CN113297473A - Data pushing method and device based on cloud computing and cloud server - Google Patents
Data pushing method and device based on cloud computing and cloud server Download PDFInfo
- Publication number
- CN113297473A CN113297473A CN202011425805.2A CN202011425805A CN113297473A CN 113297473 A CN113297473 A CN 113297473A CN 202011425805 A CN202011425805 A CN 202011425805A CN 113297473 A CN113297473 A CN 113297473A
- Authority
- CN
- China
- Prior art keywords
- data
- pushed
- pushing
- keyword
- push
- 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.)
- Withdrawn
Links
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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- 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
-
- 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
- G06N3/08—Learning methods
Abstract
The invention discloses a data pushing method and device based on cloud computing and a cloud server. According to the method, first, after first data to be pushed and second data to be pushed are obtained, pushing key data corresponding to the data to be pushed are obtained, then each key data weight in the pushing key data is determined to obtain a first key data weight set, further, a data pushing relation between any two key data weights in the first key data weight set is determined to obtain a data pushing relation sequence, further, a data pushing relation smaller than a first preset data parameter in the data pushing relation sequence is adjusted to be a first preset data parameter, and after the pushing relation sequence is obtained, pushing sequence correction is carried out on the pushing relation sequence. Therefore, the push relation sequence can be corrected one by one, and the accuracy of the correction result can be ensured.
Description
Technical Field
The invention relates to the technical field of data pushing of cloud computing, in particular to a data pushing method and device based on cloud computing and a cloud server.
Background
With the continuous development of computer technology and information technology, most enterprises currently push relevant data to users through a network in order to enlarge brand influence, however, in order to ensure the security of the pushed data in the pushing process, the pushed data needs to be corrected safely, and the existing correction is often incomplete, which results in inaccurate correction results.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides a data pushing method and device based on cloud computing and a cloud server.
The invention provides a data pushing method based on cloud computing, which comprises the following steps:
acquiring first push key data of first data to be pushed and second push key data of second data to be pushed after acquiring the first data to be pushed and the second data to be pushed based on a push request; the first data to be pushed comprises a first keyword, and the second data to be pushed comprises a second keyword;
acquiring each key data weight in the first pushed key data and each key data weight in the second pushed key data to obtain a first key data weight set;
determining a data pushing relationship between any two key data weights in the first key data weight set to obtain a data pushing relationship sequence; adjusting a data pushing relation smaller than a first preset data parameter in the data pushing relation sequence to be a first preset data parameter to obtain a pushing relation sequence;
and carrying out pushing sequence correction on the pushing relation sequence to obtain a first correction result, wherein the first correction result is used for indicating that the first keyword and the second keyword are the same keyword or different keywords.
In an alternative embodiment, the determining a data pushing relationship between any two key data weights in the first key data weight set to obtain a data pushing relationship sequence includes: determining each critical data weight in the first set of critical data weights as a current critical data weight, performing the following steps until the first set of critical data weights is traversed: and calculating a data pushing relationship between the current key data weight and each key data weight in the first key data weight set, and determining a plurality of calculated data pushing relationships as a list in the data pushing relationship sequence.
In an alternative embodiment, determining a data push relationship between two of the key data weights comprises: calculating the inner product of the two key data weights to obtain a calculation result; determining the calculation result as the data push relationship between the two key data weights.
In an alternative embodiment, the adjusting a data pushing relationship smaller than a first preset data parameter in the data pushing relationship sequence to a first preset data parameter to obtain a pushing relationship sequence includes: determining each weight in the data push relationship sequence as a current weight, and executing the following steps until the data push relationship sequence is traversed: acquiring the current weight; under the condition that the current weight is smaller than the first preset data parameter, adjusting the current weight to be the first preset data parameter; and after traversing is finished, determining the adjusted data push relation sequence as the push relation sequence.
In an alternative embodiment, the performing push sequence correction on the push relationship sequence to obtain a first correction result includes: converting the push relation sequence into a data push relation coefficient unit; inputting the push relationship sequence, the data push relationship coefficient unit, the first push key data and the second push key data into a predetermined formula to obtain a fusion weight parameter of the first data to be pushed and the second data to be pushed; and verifying the fusion weight parameters by using a target neural network model to obtain the first correction result.
In an alternative embodiment, after obtaining the first correction result, the method further comprises: determining that the first keyword and the second keyword are the same keyword under the condition that the first correction result is greater than or equal to a second preset data parameter; and under the condition that the first correction result is smaller than the second preset data parameter, determining that the first keyword and the second keyword are different keywords.
In an alternative embodiment, before obtaining the first push critical data of the first data to be pushed and the second push critical data of the second data to be pushed, the method further comprises: acquiring a group of data groups to be pushed of samples; inputting the group of data groups to be pushed into an original neural network model, and training the original neural network model until a target neural network model is obtained, wherein the target neural network model is used for verifying whether the first keyword and the second keyword are the same keyword.
In an alternative embodiment, the inputting the set of sample data to be pushed into an original neural network model, and the training the original neural network model until obtaining a target neural network model includes: determining first sample data to be pushed and second sample data to be pushed from the sample data group to be pushed; determining a fusion weight parameter of the data to be pushed of the first sample and the data to be pushed of the second sample; determining linear parameters of the original neural network model through the fusion weight parameters; and under the condition that the linear parameter is larger than a third preset data parameter, adjusting the original neural network model until the linear parameter is smaller than or equal to the third preset data parameter.
In an alternative embodiment, after determining that the first keyword and the second keyword are the same keyword, the method further comprises: and splicing the first character string of the first keyword and the second character string of the second keyword to obtain a target character string of the first keyword.
The invention also provides a data pushing device based on cloud computing, which comprises:
the key data acquisition module is used for acquiring first push key data of the first data to be pushed and second push key data of the second data to be pushed after the first data to be pushed and the second data to be pushed are acquired; the first data to be pushed comprises a first keyword, and the second data to be pushed comprises a second keyword;
a key data weight determining module, configured to obtain each key data weight in the first pushed key data and each key data weight in the second pushed key data, to obtain a first key data weight set;
a pushing relationship sequence obtaining module, configured to determine a data pushing relationship between any two key data weights in the first key data weight set, so as to obtain a data pushing relationship sequence; adjusting a data pushing relation smaller than a first preset data parameter in the data pushing relation sequence to be a first preset data parameter to obtain a pushing relation sequence;
and the pushing sequence correction module is used for carrying out pushing sequence correction on the pushing relation sequence to obtain a first correction result, wherein the first correction result is used for indicating that the first keyword and the second keyword are the same keyword or different keywords.
The invention also provides a cloud server, which comprises a processor and a memory which are communicated with each other, wherein the processor is used for calling the computer program from the memory and realizing the method of any one of the above items by running the computer program.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when run, implements the method of any of the above.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects.
The invention provides a data pushing method and device based on cloud computing and a cloud server, firstly after first data to be pushed and second data to be pushed are obtained, further acquiring first push key data of the first data to be pushed and second push key data of the second data to be pushed, secondly, determining each key data weight in the first push key data and each key data weight in the second push key data to obtain a first key data weight set, on the basis, determining the data push relationship between any two key data weights in the first key data weight set to obtain a data push relationship sequence, and then adjusting the data pushing relation smaller than the first preset data parameter in the data pushing relation sequence to the first preset data parameter, and after the pushing relation sequence is obtained, performing pushing sequence correction on the pushing relation sequence. Therefore, the push relation sequence can be corrected one by one, and the accuracy of the correction result can be ensured.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart of a data pushing method based on cloud computing according to an embodiment of the present invention.
Fig. 2 is a block diagram of a data pushing apparatus based on cloud computing according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of a cloud server according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, a flow chart of a data pushing method based on cloud computing is provided, and the following contents described in steps S11 to S14 are specifically executed when the method is implemented.
Step S11, after acquiring first data to be pushed and second data to be pushed based on a push request, acquiring first push key data of the first data to be pushed and second push key data of the second data to be pushed; the first data to be pushed comprises a first keyword, and the second data to be pushed comprises a second keyword.
Step S12, obtaining each key data weight in the first pushed key data and each key data weight in the second pushed key data, to obtain a first key data weight set.
Step S13, determining a data pushing relationship between any two key data weights in the first key data weight set, to obtain a data pushing relationship sequence; and adjusting the data pushing relation smaller than a first preset data parameter in the data pushing relation sequence to the first preset data parameter to obtain a pushing relation sequence.
Step S14, performing a push sequence correction on the push relationship sequence to obtain a first correction result, where the first correction result is used to indicate that the first keyword and the second keyword are the same keyword or different keywords.
The following advantageous effects can be achieved when the method described in the above steps S11-S14 is performed: firstly, after first data to be pushed and second data to be pushed are obtained, first pushing key data of the first data to be pushed and second pushing key data of the second data to be pushed are further obtained, secondly, each key data weight in the first pushing key data and each key data weight in the second pushing key data are determined, a first key data weight set is obtained, on the basis, a data pushing relation between any two key data weights in the first key data weight set is determined, a data pushing relation sequence is obtained, then, the data pushing relation smaller than a first preset data parameter in the data pushing relation sequence is adjusted to be a first preset data parameter, and after the pushing relation sequence is obtained, pushing sequence correction is carried out on the pushing relation sequence. Therefore, the push relation sequence can be corrected one by one, and the accuracy of the correction result can be ensured.
In an alternative embodiment, the determining a data pushing relationship between any two key data weights in the first key data weight set to obtain a data pushing relationship sequence includes: determining each critical data weight in the first set of critical data weights as a current critical data weight, performing the following steps until the first set of critical data weights is traversed: and calculating a data pushing relationship between the current key data weight and each key data weight in the first key data weight set, and determining a plurality of calculated data pushing relationships as a list in the data pushing relationship sequence.
In an alternative embodiment, determining a data push relationship between two of the key data weights comprises: calculating the inner product of the two key data weights to obtain a calculation result; determining the calculation result as the data push relationship between the two key data weights.
In an alternative embodiment, the adjusting a data pushing relationship smaller than a first preset data parameter in the data pushing relationship sequence to a first preset data parameter to obtain a pushing relationship sequence includes: determining each weight in the data push relationship sequence as a current weight, and executing the following steps until the data push relationship sequence is traversed: acquiring the current weight; under the condition that the current weight is smaller than the first preset data parameter, adjusting the current weight to be the first preset data parameter; and after traversing is finished, determining the adjusted data push relation sequence as the push relation sequence.
In an alternative embodiment, the performing push sequence correction on the push relationship sequence to obtain a first correction result includes: converting the push relation sequence into a data push relation coefficient unit; inputting the push relationship sequence, the data push relationship coefficient unit, the first push key data and the second push key data into a predetermined formula to obtain a fusion weight parameter of the first data to be pushed and the second data to be pushed; and verifying the fusion weight parameters by using a target neural network model to obtain the first correction result.
In an alternative embodiment, after obtaining the first correction result, the method further comprises: determining that the first keyword and the second keyword are the same keyword under the condition that the first correction result is greater than or equal to a second preset data parameter; and under the condition that the first correction result is smaller than the second preset data parameter, determining that the first keyword and the second keyword are different keywords.
In an alternative embodiment, before obtaining the first push critical data of the first data to be pushed and the second push critical data of the second data to be pushed, the method further comprises: acquiring a group of data groups to be pushed of samples; inputting the group of data groups to be pushed into an original neural network model, and training the original neural network model until a target neural network model is obtained, wherein the target neural network model is used for verifying whether the first keyword and the second keyword are the same keyword.
In an alternative embodiment, the inputting the set of sample data to be pushed into an original neural network model, and the training the original neural network model until obtaining a target neural network model includes: determining first sample data to be pushed and second sample data to be pushed from the sample data group to be pushed; determining a fusion weight parameter of the data to be pushed of the first sample and the data to be pushed of the second sample; determining linear parameters of the original neural network model through the fusion weight parameters; and under the condition that the linear parameter is larger than a third preset data parameter, adjusting the original neural network model until the linear parameter is smaller than or equal to the third preset data parameter.
In an alternative embodiment, after determining that the first keyword and the second keyword are the same keyword, the method further comprises: and splicing the first character string of the first keyword and the second character string of the second keyword to obtain a target character string of the first keyword.
Based on the same inventive concept, please refer to fig. 2, the present invention further provides a block diagram of a data pushing apparatus 20 based on cloud computing, and the apparatus may specifically include the following functional modules:
the key data obtaining module 21 is configured to obtain first push key data of the first data to be pushed and second push key data of the second data to be pushed after obtaining the first data to be pushed and the second data to be pushed based on a push request; the first data to be pushed comprises a first keyword, and the second data to be pushed comprises a second keyword;
a key data weight determining module 22, configured to obtain each key data weight in the first pushed key data and each key data weight in the second pushed key data, so as to obtain a first key data weight set;
a pushing relationship sequence obtaining module 23, configured to determine a data pushing relationship between any two key data weights in the first key data weight set, so as to obtain a data pushing relationship sequence; adjusting a data pushing relation smaller than a first preset data parameter in the data pushing relation sequence to be a first preset data parameter to obtain a pushing relation sequence;
and a push sequence correction module 24, configured to perform push sequence correction on the push relationship sequence to obtain a first correction result, where the first correction result is used to indicate that the first keyword and the second keyword are the same keyword or different keywords.
On the basis, please refer to fig. 3 in combination, which provides a cloud server 110, including a processor 111, and a memory 112 and a bus 113 connected to the processor 111; wherein, the processor 111 and the memory 112 complete the communication with each other through the bus 113; the processor 111 is used to call program instructions in the memory 112 to perform the above-described method.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It should be understood that, for technical terms that are not noun explanations for the above-mentioned contents, a person skilled in the art can deduce and unambiguously determine the meaning of the present invention from the above-mentioned contents, for example, for some values, coefficients, weights and other terms, a person skilled in the art can deduce and determine from the logical relationship between the above and the below, the value range of these values can be selected according to the actual situation, for example, 0 to 1, for example, 1 to 10, for example, 50 to 100, but not limited thereto, and a person skilled in the art can unambiguously determine some preset, reference, predetermined, set and target technical features/technical terms according to the above-mentioned contents of the invention. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. The foregoing will therefore be clear and complete to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the contents of the above-mentioned invention is based on the contents described in the present application, and thus the above-mentioned contents are not an appreciation of the creativity of the overall solution.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A data pushing method based on cloud computing is characterized by comprising the following steps:
acquiring first push key data of first data to be pushed and second push key data of second data to be pushed after acquiring the first data to be pushed and the second data to be pushed based on a push request; the first data to be pushed comprises a first keyword, and the second data to be pushed comprises a second keyword;
acquiring each key data weight in the first pushed key data and each key data weight in the second pushed key data to obtain a first key data weight set;
determining a data pushing relationship between any two key data weights in the first key data weight set to obtain a data pushing relationship sequence; adjusting a data pushing relation smaller than a first preset data parameter in the data pushing relation sequence to be a first preset data parameter to obtain a pushing relation sequence;
and carrying out pushing sequence correction on the pushing relation sequence to obtain a first correction result, wherein the first correction result is used for indicating that the first keyword and the second keyword are the same keyword or different keywords.
2. The method of claim 1, wherein the determining a data pushing relationship between any two key data weights in the first key data weight set to obtain a data pushing relationship sequence comprises: determining each critical data weight in the first set of critical data weights as a current critical data weight, performing the following steps until the first set of critical data weights is traversed: calculating a data pushing relationship between the current key data weight and each key data weight in the first key data weight set, and determining a plurality of calculated data pushing relationships as a list in the data pushing relationship sequence;
determining a data push relationship between two of the key data weights, comprising: calculating the inner product of the two key data weights to obtain a calculation result; determining the calculation result as the data push relationship between the two key data weights.
3. The method according to claim 1, wherein the adjusting a data pushing relationship smaller than a first preset data parameter in the data pushing relationship sequence to the first preset data parameter to obtain a pushing relationship sequence comprises: determining each weight in the data push relationship sequence as a current weight, and executing the following steps until the data push relationship sequence is traversed: acquiring the current weight; under the condition that the current weight is smaller than the first preset data parameter, adjusting the current weight to be the first preset data parameter; and after traversing is finished, determining the adjusted data push relation sequence as the push relation sequence.
4. The method according to claim 1, wherein the performing push sequence correction on the push relationship sequence to obtain a first correction result comprises: converting the push relation sequence into a data push relation coefficient unit; inputting the push relationship sequence, the data push relationship coefficient unit, the first push key data and the second push key data into a predetermined formula to obtain a fusion weight parameter of the first data to be pushed and the second data to be pushed; and verifying the fusion weight parameters by using a target neural network model to obtain the first correction result.
5. The method of claim 1, wherein after obtaining the first correction result, the method further comprises: determining that the first keyword and the second keyword are the same keyword under the condition that the first correction result is greater than or equal to a second preset data parameter; and under the condition that the first correction result is smaller than the second preset data parameter, determining that the first keyword and the second keyword are different keywords.
6. The method of claim 1, wherein prior to obtaining the first push critical data of the first data to be pushed and the second push critical data of the second data to be pushed, the method further comprises: acquiring a group of data groups to be pushed of samples; inputting the group of data groups to be pushed into an original neural network model, and training the original neural network model until a target neural network model is obtained, wherein the target neural network model is used for verifying whether the first keyword and the second keyword are the same keyword;
inputting the set of data groups to be pushed of the samples into an original neural network model, and training the original neural network model until a target neural network model is obtained comprises: determining first sample data to be pushed and second sample data to be pushed from the sample data group to be pushed; determining a fusion weight parameter of the data to be pushed of the first sample and the data to be pushed of the second sample; determining linear parameters of the original neural network model through the fusion weight parameters; and under the condition that the linear parameter is larger than a third preset data parameter, adjusting the original neural network model until the linear parameter is smaller than or equal to the third preset data parameter.
7. The method of any of claims 1-6, wherein after determining that the first keyword and the second keyword are the same keyword, the method further comprises: and splicing the first character string of the first keyword and the second character string of the second keyword to obtain a target character string of the first keyword.
8. A data pushing device based on cloud computing is characterized by comprising:
the key data acquisition module is used for acquiring first push key data of the first data to be pushed and second push key data of the second data to be pushed after acquiring the first data to be pushed and the second data to be pushed based on a push request; the first data to be pushed comprises a first keyword, and the second data to be pushed comprises a second keyword;
a key data weight determining module, configured to obtain each key data weight in the first pushed key data and each key data weight in the second pushed key data, to obtain a first key data weight set;
a pushing relationship sequence obtaining module, configured to determine a data pushing relationship between any two key data weights in the first key data weight set, so as to obtain a data pushing relationship sequence; adjusting a data pushing relation smaller than a first preset data parameter in the data pushing relation sequence to be a first preset data parameter to obtain a pushing relation sequence;
and the pushing sequence correction module is used for carrying out pushing sequence correction on the pushing relation sequence to obtain a first correction result, wherein the first correction result is used for indicating that the first keyword and the second keyword are the same keyword or different keywords.
9. A cloud server comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to implement the method of any one of claims 1 to 7 by running the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011425805.2A CN113297473A (en) | 2020-12-09 | 2020-12-09 | Data pushing method and device based on cloud computing and cloud server |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011425805.2A CN113297473A (en) | 2020-12-09 | 2020-12-09 | Data pushing method and device based on cloud computing and cloud server |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113297473A true CN113297473A (en) | 2021-08-24 |
Family
ID=77318455
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011425805.2A Withdrawn CN113297473A (en) | 2020-12-09 | 2020-12-09 | Data pushing method and device based on cloud computing and cloud server |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113297473A (en) |
-
2020
- 2020-12-09 CN CN202011425805.2A patent/CN113297473A/en not_active Withdrawn
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5864586B2 (en) | Method and apparatus for ranking search results | |
| CN111913954B (en) | Intelligent data standard catalog generation method and device | |
| CN113536081A (en) | Data center data management method and system based on artificial intelligence | |
| CN110210038A (en) | Kernel entity determines method and its system, server and computer-readable medium | |
| CN109992659B (en) | Method and device for text sorting | |
| CN111950267B (en) | Text triplet extraction method and device, electronic equipment and storage medium | |
| CN111723182B (en) | Key information extraction method and device for vulnerability text | |
| CN117271645A (en) | Test data processing method and device and computer readable storage medium | |
| WO2014021824A1 (en) | Search method | |
| CN110580265B (en) | ETL task processing method, device, equipment and storage medium | |
| CN108829699A (en) | A kind of polymerization and device of focus incident | |
| CN109753561B (en) | Automatic reply generation method and device | |
| CN111930901A (en) | Knowledge point vector obtaining method, recommendation question determining method and related device | |
| CN113297473A (en) | Data pushing method and device based on cloud computing and cloud server | |
| KR20210090706A (en) | Sort | |
| CN110413750A (en) | The method and apparatus for recalling standard question sentence according to user's question sentence | |
| CN115170196A (en) | Popularization method based on big data intelligent writing | |
| CN113297162A (en) | Data storage method and device based on cloud computing | |
| CN110110280B (en) | Curve integral calculation method, device and equipment for coordinates and storage medium | |
| CN110134575B (en) | Method and device for calculating service capacity of server cluster | |
| CN110298690B (en) | Object class purpose period judging method, device, server and readable storage medium | |
| CN114238106A (en) | Test time prediction method and device, electronic device and storage medium | |
| CN113297626A (en) | Information processing method and device based on cloud computing and block chain | |
| CN111881266A (en) | Response method and device | |
| CN116127942B (en) | Text comparison method, device, equipment and storage medium |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210824 |
|
| WW01 | Invention patent application withdrawn after publication |