CN111259821A - Cloud computing model method based on deep learning - Google Patents
Cloud computing model method based on deep learning Download PDFInfo
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- CN111259821A CN111259821A CN202010058318.0A CN202010058318A CN111259821A CN 111259821 A CN111259821 A CN 111259821A CN 202010058318 A CN202010058318 A CN 202010058318A CN 111259821 A CN111259821 A CN 111259821A
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Abstract
The invention discloses a cloud computing model method based on deep learning, which belongs to the technical field of artificial intelligence and cloud computing and comprises a data acquisition layer, a data processing layer, a cloud computing layer and a service layer, wherein the data processing layer is used for processing data acquired by the data acquisition layer, and the data processing layer trains a convolutional neural network by using the data acquired by the data acquisition layer to obtain optimal convolutional neural network parameters. The cloud computing layer is used for assisting the data processing layer, the cloud computing layer comprises a server, a convolutional neural network is deployed on the server, when the calculated amount of the data processing layer in unit time exceeds a set threshold value, the data processing layer transmits partial data to the cloud computing layer through the network, the convolutional neural network is trained by using data collected by the data collecting layer, the optimal convolutional neural network parameter is obtained and is transmitted back to the data processing layer, and the problem that in the prior art, the accuracy rate of identifying the type of the vehicle at the highway intersection is low is solved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and cloud computing, in particular to a cloud computing model method based on deep learning.
Background
With the increasing popularization of the application of the monitoring video, the trend of high-definition, high-volume, networking and intellectualization of the monitoring video appears, and the requirements for intellectualization and real-time processing of the monitoring video are increasingly prominent. The appearance of cloud computing technology makes processing and mining of massive video data possible. Deep learning has become an emerging area of the machine learning domain since 2006. In the past few years, the development of deep learning techniques has had a wide impact on the field of signal and information processing and will continue to impact other key areas of machine learning and artificial intelligence.
In the prior art, the types of vehicles are identified at the highway intersection, because the types of the vehicles are multiple and complex and the vehicles are in a moving state in the driving process, the accuracy rate of identifying the types of the vehicles is low, and the vehicles can not be identified accurately generally.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a cloud computing model method based on deep learning, and solves the problem that the accuracy rate of identifying the type of a vehicle at an expressway intersection is low in the prior art.
The purpose of the invention can be realized by the following technical scheme:
a cloud computing model method based on deep learning comprises a data acquisition layer, a data processing layer, a cloud computing layer and a service layer,
the data acquisition layer is used for acquiring data and comprises a camera which is used for acquiring video image data;
the data processing layer is used for processing the data collected by the data collecting layer, and the data processing layer trains the convolutional neural network by using the data collected by the data collecting layer to obtain the optimal convolutional neural network parameters.
The cloud computing layer is used for assisting the data processing layer, the cloud computing layer comprises a server, the server is provided with a convolutional neural network, when the calculated amount of the data processing layer in unit time exceeds a set threshold value, the data processing layer transmits partial data to the cloud computing layer through the network, the convolutional neural network is trained by using the data acquired by the data acquisition layer, and the optimal convolutional neural network parameters are obtained and transmitted back to the data processing layer.
The service layer is used for displaying and configuring the data acquisition layer, the data processing layer and the cloud computing layer, and the service layer visualizes results and provides the results for users.
As a preferable aspect of the present invention, the data acquisition layer further includes a radar for acquiring distance data.
As a preferable aspect of the present invention, the data acquisition layer further includes a recording device, and the recording device is configured to acquire audio data.
As a preferred embodiment of the present invention, the service layer accesses the internet, and the service layer is accessed through the mobile terminal device.
As a preferred scheme of the present invention, the mobile terminal device includes an android system device, an apple system, and a microsoft windows system device.
As a preferred aspect of the present invention, the deep learning based cloud computing model method is used for identifying the model number of a vehicle at a highway intersection.
The invention has the beneficial effects that:
the scheme is applied to the model of the vehicle at the highway intersection, the problem that the accuracy rate of identifying the model of the vehicle at the highway intersection is low in the prior art is solved, and the accuracy rate of identifying the model of the vehicle at the highway intersection can be improved by adopting the scheme. The data acquisition layer also comprises a radar which is used for acquiring distance data and calculating the distance between the vehicle and the highway intersection, and the running speed of the vehicle can be further calculated through the time difference. The data acquisition layer also comprises a recording device, and the recording device is used for acquiring audio data and noise generated in the driving process of the vehicle.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a block diagram of the present invention.
Detailed Description
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.
As shown in the figure, the cloud computing model method based on deep learning comprises a data acquisition layer, a data processing layer, a cloud computing layer and a service layer,
the data acquisition layer is used for acquiring data and comprises a camera which is used for acquiring video image data;
the data processing layer is used for processing the data collected by the data collecting layer, and the data processing layer trains the convolutional neural network by using the data collected by the data collecting layer to obtain the optimal convolutional neural network parameters.
The cloud computing layer is used for assisting the data processing layer, the cloud computing layer comprises a server, a convolutional neural network is deployed on the server, when the calculated amount of the data processing layer in unit time exceeds a set threshold value, the data processing layer transmits partial data to the cloud computing layer through the network, the convolutional neural network is trained by using data collected by the data collecting layer, the optimal convolutional neural network parameter is obtained, and the optimal convolutional neural network parameter is transmitted back to the data processing layer.
The service layer is used for displaying and configuring the data acquisition layer, the data processing layer and the cloud computing layer, and the service layer visualizes results and provides the results for users. The scheme is applied to the model of the vehicle at the highway intersection, the problem that the accuracy rate of identifying the model of the vehicle at the highway intersection is low in the prior art is solved, and the accuracy rate of identifying the model of the vehicle at the highway intersection can be improved by adopting the scheme.
The data acquisition layer also comprises a radar which is used for acquiring distance data and calculating the distance between the vehicle and the highway intersection, and the running speed of the vehicle can be further calculated through the time difference. The data acquisition layer also comprises a recording device, and the recording device is used for acquiring audio data and noise generated in the driving process of the vehicle.
The service layer accesses the Internet and is accessed through the mobile terminal equipment. The mobile terminal equipment comprises android system equipment, an apple system and Microsoft Windows system equipment. The scheme is convenient for users to access and manage.
The cloud computing model method based on deep learning is used for identifying the type of a vehicle at an expressway intersection.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (6)
1. A cloud computing model method based on deep learning is characterized by comprising a data acquisition layer, a data processing layer, a cloud computing layer and a service layer,
the data acquisition layer is used for acquiring data and comprises a camera which is used for acquiring video image data;
the data processing layer is used for processing the data collected by the data collecting layer, and the data processing layer trains the convolutional neural network by using the data collected by the data collecting layer to obtain the optimal convolutional neural network parameters.
The cloud computing layer is used for assisting the data processing layer, the cloud computing layer comprises a server, the server is provided with a convolutional neural network, when the calculated amount of the data processing layer in unit time exceeds a set threshold value, the data processing layer transmits partial data to the cloud computing layer through the network, the convolutional neural network is trained by using the data acquired by the data acquisition layer, and the optimal convolutional neural network parameters are obtained and transmitted back to the data processing layer.
The service layer is used for displaying and configuring the data acquisition layer, the data processing layer and the cloud computing layer, and the service layer visualizes results and provides the results for users.
2. The deep learning based cloud computing model method of claim 1, wherein the data collection layer further comprises radar for collecting distance data.
3. The deep learning based cloud computing model method of claim 1, wherein the data collection layer further comprises a recording device for collecting audio data.
4. The deep learning-based cloud computing model method according to claim 1, wherein the service layer is accessed to the internet and is accessed through a mobile terminal device.
5. The cloud computing model method based on deep learning of claim 4, wherein the mobile terminal device includes an android system device, an apple system, and a Microsoft Windows system device.
6. The deep learning based cloud computing model method of claim 1, wherein the deep learning based cloud computing model method is used for identifying the model number of a vehicle at a highway intersection.
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| CN104992147A (en) * | 2015-06-09 | 2015-10-21 | 中国石油大学(华东) | License plate identification method of deep learning based on fast and slow combination cloud calculation environment |
| CN105678214A (en) * | 2015-12-21 | 2016-06-15 | 中国石油大学(华东) | Vehicle flow statistical method based on convolutional neural network vehicle model recognition in cloud environment |
| CN106203330A (en) * | 2016-07-08 | 2016-12-07 | 西安理工大学 | A kind of vehicle classification method based on convolutional neural networks |
| US20180293664A1 (en) * | 2017-04-11 | 2018-10-11 | Alibaba Group Holding Limited | Image-based vehicle damage determining method and apparatus, and electronic device |
| CN109977908A (en) * | 2019-04-04 | 2019-07-05 | 重庆交通大学 | A kind of vehicle driving lane detection method based on deep learning |
| CN110390822A (en) * | 2019-05-31 | 2019-10-29 | 东南大学 | Bridge statistical method of traffic flow based on FBG sensor and convolutional neural networks |
-
2020
- 2020-01-19 CN CN202010058318.0A patent/CN111259821A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104992147A (en) * | 2015-06-09 | 2015-10-21 | 中国石油大学(华东) | License plate identification method of deep learning based on fast and slow combination cloud calculation environment |
| CN105678214A (en) * | 2015-12-21 | 2016-06-15 | 中国石油大学(华东) | Vehicle flow statistical method based on convolutional neural network vehicle model recognition in cloud environment |
| CN106203330A (en) * | 2016-07-08 | 2016-12-07 | 西安理工大学 | A kind of vehicle classification method based on convolutional neural networks |
| US20180293664A1 (en) * | 2017-04-11 | 2018-10-11 | Alibaba Group Holding Limited | Image-based vehicle damage determining method and apparatus, and electronic device |
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