Image data storage method, image server and cloud storage system

Abstract

The invention discloses an image data storage method, an image server and a cloud storage system, and relates to the technical field of data storage. Decomposing the uploaded video into continuous single-frame pictures; performing semantic analysis on the decomposed continuous single-frame pictures to obtain a semantic text set capable of generating an uploaded video, and taking the semantic text set as storage data; and deleting the video subjected to semantic analysis from the cache, storing the corresponding storage data according to the time sequence, and the like. The video file uploaded to the cloud storage is subjected to semantic recognition and then is converted into the semantic text set, so that the volume of the video data is greatly reduced, and the video data can be stored in the semantic text set. When the video file needs to be extracted, the video is restored by adopting a generation mode according to the semantic text in the semantic text set, the extraction of the video data is realized, compared with the prior art, the occupation of the storage space of cloud storage by the video file can be greatly reduced, and the utilization rate of the storage space of the cloud storage is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and in particular to an image data storage method, an image server and a cloud storage system. Background Art

[0002] As an emerging data storage solution, cloud storage stores data on remote servers on the network through distributed systems and virtualization technologies. While saving local storage space, it also facilitates data management and avoids data loss.

[0003] With the rapid development of Internet technology, video content has become an indispensable part of people's daily life, such as videos generated by daily records or surveillance videos generated by monitoring equipment. With the continuous iteration of equipment technology, video files will take up a lot of storage space due to their high definition, high frame rate and other characteristics. When storing the above video files in cloud storage for preservation, extremely high requirements are placed on the storage capacity of cloud storage. Summary of the invention

[0004] The embodiments of the present invention provide an image data storage method, an image server and a cloud storage system, which can reduce the volume of uploaded video data and achieve the effect of reducing the storage space occupied by the cloud storage.

[0005] A method for storing image data, comprising:

[0006] Cache the videos uploaded by the terminal in time sequence;

[0007] Decompose the uploaded video into consecutive single-frame images;

[0008] Perform semantic analysis on the decomposed continuous single-frame images to obtain a semantic text set that can generate the uploaded video and use it as storage data;

[0009] The video that has undergone semantic analysis is deleted from the cache, and its corresponding storage data is saved in time series.

[0010] Further, including:

[0011] Extract key frame images from the decomposed continuous single-frame images;

[0012] Semantic analysis is performed on the continuous single-frame images within the time series interval of the key frame images to obtain a semantic text set that can generate the video in the interval;

[0013] The above key frame images and semantic text sets are packaged to generate storage data.

[0014] Furthermore, the video is restored in a generative manner using the semantic text in the stored data.

[0015] An image server comprises: at least one cache server, a processing server and a storage server, wherein:

[0016] The cache server is connected to the terminal for communication and is suitable for caching the video uploaded by the terminal;

[0017] The processing server is in communication with the cache server and has a semantic processing unit adapted to process the cached video to obtain a semantic text set;

[0018] The storage server is in communication with the processing server and is adapted to store the semantic text set as storage data;

[0019] Processing server communication and terminal communication connection, suitable for feeding back data to the terminal.

[0020] Furthermore, the cache server includes a data cache module for collecting videos uploaded by the terminal and caching the data in time series.

[0021] Furthermore, the semantic processing unit includes a video image preprocessing module, a semantic extraction module and a semantic reverse restoration module;

[0022] Video image preprocessing module, suitable for decomposing uploaded videos into continuous single-frame images;

[0023] The semantic extraction module is suitable for performing semantic analysis on the decomposed continuous single-frame images to obtain a semantic text set that can generate the uploaded video;

[0024] The semantic reverse restoration module is suitable for restoring videos in a generative manner based on a semantic text set.

[0025] Furthermore, the video image preprocessing module includes a continuous frame image decomposition unit and a key frame recognition and extraction unit;

[0026] A continuous frame image decomposition unit, adapted to decompose the uploaded video into continuous single-frame images according to a time sequence;

[0027] The key frame recognition and extraction unit is suitable for extracting key frame videos from the above continuous single-frame images.

[0028] Further, the semantic extraction module includes a data reading unit and a semantic recognition conversion unit;

[0029] A data reading unit, adapted to read the images decomposed into continuous single frames;

[0030] The semantic recognition conversion unit is suitable for recognizing continuous single-frame images to obtain a semantic text set that can generate the original video.

[0031] Furthermore, the semantic reverse restoration module includes a semantic recognition restoration unit and a verification unit;

[0032] A semantic recognition and restoration unit, adapted to restore the video in a generative manner according to a semantic text set;

[0033] The verification unit is adapted to verify the semantic text set generated by the semantic recognition conversion unit, and includes:

[0034] Input the semantic text set into the semantic recognition and restoration unit to generate the original video;

[0035] The original video is used as a reference object and compared with the generated original video. When they are completely consistent, the semantic recognition conversion unit outputs a semantic text set. When they are not completely consistent, the generation process is modified and verified before outputting the semantic text set.

[0036] A cloud storage system comprises at least one terminal, a cache server, a processing server and a storage server, wherein the terminal is respectively connected to the cache server and the processing server for communication;

[0037] The processing server generates video data using the semantic text set according to the instruction issued by the terminal;

[0038] The terminal is adapted to upload video data to a cache server; and receive video data from a processing server.

[0039] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0040] After performing semantic recognition on video files uploaded to cloud storage, the present invention can convert video files into semantic text sets, which greatly reduces the volume of video data. When it is necessary to retrieve the video files, the video is restored in a generated manner according to the semantic text in the semantic text set to achieve the retrieval of video data. Compared with the prior art, it can greatly reduce the storage space occupied by video files in cloud storage and improve the utilization rate of storage space of cloud storage.

[0041] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0042] The technical solution of the present invention is further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 A schematic diagram of a flow chart of an image data storage method disclosed in an embodiment of the present invention;

[0045] Figure 2 A communication block diagram of an image server for storing image data disclosed in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the architecture of a cloud storage system for image data storage disclosed in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of reducing the first type of video data disclosed in an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of reducing the second type of video data disclosed in an embodiment of the present invention;

[0049] Figure 6 A schematic diagram of reducing the third type of video data disclosed in an embodiment of the present invention.

[0050] Reference numerals:

[0051] 1. Terminal; 2. Processing server; 21. Key feature processing unit; 22. Semantic processing unit; 3. Cache server; 4. Storage server; 5. Data cache module; 51. Data acquisition unit; 52. Data cache unit; 6. Video image preprocessing module; 61. Continuous frame image decomposition unit; 62. Key frame recognition and extraction unit; 7. Semantic extraction module; 71. Data reading unit; 72. Semantic recognition conversion unit; 8. Semantic reverse restoration module; 81. Semantic recognition and restoration unit; 82. Verification unit. DETAILED DESCRIPTION

[0052] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0053] In today's digital age, video has become an important form of information storage and sharing. In the past, videos were generally stored in local storage, such as hard disks, optical disks, tape drives, etc. However, the local storage method has the following problems: 1. It takes up a lot of local storage space; 2. It requires the purchase of additional storage hardware for storage, which increases storage costs; 3. There is a problem of data loss.

[0054] With the development of technology, more and more cloud storages have emerged. Users can upload the video files they need to save to the cloud storage server 4 through the terminal 1, which solves the problems of occupying a large amount of local storage space and data loss in the previous local storage method. However, with the surge in the amount of video data uploaded to the cloud storage server 4, extremely high requirements are placed on the storage capacity of the cloud storage server 4.

[0055] Currently, the videos uploaded by users to cloud storage can be roughly divided into two categories: one is active video data, which is video data that is frequently read and accessed, such as videos that often need to be played, downloaded, and modified; the other is solid-state video data, which is video data that is infrequently read and accessed, such as videos generated by daily records or surveillance videos generated by monitoring equipment. Such video data is generally rarely accessed and used after it is generated.

[0056] The technical solution provided by the present invention is mainly aimed at the above-mentioned solid-state video data, and is used to reduce the storage space occupied by the solid-state video data during the storage process.

[0057] Figure 3 The schematic diagram of the architecture of the cloud storage system for image data storage disclosed in an embodiment of the present invention is shown, which includes at least one terminal 1, a cache server 3, a processing server 2 and a storage server 4, and the terminal 1 is communicated with the cache server 3 and the processing server 2 respectively.

[0058] The processing server 2 generates video data using the semantic text set according to the instruction issued by the terminal 1 ; the terminal 1 is suitable for uploading the video data to the cache server 3 ; and receiving the video data from the processing server 2 .

[0059] The specific process includes:

[0060] 1. Terminal 1 uploads the acquired video data to a cache server 3 that is in communication with it;

[0061] 2. The cache server 3 stores the uploaded video data in chronological order;

[0062] 3. The processing server 2 reads the video data in sequence according to the time sequence, and reduces the volume of the video data to form storage data consisting of a semantic text set;

[0063] 4. Processing server 2 stores the stored data in storage server 4 for preservation;

[0064] 5. When terminal 1 sends a video processing instruction to processing server 2, processing server 2 reads the corresponding video data from storage server 4 according to the instruction, restores it, and then feeds it back to terminal 1, and terminal 1 performs the required operation.

[0065] like Figure 2 As shown, in the above-mentioned video data process, the cache server 3, the processing server 2 and the storage server 4 are combined to form an image server, which is used to reduce, restore and store the video data volume.

[0066] The cache server 3 is in communication connection with the terminal 1 and is suitable for caching the video uploaded by the terminal 1 .

[0067] The cache server 3 includes a data cache module 5 for collecting the video uploaded by the terminal 1 and caching the data in time series.

[0068] The processing server 2 is in communication with the cache server 3 and has a semantic processing unit 22 suitable for processing the cached video to obtain a semantic text set.

[0069] The semantic processing unit 22 includes a video image preprocessing module 6, a semantic extraction module 7 and a semantic reverse restoration module 8;

[0070] The video image preprocessing module 6 is adapted to decompose the uploaded video into continuous single-frame images.

[0071] The video image preprocessing module 6 includes a continuous frame image decomposition unit 61 and a key frame identification and extraction unit 62;

[0072] The continuous frame image decomposition unit 61 is adapted to decompose the uploaded video into continuous single frame images according to the time sequence;

[0073] The key frame identification and extraction unit 62 is adapted to extract key frame videos from the above continuous single frame images.

[0074] like Figures 4 to 6 As shown, the semantic extraction module 7 is suitable for performing semantic analysis on the decomposed continuous single-frame images to obtain a semantic text set that can generate an uploaded video.

[0075] The semantic extraction module 7 includes a data reading unit 71 and a semantic recognition conversion unit 72;

[0076] A data reading unit 71, adapted to read the images decomposed into continuous single frames;

[0077] The semantic recognition conversion unit 72 is adapted to recognize continuous single-frame images to obtain a semantic text set that can generate the original video.

[0078] In one example, the semantic recognition conversion unit 72 analyzes the video content based on successive single-frame images, identifies key elements therein, such as scenes, actions, and visual features such as color, texture, shape, etc., links these elements with corresponding text descriptions, and packages the text descriptions into a semantic text set that can generate the original video.

[0079] The above text means a document composed of words in a natural language. The process of semantic recognition conversion is to convert the video into a natural language that can describe its content. When restoring the video, the original video is restored in a generated manner based on the semantic text in the semantic text set, that is, the natural language.

[0080] The semantic reverse restoration module 8 is suitable for restoring the video in a generated manner according to the semantic text set.

[0081] The semantic reverse restoration module 8 includes a semantic recognition restoration unit 81 and a verification unit 82;

[0082] A semantic recognition and restoration unit 81, adapted to restore the video in a generated manner according to the semantic text set;

[0083] In another example, the verification unit 82 is adapted to verify the semantic text set generated by the semantic recognition conversion unit 72, including: inputting the semantic text set into the semantic recognition restoration unit 81 to generate the original video;

[0084] The original video is used as a reference object and compared with the generated original video. When they are completely consistent, the semantic recognition conversion unit 72 outputs a semantic text set. When they are not completely consistent, the generation process is modified and verified before outputting the semantic text set.

[0085] When the semantic text set after verification is used for video generation and restoration in the semantic reverse restoration module 8, a video consistent with the original video is generated according to the content of the semantic text set.

[0086] The above-mentioned semantic text set also includes the parameters of the video generation model, that is, the parameters of the video generation model when the semantic recognition and restoration unit 81 generates a video according to the semantic text set, so as to ensure that the generated video is consistent with the original video under the conditions that the semantic text set and the parameters of the video generation model are consistent in the semantic recognition and restoration unit 81.

[0087] like Figure 6 As shown, the processing server 2 also includes a key feature processing unit 21, which can also identify specific features in continuous single-frame images and record them in the stored data when packaging the stored data.

[0088] In the above example, the specific feature set by terminal 1 is the license plate number. When reducing the volume of the video, the license plate number appearing in the video data is identified and saved in a time series according to the time axis. The advantage of this is that when browsing the stored data through terminal 1, the video with specific features can be restored by screening, thereby improving the retrieval efficiency of the video and reducing the workload of the image server.

[0089] The storage server 4 is in communication with the processing server 2 and is adapted to store the semantic text set as storage data;

[0090] The processing server 2 is communicatively connected to the terminal 1 and is suitable for feeding back data to the terminal 1 .

[0091] like Figure 1 As shown, a method for storing image data includes:

[0092] S1, cache the video uploaded by terminal 1 in time sequence;

[0093] S2, decomposes the uploaded video into continuous single-frame images, such as Figures 4 to 6 As shown;

[0094] S3 performs semantic analysis on the decomposed continuous single-frame images to obtain a semantic text set that can generate the uploaded video and uses it as storage data, such as Figures 4 to 6 As shown;

[0095] S4, the video that has been semantically analyzed is deleted from the cache, and its corresponding storage data is saved in time series, such as Figures 4 to 6 As shown;

[0096] like Figures 4-5 FIG. 2 shows two different embodiments of the above steps S2 to S3.

[0097] exist Figure 4 In this embodiment, the method for reducing the video volume is:

[0098] 1. Decompose the uploaded video into continuous single-frame images;

[0099] 2. Extract key frame images from the decomposed continuous single-frame images;

[0100] 3. Perform semantic analysis on the continuous single-frame images within the time series interval of the key frame images to obtain a semantic text set that can generate the video in the interval;

[0101] 4. Package the above key frame images and semantic text sets to generate storage data.

[0102] The advantage of adopting the solution of the above embodiment is that, due to the existence of key frame images, the computational complexity of semantic analysis of video data can be reduced, which can reduce the computational complexity when reducing the volume of video data and restoring video data.

[0103] exist Figure 5 In this embodiment, the method for reducing the video volume is:

[0104] 1. Decompose the uploaded video into continuous single-frame images;

[0105] 2. Perform semantic analysis on the decomposed continuous single-frame images to obtain a semantic text set that can generate the video of the interval;

[0106] 3. Package the above semantic text set to generate storage data.

[0107] The advantage of adopting the solution of the above embodiment is that the storage space occupied by the video data can be greatly reduced, but the amount of calculation is relatively large when reducing the volume of the video data and restoring the video data.

[0108] The above two embodiments can be selected according to actual needs. In this embodiment, the first embodiment is preferred.

[0109] S5, restore the video in a generative manner using the semantic text in the stored data.

[0110] After performing semantic recognition on video files uploaded to cloud storage, the present invention can convert video files into semantic text sets, which greatly reduces the volume of video data. When it is necessary to retrieve the video files, the video is restored in a generated manner according to the semantic text in the semantic text set to achieve the retrieval of video data. Compared with the prior art, it can greatly reduce the storage space occupied by video files in cloud storage and improve the utilization rate of storage space of cloud storage.

[0111] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0112] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0113] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0114] The steps of the method or algorithm described in conjunction with the embodiments herein can be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module can be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. The ASIC can be located in the user terminal 1. Of course, the processor and the storage medium can also be present in the user terminal 1 as discrete components.

[0115] For software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0116] The above description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it will be appreciated by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained by "including," when used as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A method for storing image data, characterized in that: include: Cache the videos uploaded by the terminal in time sequence; Decompose the uploaded video into consecutive single-frame images; Perform semantic analysis on the decomposed continuous single-frame images to obtain a semantic text set that can generate the uploaded video and use it as storage data; The video that has undergone semantic analysis is deleted from the cache, and its corresponding storage data is saved in time series.

2. The image data storage method according to claim 1, characterized in that: include: Extract key frame images from the decomposed continuous single-frame images; Perform semantic analysis on the continuous single-frame images within the time series interval of the key frame images to obtain a semantic text set that can generate the video in the interval; The above key frame images and semantic text sets are packaged to generate storage data.

3. An image data storage method as claimed in claim 1 or 2, characterized in that: The semantic text in the stored data is used to restore the video in a generative manner.

4. An image server, characterized in that: include: At least one cache server, processing server, and storage server, where: The cache server is connected to the terminal for communication and is suitable for caching the video uploaded by the terminal; The processing server is in communication with the cache server and has a semantic processing unit adapted to process the cached video to obtain a semantic text set; The storage server is in communication with the processing server and is adapted to store the semantic text set as storage data; Processing server communication and terminal communication connection, suitable for feeding back data to the terminal.

5. An image server as claimed in claim 4, characterized in that: The cache server includes a data cache module, which is used to collect videos uploaded by the terminal and cache the data according to time series.

6. An image server as claimed in claim 4, characterized in that: The semantic processing unit includes a video image preprocessing module, a semantic extraction module and a semantic reverse restoration module; Video image preprocessing module, suitable for decomposing uploaded videos into continuous single-frame images; The semantic extraction module is suitable for performing semantic analysis on the decomposed continuous single-frame images to obtain a semantic text set that can generate the uploaded video; The semantic reverse restoration module is suitable for restoring videos in a generative manner based on a semantic text set.

7. An image server as claimed in claim 6, characterized in that: The video image preprocessing module includes a continuous frame image decomposition unit and a key frame recognition and extraction unit; A continuous frame image decomposition unit, adapted to decompose the uploaded video into continuous single-frame images according to a time sequence; The key frame recognition and extraction unit is suitable for extracting key frame videos from the above continuous single-frame images.

8. An image server as claimed in claim 6, characterized in that: The semantic extraction module includes a data reading unit and a semantic recognition conversion unit; A data reading unit, adapted to read the images decomposed into continuous single frames; The semantic recognition conversion unit is suitable for recognizing continuous single-frame images to obtain a semantic text set that can generate the original video.

9. An image server as claimed in claim 8, characterized in that: The semantic reverse restoration module includes a semantic recognition restoration unit and a verification unit; A semantic recognition and restoration unit, adapted to restore the video in a generative manner according to a semantic text set; The verification unit is adapted to verify the semantic text set generated by the semantic recognition conversion unit, and includes: Input the semantic text set into the semantic recognition and restoration unit to generate the original video; The original video is used as a reference object and compared with the generated original video. When they are completely consistent, the semantic recognition conversion unit outputs a semantic text set. When they are not completely consistent, the generation process is modified and verified before outputting the semantic text set.

10. A cloud storage system, characterized in that: It includes at least one terminal, a cache server, a processing server and a storage server, and the terminal is respectively connected to the cache server and the processing server for communication; The processing server generates video data using the semantic text set according to the instruction issued by the terminal; The terminal is suitable for uploading video data to a cache server; and, receiving video data from a processing server.

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