effectively reduce the description of product requirements only depending on a product static requirement table, and reduce the loss of product design requirement information, thereby causing unnecessary product design change; the deviation of the character understanding and the reading and understanding time of designers are reduced, and the product design efficiency is further improved.
In a text analysis processing unit, inputting the acquired product design requirement information data into a text analysis model, and outputting preprocessed data, wherein the data input into the text analysis model is that the total mass of a product cannot exceed 200g, and after the data are preprocessed by the RMM model word segmentation, a plurality of groups of text vocabulary data of the product, the total mass, the product cannot exceed 200g, the product exceeding 200g are generated; as another example, "the total mass of the product cannot exceed 200g, and the shell material is an aluminum alloy. The physical structure of the input text data is divided through a text structure analysis model to generate data of two levels, namely 'the total mass of a product cannot exceed 200 g', 'the shell material adopts aluminum alloy', and the data of the first level, namely 'the total mass of the product cannot exceed 200 g', is arranged before the data of the second level, namely 'the shell material adopts aluminum alloy'.
Inputting data processed by the text analysis processing unit into a feature extraction unit, inputting the data into a feature extraction model in the feature extraction unit, outputting feature data to construct a structured data set, screening out keywords or feature words of 'product', 'total mass', '200 g', 'shell', 'material', 'aluminum alloy' by nouns, wherein verbs are 'exceeding' and 'adopting', other words are 'unable', and obtaining the structured data set, wherein attributes of the 'product' comprise 'total mass', 'shell' and 'material', and attribute value data for the attributes comprise '200 g' and 'aluminum alloy'; for example, the attribute value of "total mass" is "200 g", and the connection is defined as "cannot", "exceed"; the "material" of the "outer shell", i.e. the "outer shell" of the "product", is an "aluminium alloy". After passing through the feature extraction unit, the output data comprises structured data of each attribute value of the product.
In order to support the product design requirement information of different forms of data such as text type data or voice type data, under the preferable condition of the invention, the acquisition unit comprises a plurality of input modules of different types, at least comprises a text input module and a voice input module, and the text input module is used for inputting the text type data of the product design requirement information; the voice input module is used for voice type data input of product design requirement information.
In order to input the input data of the text analysis processing unit as text data, so as to perform word segmentation analysis in the word segmentation model and perform text structure analysis in the text structure analysis model, in a preferred case of the present invention, the obtaining unit further includes a voice recognition module for converting voice information data of the voice input module into text data.
The embodiment of the invention also provides a storage medium, which comprises a stored program, wherein when the program runs, the device where the storage medium is located is controlled to execute the method.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a mobile terminal, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A PDM system product design requirement information semantic analysis and extraction method is characterized by comprising the following steps:
acquiring product design requirement information data;
inputting the acquired product design requirement information data into a text analysis model, and outputting preprocessed data, wherein the text analysis model comprises a word segmentation model and a text structure analysis model, and the word segmentation model is used for outputting a plurality of groups of text words after inputting the text data; the text structure analysis model is used for dividing the physical structure of input text data and dividing the input text data into N layers of data according to topics, wherein N is a positive integer greater than 0;
inputting the preprocessed data into a feature extraction model, and outputting feature data to construct a structured data set, wherein the feature extraction model is used for extracting keywords or feature words of text data and generating weights;
the structured data set is input into a visualization model, which is used for visualization of the structured data set.
2. The PDM system product design requirement information semantic analysis and extraction method according to claim 1, wherein the word segmentation model comprises an inverse maximum matching model or a bidirectional matching model.
3. The PDM system product design requirement information semantic analysis and extraction method as claimed in claim 1, wherein the word segmentation model comprises word segmentation processing of number, date, name and part of speech labels, and the plurality of groups of text words comprise word types of number, date and name.
4. The PDM system product design requirement information semantic analysis and extraction method according to claim 1, wherein the text structure analysis model comprises temporal and spatial physical segmentation on paragraphs, titles and sentences, and the N levels of data comprise structured annotation data of paragraphs, titles and sentences.
5. The PDM system product design requirement information semantic analysis extraction method according to any one of claims 1-4, wherein the feature extraction model comprises extraction and classification of feature words, keywords and abstract data.
6. The PDM system product design requirement information semantic analysis extraction method according to any one of claims 1-4, wherein the visualization model comprises an XML model.
7. A system, characterized in that the system comprises;
the acquisition unit is used for acquiring product design requirement information data;
the text analysis processing unit is used for inputting the acquired product design requirement information data into a text analysis model and outputting preprocessed data, wherein the text analysis model comprises a word segmentation model and a text structure analysis model, and the word segmentation model is used for outputting a plurality of groups of text words after the text data is input; the text structure analysis model is used for dividing the physical structure of input text data and dividing the input text data into N layers of data according to topics, wherein N is a positive integer greater than 0;
the feature extraction unit is used for inputting the preprocessed data into a feature extraction model and outputting feature data to construct a structured data set, wherein the feature extraction model is used for extracting keywords or feature words of the text data and generating weights;
and the display unit is used for inputting the structured data set into a visualization model, and the visualization model is used for visualization of the structured data set.
8. The system according to claim 7, wherein the acquiring unit comprises a plurality of different types of input modules, at least comprising a text input module and a voice input module, wherein the text input module is used for inputting text type data of product design requirement information; the voice input module is used for voice type data input of product design requirement information.
9. The system of claim 8, wherein the obtaining unit further comprises a voice recognition module for converting voice information data of the voice input module into text data.
10. A storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method of any one of claims 1-6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101067807A (en) * | 2007-05-24 | 2007-11-07 | 上海大学 | Text semantic visable representation and obtaining method |
| CN101308498A (en) * | 2008-07-03 | 2008-11-19 | 上海交通大学 | Text collection visualized system |
| CN106649713A (en) * | 2016-12-21 | 2017-05-10 | 中山大学 | Movie visualization processing method and system based on content |
| CN107291697A (en) * | 2017-06-29 | 2017-10-24 | 浙江图讯科技股份有限公司 | A kind of semantic analysis, electronic equipment, storage medium and its diagnostic system |
| CN108536762A (en) * | 2018-03-21 | 2018-09-14 | 上海蔚界信息科技有限公司 | A kind of high-volume text data automatically analyzes scheme |
| CN109992645A (en) * | 2019-03-29 | 2019-07-09 | 国家计算机网络与信息安全管理中心 | A kind of data supervision system and method based on text data |
| CN111177591A (en) * | 2019-12-10 | 2020-05-19 | 浙江工业大学 | Knowledge graph-based Web data optimization method facing visualization demand |
-
2020
- 2020-06-04 CN CN202010498256.5A patent/CN111680516A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101067807A (en) * | 2007-05-24 | 2007-11-07 | 上海大学 | Text semantic visable representation and obtaining method |
| CN101308498A (en) * | 2008-07-03 | 2008-11-19 | 上海交通大学 | Text collection visualized system |
| CN106649713A (en) * | 2016-12-21 | 2017-05-10 | 中山大学 | Movie visualization processing method and system based on content |
| CN107291697A (en) * | 2017-06-29 | 2017-10-24 | 浙江图讯科技股份有限公司 | A kind of semantic analysis, electronic equipment, storage medium and its diagnostic system |
| CN108536762A (en) * | 2018-03-21 | 2018-09-14 | 上海蔚界信息科技有限公司 | A kind of high-volume text data automatically analyzes scheme |
| CN109992645A (en) * | 2019-03-29 | 2019-07-09 | 国家计算机网络与信息安全管理中心 | A kind of data supervision system and method based on text data |
| CN111177591A (en) * | 2019-12-10 | 2020-05-19 | 浙江工业大学 | Knowledge graph-based Web data optimization method facing visualization demand |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113407164A (en) * | 2021-06-21 | 2021-09-17 | 邬恩国 | Software code generation method and system based on mind map and tree structure technology |
| CN113407164B (en) * | 2021-06-21 | 2022-07-29 | 邬恩国 | Software code generation method and system based on mind map and tree structure technology |
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