CN113286148A - Video equipment optimization method and device based on big data and Internet of things and server - Google Patents
Video equipment optimization method and device based on big data and Internet of things and server Download PDFInfo
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- CN113286148A CN113286148A CN202011337049.8A CN202011337049A CN113286148A CN 113286148 A CN113286148 A CN 113286148A CN 202011337049 A CN202011337049 A CN 202011337049A CN 113286148 A CN113286148 A CN 113286148A
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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
- H04N19/30—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
- H04N19/395—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability involving distributed video coding [DVC], e.g. Wyner-Ziv video coding or Slepian-Wolf video coding
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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
- H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
Abstract
According to the video equipment optimization method, device and server based on big data and the Internet of things, first video interaction data of online video equipment are obtained; extracting coding state data from the first video interactive data, and judging whether the online video equipment has coding optimization requirements or not according to the dynamic distribution coding state data and/or the static distribution coding state data; and if so, finishing coding optimization based on the coding optimization model. By the design, accurate and reliable coding optimization of the online video equipment can be realized.
Description
Technical Field
The invention relates to the technical field of cosmetic coding, in particular to a video equipment optimization method, a video equipment optimization device and a video equipment optimization server based on big data and the Internet of things.
Background
An online video device may involve switching multiple encoding lines during operation to meet the encoding needs of different customers.
Therefore, how to achieve accurate and reliable coding optimization of an online video device is a technical problem to be solved at present.
Disclosure of Invention
In order to solve the problems, the invention provides a video equipment optimization method, a video equipment optimization device and a video equipment optimization server based on big data and the Internet of things.
A video equipment optimization method based on big data and the Internet of things comprises the following steps:
acquiring first video interaction data of online video equipment; extracting encoding status data from the first video interaction data, wherein the encoding status data comprises: dynamically distributing coding state data and/or statically distributing coding state data; the dynamically distributed encoding state data comprises: a measured dynamic distribution within a target interval, the static distribution encoding state data comprising: at least one of a first statically distributed encoding state data set and a second statically distributed encoding state data set;
judging whether the online video equipment has the coding optimization requirement or not according to the dynamic distribution coding state data and/or the static distribution coding state data;
and if so, finishing coding optimization based on the coding optimization model.
Wherein the accomplishing of the coding optimization based on the coding optimization model comprises: triggering and executing a coding optimization starting operation to acquire second video interaction data based on a coding optimization model; judging whether the coding state data extracted from the second video interaction data has coding optimization requirements or not; and if so, triggering to execute a coding optimization confirmation operation so as to complete coding optimization based on the intelligent coding equipment configuration information corresponding to the online video equipment.
Wherein the determining whether the coding state data extracted from the second video interaction data has a coding optimization requirement includes: judging whether the working condition identification corresponding to the second video interaction data is consistent with the working condition identification of the online video equipment or not; and if so, judging whether the coding state data extracted from the second video interaction data has coding optimization requirements.
A video equipment optimizing device based on big data and the Internet of things comprises:
the data acquisition module is used for acquiring first video interaction data of the online video equipment; extracting encoding status data from the first video interaction data, wherein the encoding status data comprises: dynamically distributing coding state data and/or statically distributing coding state data; the dynamically distributed encoding state data comprises: a measured dynamic distribution within a target interval, the static distribution encoding state data comprising: at least one of a first statically distributed encoding state data set and a second statically distributed encoding state data set;
the demand judging module is used for judging whether the online video equipment has a demand for coding optimization according to the dynamic distribution coding state data and/or the static distribution coding state data;
and the coding optimization module is used for finishing coding optimization based on the coding optimization model if the judgment result is yes.
A server, a processor in the server being operable to perform the method described above.
By applying the method, the device and the server, first video interaction data of online video equipment are acquired; extracting encoding status data from the first video interaction data, wherein the encoding status data comprises: dynamically distributing coding state data and/or statically distributing coding state data; the dynamically distributed encoding state data comprises: a measured dynamic distribution within a target interval, the static distribution encoding state data comprising: at least one of a first statically distributed encoding state data set and a second statically distributed encoding state data set; judging whether the online video equipment has the coding optimization requirement or not according to the dynamic distribution coding state data and/or the static distribution coding state data; and if so, finishing coding optimization based on the coding optimization model. By the design, accurate and reliable coding optimization of the online video equipment can be realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a video device optimization method based on big data and the internet of things according to an embodiment of the present invention.
Fig. 2 is a functional block diagram of a video device optimization apparatus based on big data and the internet of things according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of a server according to an embodiment of the present invention.
Detailed Description
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
Referring to fig. 1, a video device optimization method based on big data and internet of things is shown, applied to a server, and including the contents described in the following steps S11-S13.
Step S11, acquiring first video interaction data of the online video equipment; encoding state data is extracted from the first video interaction data.
Wherein the encoding state data comprises: dynamically distributing coding state data and/or statically distributing coding state data; the dynamically distributed encoding state data comprises: a measured dynamic distribution within a target interval, the static distribution encoding state data comprising: at least one of the first statically distributed encoding state data set and the second statically distributed encoding state data set.
Step S12, determining whether the online video device has a coding optimization requirement according to the dynamic distribution coding state data and/or the static distribution coding state data.
In step S13, if the determination result is yes, the encoding optimization is completed based on the encoding optimization model.
Wherein the accomplishing of the coding optimization based on the coding optimization model comprises: triggering and executing a coding optimization starting operation to acquire second video interaction data based on a coding optimization model; judging whether the coding state data extracted from the second video interaction data has coding optimization requirements or not; and if so, triggering to execute a coding optimization confirmation operation so as to complete coding optimization based on the intelligent coding equipment configuration information corresponding to the online video equipment.
Wherein the determining whether the coding state data extracted from the second video interaction data has a coding optimization requirement includes: judging whether the working condition identification corresponding to the second video interaction data is consistent with the working condition identification of the online video equipment or not; and if so, judging whether the coding state data extracted from the second video interaction data has coding optimization requirements.
Referring to fig. 2, a video device optimization apparatus 200 based on big data and internet of things is shown, including:
a data obtaining module 210, configured to obtain first video interaction data of an online video device; extracting encoding status data from the first video interaction data, wherein the encoding status data comprises: dynamically distributing coding state data and/or statically distributing coding state data; the dynamically distributed encoding state data comprises: a measured dynamic distribution within a target interval, the static distribution encoding state data comprising: at least one of a first statically distributed encoding state data set and a second statically distributed encoding state data set;
a requirement determining module 220, configured to determine whether the online video device has a requirement for coding optimization according to the dynamic distribution coding state data and/or the static distribution coding state data;
and the coding optimization module 230 is configured to complete coding optimization based on the coding optimization model if the determination result is yes.
Fig. 3 is a block diagram illustrating a server 110 according to an embodiment of the present invention. The server 110 in the embodiment of the present invention may be a server with data storage, transmission, and processing functions, as shown in fig. 3, the server 110 includes: the video equipment optimization device comprises a memory 111, a processor 112, a network module 113 and a video equipment optimization device 200 based on big data and the internet of things.
The memory 111, the processor 112, and the network module 113 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The video device optimization apparatus 200 based on big data and internet of things is stored in the memory 111, the video device optimization apparatus 200 based on big data and internet of things includes at least one software function module which can be stored in the memory 111 in a form of software or firmware (firmware), and the processor 112 executes various function applications and data processing by running software programs and modules stored in the memory 111, for example, the video device optimization apparatus 200 based on big data and internet of things in the embodiment of the present invention, so as to implement the video device optimization method based on big data and internet of things in the embodiment of the present invention.
The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is used for storing a program, and the processor 112 executes the program after receiving the execution instruction.
The processor 112 may be an integrated circuit chip having data processing capabilities. The Processor 112 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 113 is configured to establish a communication connection between the server 110 and other communication terminal intelligent encoding devices through a network, so as to implement transceiving operations of network signals and data. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that server 110 may include more or fewer components than shown in fig. 3 or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
An embodiment of the present invention also provides a computer-readable storage medium, which includes a computer program. The computer program controls the server 110 on which the readable storage medium is executed to perform the above-mentioned method.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer intelligent coding device (which may be a personal computer, an electronic intelligent coding device 10, or a network intelligent coding device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or intelligent coding device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or intelligent coding device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or intelligent coding device that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (5)
1. A video equipment optimization method based on big data and the Internet of things comprises the following steps:
acquiring first video interaction data of online video equipment; extracting encoding status data from the first video interaction data, wherein the encoding status data comprises: dynamically distributing coding state data and/or statically distributing coding state data; the dynamically distributed encoding state data comprises: a measured dynamic distribution within a target interval, the static distribution encoding state data comprising: at least one of a first statically distributed encoding state data set and a second statically distributed encoding state data set;
judging whether the online video equipment has the coding optimization requirement or not according to the dynamic distribution coding state data and/or the static distribution coding state data;
and if so, finishing coding optimization based on the coding optimization model.
2. The method of claim 1, wherein the performing coding optimization based on a coding optimization model comprises: triggering and executing a coding optimization starting operation to acquire second video interaction data based on a coding optimization model; judging whether the coding state data extracted from the second video interaction data has coding optimization requirements or not; and if so, triggering to execute a coding optimization confirmation operation so as to complete coding optimization based on the intelligent coding equipment configuration information corresponding to the online video equipment.
3. The method of claim 2, wherein the determining whether the coding state data extracted from the second video interaction data has coding optimization requirements comprises: judging whether the working condition identification corresponding to the second video interaction data is consistent with the working condition identification of the online video equipment or not; and if so, judging whether the coding state data extracted from the second video interaction data has coding optimization requirements.
4. A video equipment optimizing device based on big data and the Internet of things comprises:
the data acquisition module is used for acquiring first video interaction data of the online video equipment; extracting encoding status data from the first video interaction data, wherein the encoding status data comprises: dynamically distributing coding state data and/or statically distributing coding state data; the dynamically distributed encoding state data comprises: a measured dynamic distribution within a target interval, the static distribution encoding state data comprising: at least one of a first statically distributed encoding state data set and a second statically distributed encoding state data set;
the demand judging module is used for judging whether the online video equipment has a demand for coding optimization according to the dynamic distribution coding state data and/or the static distribution coding state data;
and the coding optimization module is used for finishing coding optimization based on the coding optimization model if the judgment result is yes.
5. A server, a processor in the server being operable to perform the method of any of the preceding claims 1-3.
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| CN202011337049.8A CN113286148A (en) | 2020-11-25 | 2020-11-25 | Video equipment optimization method and device based on big data and Internet of things and server |
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| CN1930552A (en) * | 2004-01-14 | 2007-03-14 | 原子能源局 | System for automatically generating optimised codes |
| US20170180740A1 (en) * | 2013-04-16 | 2017-06-22 | Fastvdo Llc | Adaptive coding, transmission and efficient display of multimedia (acted) |
| CN110536168A (en) * | 2019-09-11 | 2019-12-03 | 北京达佳互联信息技术有限公司 | Video method for uploading, device, electronic equipment and storage medium |
| CN111246209A (en) * | 2020-01-20 | 2020-06-05 | 北京字节跳动网络技术有限公司 | Adaptive encoding method, apparatus, electronic device, and computer storage medium |
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- 2020-11-25 CN CN202011337049.8A patent/CN113286148A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1930552A (en) * | 2004-01-14 | 2007-03-14 | 原子能源局 | System for automatically generating optimised codes |
| US20170180740A1 (en) * | 2013-04-16 | 2017-06-22 | Fastvdo Llc | Adaptive coding, transmission and efficient display of multimedia (acted) |
| CN110536168A (en) * | 2019-09-11 | 2019-12-03 | 北京达佳互联信息技术有限公司 | Video method for uploading, device, electronic equipment and storage medium |
| CN111246209A (en) * | 2020-01-20 | 2020-06-05 | 北京字节跳动网络技术有限公司 | Adaptive encoding method, apparatus, electronic device, and computer storage medium |
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Application publication date: 20210820 |