CN111639133A - Live broadcast monitoring method and system based on block chain - Google Patents
Live broadcast monitoring method and system based on block chain Download PDFInfo
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- CN111639133A CN111639133A CN202010479699.XA CN202010479699A CN111639133A CN 111639133 A CN111639133 A CN 111639133A CN 202010479699 A CN202010479699 A CN 202010479699A CN 111639133 A CN111639133 A CN 111639133A
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- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
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Abstract
The invention provides a live broadcast monitoring method and a live broadcast monitoring system based on a block chain, and when the live broadcast monitoring system is used, the behavior and the action of a live broadcast person are independently acquired, so that the calculation force can be prevented from being occupied by the process of acquiring live broadcast images; analyzing the behavior of a live player by adopting an image recognition deep learning algorithm, recording a sensitive action frame, constructing a block chain, recording the occurrence time of the sensitive action frame, the Hash of sensitive action frame data and the Hash of the last block, recording the block head of the current block as a current block characteristic value, and calculating the Hash of the current block according to the characteristic value; recording the data of the sensitive action frame into the block body of the current block; and then carrying out distributed storage, and after the sensitive action frame is packaged and stored in a distributed manner in a block chain manner, as each item of data of the sensitive action frame is stored in each node in a distributed manner, the sensitive action frame cannot be tampered, so that the sensitive action frame can be used as a truthful non-tampered evidence for subsequent violation judgment and investigation evidence collection.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to a live broadcast monitoring method and system based on a block chain.
Background
At present, with the rapid development speed of the network, the domestic network live broadcast field shows the situation of fire development and the people's life is gradually advanced. When the live broadcasting is carried out, the audience can issue comments through the barrage and exchange and interact with the anchor. Due to the fact that the barrage is simple to operate, interaction through the barrage becomes a main interaction mode of the anchor and the audience during live broadcasting. Because the network live broadcast faces more audience groups and has huge quantity, the quality and the content of the network live broadcast can have certain influence on the public. Sometimes, live personnel are more dangerous to attract more viewers to live content involving illegal violations.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a live broadcast monitoring method and system based on a block chain, which can analyze the behavior of a live broadcast user through images and store a sensitive action frame into the block chain.
The live broadcast monitoring method based on the block chain comprises the following steps:
the method comprises the steps of independently collecting behavior of a live player during live broadcast, analyzing collected behavior image by adopting an image recognition deep learning algorithm and recording a sensitive action frame;
the image of the sensitive action frame is stored as data;
each live broadcast room is used as a node, sensitive action frames of all nodes are packaged into a block in a preset time period, and the blocks generated in sequence are mutually constructed into a block chain;
recording the occurrence time of the sensitive action frame, the hash of the sensitive action frame data and the hash of the last block into the block head of the current block as a current block characteristic value, and calculating the hash of the current block according to the characteristic value; recording the data of the sensitive action frame into the block body of the current block; all blocks are downloaded to each node.
And furthermore, at least two cameras of live broadcast equipment of a live broadcast person are called, at least one camera is used for shooting live broadcast content of the live broadcast person, and at least one other camera is used for independently collecting behavior and action of the live broadcast person so as to analyze and record sensitive action frames.
And furthermore, calling at least two processors of live broadcast equipment of a live broadcast person, wherein at least one processor is used for processing live broadcast content of the live broadcast person, and at least one other processor is used for analyzing the collected behavior action images and recording sensitive action frames by adopting an image recognition deep learning algorithm.
And furthermore, the system at least comprises two live broadcast devices, wherein at least one live broadcast device is used for shooting and processing live broadcast contents of a live broadcast person, and at least one other live broadcast device is used for independently acquiring behavior and action of the live broadcast person and analyzing and recording a sensitive action frame.
Further, the data of the block is broadcasted to all nodes through the internet or a local area network.
The invention also provides a live broadcast monitoring system based on the block chain, which comprises live broadcast equipment and an independent monitoring and acquisition module, wherein the monitoring and acquisition module is used for independently acquiring the behavior of a live broadcast person, analyzing the acquired behavior image by adopting an image recognition deep learning algorithm and recording a sensitive action frame; the monitoring acquisition module is used for transferring the image of the sensitive action frame into data;
the live broadcast equipment or the monitoring acquisition module takes each live broadcast room as a node, packs the sensitive action frames of all the nodes into a block in a preset time period, and mutually constructs the blocks generated in sequence into a block chain;
the live broadcast equipment or the monitoring acquisition module records the occurrence time of the sensitive action frame, the Hash of the sensitive action frame data and the Hash of the last block into the block head of the current block as a current block characteristic value, and calculates the Hash of the current block according to the characteristic value; the live broadcast equipment or the monitoring acquisition module records the data of the sensitive action frame into a block body of the current block; all blocks are downloaded to each node.
Further, at least two cameras of live broadcast equipment of a live broadcast person are called, at least one camera is arranged on the live broadcast equipment, and the camera is used for shooting live broadcast content of the live broadcast person;
at least one other camera is arranged on the monitoring acquisition module and used for independently acquiring the behavior action of the live broadcast person so as to analyze and record the sensitive action frame.
Further, at least two processors are called, and at least one processor is arranged in the live broadcast equipment and is used for processing live broadcast content of a live broadcast person;
at least one other processor is arranged in the monitoring acquisition module and used for analyzing the acquired behavior action image by adopting an image recognition deep learning algorithm and recording a sensitive action frame.
Further, the live broadcast equipment and the monitoring acquisition module are two independent terminal devices, the live broadcast equipment is used for shooting and processing live broadcast contents of a live broadcast person, and the monitoring acquisition module is used for independently acquiring behavior and action of the live broadcast person and analyzing and recording sensitive action frames.
Further, the data of the block is broadcasted to all nodes through the internet or a local area network.
When the invention is used, the behavior and the action of the live broadcast person are independently collected, so that the calculation force can be prevented from being occupied by the process of collecting live broadcast images; analyzing the behavior of a live player by adopting an image recognition deep learning algorithm, recording a sensitive action frame, constructing a block chain, recording the occurrence time of the sensitive action frame, the hash of sensitive action frame data and the hash of the last block in the block chain construction process, recording the block head of the current block as a current block characteristic value, and calculating the hash of the current block according to the characteristic value; recording the data of the sensitive action frame into the block body of the current block; and then carrying out distributed storage, and after the sensitive action frame is packaged and stored in a distributed manner in a block chain manner, as each item of data of the sensitive action frame is stored in each node in a distributed manner, the sensitive action frame cannot be tampered, so that the sensitive action frame can be used as a truthful non-tampered evidence for subsequent violation judgment and investigation evidence collection.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby. It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
The invention firstly provides a live broadcast monitoring method based on a block chain, which comprises the following steps:
the method comprises the steps of independently collecting behavior of a live player during live broadcast, analyzing collected behavior image by adopting an image recognition deep learning algorithm and recording a sensitive action frame;
the image of the sensitive action frame is stored as data;
each live broadcast room is used as a node, sensitive action frames of all nodes are packaged into a block in a preset time period, and the blocks generated in sequence are mutually constructed into a block chain;
recording the occurrence time of the sensitive action frame, the hash of the sensitive action frame data and the hash of the last block into the block head of the current block as a current block characteristic value, and calculating the hash of the current block according to the characteristic value; recording the data of the sensitive action frame into the block body of the current block; all blocks are downloaded to each node.
In the above embodiment, specifically, the behavior and actions of the live broadcast viewer are collected independently means that the collection process and the live broadcast video collection during normal live broadcast are independent and do not interfere with each other, so that mutual influence can be prevented from causing respective processing to be not smooth enough, the processing speed is not fast enough, the process for analyzing and recording the sensitive action frame is ensured to be performed smoothly, and the calculation power of the process cannot be occupied by the live broadcast video collection process;
the method comprises the steps that an image recognition deep learning algorithm is adopted to analyze the process of behavior and action of a live broadcast person, the image recognition deep learning algorithm is already the prior art in the field of image processing, the image recognition deep learning algorithm can be in butt joint with an existing sensitive action or a database of sensitive images, and when the image recognition deep learning algorithm is adopted to detect that the live broadcast person has the sensitive action, a sensitive action frame at the moment is collected and recorded;
then, converting the image of the sensitive action frame into accessible data through computing equipment such as a computer and the like, constructing a block chain through the computing equipment such as the computer or a computing server and the like, and in the block chain construction process, specifically, recording the occurrence time of the sensitive action frame, the hash of the sensitive action frame data and the hash of the last block, recording the block head of the current block as a characteristic value of the current block, and calculating the hash of the current block according to the characteristic value; recording the data of the sensitive action frame into the block body of the current block; then, distributed storage is carried out, namely all blocks are downloaded to each node;
in the block packing process, the preset time period may be set manually, for example, set to be 12 hours or 24 hours.
After the sensitive action frame is packaged and stored in a distributed mode in a block chain mode, as various data of the sensitive action frame are stored in various nodes in a distributed mode, the sensitive action frame cannot be tampered, and the sensitive action frame can be used as authentic non-tamperproof evidence to be used in subsequent violation judgment and investigation evidence collection.
Preferably, at least two cameras of live broadcast equipment of a live broadcast person are called, at least one camera is used for shooting live broadcast content of the live broadcast person, and at least one other camera is used for independently collecting behavior and action of the live broadcast person so as to analyze and record sensitive action frames. For example, in practical use, two cameras on a live broadcast device for live broadcast can be adopted, wherein one camera is used for shooting a live broadcast person to perform normal live broadcast, and the other camera is specially used for independently acquiring the behavior of the live broadcast person for analyzing a sensitive action frame. The collection process that can prevent to gather live broadcast person's action like this influences the shooting process of shooting live broadcast person, and both are independent each other, and each other does not influence the interference.
More preferably, at least two processors of a live device of a live player are called, at least one processor is used for processing live content of the live player, and at least another processor is used for analyzing the collected behavior action image and recording a sensitive action frame by adopting an image recognition deep learning algorithm. The two processes are processed independently, so that the two processes are smoother, do not influence each other and do not occupy computational power.
In another alternative, the system at least comprises two live broadcast devices, wherein at least one live broadcast device is used for shooting and processing live broadcast content of a live broadcast person, and at least another live broadcast device is used for independently acquiring behavior and action of the live broadcast person and analyzing and recording a sensitive action frame. This scheme can make two processes more independent, and both mutually noninterfere.
Further, the data of the block is broadcasted to all nodes through the internet or a local area network.
In another embodiment, the invention further provides a live broadcast monitoring system based on the block chain, which comprises live broadcast equipment and an independent monitoring and acquisition module, wherein the live broadcast equipment is used for shooting live broadcasters to carry out live broadcast, the monitoring and acquisition module is used for independently acquiring the behavior of the live broadcasters, analyzing the acquired behavior image by adopting an image recognition deep learning algorithm and recording sensitive action frames; the monitoring acquisition module is used for transferring the image of the sensitive action frame into data;
the live broadcast equipment or the monitoring acquisition module takes each live broadcast room as a node, packs the sensitive action frames of all the nodes into a block in a preset time period, and mutually constructs the blocks generated in sequence into a block chain;
the live broadcast equipment or the monitoring acquisition module records the occurrence time of the sensitive action frame, the Hash of the sensitive action frame data and the Hash of the last block into the block head of the current block as a current block characteristic value, and calculates the Hash of the current block according to the characteristic value; the live broadcast equipment or the monitoring acquisition module records the data of the sensitive action frame into a block body of the current block; all blocks are downloaded to each node.
In this embodiment, an independent monitoring and collecting module collects the behavior of the live broadcast, the monitoring and collecting module may be a storable computing device or module, and the monitoring and collecting module analyzes the behavior of the live broadcast by using an image recognition deep learning algorithm, the monitoring and collecting module may be docked with an existing database of sensitive actions or sensitive images, and when the image recognition deep learning algorithm is used to detect that the live broadcast has sensitive actions, the monitoring and collecting module collects and records the frames of the sensitive actions at that moment;
then the monitoring acquisition module converts the image of the sensitive action frame into accessible data, and the live broadcast equipment or the monitoring acquisition module takes the live broadcast room as a node; when packaging, packaging can be carried out through live broadcast equipment or a monitoring acquisition module;
in the block chain construction process, specifically, the occurrence time of a sensitive action frame, the hash of sensitive action frame data and the hash of a previous block are recorded into the block head of the current block as a current block characteristic value, and the hash of the current block is calculated according to the characteristic value; recording the data of the sensitive action frame into the block body of the current block; then, distributed storage is carried out, namely all blocks are downloaded to each node;
in the block packing process, the preset time period may be set manually, for example, set to be 12 hours or 24 hours.
After the sensitive action frame is packaged and stored in a distributed mode in a block chain mode, as various data of the sensitive action frame are stored in various nodes in a distributed mode, the sensitive action frame cannot be tampered, and the sensitive action frame can be used as authentic non-tamperproof evidence to be used in subsequent violation judgment and investigation evidence collection.
In this embodiment, specifically, the at least two cameras of the live broadcast device of the live broadcast user are called, and the monitoring acquisition module may be integrated in the live broadcast device, and at least one camera is arranged on the live broadcast device and used for shooting live broadcast content of the live broadcast user; at least one other camera is arranged on the monitoring acquisition module and used for independently acquiring the behavior action of the live broadcast so as to analyze and record the sensitive action frame; thereby make the camera of two purposes mutually noninterference.
More preferably, at least two processors are called, and at least one processor is arranged in the live broadcast equipment and is used for processing live broadcast content of a live broadcast person; at least one other processor is arranged in the monitoring acquisition module and used for analyzing the acquired behavior action image by adopting an image recognition deep learning algorithm and recording a sensitive action frame. Therefore, the processors with two purposes do not interfere with each other and occupy no computational power.
In another embodiment, the live broadcast device and the monitoring acquisition module are two independent terminal devices, that is, at least two devices are adopted to shoot a live broadcast person when the live broadcast person broadcasts directly. The live broadcast equipment is used for shooting and processing live broadcast contents of a live broadcast person, and the monitoring and collecting module is used for independently collecting behavior and action of the live broadcast person and analyzing and recording a sensitive action frame. So that the two processes are not interfered with each other and do not occupy computational power.
Further, the data of the block is broadcasted to all nodes through the internet or a local area network.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. The live broadcast monitoring method based on the block chain is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the steps of independently collecting behavior of a live player during live broadcast, analyzing collected behavior image by adopting an image recognition deep learning algorithm and recording a sensitive action frame;
the image of the sensitive action frame is stored as data;
each live broadcast room is used as a node, sensitive action frames of all nodes are packaged into a block in a preset time period, and the blocks generated in sequence are mutually constructed into a block chain;
recording the occurrence time of the sensitive action frame, the hash of the sensitive action frame data and the hash of the last block into the block head of the current block as a current block characteristic value, and calculating the hash of the current block according to the characteristic value; recording the data of the sensitive action frame into the block body of the current block; all blocks are downloaded to each node.
2. The blockchain-based live broadcast monitoring method according to claim 1, wherein:
at least two cameras of live broadcast equipment of a live broadcast person are called, at least one camera is used for shooting live broadcast content of the live broadcast person, and at least one other camera is used for independently collecting behavior and action of the live broadcast person and used for analyzing and recording sensitive action frames.
3. The blockchain-based live broadcast monitoring method according to claim 2, wherein:
and calling at least two processors of live broadcast equipment of a live broadcast person, wherein at least one processor is used for processing live broadcast content of the live broadcast person, and at least one other processor is used for analyzing the collected behavior action images by adopting an image recognition deep learning algorithm and recording sensitive action frames.
4. The blockchain-based live broadcast monitoring method according to claim 1, wherein:
the system at least comprises two live broadcast devices, at least one live broadcast device is used for shooting and processing live broadcast content of a live broadcast person, and at least one other live broadcast device is used for independently collecting behavior and action of the live broadcast person and analyzing and recording a sensitive action frame.
5. The live broadcast monitoring method based on the block chain as claimed in any one of claims 1 to 4, wherein:
and the data of the block is broadcasted to all nodes through the Internet or a local area network.
6. Live monitored control system based on block chain, its characterized in that:
the system comprises live broadcast equipment and an independent monitoring and acquisition module, wherein the monitoring and acquisition module is used for independently acquiring the behavior of a live broadcast person, analyzing the acquired behavior and action image by adopting an image recognition deep learning algorithm and recording a sensitive action frame; the monitoring acquisition module is used for transferring the image of the sensitive action frame into data;
the live broadcast equipment or the monitoring acquisition module takes each live broadcast room as a node, packs the sensitive action frames of all the nodes into a block in a preset time period, and mutually constructs the blocks generated in sequence into a block chain;
the live broadcast equipment or the monitoring acquisition module records the occurrence time of the sensitive action frame, the Hash of the sensitive action frame data and the Hash of the last block into the block head of the current block as a current block characteristic value, and calculates the Hash of the current block according to the characteristic value; the live broadcast equipment or the monitoring acquisition module records the data of the sensitive action frame into a block body of the current block; all blocks are downloaded to each node.
7. The blockchain-based live broadcast monitoring system of claim 6, wherein:
calling at least two cameras of live broadcast equipment of a live broadcast person, wherein at least one camera is arranged on the live broadcast equipment and is used for shooting live broadcast content of the live broadcast person;
at least one other camera is arranged on the monitoring acquisition module and used for independently acquiring the behavior action of the live broadcast person so as to analyze and record the sensitive action frame.
8. The blockchain-based live broadcast monitoring system of claim 7, wherein:
calling at least two processors, wherein at least one processor is arranged in the live broadcast equipment and is used for processing live broadcast content of a live broadcast person;
at least one other processor is arranged in the monitoring acquisition module and used for analyzing the acquired behavior action image by adopting an image recognition deep learning algorithm and recording a sensitive action frame.
9. The blockchain-based live broadcast monitoring system of claim 6, wherein:
the live broadcast equipment and the monitoring acquisition module are two independent terminal equipment, the live broadcast equipment is used for shooting and processing live broadcast contents of a live broadcast person, and the monitoring acquisition module is used for independently acquiring behavior and action of the live broadcast person and analyzing and recording a sensitive action frame.
10. The blockchain-based live broadcast monitoring system according to any one of claims 6 to 9, wherein:
and the data of the block is broadcasted to all nodes through the Internet or a local area network.
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| CN202010479699.XA CN111639133A (en) | 2020-05-30 | 2020-05-30 | Live broadcast monitoring method and system based on block chain |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114491648A (en) * | 2022-04-02 | 2022-05-13 | 北京嘉沐安科技有限公司 | Block chain data privacy protection method for video live broadcast social big data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114491648A (en) * | 2022-04-02 | 2022-05-13 | 北京嘉沐安科技有限公司 | Block chain data privacy protection method for video live broadcast social big data |
| CN114491648B (en) * | 2022-04-02 | 2022-10-25 | 上海饼戈信息科技有限公司 | Block chain data privacy protection method for video live broadcast social big data |
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