Multimedia data optimization processing method and device for network set-top box

Abstract

The present invention provides a multimedia data optimization processing method and device for a network set-top box. The method includes: obtaining a target data frame to be processed currently, and determining an optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality judgment result of the target data frame; processing the target data frame based on the optimization processing method to obtain a data frame to be executed after the optimization processing. By adopting the method provided by the present invention, the optimization processing method for the target data frame is dynamically adjusted based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality judgment result of the target data frame, which can reduce the occurrence of freezes, a large number of screen distortions and other phenomena encountered during the viewing process, improve the processing efficiency and accuracy of abnormal data frames, and thus enhance the viewing perception experience of TV users.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and more particularly to a method and device for optimizing multimedia data processing for a network set-top box. Furthermore, the present invention also relates to an electronic device and a processor-readable storage medium. Background Art

[0002] In recent years, with the rapid development of network technology, various network-based multimedia terminals, such as digital video conversion devices like network set-top boxes, have become increasingly widespread. Currently, network set-top boxes often experience screen distortion and freezing during video projection. This phenomenon is caused by improper processing of "abnormal data frames" by the set-top box chip.

[0003] In existing technologies, when a set-top box receives an error frame, it typically relies on defined fixed parameters. Improperly configured fixed parameters can result in excessive screen distortion or prolonged, noticeable freezes, leading to a poor user experience. Therefore, designing a stable multimedia data optimization solution for network set-top boxes has become an urgent challenge. Summary of the Invention

[0004] To this end, the present invention provides a multimedia data optimization processing method and device for a network set-top box to solve the problem that the abnormal data frame processing scheme for a network set-top box in the prior art has high limitations, is prone to screen distortion, freezes and other phenomena, and leads to a poor experience.

[0005] In a first aspect, the present invention provides a multimedia data optimization processing method for a network set-top box, comprising:

[0006] Obtaining a target data frame to be processed currently, and determining an optimization processing method for the target data frame based on processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and a quality judgment result of the target data frame;

[0007] The target data frame is processed based on the optimization processing method to obtain a data frame to be executed after the optimization processing.

[0008] In one embodiment, determining the optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame specifically includes:

[0009] If there are no consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, determining an optimization processing method for the target data frame according to the quality difference judgment result of the target data frame; or

[0010] If there are fewer than a first number of consecutive abnormal data frames in a preset number of consecutive data frames before the target data frame, determining an optimization processing method for the target data frame based on a processing result of an abnormal data frame adjacent to the target data frame in the consecutive abnormal data frames; wherein the preset number is greater than the first number; or

[0011] If there are more than the first number of consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, an optimization processing method for the target data frame is determined according to processing results of two abnormal data frames adjacent to the target data frame in the consecutive abnormal data frames.

[0012] In one embodiment, the tuning processing method is to determine whether to discard the target data frame according to a preset abnormal data frame processing rule.

[0013] In one embodiment, the abnormal data frame processing rules include: if the abnormal data frame among the preset number of continuous data frames before the target data frame is a non-continuous abnormal data frame, the target data frame is discarded; if an abnormal data frame adjacent to the target data frame has been discarded, the target data frame is retained; if an abnormal data frame adjacent to the target data frame has not been discarded, the target data frame is discarded; if two abnormal data frames adjacent to the target data frame have been discarded, the target data frame is retained; if the two abnormal data frames adjacent to the target data frame have not been discarded, the target data frame is discarded.

[0014] In one embodiment, the abnormal data frame is a data frame whose quality difference meets a preset indicator.

[0015] In one embodiment, the multimedia data optimization processing method for a network set-top box further includes:

[0016] Determining the quality of the data frame according to the number of error slices in the data frame;

[0017] The data frames are separated according to the quality difference degree of the data frames to obtain the target data frames; wherein the target data frames are normal data frames or abnormal data frames.

[0018] In one embodiment, determining the quality degree of the data frame according to the number of error slices in the data frame specifically includes: determining the quality degree of the data frame based on an actual accuracy rate of the data frame and a preset accuracy rate standard value.

[0019] In a second aspect, the present invention further provides a multimedia data optimization processing device for a network set-top box, comprising:

[0020] A dynamic result optimization processing judgment unit is used to obtain a target data frame to be processed currently, and determine an optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame;

[0021] The optimization processing result obtaining unit is used to process the target data frame based on the optimization processing method to obtain a data frame to be executed after the optimization processing.

[0022] In one embodiment, the dynamic result optimization processing and judgment unit is specifically configured to:

[0023] If there are no consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, determining an optimization processing method for the target data frame according to the quality difference judgment result of the target data frame; or

[0024] If there are fewer than a first number of consecutive abnormal data frames in a preset number of consecutive data frames before the target data frame, determining an optimization processing method for the target data frame based on a processing result of an abnormal data frame adjacent to the target data frame in the consecutive abnormal data frames; wherein the preset number is greater than the first number; or

[0025] If there are more than the first number of consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, an optimization processing method for the target data frame is determined according to processing results of two abnormal data frames adjacent to the target data frame in the consecutive abnormal data frames.

[0026] In one embodiment, the tuning processing method is to determine whether to discard the target data frame according to a preset abnormal data frame processing rule.

[0027] In one embodiment, the abnormal data frame processing rules include: if the abnormal data frame among the preset number of continuous data frames before the target data frame is a non-continuous abnormal data frame, the target data frame is discarded; if an abnormal data frame adjacent to the target data frame has been discarded, the target data frame is retained; if an abnormal data frame adjacent to the target data frame has not been discarded, the target data frame is discarded; if two abnormal data frames adjacent to the target data frame have been discarded, the target data frame is retained; if the two abnormal data frames adjacent to the target data frame have not been discarded, the target data frame is discarded.

[0028] In one embodiment, the abnormal data frame is a data frame whose quality difference meets a preset indicator.

[0029] In one embodiment, the multimedia data optimization processing device for a network set-top box further includes: an initial data frame separation unit, configured to:

[0030] Determining the quality of the data frame according to the number of error slices in the data frame;

[0031] The data frames are separated according to the quality difference degree of the data frames to obtain the target data frames; wherein the target data frames are normal data frames or abnormal data frames.

[0032] In one embodiment, determining the quality degree of the data frame according to the number of error slices in the data frame specifically includes: determining the quality degree of the data frame based on an actual accuracy rate of the data frame and a preset accuracy rate standard value.

[0033] In a third aspect, the present invention also provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the program, the steps of the multimedia data optimization processing method for a network set-top box as described in any one of the above items are implemented.

[0034] In a fourth aspect, the present invention further provides a processor-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the multimedia data optimization processing method for a network set-top box as described in any one of the above items are implemented.

[0035] The multimedia data optimization processing method for a network set-top box provided in an embodiment of the present invention dynamically adjusts the tuning processing method for the target data frame through the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality judgment results of the target data frame. It can reduce the occurrence of phenomena such as freezes and large-scale screen distortion encountered during the viewing process, improve the processing efficiency and accuracy of abnormal data frames, and thus enhance the viewing perception experience of TV users. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 A flowchart of a multimedia data optimization processing method for a network set-top box provided by an embodiment of the present invention;

[0038] Figure 2A complete flow chart of a multimedia data optimization processing method for a network set-top box provided by an embodiment of the present invention;

[0039] Figure 3 A schematic structural diagram of a multimedia data optimization processing device for a network set-top box provided by an embodiment of the present invention;

[0040] Figure 4 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0042] The multimedia data optimization processing method for a network set-top box provided by the present invention adds the recording and judgment of abnormal data frames, and can dynamically adjust the processing method of the target data frame according to the processing results of the first n consecutive data frames and the quality judgment result of the target data frame. The processing method can change with the number of abnormal data frames. The more error frames, the more processing, and the fewer error frames, the less processing, so as to ensure that the abnormal data frames are not continuously discarded, thereby avoiding causing long-term obvious freezes; at the same time, some abnormal data frames are appropriately discarded to reduce the occurrence of the screen distortion phenomenon, thereby effectively improving the viewing perception of TV users.

[0043] The following is a detailed description of an embodiment of the multimedia data optimization processing method for a network set-top box based on the present invention. Figure 1 , which is a flow chart of a multimedia data optimization processing method for a network set-top box provided by an embodiment of the present invention. The specific implementation process includes the following steps:

[0044] Step 101: Obtain a target data frame to be processed currently, and determine an optimization processing method for the target data frame based on processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and a quality judgment result of the target data frame.

[0045] Before performing this step, the data frames must be separated to obtain a preliminary result containing the target data frames. Specifically, the quality level of the data frames is first determined based on the number of error segments within the data frames. The data frames are then separated based on the quality level of the data frames to obtain a preliminary result containing the target data frames. The target data frames can be normal data frames or abnormal data frames. Abnormal data frames can be error data frames or poor quality data frames, such as video frames with poor signal quality.

[0046] The step of determining the quality difference of the data frame according to the number of error slices in the data frame specifically includes: determining the quality difference of the data frame based on the actual accuracy of the data frame and a preset accuracy standard value.

[0047] For example, if Figure 2 As shown, in the actual implementation process, it is necessary to first determine the actual accuracy R of the i-th data frame (i.e., the target data frame) i Among them, R i =(total number of slices in the i-th data frame - number of error slices in the i-th data frame) / total number of slices in the i-th data frame*100%; then, a standard value P of the accuracy rate corresponding to the i-th data frame is preset; when R i When ≤P, it is determined that the quality of the data frame is high, so the i-th data frame can be determined to be an abnormal data frame. The network set-top box makes a preliminary judgment based on the above preset accuracy standard value, and the judgment result for each data frame is D i When D i =1,0 <R i ≤P, that is, when the actual accuracy of a data frame is less than P, the data frame is determined to be an abnormal data frame, and the quality difference judgment result is 1; when D i =0,P <R i ≤1, that is, when the actual accuracy of a data frame is greater than P, the data frame is determined to be a normal data frame, and the poor quality judgment result is 0.

[0048] From the above, we can see that when the quality judgment result D of the i-th data frame i = 0, it means that the i-th data frame has no quality difference or has a slight quality difference, that is, it is determined to be a normal data frame, and then compensation processing is performed or it is directly output without processing; when the quality difference judgment result D i =1, it indicates that the i-th frame has a relatively serious quality problem, and further judgment is needed to determine whether to discard the abnormal quality frame.

[0049] After obtaining the target data frame based on the above content, in this step, by obtaining the target data frame to be processed, and based on the processing results of the abnormal data frames in the preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame, the corresponding tuning processing method for the target data frame is determined.

[0050] The tuning processing method for the target data frame is determined based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame. The specific implementation process includes: if there are no consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, then the tuning processing method for the target data frame is determined according to the quality difference judgment result of the target data frame; or, if there are fewer than a first number of consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, then the tuning processing method for the target data frame is determined according to the processing results of an abnormal data frame adjacent to the target data frame in the continuous abnormal data frames; wherein the preset number is greater than the first number; or, if there are more than the first number of consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, then the tuning processing method for the target data frame is determined according to the processing results of two abnormal data frames adjacent to the target data frame in the continuous abnormal data frames.

[0051] In an embodiment of the present invention, the tuning processing method is to determine whether to discard the target data frame according to a preset abnormal data frame processing rule. Wherein, the abnormal data frame processing rule includes but is not limited to: if the abnormal data frame in the preset number of continuous data frames before the target data frame is a non-continuous abnormal data frame, the target data frame is discarded; if an abnormal data frame adjacent to the target data frame has been discarded, the target data frame is retained, if an abnormal data frame adjacent to the target data frame has not been discarded, the target data frame is discarded; the two abnormal data frames adjacent to the target data frame have been discarded, the target data frame is retained, if the two abnormal data frames adjacent to the target data frame have not been discarded, the target data frame is discarded. The abnormal data frame is a data frame whose quality difference meets the preset indicators.

[0052] like Figure 2 As shown, in the dynamic result optimization process of this step, the quality difference judgment result D′ is introduced i , let i≤0 when D i =0, the specific implementation scheme includes:

[0053] D′ i =D i , It corresponds to situation 1. At this time, it is considered that there are no consecutive abnormal data frames before the i-th data frame (i.e., the target data frame). It is only necessary to calculate the abnormal data frame according to D i The value determines whether the target data frame is discarded;

[0054] D′ i =0, This corresponds to situation 2. At this time, a small number of consecutive abnormal data frames appear before the i-th data frame (for example, less than the first number of consecutive abnormal data frames). In order to avoid excessive discarding of abnormal data frames, which causes a long period of lag and affects perception, the current data frame has been discarded (that is, an abnormal data frame adjacent to the target data frame has been discarded), that is, D i-1 =1, retain the target data frame;

[0055] D′ i =D i , This corresponds to situation 2. At this time, a small number of consecutive abnormal data frames appear before the i-th data frame (for example, less than the first number of consecutive abnormal data frames). In order to avoid excessive discarding of abnormal data frames, which causes a long period of lag and affects perception, the current data frame is not discarded (that is, an abnormal data frame adjacent to the target data frame is not discarded), that is, D i-1 = 0, the quality difference judgment result D of the target data frame is i value to determine whether the target data frame is discarded;

[0056] D′ i =0, This corresponds to situation 3. In this case, there are many consecutive abnormal data frames before the i-th data frame (i.e., more than the first number of consecutive abnormal data frames). To avoid long-term lag, when the two frames before the target data frame are both abnormal data frames, the target data frame is retained;

[0057] D′ i =D i , This corresponds to situation 3. At this time, there are more consecutive abnormal data frames before the i-th data frame (that is, more than the first number of consecutive abnormal data frames). When the two data frames before the target data frame are not all abnormal data frames, it can be decided whether to discard the target data frame based on the quality difference of the target data frame.

[0058] Step 102: Process the target data frame based on the optimization processing method to obtain a data frame to be executed after optimization processing.

[0059] From the above step 101, it can be seen that according to the dynamic quality difference judgment result D' i It can be judged whether the i-th data frame (i.e. the target data frame) is discarded. When D′ i=1, the target data frame can be discarded. i =0, the target data frame is retained.

[0060] For example, when judging whether the i-th data frame (i.e., the target data frame) should be discarded, it is necessary to judge the poor quality of the n (i.e., a preset number) consecutive data frames before the target data frame. In this example, n=5. When there is less than or equal to 1 abnormal data frame in the current 5 data frames, it is determined that there will be no possibility of continuous discarding of poor quality frames causing jamming, so it is sufficient to directly judge whether to discard the target data frame based on the poor quality of the i-th data frame; when there are 1 to 3 abnormal data frames in the current 5 data frames, it is necessary to judge the poor quality of the data frame before the i-th data frame. The current data frame has already had poor quality. In order to avoid jamming caused by continuous discarding, the target data frame is retained regardless of whether the i-th data frame has poor quality. On the contrary, if the current data frame has not had poor quality, it is sufficient to judge whether to discard it based on the actual poor quality of the i-th data frame; if there are 3 to 5 abnormal data frames in the current 5 data frames, the target data frame is retained regardless of whether the i-th data frame has poor quality. On the contrary, if the current data frame has not had poor quality, it is sufficient to judge whether to discard it based on the actual poor quality of the i-th data frame; When an abnormal data frame is found, it means that the quality of this data is poor. If the abnormal data frame is discarded directly without judgment, it is very easy to cause jamming. Therefore, it is necessary to judge whether the target data frame should be discarded based on the quality difference of the two data frames before the i-th data frame. If the product of the quality difference judgment results of the first two data frames is 1, it means that the first two data frames are abnormal data frames (i.e., poor quality data frames), then the i-th data frame should be retained regardless of whether the i-th data frame is of poor quality. If the product of the quality difference judgment results of the first two data frames is 0, it means that at least one of the first two data frames is a non-abnormal data frame (i.e., poor quality data frame), then the target data frame can be retained based on the actual quality difference of the i-th data frame.

[0061] The multimedia data optimization processing method for a network set-top box provided in an embodiment of the present invention dynamically adjusts the tuning processing method for the target data frame through the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality judgment results of the target data frame. It can reduce the occurrence of phenomena such as freezes and large-scale screen distortion encountered during the viewing process, improve the processing efficiency and accuracy of abnormal data frames, and thus enhance the viewing perception experience of TV users.

[0062] Corresponding to the above-mentioned method for optimizing multimedia data processing for a network set-top box, the present invention also provides a device for optimizing multimedia data processing for a network set-top box. Since the embodiment of the device is similar to the above-mentioned method embodiment, the description is relatively simple. For relevant details, please refer to the description of the above-mentioned method embodiment. The embodiment of the device for optimizing multimedia data processing for a network set-top box described below is only illustrative. Please refer to Figure 3 , which is a structural diagram of a multimedia data optimization processing device for a network set-top box provided by an embodiment of the present invention.

[0063] The multimedia data optimization processing device for a network set-top box according to the present invention comprises the following parts:

[0064] The dynamic result optimization processing judgment unit 301 is used to obtain a target data frame to be processed and determine an optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality judgment result of the target data frame;

[0065] The optimization processing result obtaining unit 302 is configured to process the target data frame based on the optimization processing method to obtain a data frame to be executed after the optimization processing.

[0066] The multimedia data optimization processing device for a network set-top box provided in an embodiment of the present invention dynamically adjusts the tuning processing method for the target data frame through the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality judgment result of the target data frame. It can reduce the occurrence of phenomena such as freezes and large-scale screen distortion encountered during the viewing process, improve the processing efficiency and accuracy of abnormal data frames, and thus enhance the viewing perception experience of TV users.

[0067] Corresponding to the multimedia data optimization processing method for a network set-top box provided above, the present invention also provides an electronic device. Since the embodiment of the electronic device is similar to the above method embodiment, the description is relatively simple. For relevant details, please refer to the description of the above method embodiment. The electronic device described below is only illustrative. Figure 4 As shown, it is a schematic diagram of the physical structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may include: a processor 401, a memory 402 and a communication bus 403, wherein the processor 401 and the memory 402 complete communication with each other through the communication bus 403 and communicate with the outside through the communication interface 404. The processor 401 can call the logic instructions in the memory 402 to execute the multimedia data optimization processing method for the network set-top box. The method includes: obtaining the target data frame to be processed currently, and determining the tuning processing method for the target data frame based on the processing results of the abnormal data frames in the preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame; processing the target data frame based on the tuning processing method to obtain the data frame to be executed after the tuning processing.

[0068] In addition, the logic instructions in the above-mentioned memory 402 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a memory chip, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code.

[0069] On the other hand, an embodiment of the present invention further provides a computer program product, the computer program product including a computer program stored on a processor-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer can execute the multimedia data optimization processing method for a network set-top box provided by each of the above method embodiments. The method includes: obtaining a target data frame to be processed currently, and determining an optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame; processing the target data frame based on the optimization processing method to obtain a data frame to be executed after the optimization processing.

[0070] In another aspect, an embodiment of the present invention further provides a processor-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multimedia data optimization processing method for a network set-top box provided in each of the above embodiments. The method comprises: obtaining a target data frame to be processed, determining an optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame; processing the target data frame based on the optimization processing method to obtain a data frame to be executed after the optimization processing.

[0071] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs)), etc.

[0072] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0073] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multimedia data optimization processing method for a network set-top box, characterized in that: include: Obtaining a target data frame to be processed currently, and determining an optimization processing method for the target data frame based on processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and a quality judgment result of the target data frame; Processing the target data frame based on the optimization processing method to obtain a data frame to be executed after the optimization processing; The step of determining an optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame specifically includes: If there are no consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, determining an optimization processing method for the target data frame according to the quality difference judgment result of the target data frame; or If there are fewer than a first number of consecutive abnormal data frames in a preset number of consecutive data frames before the target data frame, determining an optimization processing method for the target data frame based on a processing result of an abnormal data frame adjacent to the target data frame in the consecutive abnormal data frames; wherein the preset number is greater than the first number; or If there are more than the first number of consecutive abnormal data frames in the preset number of consecutive data frames before the target data frame, an optimization processing method for the target data frame is determined according to processing results of two abnormal data frames adjacent to the target data frame in the consecutive abnormal data frames.

2. The multimedia data optimization processing method for a network set-top box according to claim 1, characterized in that: The tuning processing method is to determine whether to discard the target data frame according to a preset abnormal data frame processing rule.

3. The multimedia data optimization processing method for a network set-top box according to claim 2, characterized in that: The abnormal data frame processing rules include: if the abnormal data frame among the preset number of continuous data frames before the target data frame is a non-continuous abnormal data frame, the target data frame is discarded; if an abnormal data frame adjacent to the target data frame has been discarded, the target data frame is retained; if an abnormal data frame adjacent to the target data frame has not been discarded, the target data frame is discarded; if two abnormal data frames adjacent to the target data frame have been discarded, the target data frame is retained; if the two abnormal data frames adjacent to the target data frame have not been discarded, the target data frame is discarded.

4. The multimedia data optimization processing method for a network set-top box according to any one of claims 1 to 3, characterized in that: The abnormal data frame is a data frame whose quality difference meets the preset index.

5. The multimedia data optimization processing method for a network set-top box according to claim 1, characterized in that: Also includes: Determining the quality of the data frame according to the number of error slices in the data frame; The data frames are separated according to the quality difference degree of the data frames to obtain the target data frames; wherein the target data frames are normal data frames or abnormal data frames.

6. The multimedia data optimization processing method for a network set-top box according to claim 5, characterized in that: Determining the quality degree of the data frame according to the number of error slices in the data frame specifically includes: determining the quality degree of the data frame based on the actual accuracy of the data frame and a preset accuracy standard value.

7. A multimedia data optimization processing device for a network set-top box, characterized in that: The method for optimizing multimedia data processing for a network set-top box according to claim 1 comprises: A dynamic result optimization processing judgment unit is used to obtain a target data frame to be processed currently, and determine an optimization processing method for the target data frame based on the processing results of abnormal data frames in a preset number of consecutive data frames before the target data frame and the quality difference judgment result of the target data frame; The optimization processing result obtaining unit is used to process the target data frame based on the optimization processing method to obtain a data frame to be executed after the optimization processing.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the multimedia data optimization processing method for a network set-top box as claimed in any one of claims 1 to 6 are implemented.

9. A processor-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multimedia data optimization processing method for a network set-top box as claimed in any one of claims 1 to 6 are implemented.

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