Video processing method, device and equipment

Abstract

The present invention discloses a video processing method, device and equipment, the method comprising: obtaining task processing information of a video to be processed; inputting the task processing information into a trained video processing duration determination model for processing to obtain the processing duration of the video to be processed; determining the type of processing channel for processing the video to be processed according to the processing duration of the video to be processed and the preset processing duration threshold range of the video processing channel; determining the target processing channel for processing the video to be processed according to the load of the processing channel of the processing channel type. Through the above manner, the present invention can realize automatic adaptation of the target processing channel and improve the efficiency of video processing.

Description

Technical Field

[0001] The present invention relates to the field of video processing technology, and in particular to a video processing method, device and equipment. Background Art

[0002] When video processing tasks are issued, they are issued to the task channel randomly or according to a certain balancing algorithm, but the problem of isolating tasks that take a long, medium, or short time to process cannot be solved, resulting in long waiting times for tasks that could have been processed in a short time.

[0003] The existing video processing task distribution mode is based on the active pulling method of cluster nodes. At the same time, because the current video processing task distribution channel is fixed and single, the video processing task does not consider the information of the video itself when distributing it. The service nodes can only blindly obtain tasks indiscriminately, which may cause the service nodes with low configuration to be overloaded, while the nodes with high configuration cannot give full play to the machine performance. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a video processing method, apparatus and device that overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of an embodiment of the present invention, there is provided a video processing method, including:

[0006] Get task processing information of the video to be processed;

[0007] Input the task processing information into a trained video processing time determination model for processing to obtain the processing time of the video to be processed;

[0008] Determining a processing channel type for processing the video to be processed according to the processing time of the video to be processed and a preset processing time threshold range of the video processing channel;

[0009] A target processing channel for processing the video to be processed is determined according to the load of the processing channel of the processing channel type.

[0010] According to another aspect of an embodiment of the present invention, there is provided a video processing device, including:

[0011] An acquisition module is used to obtain task processing information of the video to be processed;

[0012] The processing module is used to input the task processing information into a trained video processing time determination model for processing to obtain the processing time of the video to be processed; determine the type of processing channel for processing the video to be processed based on the processing time of the video to be processed and the preset processing time threshold range of the video processing channel; and determine the target processing channel for processing the video to be processed based on the load of the processing channel of the processing channel type.

[0013] According to another aspect of an embodiment of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0014] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned video processing method.

[0015] According to another aspect of the embodiments of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned video processing method.

[0016] According to the solution provided by the above-mentioned embodiment of the present invention, task processing information of the video to be processed is obtained; the task processing information is input into a trained video processing time determination model for processing to obtain the processing time of the video to be processed; the type of processing channel for processing the video to be processed is determined according to the processing time of the video to be processed and the preset processing time threshold range of the video processing channel; the target processing channel for processing the video to be processed is determined according to the load of the processing channel of the processing channel type; the target processing channel is determined according to the actual processing time of the video and the load of the processing channel, the target processing channel is automatically adapted, and the beneficial effect of improving the video processing efficiency is achieved.

[0017] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation method of the embodiment of the present invention is specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the embodiments of the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0019] Figure 1 A flow chart of a video processing method provided by an embodiment of the present invention is shown;

[0020] Figure 2 A flowchart of a video processing method provided by another embodiment of the present invention is shown;

[0021] Figure 3 A schematic diagram of the architecture of a video processing system provided by an embodiment of the present invention is shown;

[0022] Figure 4 A schematic diagram showing the structure of a video processing device provided by an embodiment of the present invention is shown;

[0023] Figure 5 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0024] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0025] Figure 1 FIG. 4 is a flow chart showing a video processing method provided by an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0026] Step 11, obtaining task processing information of the video to be processed;

[0027] Step 12, inputting the task processing information into a trained video processing time determination model for processing to obtain the processing time of the video to be processed;

[0028] Step 13, determining the type of processing channel for processing the video to be processed according to the processing time of the video to be processed and a preset processing time threshold range of the video processing channel;

[0029] Step 14: Determine a target processing channel for processing the video to be processed according to the load of the processing channel of the processing channel type.

[0030] The video processing method described in this embodiment obtains task processing information of a video to be processed; inputs the task processing information into a trained video processing time determination model for processing to obtain the processing time of the video to be processed; determines the type of processing channel for processing the video to be processed based on the processing time of the video to be processed and a preset processing time threshold range of the video processing channel; determines the target processing channel for processing the video to be processed based on the load of the processing channel of the processing channel type, thereby achieving the beneficial effect of determining the target processing channel based on the actual processing time of the video and the load of the processing channel, realizing automatic adaptation of the target processing channel, and improving video processing efficiency.

[0031] Figure 2 FIG. 2 is a flow chart showing a processing method provided by another embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0032] Step 21, obtaining a frame rate range of the video according to the video medium;

[0033] Specifically, the open source multimedia processing tool FFmpeg is used to analyze the video medium, and the required data is collected from the output detailed media information, including key data such as fps (frame rate), bitRate (bit rate), t (audio and video duration), but not limited to the above. And according to the existing video production specifications, the frame rate range is determined, and the frame rate range can be (20,30], (30,60], (60,~].

[0034] Step 22, selecting a plurality of training videos from the video training set according to the frame rate range;

[0035] Specifically, a batch of training videos with frame rates within the frame rate range but different durations and bit rates may be randomly selected at the audio and video convergence center.

[0036] Step 23, inputting the task processing information of the plurality of training videos into a preset video processing duration determination model for training, and obtaining a trained video processing duration determination model.

[0037] In this embodiment, frame rates in different intervals are trained and the models are determined in the same manner.

[0038] In another optional embodiment of the present invention, step 23 may include:

[0039] Step 231, inputting the video duration, bit rate and actual processing duration information of the plurality of training videos into Z=[αx+βy] / γ for training to obtain the values ​​of α, β, and γ; wherein Z is the processing duration, x is the video duration, and y is the bit rate;

[0040] Step 232, determine Z with the determined values ​​of α, β, and γ as the trained video processing duration determination model.

[0041] In this embodiment, while ensuring that other factors remain unchanged, the only variable factors are duration and bit rate. Jenkins (an open source software project, a continuous integration tool developed based on Java) is used to send requests concurrently to simulate online audio and video production, and collect relevant data such as video duration, processing duration, and bit rate. Model training is performed on the collected data, and data fitting is performed through a regression model to obtain model parameters such as α, β, γ in Z = [αx + βy] / γ.

[0042] In yet another optional embodiment of the present invention, step 12 may include:

[0043] The model trained by the processing method provided by the above embodiment uses the task bit rate and video duration in the video to be processed as model input data to obtain the processing duration of the video to be processed.

[0044] In another optional embodiment of the present invention, step 13 may include:

[0045] Step 131, comparing the processing time of the video to be processed with a preset processing time threshold range of the video processing channel to obtain a comparison result;

[0046] Step 132: If the comparison result indicates that the processing time of the to-be-processed video is within a preset processing time threshold range of a target processing channel type, the target processing channel is determined as the processing channel type of the to-be-processed video.

[0047] In this embodiment, the processing duration of the video to be processed is compared with the duration thresholds corresponding to the long, medium or short processing channels of the video processing channel, respectively, to determine the processing channel type of the video to be processed. For example, if the comparison result indicates that the processing duration of the video to be processed is within the duration threshold range of the long channel, the processing channel type of the video to be processed is determined to be the long processing channel;

[0048] In yet another optional embodiment of the present invention, step 14 may include:

[0049] Step 141, obtaining a load threshold of a processing channel of the processing channel type;

[0050] Step 142, selecting a target processing channel set that does not exceed the load threshold from a plurality of processing channels corresponding to the processing channel type according to the load threshold;

[0051] Step 143: Determine a target processing channel for processing the to-be-processed video from the target processing channel set according to a preset algorithm.

[0052] In yet another optional embodiment of the present invention, step 141 may include:

[0053] Step 1411, by formula C k =A*B k +B*T k +C*F k Obtain the processing performance of a single video of a processing channel of the processing channel type; wherein, C k is the processing performance of a single video of the processing channel of the processing channel type; A is the coefficient of bit rate on performance consumption, B is the coefficient of video duration on performance consumption, C is the coefficient of frame rate on performance consumption, B K is the bit rate, T K is the video duration, F K is the frame rate, k ranges from 1 to n;

[0054] Step 1412, according to the formula Get the total number of processing channel tasks performance load of the processing channel type, where L m is the performance load of the total number of processing channel tasks of the processing channel type, and N is the number of processing channels in the target processing channel set;

[0055] Step 1413: The total number of processing channel tasks of the processing channel type is processed, and the coefficient matrix I is obtained according to the performance level. 1 , I 2 …I m ), (0<I m ≤1);

[0056] Step 1414, according to formula O n =L n / I n , obtain the load threshold of the processing channel of the processing channel type, where O n A load threshold for a processing channel of the processing channel type.

[0057] In this embodiment, since each channel type can be bound to multiple channels, the number of tasks that each channel can accommodate is different. After determining the channel type, it is also necessary to determine a threshold that each channel can reach based on the bit rate, duration, frame rate and inherent machine performance.

[0058] In yet another optional embodiment of the present invention, step 143 may include:

[0059] Step 1431, according to the formula avg(n)=N0+N1+...Np, obtain the channel of the critical average task, where avg(n) is the channel of the critical average task;

[0060] Specifically, the load threshold O of each channel is calculated, and the number of existing tasks n of each channel is obtained, the tasks are sorted, and the channels that do not exceed the threshold are taken out. Finally, the channel of the critical average task is obtained through the formula avg(n)=N0+N1+...Np.

[0061] Step 1432, input the channel of the critical average task into the formula Hash(avg(n)) to obtain the target processing channel for processing the video to be processed, where n is the number of existing tasks for each channel, N is the number of processing channels in the target processing channel set, and Hash is a hash function.

[0062] Combine the following Figure 3 The application scenarios of the above embodiments of the present invention are described as follows:

[0063] like Figure 3 As shown, it is a schematic diagram of the architecture of the video processing system; in the video processing system, the video to be processed is accessed through the access party interface, and the task scheduling computing center of the system decomposes the video to be processed into multiple video processing tasks, and writes them into the corresponding processing channels through services 1, 2, and 3 of the task scheduling computing center. For example, a short processing channel, a long processing channel, or a medium processing channel can be written. Of course, the type of processing channel here is not limited to this; of course, each video processing task here can also be a video to be processed; when these video processing tasks are specifically written to the corresponding target processing channel, the method described in the above embodiment of the present invention can be used to realize the judgment of the processing channel type and the determination of the target processing channel; it can be seen from the system architecture that the video processing tasks in one type of processing channel can be processed by one node, and there can be multiple video processing tasks processed on one node;

[0064] The node heartbeat detection center is used to detect the health status of each node and feed back the relevant processing channel information to the task scheduling computing center.

[0065] In an embodiment of the present invention, a video medium is pre-analyzed, where the video medium may be a medium such as a file where the video is located, and the processing channel may be a processing queue, etc. The method described in the embodiment of the present invention automatically adapts a suitable processing channel according to the processing time of the video to be processed and the load performance of the corresponding processing channel, thereby improving the video processing efficiency. At the same time, the nodes in the system also implement channel binding, which can be intelligently scaled, and the channel tasks can be transferred. Moreover, after a large number of tests, the video processing speed is increased by at least 10 times compared with the existing method.

[0066] Figure 4 FIG. 4 is a schematic diagram showing the structure of a video processing device 40 provided in an embodiment of the present invention. Figure 4 As shown, the device comprises:

[0067] An acquisition module 41 is used to acquire task processing information of a video to be processed;

[0068] The processing module 42 is used to input the task processing information into a trained video processing time determination model for processing to obtain the processing time of the video to be processed; determine the type of processing channel for processing the video to be processed based on the processing time of the video to be processed and the preset processing time threshold range of the video processing channel; determine the target processing channel for processing the video to be processed based on the load of the processing channel of the processing channel type.

[0069] Optionally, the processing module 42 is further used to obtain a frame rate range of the video according to the video medium;

[0070] According to the frame rate range, selecting a plurality of training videos from a video training set;

[0071] The task processing information of the plurality of training videos is input into a preset video processing duration determination model for training to obtain a trained video processing duration determination model.

[0072] Optionally, the processing module 42 is further used to input the video duration, bit rate and actual processing duration information of the multiple training videos into Z=[αx+βy] / γ for training to obtain the values ​​of α, β, and γ; wherein Z is the processing duration, x is the video duration, and y is the bit rate;

[0073] The Z with the determined values ​​of α, β, and γ is determined as the trained video processing duration determination model.

[0074] Optionally, the processing module 42 is further used to compare the processing time of the video to be processed with a preset processing time threshold range of the video processing channel to obtain a comparison result;

[0075] If the comparison result indicates that the processing time of the to-be-processed video is within a preset processing time threshold range of a target processing channel type, the target processing channel is determined as the processing channel type of the to-be-processed video.

[0076] Optionally, the processing module 42 is further used to obtain a load threshold of a processing channel of the processing channel type;

[0077] According to the load threshold, selecting a target processing channel set that does not exceed the load threshold from a plurality of processing channels corresponding to the processing channel type;

[0078] A target processing channel for processing the video to be processed is determined from the target processing channel set according to a preset algorithm.

[0079] Optionally, the processing module 42 is further configured to: k =A*B k +B*T k +C*F k Obtain the processing performance of a single video of a processing channel of the processing channel type; wherein, C k is the processing performance of a single video of the processing channel of the processing channel type; A is the coefficient of bit rate on performance consumption, B is the coefficient of video duration on performance consumption, C is the coefficient of frame rate on performance consumption, B K is the bit rate, T K is the video duration, F K is the frame rate, k ranges from 1 to n;

[0080] According to the formula The sum is calculated to obtain the total number of processing channel tasks performance load of the processing channel type, where L m is the performance load of the total number of processing channel tasks of the processing channel type, and N is the number of processing channels in the target processing channel set;

[0081] The total number of processing channel tasks of the processing channel type is processed according to the performance level to obtain the coefficient matrix I = (1 1 , I 2 …I m ), (0<I m ≤1);

[0082] According to the formula n =L n / I n , obtain the load threshold of the processing channel of the processing channel type, where O n A load threshold for a processing channel of the processing channel type.

[0083] Optionally, the processing module 42 is further used to obtain the channel of the critical average task according to the formula avg(n)=N0+N1+...Np, where avg(n) is the channel of the critical average task;

[0084] The channel of the critical average task is input into the formula Hash(avg(n)) to obtain the target processing channel for processing the video to be processed, where n is the number of existing tasks for each channel and N is the number of processing channels in the target processing channel set.

[0085] It should be noted that this embodiment is a device embodiment corresponding to the above method embodiment, and all implementation methods in the above method embodiment are applicable to the embodiment of this device and can achieve the same technical effect.

[0086] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the video processing method in any of the above method embodiments.

[0087] Figure 5 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention is shown, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0088] like Figure 5 As shown, the computing device may include: a processor, a communications interface, a memory, and a communication bus.

[0089] Wherein: the processor, the communication interface, and the memory communicate with each other via a communication bus. The communication interface is used to communicate with other devices such as a client or other server network elements. The processor is used to execute a program, specifically, to execute the relevant steps in the above-mentioned video processing method embodiment for a computing device.

[0090] Specifically, the program may include program codes including computer operation instructions.

[0091] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0092] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0093] The program can be specifically used to enable the processor to execute the video processing method in any of the above method embodiments. The specific implementation of each step in the program can refer to the corresponding descriptions in the corresponding steps and units in the above video processing method embodiments, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above method embodiments, which will not be repeated here.

[0094] The algorithm or display provided herein is not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the embodiment of the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the embodiment of the present invention described herein, and the description of the above specific language is to disclose the best mode of the embodiment of the present invention.

[0095] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0096] Similarly, it should be understood that in order to streamline the embodiments of the present invention and to aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: that the claimed embodiments of the present invention require more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0097] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0098] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0099] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing an embodiment of the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0100] It should be noted that the above embodiments illustrate rather than limit the present invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim listing a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A video processing method, characterized in that: The method comprises: Get task processing information of the video to be processed; Input the task processing information into a trained video processing duration determination model for processing to obtain the processing duration of the video to be processed; wherein the task bit rate and video duration of the video to be processed are used as model input data; Determining a processing channel type for processing the video to be processed according to the processing time of the video to be processed and a preset processing time threshold range of the video processing channel; According to the load threshold of the processing channel of the processing channel type, a target processing channel for processing the video to be processed is determined, wherein the load threshold of the processing channel is obtained according to the bit rate, video duration, frame rate and inherent machine performance of a single video of the processing channel of the processing channel type, specifically including: obtaining the processing performance of a single video of the processing channel, and then obtaining the performance load of the total number of tasks of the processing channel; obtaining a coefficient matrix according to the performance level, and obtaining the load threshold of the processing channel according to the performance load of the total number of tasks of the processing channel and the coefficient matrix; according to the load threshold, selecting a set of target processing channels that do not exceed the load threshold from a plurality of processing channels corresponding to the processing channel type; determining the target processing channel for processing the video to be processed from the set of target processing channels according to a preset algorithm, specifically, first obtaining a channel of a critical average task, and inputting the channel of the critical average task into a hash function formula to obtain a target processing channel for processing the video to be processed.

2. The video processing method according to claim 1, characterized in that: The video processing duration determination model is trained through the following process: Get the frame rate range of the video according to the video medium; According to the frame rate range, selecting a plurality of training videos from a video training set; The task processing information of the plurality of training videos is input into a preset video processing duration determination model for training to obtain a trained video processing duration determination model.

3. The video processing method according to claim 2, characterized in that: Inputting the task processing information of the plurality of training videos into a preset video processing duration determination model for training to obtain a trained video processing duration determination model, including: Input the video duration, bit rate and actual processing duration information of the multiple training videos into Z=[αx+βy] / γ for training to obtain the values ​​of α, β, and γ; wherein Z is the processing duration, x is the video duration, and y is the bit rate; The Z with the determined values ​​of α, β, and γ is determined as the trained video processing duration determination model.

4. The video processing method according to claim 1, characterized in that: Determining the type of processing channel for processing the video to be processed according to the processing time of the video to be processed and a preset processing time threshold range of the video processing channel includes: Compare the processing time of the video to be processed with a preset processing time threshold range of the video processing channel to obtain a comparison result; If the comparison result indicates that the processing time of the to-be-processed video is within a preset processing time threshold range of a target processing channel type, the target processing channel is determined as the processing channel type of the to-be-processed video.

5. The video processing method according to claim 1, characterized in that: Obtaining a load threshold of a processing channel of the processing channel type includes: By formula Obtaining the processing performance of a single video of a processing channel of the processing channel type; wherein, is the processing performance of a single video of the processing channel of the processing channel type; A is the coefficient of bit rate on performance consumption, B is the coefficient of video duration on performance consumption, C is the coefficient of frame rate on performance consumption, B K is the bit rate, T K is the video duration, F K is the frame rate, k ranges from 1 to N; According to the formula = , get the total number of processing channel tasks performance load of the processing channel type, where, is the performance load of the total number of processing channel tasks of the processing channel type, and N is the total number of processing channel tasks; The total number of processing channel tasks of the processing channel type is loaded, and the coefficient matrix is ​​obtained according to the performance level. =( … ), (0< ≤1); According to the formula , obtain the load threshold of the processing channel of the processing channel type, where, A load threshold for a processing channel of the processing channel type.

6. A video processing device, characterized in that: The device comprises: An acquisition module is used to obtain task processing information of the video to be processed; The processing module is used to input the task processing information into a trained video processing time determination model for processing, so as to obtain the processing time of the video to be processed; wherein the task bit rate and video time of the video to be processed are used as model input data; the type of processing channel for processing the video to be processed is determined according to the processing time of the video to be processed and the preset processing time threshold range of the video processing channel; the target processing channel for processing the video to be processed is determined according to the load threshold of the processing channel of the processing channel type, wherein the load threshold of the processing channel is obtained according to the bit rate, video time, frame rate and inherent machine performance of a single video of the processing channel of the processing channel type, The method comprises: obtaining the processing performance of a single video of a processing channel, and then obtaining the performance load of the total number of tasks of the processing channel; obtaining a coefficient matrix according to the performance level, and obtaining the load threshold of the processing channel according to the performance load of the total number of tasks of the processing channel and the coefficient matrix; selecting a target processing channel set that does not exceed the load threshold from a plurality of processing channels corresponding to the processing channel type according to the load threshold; determining a target processing channel for processing the video to be processed from the target processing channel set according to a preset algorithm, specifically, first obtaining a channel of a critical average task, inputting the channel of the critical average task into a hash function formula, and obtaining a target processing channel for processing the video to be processed.

7. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the video processing method according to any one of claims 1 to 5.

8. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform an operation corresponding to the video processing method according to any one of claims 1 to 5.

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