CN110636260A - Bright kitchen range management method based on big data - Google Patents
Bright kitchen range management method based on big data Download PDFInfo
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- CN110636260A CN110636260A CN201910860858.8A CN201910860858A CN110636260A CN 110636260 A CN110636260 A CN 110636260A CN 201910860858 A CN201910860858 A CN 201910860858A CN 110636260 A CN110636260 A CN 110636260A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/12—Hotels or restaurants
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/40—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using video transcoding, i.e. partial or full decoding of a coded input stream followed by re-encoding of the decoded output stream
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
- H04N19/423—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
Abstract
The invention relates to catering health and safety, in particular to a bright kitchen light management method based on big data, wherein a plurality of front-end video shooting devices with different angles are arranged in a kitchen and used for shooting the whole cooking process, a food material sampling box is arranged in the kitchen, a sampling video shooting device is arranged in the food material sampling box, a first video encoder is used for encoding videos shot by the front-end video shooting devices, a second video encoder is used for encoding videos shot by the sampling video shooting devices, the encoded videos are transmitted to a server through network transmission, a video decoder is used for decoding the encoded videos and transmitting the videos to a display device arranged in a hall for display; the technical scheme provided by the invention can effectively overcome the defect that the daily production operation of catering enterprises cannot be comprehensively and efficiently supervised in the prior art.
Description
Technical Field
The invention relates to catering sanitary safety, in particular to a bright kitchen range management method based on big data.
Background
With the continuous improvement of living standard of people, the quality and safety of food are more and more concerned, and the concept of people is changed from how to eat full to how to eat good and safe. At present, the food safety situation in China is still severe, the food safety problem is frequent, and how to quickly and effectively solve the food safety problem is a current major subject in China.
At present, many catering enterprises still have many non-normative phenomena on daily production operation, and the dishonest phenomenon in operation is very serious, so that great risk potential is brought to the health and safety of consumers. In the face of the frequent illegal behaviors and dishonest phenomena, the traditional supervision mode relying on the home inspection of law enforcement supervisors cannot achieve good effects, the number of catering enterprises is large, the number of the law enforcement supervisors is relatively insufficient, effective supervision is difficult to achieve by relying on the existing supervision mode mainly based on the home spot inspection, and the supervision efficiency needs to be improved urgently.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a bright kitchen range management method based on big data, which can effectively overcome the defect that the daily production operation of catering enterprises cannot be comprehensively and efficiently supervised in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a bright kitchen range management method based on big data comprises the following steps:
s1, arranging a plurality of front-end video shooting devices for shooting the whole cooking process at different angles in a kitchen, arranging a food material sampling box in the kitchen, and arranging a sampling video shooting device in the food material sampling box;
s2, coding the video shot by the front-end video shooting device by using the first video coder, coding the video shot by the sampling inspection video shooting device by using the second video coder, and sending the coded video to the server through network transmission;
s3, decoding the coded video by using a video decoder, and transmitting the decoded video to a display device arranged in a hall for display;
s4, carrying out self deep learning on the violation behavior recognition unit;
s5, extracting a character image from a video shot by the front-end video shooting device by using the image acquisition module, and judging whether an illegal behavior exists in the character image extracted by the image acquisition module by the illegal behavior identification unit;
s6, the violation recording module records the violation identified by the violation identification unit and generates an event report;
and S7, the intelligent scoring module intelligently scores according to the identification result of the violation behavior recording module, and the server sends the event report generated by the violation behavior recording module to the supervision platform through the wireless communication module.
Preferably, a storage unit for storing the video decoded by the video decoder and a video retrieval module for inputting retrieval conditions to retrieve the corresponding decoded video from the storage unit are arranged in the server;
the storage unit is internally divided into a first storage space for storing and decoding the video coded by the first video coder and a second storage space for storing and decoding the video coded by the second video coder.
Preferably, the first storage space stores decoded video in a temporal order of the video encoded by the first video encoder, and the second storage space stores decoded video in a temporal order of the video encoded by the second video encoder.
Preferably, the retrieval condition includes a retrieval video type and a retrieval video time, and if the retrieval video type is the cooking whole process, the first storage space calls a corresponding decoding video according to the retrieval video time; and if the type of the retrieval video is food material, the second storage space calls a corresponding decoding video according to the retrieval video time.
Preferably, the violation behavior recognition unit performs self deep learning, including the steps of:
s1, storing a large number of violation images containing violation behaviors;
s2, cutting the part around the person in the violation image;
s3, extracting the characteristic information of the cut image and establishing a characteristic information base;
and S4, classifying each piece of feature information in a feature information base according to the corresponding violation behavior.
Preferably, the violation images comprise a person wearing an irregular violation image, a person calling the violation image, a person playing a mobile phone violation image, and a person smoking the violation image.
Preferably, the method for cropping the part around the person in the violation image is as follows: and taking a person in the illegal image as a center, and extending a certain area outwards for clipping.
Preferably, the method for judging whether the violation behavior exists in the person image extracted by the image acquisition module by the violation behavior identification unit is as follows: the illegal action recognition unit judges whether the feature information in the figure image extracted by the image acquisition module is the same as the feature information stored in the feature information base established by the illegal action recognition unit.
Preferably, the intelligent scoring module performs intelligent scoring according to the number of times of violation behaviors recorded by the violation behavior recording module in unit time.
(III) advantageous effects
Compared with the prior art, the bright kitchen range management method based on big data has the following beneficial effects:
1. the method comprises the steps that a plurality of front-end video shooting devices which are used for shooting the whole cooking process and have different angles are arranged in a kitchen, a food material sampling box is arranged in the kitchen, the sampling video shooting devices are arranged in the food material sampling box, videos shot by the front-end video shooting devices are coded by a first video coder, videos shot by the sampling video shooting devices are coded by a second video coder, the coded videos are transmitted to a server through network transmission, the coded videos are decoded by a video decoder and transmitted to display equipment arranged in a hall for display, kitchen operation of catering enterprises is enabled to be transparent, and customers can monitor the kitchen operation better;
2. the method comprises the steps that self-deep learning is conducted on an illegal behavior recognition unit, a character image is extracted from a video shot by a front-end video shooting device through an image acquisition module, the illegal behavior recognition unit judges whether illegal behaviors exist in the character image extracted by the image acquisition module, the illegal behavior recording module records the illegal behaviors recognized by the illegal behavior recognition unit and generates an event report server, and the event report generated by the illegal behavior recording module is sent to a supervision platform through a wireless communication module;
3. the storage unit is internally divided into a first storage space for storing and decoding a first video encoder coded video and a second storage space for storing and decoding a second video encoder coded video, and the video retrieval module is used for inputting retrieval conditions to retrieve corresponding decoded videos from the storage unit, so that corresponding monitoring videos can be quickly found through the retrieval conditions, a supervision platform can conveniently and timely obtain evidence, and catering enterprises can be pertinently reformed.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all 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.
A bright kitchen range management method based on big data comprises the following steps:
s1, arranging a plurality of front-end video shooting devices for shooting the whole cooking process at different angles in a kitchen, arranging a food material sampling box in the kitchen, and arranging a sampling video shooting device in the food material sampling box;
s2, coding the video shot by the front-end video shooting device by using the first video coder, coding the video shot by the sampling inspection video shooting device by using the second video coder, and sending the coded video to the server through network transmission;
s3, decoding the coded video by using a video decoder, and transmitting the decoded video to a display device arranged in a hall for display;
s4, carrying out self deep learning on the violation behavior recognition unit;
s5, extracting a character image from a video shot by the front-end video shooting device by using the image acquisition module, and judging whether an illegal behavior exists in the character image extracted by the image acquisition module by the illegal behavior identification unit;
s6, the violation recording module records the violation identified by the violation identification unit and generates an event report;
and S7, the intelligent scoring module intelligently scores according to the identification result of the violation behavior recording module, and the server sends the event report generated by the violation behavior recording module to the supervision platform through the wireless communication module.
The server is internally provided with a storage unit for storing the video decoded by the video decoder and a video retrieval module for inputting retrieval conditions to retrieve the corresponding decoded video from the storage unit;
the storage unit is internally divided into a first storage space for storing video encoded by a first video encoder to be decoded and a second storage space for storing video encoded by a second video encoder to be decoded.
The first storage space stores decoded video in chronological order of video encoded by the first video encoder, and the second storage space stores decoded video in chronological order of video encoded by the second video encoder.
The retrieval conditions comprise retrieval video types and retrieval video time, and if the retrieval video types are in the whole cooking process, the first storage space calls corresponding decoding videos according to the retrieval video time; and if the type of the retrieval video is food material, the second storage space calls the corresponding decoding video according to the retrieval video time.
The violation behavior recognition unit for performing self deep learning comprises the following steps:
s1, storing a large number of violation images containing violation behaviors;
s2, cutting the part around the person in the violation image;
s3, extracting the characteristic information of the cut image and establishing a characteristic information base;
and S4, classifying each piece of feature information in a feature information base according to the corresponding violation behavior.
The violation images comprise a violation image of the dress irregularity of the person, a violation image of the call making of the person, a violation image of the mobile phone playing of the person and a violation image of the smoking of the person.
The method for cutting the part around the person in the illegal image comprises the following steps: and taking a person in the illegal image as a center, and extending a certain area outwards for clipping.
The method for judging whether the illegal behavior exists in the figure image extracted by the image acquisition module by the illegal behavior recognition unit comprises the following steps: the illegal behavior recognition unit judges whether the feature information in the figure image extracted by the image acquisition module is the same as the feature information stored in the feature information base established by the illegal behavior recognition unit.
And the intelligent scoring module intelligently scores according to the times of the illegal behaviors recorded by the illegal behavior recording module in unit time.
Set up the front end video shooting device that is used for shooting culinary art overall process of a plurality of different angles in the kitchen, and set up edible material sampling box in the kitchen, set up sampling video shooting device in edible material sampling box, utilize first video encoder to carry out the coding to the video that front end video shooting device was shot, utilize second video encoder to carry out the coding to the video that sampling video shooting device was shot, and send the video after the coding for the server through network transmission, utilize video decoder to decode the video after the coding, and transmit the display device that sets up in the hall for the display, make catering enterprise kitchen operation transparence, customer can supervise better.
The violation behavior recognition unit is subjected to self deep learning, the image acquisition module is used for extracting character images from videos shot by the front-end video shooting device, the violation behavior recognition unit is used for judging whether violation behaviors exist in the character images extracted by the image acquisition module, the violation behavior recording module is used for recording the violation behaviors identified by the violation behavior recognition unit and generating an event report server, and the event report generated by the violation behavior recording module is sent to the supervision platform through the wireless communication module.
The violation behavior recognition unit for performing self deep learning comprises the following steps:
s1, storing a large number of violation images containing violation behaviors;
s2, cutting the part around the person in the violation image;
s3, extracting the characteristic information of the cut image and establishing a characteristic information base;
and S4, classifying each piece of feature information in a feature information base according to the corresponding violation behavior.
The violation images comprise a violation image of the dress irregularity of the person, a violation image of the call making of the person, a violation image of the mobile phone playing of the person and a violation image of the smoking of the person.
The method for cutting the part around the person in the illegal image comprises the following steps: and taking a person in the illegal image as a center, and extending a certain area outwards for clipping.
The method for judging whether the illegal behavior exists in the figure image extracted by the image acquisition module by the illegal behavior recognition unit comprises the following steps: the illegal behavior recognition unit judges whether the feature information in the figure image extracted by the image acquisition module is the same as the feature information stored in the feature information base established by the illegal behavior recognition unit.
The storage unit is internally divided into a first storage space for storing and decoding a first video encoder coded video and a second storage space for storing and decoding a second video encoder coded video, and the video retrieval module is used for inputting retrieval conditions to retrieve corresponding decoded videos from the storage unit, so that corresponding monitoring videos can be quickly found through the retrieval conditions, a supervision platform can conveniently and timely obtain evidence, and catering enterprises can be pertinently reformed.
The server is internally provided with a storage unit for storing the video decoded by the video decoder and a video retrieval module for inputting retrieval conditions to retrieve the corresponding decoded video from the storage unit;
the storage unit is internally divided into a first storage space for storing video encoded by a first video encoder to be decoded and a second storage space for storing video encoded by a second video encoder to be decoded.
The first storage space stores decoded video in chronological order of video encoded by the first video encoder, and the second storage space stores decoded video in chronological order of video encoded by the second video encoder.
The retrieval conditions comprise retrieval video types and retrieval video time, and if the retrieval video types are in the whole cooking process, the first storage space calls corresponding decoding videos according to the retrieval video time; and if the type of the retrieval video is food material, the second storage space calls the corresponding decoding video according to the retrieval video time.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. A bright kitchen range management method based on big data is characterized by comprising the following steps: the method comprises the following steps:
s1, arranging a plurality of front-end video shooting devices for shooting the whole cooking process at different angles in a kitchen, arranging a food material sampling box in the kitchen, and arranging a sampling video shooting device in the food material sampling box;
s2, coding the video shot by the front-end video shooting device by using the first video coder, coding the video shot by the sampling inspection video shooting device by using the second video coder, and sending the coded video to the server through network transmission;
s3, decoding the coded video by using a video decoder, and transmitting the decoded video to a display device arranged in a hall for display;
s4, carrying out self deep learning on the violation behavior recognition unit;
s5, extracting a character image from a video shot by the front-end video shooting device by using the image acquisition module, and judging whether an illegal behavior exists in the character image extracted by the image acquisition module by the illegal behavior identification unit;
s6, the violation recording module records the violation identified by the violation identification unit and generates an event report;
and S7, the intelligent scoring module intelligently scores according to the identification result of the violation behavior recording module, and the server sends the event report generated by the violation behavior recording module to the supervision platform through the wireless communication module.
2. The big-data-based Mingchi light range management method according to claim 1, wherein: the server is internally provided with a storage unit for storing the video decoded by the video decoder and a video retrieval module for inputting retrieval conditions to retrieve the corresponding decoded video from the storage unit;
the storage unit is internally divided into a first storage space for storing and decoding the video coded by the first video coder and a second storage space for storing and decoding the video coded by the second video coder.
3. The big-data-based Mingchi light range management method according to claim 2, wherein: the first storage space stores decoded video according to the time sequence of the video encoded by the first video encoder, and the second storage space stores decoded video according to the time sequence of the video encoded by the second video encoder.
4. The big-data-based Mingchi light range management method according to claim 2, wherein: the retrieval conditions comprise retrieval video types and retrieval video time, and if the retrieval video types are in the whole cooking process, the first storage space calls corresponding decoding videos according to the retrieval video time; and if the type of the retrieval video is food material, the second storage space calls a corresponding decoding video according to the retrieval video time.
5. The big-data-based Mingchi light range management method according to claim 1, wherein: the violation behavior recognition unit performs self deep learning, and comprises the following steps:
s1, storing a large number of violation images containing violation behaviors;
s2, cutting the part around the person in the violation image;
s3, extracting the characteristic information of the cut image and establishing a characteristic information base;
and S4, classifying each piece of feature information in a feature information base according to the corresponding violation behavior.
6. The big-data-based Mingchi light range management method according to claim 5, wherein: the violation images comprise a violation image of the dress irregularity of the person, a violation image of the call making of the person, a violation image of the mobile phone playing of the person and a violation image of the smoking of the person.
7. The big-data-based Mingchi light range management method according to claim 5, wherein: the method for cutting the part around the character in the illegal image comprises the following steps: and taking a person in the illegal image as a center, and extending a certain area outwards for clipping.
8. The big-data-based Mingchi light range management method according to claim 1, wherein: the method for judging whether the illegal behavior exists in the figure image extracted by the image acquisition module by the illegal behavior identification unit comprises the following steps: the illegal action recognition unit judges whether the feature information in the figure image extracted by the image acquisition module is the same as the feature information stored in the feature information base established by the illegal action recognition unit.
9. The big-data-based Mingchi light range management method according to claim 1, wherein: and the intelligent scoring module intelligently scores according to the times of the illegal behaviors recorded by the illegal behavior recording module in unit time.
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Application publication date: 20191231 |