CN110688457A - Steam-massage industry text information input method based on identification analysis - Google Patents
Steam-massage industry text information input method based on identification analysis Download PDFInfo
- Publication number
- CN110688457A CN110688457A CN201910913825.5A CN201910913825A CN110688457A CN 110688457 A CN110688457 A CN 110688457A CN 201910913825 A CN201910913825 A CN 201910913825A CN 110688457 A CN110688457 A CN 110688457A
- Authority
- CN
- China
- Prior art keywords
- feature vector
- feature
- input
- text information
- identification
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000004458 analytical method Methods 0.000 title claims abstract description 14
- 239000013598 vector Substances 0.000 claims abstract description 106
- 238000000605 extraction Methods 0.000 abstract description 3
- 210000003169 central nervous system Anatomy 0.000 description 1
- 238000013479 data entry Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3344—Query execution using natural language analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a method for inputting text information in the automobile and motorcycle industry based on identification analysis, which comprises the following steps: s1, acquiring text information to be input; s2, extracting a feature vector to be input from the text information to be input; s3, calculating the similarity between the feature vector to be recorded and a preset feature vector; s4, classifying the feature vectors to be input based on the similarity; and S5, generating the identification code of the text to be recorded based on the identification code rule corresponding to the category to which the feature vector to be recorded belongs. The method and the device can realize automatic extraction of the feature vectors of the text information, and classify the extracted feature vectors based on the similarity between the feature vectors and the preset feature vectors, thereby realizing automatic generation of the identification codes of the text information, and further greatly improving the efficiency of generating the corresponding identification codes by mass input data in the automobile and motorcycle industry.
Description
Technical Field
The invention relates to the technical field of identification analysis, in particular to a method for inputting text information in the automobile and motorcycle industry based on identification analysis.
Background
In the internet era, an analysis system becomes a central nervous system of the internet, and the ecology of the whole internet is prosperous; in the era of internet of things with interconnection of everything, the strategic need is to consider in advance and build a layout identification analysis system to construct the ecology of the internet of things with interconnection of everything. Particularly in the automobile and motorcycle industry, in order to achieve information intercommunication between each enterprise and each mechanism in the industry, a data format in the industry needs to be unified through a unified identification and analysis method, however, in order to achieve unified identification analysis in the whole industry, data in the industry needs to be input according to preset rules to generate corresponding identification codes.
In the prior art, data is input according to a well-specified coding rule, manual entry is usually required one by one, when an enterprise preliminarily adopts identification analysis, a large amount of historical data needs to be manually entered, the workload is huge, and the data entry efficiency is low.
Therefore, how to improve the efficiency of generating the corresponding identification code by inputting a large amount of data in the automobile and motorcycle industry becomes a problem which needs to be solved urgently by technical personnel in the field.
Disclosure of Invention
Aiming at the defects of the prior art, the problems to be solved by the invention are as follows: how to improve the efficiency when a large amount of input data in the automobile and motorcycle industry generate corresponding identification codes.
The invention adopts the following technical scheme:
a method for inputting text information in the automobile and motorcycle industry based on identification analysis comprises the following steps:
s1, acquiring text information to be input;
s2, extracting a feature vector to be input from the text information to be input;
s3, calculating the similarity between the feature vector to be recorded and a preset feature vector;
s4, classifying the feature vectors to be input based on the similarity;
and S5, generating the identification code of the text to be recorded based on the identification code rule corresponding to the category to which the feature vector to be recorded belongs.
Preferably, step S2 includes:
dividing the text information to be input into information terms to be input according to a preset division format;
and collecting the feature vector to be recorded corresponding to the preset feature vector from the information clause to be recorded.
Preferably, the preset feature vector format comprises a plurality of preset feature vectors, and the preset feature vectors corresponding to the preset feature vectors are collected from the information clauses to be recorded.
Preferably, the predetermined feature vector is wa1，xa1；wa2，xa2；......；wai，xai；......；wan,xanWhere n is the total number of feature words, xaiThe ith feature word coded for the a-th class mark in the preset feature vector is a vocabulary or a range value, waiThe weight of the ith characteristic word coded for the a-type identification is obtained, and the characteristic vector to be recorded is Xa1，Ya1；Xa2，Ya2；......；Xai，Yai；......；Xan，YanWherein X isaiIdentifying the coded ith feature word for the a-th class in the preset feature vector when x isaiWhen it is a word, XaiIs equal to xaiThe information clauses to be entered include corresponding xaiWhen, Yai1, the characteristic vector to be recorded does not include corresponding xaiWhen, Yai＝0，YaiIs XaiA corresponding judgment value; when x isaiIs a range value and the term of information to be entered includes being at xaiWhen numerical values in (1) are, XaiIs equal toThe above numerical value, YaiNot all but 1, otherwise, XaiIs zero, Yai0; the similarity between the feature vector to be input and the preset feature vector is Sa，Sa＝wa1Ya1+wa2Ya2+wa3Ya3+...+wanYanIf S isaAnd if the similarity is greater than or equal to the similarity threshold of the a-th type identification code, distributing the corresponding characteristic vector to be recorded into the a-th type identification code.
Preferably, each type of identification code comprises a plurality of fields, each field corresponds to one or more feature words, and if each field has a corresponding and unique feature word for the feature vector to be entered, a code corresponding to the field and the feature word is generated, and the identification code of the feature vector to be entered is obtained.
Preferably, each type of identification code comprises a plurality of fields, each field corresponds to one or more feature words, and if any field has no corresponding feature word for the feature vector to be entered, the feature vector to be entered is discarded.
Preferably, each type of identification code includes a plurality of fields, each field corresponds to one or more feature words, if for the feature vector to be entered, any one or more fields have a plurality of corresponding feature words, and the remaining fields have corresponding and unique feature words, then codes of fields corresponding to the plurality of feature words in all permutation and combination forms are generated, codes of fields corresponding to the unique feature words are generated, and the identification code of the feature vector to be entered is obtained.
In summary, the invention discloses a method for inputting text information in the automobile and motorcycle industry based on identification analysis, which comprises the following steps: s1, acquiring text information to be input; s2, extracting a feature vector to be input from the text information to be input; s3, calculating the similarity between the feature vector to be recorded and a preset feature vector; s4, classifying the feature vectors to be input based on the similarity; and S5, generating the identification code of the text to be recorded based on the identification code rule corresponding to the category to which the feature vector to be recorded belongs. The method and the device can realize automatic extraction of the feature vectors of the text information, and classify the extracted feature vectors based on the similarity between the feature vectors and the preset feature vectors, thereby realizing automatic generation of the identification codes of the text information, and further greatly improving the efficiency of generating the corresponding identification codes by mass input data in the automobile and motorcycle industry.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
fig. 1 is a flow chart of a method for inputting text information in the automobile and motorcycle industry based on identification parsing disclosed by the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the invention discloses a method for inputting text information in the automobile and motorcycle industry based on identification analysis, which comprises the following steps:
s1, acquiring text information to be input;
s2, extracting a feature vector to be input from the text information to be input;
s3, calculating the similarity between the feature vector to be recorded and a preset feature vector;
s4, classifying the feature vectors to be input based on the similarity;
and S5, generating the identification code of the text to be recorded based on the identification code rule corresponding to the category to which the feature vector to be recorded belongs.
The method and the device can realize automatic extraction of the feature vectors of the text information, and classify the extracted feature vectors based on the similarity between the feature vectors and the preset feature vectors, thereby realizing automatic generation of the identification codes of the text information, and further greatly improving the efficiency of generating the corresponding identification codes by mass input data in the automobile and motorcycle industry.
In specific implementation, step S2 includes:
dividing the text information to be input into information terms to be input according to a preset division format;
and collecting the feature vector to be recorded corresponding to the preset feature vector from the information clause to be recorded.
In the invention, because the whole text is input and the information content included in the text is large, the text needs to be segmented into the information clauses to be input according to the preset segmentation format. The preset cutting format is a preset cutting rule, for example, each line is divided as one line according to the format of the text to be entered, or a period or a comma is used as a cutting boundary. The text can be divided according to the type of the input text, if the text is a form text with clear format, the text can be divided according to lines, and if the text is an article text with main paragraphs, the text can be divided according to punctuation marks.
In specific implementation, the preset feature vector formats comprise a plurality of preset feature vector formats, and the feature vectors to be recorded corresponding to the preset feature vectors are collected from the information clauses to be recorded.
Each type of identification code has a corresponding preset feature vector, and recording habits of different enterprises or workers are different, so that contents capable of generating a plurality of different types of identification codes are possibly recorded in the same information clause to be recorded, and for each information clause to be recorded, all the feature vectors to be recorded corresponding to the preset feature vectors need to be generated, so that missing recording of information is avoided.
In specific implementation, the predetermined feature vector is wa1，xa1；wa2，xa2；......；wai，xai；......；wan,xanWhere n is the total number of feature words, xaiThe ith feature word coded for the a-th class mark in the preset feature vector is a vocabulary or a range value, waiThe weight of the ith characteristic word coded for the a-type identification is obtained, and the characteristic vector to be recorded is Xa1，Ya1；Xa2，Ya2；......；Xai，Yai；......；Xan，YanWherein X isaiIdentifying the coded ith feature word for the a-th class in the preset feature vector when x isaiWhen it is a word, XaiIs equal to xaiThe information clauses to be entered include corresponding xaiWhen, Yai1, to enter the feature vectorDoes not include a corresponding xaiWhen, Yai＝0，YaiIs XaiA corresponding judgment value; when x isaiIs a range value and the term of information to be entered includes being at xaiWhen numerical values in (1) are, XaiIs equal to the value, YaiNot all but 1, otherwise, XaiIs zero, Yai0; the similarity between the feature vector to be input and the preset feature vector is Sa，Sa＝wa1Ya1+wa2Ya2+wa3Ya3+...+wanYanIf S isaAnd if the similarity is greater than or equal to the similarity threshold of the a-th type identification code, distributing the corresponding characteristic vector to be recorded into the a-th type identification code.
Due to the particularity of the enterprise information, the characteristic words can comprise words or range values, so that all information can be input at one time. For example, if the identification code of a certain category is the quantity of a certain product in stock or shipment, the feature words may include the product name, the quantity in stock or shipment, and the quantity (in order to ensure that the quantity corresponds to the preceding words, the quantity may be in units). When the similarity is higher than the similarity threshold, the category of the feature vector to be entered can be preliminarily determined. In the invention, in order to ensure that the information is completely input without missing input, the characteristic words are not necessarily finally embodied in the identification codes, the words directly embodied in the identification codes have higher weight, and the higher the association degree of the rest of the characteristic words with the identification codes is, the higher the weight is.
In specific implementation, each type of identification code comprises a plurality of fields, each field corresponds to one or more feature words, if each field has a corresponding and unique feature word for the feature vector to be input, the code corresponding to the field and the feature word is generated, and the identification code of the feature vector to be input is obtained.
Taking the identification code of the product for shipment as an example, three fields can be included, field 1: product name, field 2: behavior, field 3: the number of the cells. Wherein, the product name can correspond to a plurality of characteristic words (each characteristic word identifies a product name), and the behavior includes: the fields 3 are numbers, when each field corresponds to a unique feature word, the to-be-input feature vector is identified to include the content capable of generating an identification code, so that codes corresponding to the feature words of each field are generated, and the codes corresponding to the fields are combined to form the identification code. The corresponding relation between the feature words and the codes is recorded in a preset coding rule, and can be set according to the actual needs of different industries, which is not described herein again.
In specific implementation, each type of identification code comprises a plurality of fields, each field corresponds to one or more feature words, and if any field does not have a corresponding feature word for the feature vector to be input, the feature vector to be input is discarded.
In the invention, the classification of the feature vectors to be input is judged according to the similarity, so as to avoid missing classification, not all feature words are corresponding to the fields, and all feature words are required to be included when classification is not required, thus, the situation of multi-classification can exist, namely, a certain feature vector to be input is classified into the class which cannot generate identification codes, at the moment, the feature vector to be input does not include the feature words corresponding to all the fields, at the moment, the feature vector to be input is discarded when the identification codes of the class are generated, and the generation of invalid or wrong identification codes is avoided. The skilled person will know that after the feature vector to be recorded is discarded when a certain type of identification code is generated, other types of identification codes can also be generated.
In specific implementation, each type of identification code comprises a plurality of fields, each field corresponds to one or more feature words, if any one or more fields have a plurality of corresponding feature words and the rest fields have corresponding and unique feature words for the feature vector to be entered, codes of the fields corresponding to the plurality of feature words in all permutation and combination forms are generated, codes of the fields corresponding to the unique feature words are generated, and the identification code of the feature vector to be entered is obtained.
In the history data to be recorded, because a uniform recording mode is not adopted, there may be a case that a sentence records information that can generate a plurality of identification codes of the same kind. For example, product 1 and product 2 are each shipped and sold 50 pieces, assuming that the fields whose identification codes include three fields, field 1: product name, field 2: behavior, field 3: the number of the cells. Firstly, the conditions of different fields and corresponding to a plurality of feature words are arranged and combined, and then the product 1 is fed, the product 2 is fed, the product 1 is sold and the product 2 is sold, the number of the four conditions is 50, therefore, the finally obtained identification codes of all arrangement and combination modes are 4, and thus, the missing of information or the identification error of the feature vector to be recorded can be avoided.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A method for inputting text information in the automobile and motorcycle industry based on identification analysis is characterized by comprising the following steps:
s1, acquiring text information to be input;
s2, extracting a feature vector to be input from the text information to be input;
s3, calculating the similarity between the feature vector to be recorded and a preset feature vector;
s4, classifying the feature vectors to be input based on the similarity;
and S5, generating the identification code of the text to be recorded based on the identification code rule corresponding to the category to which the feature vector to be recorded belongs.
2. The method for entering automotive and motorcycle industry text information based on identity resolution as claimed in claim 1, wherein step S2 includes:
dividing the text information to be input into information terms to be input according to a preset division format;
and collecting the feature vector to be recorded corresponding to the preset feature vector from the information clause to be recorded.
3. The method for entering the text information of the automotive and motorcycle industry based on the identification parsing as claimed in claim 2, wherein the preset feature vector format comprises a plurality of formats, and the feature vector to be entered corresponding to each preset feature vector is collected from the terms of the information to be entered.
4. The method for entering automo-mobile industry text information based on identity resolution as claimed in claim 2 or 3, wherein the preset feature vector is wa1，xa1；wa2，xa2；......；wai，xai；......；wan,xanWhere n is the total number of feature words, xaiThe ith feature word coded for the a-th class mark in the preset feature vector is a vocabulary or a range value, waiThe weight of the ith characteristic word coded for the a-type identification is obtained, and the characteristic vector to be recorded is Xa1，Ya1；Xa2，Ya2；......；Xai，Yai；......；Xan，YanWherein X isaiIdentifying the coded ith feature word for the a-th class in the preset feature vector when x isaiWhen it is a word, XaiIs equal to xaiThe information clauses to be entered include corresponding xaiWhen, Yai1, the characteristic vector to be recorded does not include corresponding xaiWhen, Yai＝0，YaiIs XaiA corresponding judgment value; when x isaiIs a range value and the term of information to be entered includes being at xaiWhen numerical values in (1) are, XaiIs equal to the value, YaiNot all but 1, otherwise, XaiIs zero, Yai0; the similarity between the feature vector to be input and the preset feature vector is Sa，Sa＝wa1Ya1+wa2Ya2+wa3Ya3+…+wanYanIf S isaGreater than or equal to class aAnd if the similarity threshold of the identification codes is determined, classifying the corresponding characteristic vectors to be recorded into the class a identification codes.
5. The method for inputting automotive and motorcycle industry text information based on identification parsing as claimed in claim 4, wherein each type of identification code includes a plurality of fields, each field corresponds to one or more feature words, if each field has a corresponding and unique feature word for the feature vector to be input, a code corresponding to the field and the feature word is generated, and the identification code of the feature vector to be input is obtained.
6. The method for entering the text information in the automotive and motorcycle industry based on the identification parsing as claimed in claim 4, wherein each type of identification code comprises a plurality of fields, each field corresponds to one or more feature words, and if any field does not have a corresponding feature word for the feature vector to be entered, the feature vector to be entered is discarded.
7. The method for inputting the text information in the automotive and motorcycle industry based on the identification parsing as claimed in claim 4, wherein each type of identification code comprises a plurality of fields, each field corresponds to one or more feature words, if any one or more fields have a plurality of corresponding feature words and the remaining fields have corresponding and unique feature words for the feature vector to be input, codes of the fields corresponding to the plurality of feature words in all permutation and combination forms are generated, codes of the fields corresponding to the unique feature words are generated, and the identification codes of the feature vector to be input are obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910913825.5A CN110688457A (en) | 2019-09-25 | 2019-09-25 | Steam-massage industry text information input method based on identification analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910913825.5A CN110688457A (en) | 2019-09-25 | 2019-09-25 | Steam-massage industry text information input method based on identification analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110688457A true CN110688457A (en) | 2020-01-14 |
Family
ID=69110067
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910913825.5A Pending CN110688457A (en) | 2019-09-25 | 2019-09-25 | Steam-massage industry text information input method based on identification analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110688457A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111222496A (en) * | 2020-02-17 | 2020-06-02 | 重庆忽米网络科技有限公司 | Identification code generation method and system based on 5G communication and image recognition |
| CN114240241A (en) * | 2021-12-30 | 2022-03-25 | 南京戎智信息创新研究院有限公司 | Factory equipment collaborative operation and maintenance management system based on identification analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015139497A1 (en) * | 2014-03-19 | 2015-09-24 | 北京奇虎科技有限公司 | Method and apparatus for determining similar characters in search engine |
| CN106920019A (en) * | 2015-12-24 | 2017-07-04 | 北京计算机技术及应用研究所 | Multi-class coding management system |
| CN108241867A (en) * | 2016-12-26 | 2018-07-03 | 阿里巴巴集团控股有限公司 | A kind of sorting technique and device |
| CN108563722A (en) * | 2018-04-03 | 2018-09-21 | 有米科技股份有限公司 | Trade classification method, system, computer equipment and the storage medium of text message |
| CN108616413A (en) * | 2016-12-13 | 2018-10-02 | 百度在线网络技术（北京）有限公司 | Information calibration method and device |
-
2019
- 2019-09-25 CN CN201910913825.5A patent/CN110688457A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015139497A1 (en) * | 2014-03-19 | 2015-09-24 | 北京奇虎科技有限公司 | Method and apparatus for determining similar characters in search engine |
| CN106920019A (en) * | 2015-12-24 | 2017-07-04 | 北京计算机技术及应用研究所 | Multi-class coding management system |
| CN108616413A (en) * | 2016-12-13 | 2018-10-02 | 百度在线网络技术（北京）有限公司 | Information calibration method and device |
| CN108241867A (en) * | 2016-12-26 | 2018-07-03 | 阿里巴巴集团控股有限公司 | A kind of sorting technique and device |
| CN108563722A (en) * | 2018-04-03 | 2018-09-21 | 有米科技股份有限公司 | Trade classification method, system, computer equipment and the storage medium of text message |
Non-Patent Citations (1)
| Title |
|---|
| 鲍庆升等: "基于文本分析的自动化疾病编码方法", 《计算机系统应用》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111222496A (en) * | 2020-02-17 | 2020-06-02 | 重庆忽米网络科技有限公司 | Identification code generation method and system based on 5G communication and image recognition |
| CN114240241A (en) * | 2021-12-30 | 2022-03-25 | 南京戎智信息创新研究院有限公司 | Factory equipment collaborative operation and maintenance management system based on identification analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112597312A (en) | Text classification method and device, electronic equipment and readable storage medium | |
| CN110851598B (en) | Text classification method and device, terminal equipment and storage medium | |
| CN107145584B (en) | Resume parsing method based on n-gram model | |
| DE102011106550A1 (en) | Methodology for extracting data for unstructured data using ontology-based text mining | |
| CN110704880B (en) | Correlation method of engineering drawings | |
| CN109240258A (en) | Vehicle failure intelligent auxiliary diagnosis method and system based on term vector | |
| CN113033198B (en) | Similar text pushing method and device, electronic equipment and computer storage medium | |
| CN110688457A (en) | Steam-massage industry text information input method based on identification analysis | |
| CN111950922A (en) | Equipment economic data evaluation method based on multi-source data interaction analysis | |
| US10877989B2 (en) | Data conversion system and method of converting data | |
| CN114969484A (en) | Service data searching method, device, equipment and storage medium | |
| CN112801222A (en) | Multi-classification method and device based on two-classification model, electronic equipment and medium | |
| CN112215002A (en) | Electric power system text data classification method based on improved naive Bayes | |
| CN109165295B (en) | Intelligent resume evaluation method | |
| JP2009223809A (en) | Sort condition preparation program, sort condition preparation device, and sort condition preparation method | |
| CN111104422A (en) | Training method, device, equipment and storage medium of data recommendation model | |
| CN113254583B (en) | Document marking method, device and medium based on semantic vector | |
| CN115794798A (en) | Market supervision informationized standard management and dynamic maintenance system and method | |
| CN113642291B (en) | Method, system, storage medium and terminal for constructing logical structure tree reported by listed companies | |
| JP6556658B2 (en) | Table motif extraction device, classifier learning device, table type classification device, method, and program | |
| CN111522902A (en) | Data entry method and device, electronic equipment and computer readable storage medium | |
| CN111047261A (en) | Warehouse logistics order identification method and system | |
| CN117272198B (en) | Abnormal user generated content identification method based on business travel business data | |
| CN113064597B (en) | Redundant code identification method, device and equipment | |
| CN117150439B (en) | Automobile manufacturing parameter detection method and system based on multi-source heterogeneous data fusion |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200114 |
|
| RJ01 | Rejection of invention patent application after publication |