CN116095414A - Method and device for obtaining video screenshots - Google Patents

Abstract

Embodiments of the present disclosure provide a method and device for acquiring video screenshots. The method for obtaining video screenshots includes: first, in response to obtaining a video sequence set for performing a screenshot task, acquiring video information of multiple candidate video sequences in the video sequence set, and then based on the video information of the multiple candidate video sequences, calculating The estimated screenshot load corresponding to multiple candidate video sequences, and based on the local available resources and the estimated screenshot load corresponding to multiple candidate video sequences, select the target video sequence from the video sequence set, and finally execute the screenshot task of the target video sequence to obtain the target video For the video screenshot corresponding to the sequence, the expected screenshot load corresponding to the screenshot task of each video sequence can be determined in advance before executing the screenshot task, and the number of screenshot tasks can be accurately allocated in advance, which improves load balancing and resource utilization.

Description

Method and device for obtaining video screenshots

technical field

The embodiments of the present disclosure relate to the field of computer technology and the field of Internet technology, specifically to the field of video processing technology and the field of image processing technology, especially the method and device for obtaining video screenshots.

Background technique

With the continuous development of video-on-demand, more and more users have a demand for video-on-demand screenshots. The processing flow of CPU-intensive tasks such as video-on-demand screenshots is as follows: configure the default number of threads for each screenshot task, and determine the number of concurrent tasks on the node according to the overall core configuration of the machine.

The entire processing process of the screenshot task can be divided into steps such as downloading of media resources, decoding of video parts in media resources, encoding of pictures, and uploading of finished pictures. In these steps, downloading media resources consumes the system's downlink bandwidth, video decoding and picture encoding mainly consume the system's cpu load, and uploading pictures mainly consumes the system's uplink bandwidth. The number of concurrent downloads and uploads is limited by the bandwidth of the entire network where the server is located. This generally does not become a bottleneck first, and is looser than the parallel restrictions on video decoding and image encoding. The decoding consumption of different media resources is different. All screenshot tasks are processed with the same number of threads, which will affect the processing speed of the task, especially for tasks with high video complexity. A waste of system resources.

Contents of the invention

Embodiments of the present disclosure provide a method, device, electronic device, and computer-readable medium for acquiring video screenshots.

In a first aspect, an embodiment of the present disclosure provides a method for acquiring video screenshots, the method including: in response to acquiring a video sequence set for performing a screenshot task, acquiring video information of multiple candidate video sequences in the video sequence set ; Based on the video information of multiple candidate video sequences, calculate the expected screenshot load corresponding to the multiple candidate video sequences; based on the local available resources and the expected screenshot load corresponding to the multiple candidate video sequences, select the target video sequence from the video sequence set; Execute The screenshot task of the target video sequence obtains the video screenshot corresponding to the target video sequence.

In some embodiments, based on the video information of the multiple candidate video sequences, calculating the expected screenshot load corresponding to the multiple candidate video sequences includes: obtaining a load calculation formula for calculating the screenshot load; based on the load calculation formula and multiple candidate videos The video information of the sequence is used to calculate the expected screenshot load corresponding to multiple candidate video sequences.

In some embodiments, obtaining a load calculation formula for calculating screenshot load includes: obtaining a set of sample video sequences, wherein the set of sample video sequences includes multiple sample video sequences, sample video information of multiple sample video sequences, and multiple The sample screenshot load corresponding to the sample video sequence; use the regression statistical method to analyze the data of multiple sample video sequences, sample video information of multiple sample video sequences, and sample screenshot loads corresponding to multiple sample video sequences, and obtain information related to the sample video and The load calculation formula of the sample screenshot load association.

In some embodiments, the method further includes: in response to executing the screenshot task of the target video sequence, acquiring a real-time screenshot load corresponding to the target video sequence; based on the real-time screenshot load corresponding to the target video sequence and the estimated screenshot load corresponding to the target video sequence, The load calculation formula is updated to obtain a new load calculation formula.

In some embodiments, based on the real-time screenshot load corresponding to the target video sequence and the estimated screenshot load corresponding to the target video sequence, the load calculation formula is updated to obtain a new load calculation formula, including: the real-time screenshot load corresponding to the target video sequence Compared with the expected screenshot load corresponding to the target video sequence, it is judged whether the real-time screenshot load corresponding to the target video sequence meets the load condition; in response to determining that the real-time screenshot load corresponding to the target video sequence meets the load condition, the real-time screenshot load corresponding to the target video sequence is and the video information of the target video sequence are stored in the sample video sequence set to obtain a new sample video sequence set; based on the new sample video sequence set, the load calculation formula is updated to obtain a new load calculation formula.

In some embodiments, the method further includes: calculating a new estimated screenshot load corresponding to the target video sequence based on the new load calculation formula and video information of the target video sequence; Screenshot payloads that perform preset adjustment operations on the target video sequence.

In a second aspect, an embodiment of the present disclosure provides an apparatus for acquiring video screenshots, the apparatus including: an acquisition module configured to acquire multiple The video information of the candidate video sequence; the calculation module is configured to calculate the expected screenshot load corresponding to the multiple candidate video sequences based on the video information of the multiple candidate video sequences; the selection module is configured to be based on local available resources and multiple candidate videos The expected screenshot load corresponding to the sequence selects the target video sequence from the video sequence set; the execution module is configured to execute the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.

In some embodiments, the calculation module includes: an acquisition unit configured to acquire a load calculation formula for calculating the screenshot load; a calculation unit configured to calculate multiple load calculation formulas based on the load calculation formula and video information of multiple candidate video sequences The estimated screenshot load corresponding to candidate video sequences.

In some embodiments, the calculation unit is further configured to: acquire a set of sample video sequences, wherein the set of sample video sequences includes multiple sample video sequences, sample video information of the multiple sample video sequences, and multiple sample video sequences corresponding to Sample screenshot load; use the regression statistical method to analyze the data of multiple sample video sequences, sample video information of multiple sample video sequences, and sample screenshot loads corresponding to multiple sample video sequences, and obtain the data associated with the sample video information and sample screenshot load. Load calculation formula.

In some embodiments, the device further includes an update module; the acquisition module is further configured to: obtain the real-time screenshot load corresponding to the target video sequence in response to executing the screenshot task of the target video sequence; the update module is configured to: based on the target The real-time screenshot load corresponding to the video sequence and the estimated screenshot load corresponding to the target video sequence update the load calculation formula to obtain a new load calculation formula.

In some embodiments, the update module is further configured to: compare the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and determine whether the real-time screenshot load corresponding to the target video sequence meets the load condition; respond To determine that the real-time screenshot load corresponding to the target video sequence meets the load condition, the real-time screenshot load corresponding to the target video sequence and the video information of the target video sequence are stored in the sample video sequence set to obtain a new sample video sequence set; based on the new sample video Sequence collection, update the load calculation formula to get a new load calculation formula.

In some embodiments, the calculation module is further configured to: calculate the new expected screenshot load corresponding to the target video sequence based on the new load calculation formula and the video information of the target video sequence; the execution module is further configured to: according to the local A new estimated screenshot load corresponding to the available resources and the target video sequence, and a preset adjustment operation is performed on the target video sequence.

In a third aspect, an embodiment of the present disclosure provides an electronic device, the electronic device includes: one or more processors; a storage device, on which one or more programs are stored; when one or more programs are used by one or more A plurality of processors are executed, so that one or more processors implement the method for acquiring video screenshots as described in any embodiment of the first aspect.

In a fourth aspect, embodiments of the present disclosure provide a computer-readable medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for obtaining video screenshots as described in any embodiment of the first aspect is implemented .

In the method for obtaining video screenshots provided by the embodiments of the present disclosure, the execution subject first acquires the video information of multiple candidate video sequences in the video sequence collection in response to the acquisition of the video sequence set used to perform the screenshot task, and then based on the multiple candidate The video information of the video sequence calculates the estimated screenshot load corresponding to multiple candidate video sequences, and based on the local available resources and the estimated screenshot load corresponding to multiple candidate video sequences, selects the target video sequence from the video sequence set, and finally executes the target video sequence Screenshot task, to get the video screenshot corresponding to the target video sequence. Before executing the screenshot task, the expected screenshot load corresponding to the screenshot task of each video sequence can be determined in advance, and the number of screenshot tasks can be accurately allocated in advance. No need to set the task The number of threads limits the machine resources that can be used by the task. Just assign resource quotas to the screenshot task based on the accurately calculated expected screenshot load. More screenshot tasks can be allocated based on the estimated screenshot load, making full use of the local available resources of the server and improving the performance of the screenshot. Load balancing and resource utilization.

Description of drawings

Other characteristics, objects and advantages of the present disclosure will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present disclosure can be applied;

FIG. 2 is a flow chart of an embodiment of a method for obtaining a screenshot of a video according to the present disclosure;

FIG. 3 is a schematic diagram of an application scenario of a method for obtaining video screenshots according to the present disclosure;

Fig. 4 is a flow chart of an embodiment of calculating the expected screenshot load corresponding to multiple candidate video sequences according to the present disclosure;

FIG. 5 is a flowchart of an embodiment of obtaining a load calculation formula according to the present disclosure;

FIG. 6 is a flow chart of another embodiment of a method for obtaining a screenshot of a video according to the present disclosure;

FIG. 7 is a flowchart of an embodiment of updating a load calculation formula according to the present disclosure;

FIG. 8 is a schematic structural diagram of an embodiment of a device for obtaining video screenshots according to the present disclosure;

FIG. 9 is a schematic structural diagram of an electronic device suitable for implementing an embodiment of the present disclosure.

Detailed ways

The present disclosure will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the relevant disclosure, not to limit the disclosure. It should also be noted that, for the convenience of description, only the parts related to the relevant disclosure are shown in the drawings.

It should be noted that, in the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and embodiments.

FIG. 1 shows an exemplary system architecture 100 to which the method and device for acquiring video screenshots according to the embodiments of the present disclosure can be applied.

As shown in FIG. 1 , the system architecture 100 may include terminal devices 104 , 105 , and 106 , a network 107 , and servers 101 , 102 , and 103 . The network 107 is used as a medium for providing communication links between the terminal devices 104 , 105 , 106 and the servers 101 , 102 , 103 . Network 107 may include various connection types, such as wires, wireless communication links, or fiber optic cables, among others.

Users can interact with the servers 101 , 102 , 103 belonging to the same server cluster through the network 107 through the terminal devices 104 , 105 , 106 to receive or send information and the like. Various applications may be installed on the terminal devices 104, 105, 106, such as item display applications, data analysis applications, search applications, and the like.

The terminal devices 104, 105, and 106 may be hardware or software. When the terminal device is hardware, it may be various electronic devices with display screens and supporting communication with servers, including but not limited to smartphones, tablet computers, laptop computers, desktop computers, and the like. When the terminal device is software, it can be installed in the electronic devices listed above. It can be implemented as a plurality of software or software modules, or as a single software or software module. No specific limitation is made here.

The servers 101, 102, and 103 may be servers that provide various services, for example, background servers that receive requests sent by terminal devices that establish communication connections with them. The background server can receive and analyze the request sent by the terminal device, and generate processing results.

The servers 101, 102, and 103 can acquire video sequence sets used to perform screenshot tasks, analyze and process multiple candidate video sequences in the video sequence set, obtain video information of multiple candidate video sequences in the video sequence set, and then Based on the video information of multiple candidate video sequences, calculate the expected screenshot load corresponding to the multiple candidate video sequences, and select the target video sequence from the video sequence set based on the local available resources and the estimated screenshot load corresponding to the multiple candidate video sequences, and finally Execute the screenshot task of the target video sequence, and obtain the video screenshot corresponding to the target video sequence.

It should be noted that the server may be hardware or software. When the server is hardware, it may be various electronic devices that provide various services for terminal devices. When the server is software, it can be implemented as a plurality of software or software modules that provide various services for the terminal device, or as a single software or software module that provides various services for the terminal device. No specific limitation is made here.

It should be noted that the method for acquiring video screenshots provided by the embodiments of the present disclosure may be executed by the servers 101 , 102 , and 103 . Correspondingly, the device for obtaining video screenshots is set in the servers 101 , 102 , and 103 .

It should be understood that the numbers of terminal devices, networks and servers in Fig. 1 are only illustrative. According to the implementation needs, there can be any number of terminal devices, networks and servers.

Continuing to refer to FIG. 2 , a flow 200 of an embodiment of the method for obtaining video screenshots according to the present disclosure is shown. The method for obtaining video screenshots includes the following steps:

Step 210, in response to acquiring a video sequence set for executing a screenshot task, acquire video information of multiple candidate video sequences in the video sequence set.

In this step, the execution subject on which the method for obtaining video screenshots runs (for example, the servers 101, 102, and 103 in FIG. 1 ) can send or read a video sequence set composed of multiple candidate video sequences through the receiving terminal. , wherein each candidate video sequence is used to perform a screenshot task to obtain a corresponding video screenshot, and each candidate video sequence is a sequence of multiple video images connected in time and space arranged in a certain order.

The above execution subject can perform video analysis processing on each candidate video sequence in the video sequence set, detect the media information of each candidate video sequence, and obtain the video information of each candidate video sequence, and the video information represents the video of the candidate video sequence The complexity information may include information such as resolution, code rate, frame rate, and encoding format of the candidate video sequence, and the video information of different candidate video sequences may be different. Among them, the resolution is a parameter used to measure the amount of data in the image, usually expressed as ppi (Pixel per inch). For example, the resolution of a certain video sequence is 320*180, which means that it For the effective pixels in the vertical direction, the ppi value of the window is higher when the window is small, and it looks clear. When the window is enlarged, because there are not so many effective pixels to fill the window, the ppi value of the effective pixels decreases and becomes blurred; the code rate is transmitted per unit time during data transmission The number of data bits, the unit is kbps, that is, thousand bits per second, that is, the sampling rate. The larger the sampling rate per unit time, the higher the precision, and the closer the processed file is to the original file; the frame rate is used to measure the display frame number measure. The so-called unit of measurement is the number of frames per second (Frames per Second, FPS) or "Hertz" (Hz), the number of frames per second (fps) or the frame rate means that the graphics processor can update every second when processing the field. High frame rate can get smoother and more realistic animation; encoding format is the existence form of video files, which can include H264, VP8, AVS, RMVB, WMV, QuickTime (mov), H265, VP9, AV1 and other formats.

Step 220, based on the video information of the multiple candidate video sequences, calculate the expected screenshot load corresponding to the multiple candidate video sequences.

In this step, since different video information can cause different loads used by the video sequence in the screenshot task, the video sequence with more complex video information has a higher load used in the screenshot task, and the above-mentioned execution subject obtains multiple candidate videos After the video information of each candidate video sequence, the load calculation can be performed on each candidate video sequence according to the video information of each candidate video sequence and the screenshot task corresponding to each candidate video sequence, and the corresponding An estimated screenshot load, which may represent a load value that may be required by the candidate video sequence during the execution of the screenshot task.

The execution subject may associate and mark the expected screenshot load corresponding to each candidate video sequence with the candidate video sequence, so as to obtain the expected screenshot load corresponding to multiple candidate video sequences.

Step 230: Select a target video sequence from the video sequence set based on the locally available resources and the expected screenshot loads corresponding to the plurality of candidate video sequences.

In this step, after the execution subject obtains the expected screenshot loads corresponding to multiple candidate video sequences, it can obtain local available resources, which can represent the local resource values used for screenshot tasks, such as the value of available cpu .

The above execution subject can determine how many candidate video sequences can be provided by locally available resources according to the expected screenshot load corresponding to the local available resources and multiple candidate video sequences, that is, from the estimated screenshot load corresponding to the multiple candidate video sequences , calculate the expected screenshot load sum is not greater than the candidate video sequence corresponding to the local available resources, determine the determined candidate video sequence as the target video sequence, so that the estimated screenshot load sum can be selected from the video sequence set is not greater than the locally available resources The resource corresponds to the target video sequence, and the sum of the expected screenshot loads is equal to the local available resources as much as possible. The target video sequence may include one or more, which is not specifically limited.

As an example, the value of the above-mentioned locally available resources is 100, and the multiple candidate video sequences include candidate video sequence A, candidate video sequence B, candidate video sequence C, candidate video sequence D, and candidate video sequence E, and candidate video sequence A corresponds to the expected The screenshot load is 30, the estimated screenshot load corresponding to candidate video sequence B is 50, the estimated screenshot load corresponding to candidate video sequence C is 60, the estimated screenshot load corresponding to candidate video sequence D is 70, and the estimated screenshot load corresponding to candidate video sequence E is 60. The above execution The subject can compare the local available resources with each expected screenshot load, calculate the sum of the loads between each expected screenshot load, and determine the estimated screenshot load 30 corresponding to candidate video sequence A and the estimated screenshot load corresponding to candidate video sequence D The sum of 70 is equal to the value of local available resources, and candidate video sequence A and candidate video sequence D can be used as target video sequences.

Step 240, execute the screenshot task of the target video sequence, and obtain the video screenshot corresponding to the target video sequence.

In this step, after the above-mentioned executive body selects the target video sequence, it can start to execute the screenshot task of the target video sequence, and start to execute the download of media resources, decoding of video parts in media resources, picture encoding, and uploading finished pictures of the target video sequence, etc. step, obtaining a video screenshot corresponding to the target video sequence.

If the target video sequence includes multiple video sequences, the above execution subject can perform steps such as downloading media resources, decoding video parts in media resources, encoding pictures, uploading finished pictures, etc. for each video sequence, and obtains the corresponding Video screenshot.

Continue to refer to FIG. 3 , which is a schematic diagram of an application scenario of the method for obtaining video screenshots according to this embodiment. This method can be applied to the application scenario in FIG. 3 , and the terminal 301 can send to the server 302 a video sequence set for executing a screenshot task. After the server 302 receives the video sequence set, it can perform video analysis on multiple candidate video sequences to obtain the video information of multiple candidate video sequences in the video sequence set, and then the server 302 calculates multiple candidate video sequences based on the video information of the multiple candidate video sequences. Estimated screenshot payloads for candidate video sequences. Afterwards, the server 302 can select the target video sequence from the video sequence set based on the local available resources and the expected screenshot load corresponding to multiple candidate video sequences, and start to execute the screenshot task of the target video sequence, and obtain the video screenshot corresponding to the target video sequence. The server 302 sends the video screenshot to the terminal 301, and the terminal 301 can display the video screenshot to the user through a screen.

In the method for obtaining video screenshots provided by the embodiments of the present disclosure, the execution subject first acquires the video information of multiple candidate video sequences in the video sequence collection in response to the acquisition of the video sequence set used to perform the screenshot task, and then based on the multiple candidate The video information of the video sequence calculates the estimated screenshot load corresponding to multiple candidate video sequences, and based on the local available resources and the estimated screenshot load corresponding to multiple candidate video sequences, selects the target video sequence from the video sequence set, and finally executes the target video sequence Screenshot task, to get the video screenshot corresponding to the target video sequence. Before executing the screenshot task, the expected screenshot load corresponding to the screenshot task of each video sequence can be determined in advance, and the number of screenshot tasks can be accurately allocated in advance. No need to set the task The number of threads limits the machine resources that can be used by the task. Just assign resource quotas to the screenshot task based on the accurately calculated expected screenshot load. More screenshot tasks can be allocated based on the estimated screenshot load, making full use of the local available resources of the server and improving the performance of the screenshot. Load balancing and resource utilization.

Referring to FIG. 4, FIG. 4 shows a flow chart 400 of an embodiment of calculating the expected screenshot load corresponding to multiple candidate video sequences, that is, the above-mentioned step 220, based on the video information of multiple candidate video sequences, calculating multiple candidate video sequences The corresponding expected screenshot load may include the following steps:

Step 410, obtaining a load calculation formula used to calculate the screenshot load.

In this step, after obtaining the video information of multiple candidate video sequences, the execution subject can locally read the load calculation formula used to calculate the screenshot load, and the load calculation formula is positively correlated with the video information of the video sequence. relation. The load calculation formula may be expressed as (x)*video information, where (x) may be a calculation parameter, and may be a parameter obtained through fitting using multiple test samples.

Wherein, the video information may include the resolution, bit rate, frame rate and encoding format of the video sequence, and the load calculation formula may be expressed in the following form: expected screenshot load=(x1)*resolution+(x2)*bit rate+( x3)*frame rate+(x4)*coding format.

Step 420, based on the load calculation formula and the video information of the multiple candidate video sequences, calculate the expected screenshot load corresponding to the multiple candidate video sequences.

In this step, after the execution subject obtains the load calculation formula and the video information of multiple candidate video sequences, it can respectively bring the video information of each candidate video sequence into the load calculation formula to calculate the load calculation formula of each candidate video sequence Corresponding estimated screenshot load.

As an example, the video information of the candidate video sequence A acquired by the above execution subject includes resolution a1, code rate a2, frame rate a3 and encoding format a4, and the video information of the candidate video sequence B includes resolution b1, code rate b2, frame rate b3 and encoding format b4, the above execution subject can bring the resolution a1, code rate a2, frame rate a3 and encoding format a4 of the candidate video sequence A into the load calculation formula to obtain the expected screenshot load of the candidate video sequence A=( x1)*a1+(x2)*a2+(x3)*a3+(x4)*a4, the resolution b1, code rate b2, frame rate b3 and encoding format b4 of the candidate video sequence B can also be brought into the load calculation formula , to obtain the expected screenshot load of the candidate video sequence B=(x1)*b1+(x2)*b2+(x3)*b3+(x4)*b4.

In this implementation, by using the load calculation formula that is positively correlated with the video information of the video sequence to calculate the expected screenshot load corresponding to multiple candidate video sequences, the expected screenshot load can be calculated more accurately, and the screenshot load is realized. The pre-estimation of , which can improve the load balancing and resource utilization of the screenshot task.

Referring to FIG. 5, FIG. 5 shows a flow chart 500 of an embodiment of obtaining a load calculation formula, that is, the above-mentioned step 410, obtaining a load calculation formula for calculating the screenshot load may include the following steps:

Step 510, acquiring a set of sample video sequences.

In this step, the above-mentioned executive body can obtain multiple sample video sequences in advance, and perform video analysis processing on each sample video sequence, detect the media information of each sample video sequence, and obtain the sample video of each sample video sequence Information, the sample video information represents the video complexity information of the sample video sequence, and may include information such as resolution, bit rate, frame rate, and encoding format of the sample video sequence. The execution subject may also acquire a sample screenshot load corresponding to each sample video sequence, and the sample screenshot load may be a screenshot load corresponding to each sample video sequence for a screenshot task. The above-mentioned execution subject may form a sample video sequence set by correspondingly combining multiple sample video sequences, sample video information of the multiple sample video sequences, and sample screenshot payloads corresponding to the multiple sample video sequences.

Step 520: Perform data analysis on the multiple sample video sequences, the sample video information of the multiple sample video sequences, and the sample screenshot loads corresponding to the multiple sample video sequences by using the regression statistics method, and obtain the load associated with the sample video information and the sample screenshot load calculation formula.

In this step, after the execution subject obtains the set of sample video sequences, data analysis can be performed on multiple sample video sequences, sample video information of multiple sample video sequences, and sample screenshot loads corresponding to multiple sample video sequences. The regression statistical method in the data analysis method performs data fitting on multiple sample video sequences, sample video information of multiple sample video sequences, and sample screenshot loads corresponding to multiple sample video sequences, and obtains the correlation between sample video information and sample screenshot loads The load calculation formula.

In this implementation, the load calculation formula associated with the sample video information and sample screenshot load is obtained through the sample video sequence set, and the load calculation formula can be obtained through fitting according to the current test samples, so as to use the load calculation formula to more accurately The estimated screenshot load is calculated, and the pre-estimation of the screenshot load is realized, so that the load balance and resource utilization of the screenshot task can be improved.

Referring to FIG. 6, FIG. 6 shows a flow chart 600 of another embodiment of a method for obtaining video screenshots, which may include the following steps:

Step 610, in response to acquiring a video sequence set for executing a screenshot task, acquire video information of multiple candidate video sequences in the video sequence set.

Step 610 of this embodiment may be performed in a manner similar to step 210 in the embodiment shown in FIG. 2 , and details are not described here.

Step 620, based on the video information of the multiple candidate video sequences, calculate the expected screenshot load corresponding to the multiple candidate video sequences.

Step 620 of this embodiment may be performed in a manner similar to step 220 in the embodiment shown in FIG. 2 , and details are not described here.

Step 630: Select a target video sequence from the video sequence set based on the locally available resources and the expected screenshot loads corresponding to the plurality of candidate video sequences.

Step 630 of this embodiment may be performed in a manner similar to step 230 in the embodiment shown in FIG. 2 , and details are not described here.

Step 640, execute the screenshot task of the target video sequence, and obtain the video screenshot corresponding to the target video sequence.

Step 640 of this embodiment may be performed in a manner similar to step 240 in the embodiment shown in FIG. 2 , and details are not described here.

Step 650, in response to executing the screenshot task of the target video sequence, acquire the real-time screenshot load corresponding to the target video sequence.

In this step, when the execution subject executes the screenshot task of the target video sequence, it can also start the timer corresponding to the screenshot task, and read the load value during the execution of the screenshot task every preset time, and according to the The load value in the time period calculates the cumulative average value occupied by the screenshot task process, and obtains the real-time screenshot load corresponding to the target video sequence. The real-time screenshot load can represent the average value of the screenshot load within a period of time.

Step 640: Based on the real-time screenshot load corresponding to the target video sequence and the estimated screenshot load corresponding to the target video sequence, the load calculation formula is updated to obtain a new load calculation formula.

In this step, after the above execution subject obtains the real-time screenshot load corresponding to the target video sequence, it can compare the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and based on the real-time screenshot load corresponding to the target video sequence The screenshot load updates the load calculation formula, adjusts the calculation parameters in the load calculation formula, and obtains a new load calculation formula.

The above execution subject can use the new load calculation formula to replace the previous load calculation formula, and the subsequent candidate video sequences can use the new load calculation formula to calculate the expected screenshot load.

As an optional implementation, refer to FIG. 7, which shows a flow chart 700 of an embodiment of updating the load calculation formula, that is, the above step 640, based on the real-time screenshot load corresponding to the target video sequence corresponding to the target video sequence The expected screenshot load, update the load calculation formula, and obtain a new load calculation formula, which may include the following steps:

Step 710: Compare the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and determine whether the real-time screenshot load corresponding to the target video sequence meets the load condition.

In this step, after the execution subject obtains the real-time screenshot load corresponding to the target video sequence, it can compare the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and determine the real-time screenshot corresponding to the target video sequence Whether the load meets the load condition. The load condition can include that the real-time screenshot load is within the preset range. The preset range can be [minimum load value, maximum load value], where the minimum load value is the estimated screenshot load*0.3, and the maximum load value *1.3 for estimated screenshot load.

The execution subject can compare the real-time screenshot load corresponding to the target video sequence with a preset range, and judge whether the real-time screenshot load is within the preset range, so as to determine whether the real-time screenshot load corresponding to the target video sequence meets the load condition.

Step 720: In response to determining that the real-time screenshot load corresponding to the target video sequence meets the load condition, store the real-time screenshot load corresponding to the target video sequence and the video information of the target video sequence in the sample video sequence set to obtain a new sample video sequence set.

In this step, the execution subject determines that the real-time screenshot load corresponding to the target video sequence meets the load condition after judging, can associate the real-time screenshot load corresponding to the target video sequence with the target video sequence, and determine the video information corresponding to the target video sequence . The execution subject stores the real-time screenshot load corresponding to the target video sequence and the video information of the target video sequence in the sample video sequence set, updates the sample video sequence set, and obtains a new sample video sequence set.

Step 710, based on the new sample video sequence set, update the load calculation formula to obtain a new load calculation formula.

In this step, after the above-mentioned execution subject obtains the new set of sample video sequences, the multiple sample video sequences in the new set of sample video sequences, the sample video information of the multiple sample video sequences, and the multiple sample video sequences Perform data analysis on the corresponding sample screenshot load, and use the regression statistical method in the data analysis method to perform data fitting on multiple sample video sequences, sample video information of multiple sample video sequences, and sample screenshot loads corresponding to multiple sample video sequences, A load calculation formula corresponding to the new set of sample video sequences is obtained. Then, the executive body can also use variance analysis to perform error calculation and fine-tuning of formula parameters on the obtained load calculation formula and real-time screenshot load to obtain a new load calculation formula.

The above execution subject can use the new load calculation formula to replace the previous load calculation formula, and the subsequent candidate video sequences can use the new load calculation formula to calculate the expected screenshot load.

In this implementation, the load calculation formula is updated and adjusted in real time through the real-time load feedback mechanism, which can ensure the accuracy of the load calculation formula and more accurately calculate the expected screenshot load corresponding to the video sequence.

And, if the execution subject determines that the real-time screenshot payload corresponding to the target video sequence does not meet the load condition, the real-time screenshot payload can be discarded as new data in the new sample video sequence set.

In this embodiment, the real-time screenshot load is obtained by adding a real-time load feedback mechanism, and the load calculation formula can be updated and adjusted in real time to ensure the accuracy of the load calculation formula, so that the estimated video sequence can be calculated more accurately. screenshot load.

Continuing to refer to FIG. 6, the above-mentioned method for obtaining video screenshots may also include the following steps:

Step 670, based on the new load calculation formula and the video information of the target video sequence, calculate a new expected screenshot load corresponding to the target video sequence.

In this step, after the execution subject obtains the new load calculation formula, the video information of the target video sequence can be brought into the new load calculation formula to calculate the new expected screenshot load corresponding to the target video sequence.

Step 680: Perform a preset adjustment operation on the target video sequence according to the local available resources and the new expected screenshot load corresponding to the target video sequence.

In this step, after the execution subject obtains the new expected screenshot load corresponding to the target video sequence, it can compare the new estimated screenshot load corresponding to the target video sequence with the local available resources, and execute the target video sequence according to the comparison result. A preset adjustment operation, where the preset adjustment operation may include adjusting the number of target video sequences, or adjusting the screenshot multiple of the target video sequence.

If the new estimated screenshot load corresponding to the target video sequence is less than the local available resources, the above execution subject can calculate the new estimated screenshot load of other candidate video sequences in the video sequence set according to the new load calculation formula, and calculate the new estimated screenshot load according to the new estimated screenshot load. , the new expected screenshot load and local available resources corresponding to the target video sequence, and then add a new target video sequence, thereby increasing the number of target video sequences and improving resource utilization.

If the new expected screenshot load corresponding to the target video sequence is greater than the local available resources, then the above-mentioned executive body can determine that the new expected screenshot load corresponding to the current target video sequence has exceeded the local available resources, and the screenshot multiple speed of the target video sequence needs to be reduced to ensure Load balancing.

In this embodiment, by performing a preset adjustment operation on the target video sequence according to the local available resources and the new expected screenshot load corresponding to the target video sequence, the number of target video sequences or the screenshot multiplication speed during task execution can be adjusted in real time, so as to Ensure resource utilization and load balancing.

Further referring to FIG. 8 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for acquiring video screenshots. This device embodiment corresponds to the method embodiment shown in FIG. 2 .

As shown in FIG. 8 , the apparatus 800 for acquiring video screenshots in this embodiment may include: an acquisition module 810 , a calculation module 820 , a selection module 830 and an execution module 840 .

Wherein, the acquiring module 810 is configured to acquire the video information of multiple candidate video sequences in the video sequence set in response to acquiring the video sequence set for performing the screenshot task;

The calculation module 820 is configured to calculate the expected screenshot load corresponding to the multiple candidate video sequences based on the video information of the multiple candidate video sequences;

The selection module 830 is configured to select a target video sequence from the video sequence set based on locally available resources and the expected screenshot load corresponding to the plurality of candidate video sequences;

The execution module 840 is configured to execute the screenshot task of the target video sequence, and obtain a video screenshot corresponding to the target video sequence.

In some optional implementations of this embodiment, the calculation module 820 includes: an acquisition unit configured to acquire a load calculation formula used to calculate the screenshot load; a calculation unit configured to obtain a load calculation formula based on the load calculation formula and a plurality of candidate videos The video information of the sequence is used to calculate the expected screenshot load corresponding to multiple candidate video sequences.

In some optional implementation manners of this embodiment, the computing unit is further configured to: acquire a set of sample video sequences, where the set of sample video sequences includes multiple sample video sequences, sample video information of multiple sample video sequences, and multiple A sample screenshot load corresponding to a sample video sequence; using a regression statistical method to perform data analysis on a plurality of sample video sequences, sample video information of a plurality of sample video sequences, and sample screenshot loads corresponding to a plurality of sample video sequences, to obtain information related to the sample video The load calculation formula associated with the sample screenshot load.

In some optional implementations of this embodiment, the device also includes an update module; the acquisition module 810 is further configured to: in response to executing the screenshot task of the target video sequence, acquire the real-time screenshot load corresponding to the target video sequence; the update module , is configured to: update the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the estimated screenshot load corresponding to the target video sequence, to obtain a new load calculation formula.

In some optional implementations of this implementation, the update module is further configured to: compare the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and determine the real-time screenshot load corresponding to the target video sequence Whether it meets the load condition; in response to determining that the real-time screenshot load corresponding to the target video sequence meets the load condition, the real-time screenshot load corresponding to the target video sequence and the video information of the target video sequence are stored in the sample video sequence set to obtain a new sample video sequence set ; Based on the new sample video sequence set, the load calculation formula is updated to obtain a new load calculation formula.

In some optional implementations of this embodiment, the calculation module 820 is further configured to: calculate the new expected screenshot load corresponding to the target video sequence based on the new load calculation formula and the video information of the target video sequence; the execution module 840 , is further configured to: perform a preset adjustment operation on the target video sequence according to the local available resources and the new expected screenshot load corresponding to the target video sequence.

In the device for acquiring video screenshots provided by the above-mentioned embodiments of the present disclosure, the execution subject first acquires the video information of multiple candidate video sequences in the video sequence set in response to acquiring the video sequence set used to execute the screenshot task, and then based on the multiple The video information of the candidate video sequences, calculate the estimated screenshot load corresponding to multiple candidate video sequences, and based on the local available resources and the estimated screenshot load corresponding to multiple candidate video sequences, select the target video sequence from the video sequence set, and finally execute the target video sequence Sequence screenshot task, get the video screenshot corresponding to the target video sequence, before executing the screenshot task, you can pre-determine the expected screenshot load corresponding to the screenshot task of each video sequence, you can accurately allocate the number of screenshot tasks in advance, no need to set the task The number of threads, limit the machine resources that can be used by the task, and allocate resource quotas for the screenshot task based on the accurately calculated expected screenshot load. More screenshot tasks can be allocated based on the estimated screenshot load, making full use of the local available resources of the server, and improving load balancing and resource utilization.

Those skilled in the art can understand that the above-mentioned apparatus also includes some other known structures, such as a processor, a memory, etc., and these known structures are not shown in FIG. 8 in order to unnecessarily obscure the embodiments of the present disclosure.

Referring now to FIG. 9 , it shows a schematic structural diagram of an electronic device 900 suitable for implementing an embodiment of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as smart screens, notebook computers, PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and mobile terminals such as Stationary terminal for digital TV, desktop computer, etc. The terminal device shown in FIG. 9 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.

As shown in FIG. 9, an electronic device 900 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 901, which may be randomly accessed according to a program stored in a read-only memory (ROM) 902 or loaded from a storage device 908. Various appropriate actions and processes are executed by programs in the memory (RAM) 903 . In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are also stored. The processing device 901, ROM 902, and RAM 903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904 .

Typically, the following devices can be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speakers, vibration an output device 907 such as a computer; a storage device 908 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 909. The communication means 909 may allow the electronic device 900 to perform wireless or wired communication with other devices to exchange data. While FIG. 9 shows electronic device 900 having various means, it is to be understood that implementing or having all of the means shown is not a requirement. More or fewer means may alternatively be implemented or provided. Each block shown in FIG. 9 may represent one device, or may represent multiple devices as required.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, where the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 909, or from storage means 908, or from ROM 902. When the computer program is executed by the processing device 901, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed. It should be noted that the computer-readable medium in the embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the embodiments of the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

Computer program code for carrying out the operations of the embodiments of the present disclosure may be written in one or more programming languages, or combinations thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional A procedural programming language such as "C" or a similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. Where a remote computer is involved, the remote computer may be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g. via the Internet using an Internet Service Provider). .

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. 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 they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The units involved in the embodiments described in the present application may be implemented by means of software or by means of hardware. The described unit can also be set in a processor, for example, it can be described as: a processor includes an acquisition module, a calculation module, a selection module and an execution module, wherein the names of these modules do not constitute a reference to The module itself is defined.

As another aspect, the present application also provides a computer-readable medium. The above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: in response to acquiring the video sequence set for performing the screenshot task, acquires the video sequence set The video information of multiple candidate video sequences; based on the video information of multiple candidate video sequences, calculate the expected screenshot load corresponding to multiple candidate video sequences; based on the local available resources and the estimated screenshot load corresponding to multiple candidate video sequences, from the video The target video sequence is selected from the sequence set; the screenshot task of the target video sequence is executed to obtain a video screenshot corresponding to the target video sequence.

The above description is only a preferred embodiment of the present disclosure and an illustration of the applied technical principle. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solution formed by the specific combination of the above-mentioned technical features, but also covers the above-mentioned invention without departing from the above-mentioned inventive concept. Other technical solutions formed by any combination of technical features or equivalent features. For example, a technical solution formed by replacing the above-mentioned features with technical features with similar functions disclosed in (but not limited to) the embodiments of the present disclosure.

Claims (14)

1. A method of obtaining a video screenshot, the method comprising:

in response to acquiring a set of video sequences for performing a screenshot task, acquiring video information of a plurality of candidate video sequences in the set of video sequences;

calculating predicted screenshot loads corresponding to the plurality of candidate video sequences based on video information of the plurality of candidate video sequences;

selecting a target video sequence from the video sequence set based on locally available resources and expected screenshot loads corresponding to the plurality of candidate video sequences;

and executing the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.

2. The method of claim 1, wherein the calculating, based on video information of the plurality of candidate video sequences, an expected screenshot load for the plurality of candidate video sequences comprises:

acquiring a load calculation formula for calculating a screenshot load;

and calculating the expected screenshot loads corresponding to the candidate video sequences based on the load calculation formula and the video information of the candidate video sequences.

3. The method of claim 2, wherein the obtaining a load calculation formula for calculating a screenshot load comprises:

acquiring a sample video sequence set, wherein the sample video sequence set comprises a plurality of sample video sequences, sample video information of the plurality of sample video sequences and sample screenshot loads corresponding to the plurality of sample video sequences;

and carrying out data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula associated with the sample video information and the sample screenshot loads.

4. A method according to any one of claims 1-3, the method further comprising:

Responding to the execution of the screenshot task of the target video sequence, and acquiring a real-time screenshot load corresponding to the target video sequence;

and updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.

5. The method of claim 4, wherein updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the predicted screenshot load corresponding to the target video sequence to obtain a new load calculation formula comprises:

comparing the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not;

in response to determining that the real-time screenshot load corresponding to the target video sequence meets a load condition, storing the real-time screenshot load corresponding to the target video sequence and video information of the target video sequence into a sample video sequence set to obtain a new sample video sequence set;

And updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.

6. The method of claim 4, the method further comprising:

calculating a new expected screenshot load corresponding to the target video sequence based on the new load calculation formula and the video information of the target video sequence;

and executing preset adjustment operation on the target video sequence according to the locally available resources and the new expected screenshot load corresponding to the target video sequence.

7. An apparatus for obtaining a video screenshot, the apparatus comprising:

an acquisition module configured to acquire video information of a plurality of candidate video sequences in a set of video sequences in response to acquiring the set of video sequences for performing a screenshot task;

a calculation module configured to calculate predicted screenshot loads corresponding to the plurality of candidate video sequences based on video information of the plurality of candidate video sequences;

a selection module configured to select a target video sequence from the set of video sequences based on locally available resources and predicted screenshot loads corresponding to the plurality of candidate video sequences;

And the execution module is configured to execute the screenshot task of the target video sequence to obtain the video screenshot corresponding to the target video sequence.

8. The apparatus of claim 7, wherein the computing module comprises:

an acquisition unit configured to acquire a load calculation formula for calculating a screenshot load;

and a calculating unit configured to calculate predicted screenshot loads corresponding to the plurality of candidate video sequences based on the load calculation formula and video information of the plurality of candidate video sequences.

9. The apparatus of claim 8, wherein the computing unit is further configured to:

acquiring a sample video sequence set, wherein the sample video sequence set comprises a plurality of sample video sequences, sample video information of the plurality of sample video sequences and sample screenshot loads corresponding to the plurality of sample video sequences;

and carrying out data analysis on the plurality of sample video sequences, the sample video information of the plurality of sample video sequences and the sample screenshot loads corresponding to the plurality of sample video sequences by using a regression statistical method, and obtaining a load calculation formula associated with the sample video information and the sample screenshot loads.

10. The apparatus of any of claims 7-9, wherein the apparatus further comprises an update module;

the acquisition module is further configured to: responding to the execution of the screenshot task of the target video sequence, and acquiring a real-time screenshot load corresponding to the target video sequence;

the update module is configured to: and updating the load calculation formula based on the real-time screenshot load corresponding to the target video sequence and the expected screenshot load corresponding to the target video sequence to obtain a new load calculation formula.

11. The apparatus of claim 10, wherein the update module is further configured to:

comparing the real-time screenshot load corresponding to the target video sequence with the expected screenshot load corresponding to the target video sequence, and judging whether the real-time screenshot load corresponding to the target video sequence meets the load condition or not;

in response to determining that the real-time screenshot load corresponding to the target video sequence meets a load condition, storing the real-time screenshot load corresponding to the target video sequence and video information of the target video sequence into a sample video sequence set to obtain a new sample video sequence set;

And updating the load calculation formula based on the new sample video sequence set to obtain a new load calculation formula.

12. The apparatus of claim 10, wherein the computing module is further configured to: calculating a new expected screenshot load corresponding to the target video sequence based on the new load calculation formula and the video information of the target video sequence;

the execution module is further configured to: and executing preset adjustment operation on the target video sequence according to the locally available resources and the new expected screenshot load corresponding to the target video sequence.

13. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.

14. A computer readable medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method according to any one of claims 1-6.

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