CN109460828A - A kind of artificial intelligence deep learning method based on network cloud collaboration - Google Patents
A kind of artificial intelligence deep learning method based on network cloud collaboration Download PDFInfo
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- CN109460828A CN109460828A CN201811260633.0A CN201811260633A CN109460828A CN 109460828 A CN109460828 A CN 109460828A CN 201811260633 A CN201811260633 A CN 201811260633A CN 109460828 A CN109460828 A CN 109460828A
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- layer data
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Abstract
The invention discloses a kind of artificial intelligence deep learning methods based on network cloud collaboration.First locally obtaining input layer data, and input layer data is output to Cloud Server, then it will be inputted in artificial intelligence analysis's model that layer data is input to Cloud Server in cloud server end, obtain output layer data, the technical effect for improving the study precision of artificial intelligence can only be realized the technical issues of user locally carries out artificial intelligence study in the prior art by efficiently solving.
Description
Technical field
The present invention relates to field of artificial intelligence more particularly to a kind of artificial intelligence depth based on network cloud collaboration
Learning method.
Background technique
With the continuous development of science and technology, artificial intelligence is also developed rapidly, and increasingly by people
Attention.
Currently, for the study of artificial intelligence being carried out by the learning network of user local.Local into
The study of pedestrian's work intelligence learning network has limitation, to will affect the learning effect of artificial intelligence.
Summary of the invention
The present invention solves the prior art by providing a kind of artificial intelligence deep learning method based on network cloud collaboration
In can only the technical issues of user locally carries out artificial intelligence study, realize improve artificial intelligence study precision technology
Effect.
The present invention provides a kind of artificial intelligence deep learning methods based on network cloud collaboration, include at least:
It is local to obtain input layer data；
The input layer data is output to Cloud Server；
The input layer data is input in artificial intelligence analysis's model of the Cloud Server, the output number of plies is obtained
According to.
Further, it is described the input layer data is output to Cloud Server before, further includes:
The input layer data is compressed.
Further, it is described the input layer data is output to Cloud Server before, further includes:
Compressed data are encrypted.
Further, described that the input layer data is output to Cloud Server, it specifically includes:
The input layer data is output to load-balancing device, corresponding cloud is assigned to by the load-balancing device and is taken
It is engaged in device.
Further, in artificial intelligence analysis's model that the input layer data is input to the Cloud Server
Before, further includes:
The data received are decrypted and are decompressed.
Further, it is described obtain output layer data after, further includes:
By the output layer data back to locally.
One or more technical solution provided in the present invention, has at least the following technical effects or advantages:
Input layer data is locally first being obtained, and input layer data is output to Cloud Server, then in cloud server end
Input layer data is input in artificial intelligence analysis's model of Cloud Server, output layer data is obtained, efficiently solves existing
In technology the study precision for improving artificial intelligence can only be realized the technical issues of user locally carries out artificial intelligence study
Technical effect.
Detailed description of the invention
Fig. 1 is the flow chart of the artificial intelligence deep learning method provided in an embodiment of the present invention based on network cloud collaboration.
Specific embodiment
The embodiment of the present invention solves existing by providing a kind of artificial intelligence deep learning method based on network cloud collaboration
The study precision for improving artificial intelligence can only be realized the technical issues of user locally carries out artificial intelligence study by having in technology
Technical effect.
Technical solution in the embodiment of the present invention is in order to solve the above technical problems, general thought is as follows:
Input layer data is locally first being obtained, and input layer data is output to Cloud Server, then in cloud server end
Input layer data is input in artificial intelligence analysis's model of Cloud Server, output layer data is obtained, efficiently solves existing
In technology the study precision for improving artificial intelligence can only be realized the technical issues of user locally carries out artificial intelligence study
Technical effect.
Above-mentioned technical proposal in order to better understand, in conjunction with appended figures and specific embodiments to upper
Technical solution is stated to be described in detail.
Referring to Fig. 1, the artificial intelligence deep learning method provided in an embodiment of the present invention based on network cloud collaboration is at least wrapped
It includes:
Step S110: local to obtain input layer data；
Step S120: input layer data is output to Cloud Server；
Step S130: input layer data is input in artificial intelligence analysis's model of Cloud Server, obtains the output number of plies
According to carry out artificial intelligence analysis beyond the clouds.
Specifically, before step S120, further includes:
Input layer data is compressed, to improve the efficiency of transmission of data.
Compressed data are encrypted, to improve the safety of data.
In this case, before step S130, further includes:
The data received are decrypted and are decompressed.
Step S130 is specifically included:
Input layer data is output to load-balancing device, is assigned in corresponding Cloud Server by load-balancing device,
To which data to be assigned to the Cloud Server of relative free, to improve the utilization rate of communication network.
Step S140: by output layer data back to locally.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (6)
1. a kind of artificial intelligence deep learning method based on network cloud collaboration, which is characterized in that include at least:
It is local to obtain input layer data；
The input layer data is output to Cloud Server；
The input layer data is input in artificial intelligence analysis's model of the Cloud Server, output layer data is obtained.
2. the method as described in claim 1, which is characterized in that it is described by the input layer data be output to Cloud Server it
Before, further includes:
The input layer data is compressed.
3. method according to claim 2, which is characterized in that it is described by the input layer data be output to Cloud Server it
Before, further includes:
Compressed data are encrypted.
4. method as claimed in claim 3, which is characterized in that described that the input layer data is output to Cloud Server, tool
Body includes:
The input layer data is output to load-balancing device, corresponding Cloud Server is assigned to by the load-balancing device
In.
5. method as claimed in claim 4, which is characterized in that the input layer data is input to the cloud service described
Before in artificial intelligence analysis's model of device, further includes:
The data received are decrypted and are decompressed.
6. method according to any one of claims 1 to 5, which is characterized in that it is described obtain output layer data after, also
Include:
By the output layer data back to locally.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811260633.0A CN109460828A (en) | 2018-10-26 | 2018-10-26 | A kind of artificial intelligence deep learning method based on network cloud collaboration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201811260633.0A CN109460828A (en) | 2018-10-26 | 2018-10-26 | A kind of artificial intelligence deep learning method based on network cloud collaboration |
Publications (1)
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| CN109460828A true CN109460828A (en) | 2019-03-12 |
Family
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| CN201811260633.0A Pending CN109460828A (en) | 2018-10-26 | 2018-10-26 | A kind of artificial intelligence deep learning method based on network cloud collaboration |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103093034A (en) * | 2012-12-28 | 2013-05-08 | 浙江理工大学 | Product collaborative design method based on cloud computing |
| CN104569842A (en) * | 2014-12-30 | 2015-04-29 | 北京海博思创科技有限公司 | Vehicle monitoring management system and vehicle-mounted intelligent acquisition terminal |
| CN107222493A (en) * | 2017-06-26 | 2017-09-29 | 浪潮软件股份有限公司 | A kind of ORACLE JDBC data transmission channels ciphered compressed system and method |
| CN107276816A (en) * | 2016-11-03 | 2017-10-20 | 厦门嵘拓物联科技有限公司 | A kind of long-range monitoring and fault diagnosis system and method for diagnosing faults based on cloud service |
| US20180307968A1 (en) * | 2017-04-21 | 2018-10-25 | International Business Machines Corporation | Parameter criticality-aware resilience |
-
2018
- 2018-10-26 CN CN201811260633.0A patent/CN109460828A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103093034A (en) * | 2012-12-28 | 2013-05-08 | 浙江理工大学 | Product collaborative design method based on cloud computing |
| CN104569842A (en) * | 2014-12-30 | 2015-04-29 | 北京海博思创科技有限公司 | Vehicle monitoring management system and vehicle-mounted intelligent acquisition terminal |
| CN107276816A (en) * | 2016-11-03 | 2017-10-20 | 厦门嵘拓物联科技有限公司 | A kind of long-range monitoring and fault diagnosis system and method for diagnosing faults based on cloud service |
| US20180307968A1 (en) * | 2017-04-21 | 2018-10-25 | International Business Machines Corporation | Parameter criticality-aware resilience |
| CN107222493A (en) * | 2017-06-26 | 2017-09-29 | 浪潮软件股份有限公司 | A kind of ORACLE JDBC data transmission channels ciphered compressed system and method |
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Application publication date: 20190312 |