Video preloading method and device, storage medium and electronic equipment

Abstract

The invention provides a video preloading method and device, a storage medium and electronic equipment, which are applied to the technical field of video playing, and comprise the following steps: when video playing software of the equipment is in a video playing state, collecting resource monitoring data of the equipment; based on the resource monitoring data, determining a comprehensive evaluation grade of the video playing software in each preloading comprehensive performance index and a comprehensive evaluation grade of the equipment comprehensive performance index, and based on each comprehensive evaluation grade, generating a video preloading strategy corresponding to the current residual performance resource of the equipment, and when the preloading opportunity in the video preloading strategy is immediate loading, performing video preloading based on the video preloading strategy. The generated video preloading strategy corresponds to the current residual performance resources of the equipment, so that the equipment resources of the currently played video are not preempted during video preloading, the effect of the currently played video is not influenced during video preloading, and the video watching experience of a user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of video playback, and particularly relates to a video preloading method, an apparatus, a storage medium, and an electronic device. Background Art

[0002] With the rapid development of information technology and the increasing richness of Internet content, video playback software has become an indispensable part of people's daily lives. Such software can not only provide high-quality video playback services, but also strive to attract and retain users by optimizing the user experience. Among them, preloading technology, as an important means to improve playback fluency and user experience, is widely used in various video playback software.

[0003] The basic principle of preloading technology is to pre-download or cache other video resources that the user may watch next while the current video is being played. This mechanism aims to reduce the waiting time when the user switches videos or starts playing a new video, thereby improving the overall playback coherence and immediacy.

[0004] The video preloading process requires resources such as the storage space, memory, and network bandwidth of the device. Traditional video preloading methods do not consider the resource status of the device, resulting in resource competition when preloading videos during video playback, and problems such as video frame freezes and frequent caching during video playback. Summary of the Invention

[0005] In view of this, embodiments of the present application provide a video preloading method, an apparatus, a storage medium, and an electronic device. By applying the solution provided by the present application, when preloading a video during video playback, the system resources of the video being played are not contended, thereby avoiding the situation of video frame freezes caused by preloading a video while playing a video, ensuring the fluency of the video being played, and providing a good viewing experience for users.

[0006] To achieve the above object, embodiments of the present application provide the following technical solutions:

[0007] The first aspect of the present application discloses a video preloading method, including:

[0008] When the video playback software of the device is in the state of playing a video, based on the resource metrics of each performance monitoring dimension preset, collect the resource monitoring data of the device;

[0009] Based on the resource monitoring data, determine the comprehensive evaluation level of the device in each preloading comprehensive performance index preset and the comprehensive evaluation level of the device in the device comprehensive performance index preset;

[0010] Generate a video preloading policy corresponding to the remaining performance resources of the device based on the comprehensive evaluation level of the comprehensive performance indicators of the device and the comprehensive evaluation levels of the respective preloading comprehensive performance indicators. When the preloading timing in the video preloading policy is immediate loading, preload the target video based on the video preloading policy, where the target video is the video that needs to be played after the current video played by the video playback software ends.

[0011] The second aspect of the present application discloses a video preloading device, including:

[0012] An acquisition unit, configured to collect resource monitoring data corresponding to a target video based on the resource indicators of each preset monitoring dimension when the video playback software is in the state of playing a video, where the target video is the video that needs to be played after the current video played by the video playback software ends;

[0013] A determination unit, configured to determine the comprehensive evaluation level of the device in each preset preloading comprehensive performance indicator and the comprehensive evaluation level of the device in the preset comprehensive performance indicator based on the resource monitoring data;

[0014] A preloading unit, configured to generate a video preloading policy corresponding to the remaining performance resources of the device based on the comprehensive evaluation level of the comprehensive performance indicators of the device and the comprehensive evaluation levels of the respective preloading comprehensive performance indicators, and when the preloading timing in the video preloading policy is immediate loading, preload the target video based on the video preloading policy.

[0015] The third aspect of the present application discloses a storage medium, where the storage medium includes stored instructions, and when the instructions are running, the device where the storage medium is located is controlled to execute the video preloading method as described above.

[0016] The fourth aspect of the present application discloses an electronic device, including a memory, and one or more instructions, where one or more instructions are stored in the memory and are configured to be executed by one or more processors to implement the video preloading method as described above.

[0017] Compared with the prior art, the present application has the following advantages:

[0018] The present application provides a video preloading method, apparatus, storage medium, and electronic device, including: when the video playback software of the device is in the state of playing a video, collecting the resource monitoring data of the device; based on the resource monitoring data, determining the comprehensive evaluation levels of the video playback software in various preloading comprehensive performance indicators and the comprehensive evaluation level of the device in the device comprehensive performance indicators, generating a video preloading strategy corresponding to the remaining performance resources of the device based on the comprehensive evaluation level of the device comprehensive performance indicators and the comprehensive evaluation levels of various preloading comprehensive performance indicators, and when the preloading timing in the video preloading strategy is immediate loading, performing video preloading based on the video preloading strategy. The video preloading strategy generated by the present application corresponds to the remaining performance resources of the device. Therefore, when preloading the video, it does not preempt the system resources of the currently playing video, and does not affect the effect of the currently playing video while preloading the video, improving the user experience of watching the video. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0020] Figure 1 It is a flowchart of a video preloading method provided by an embodiment of the present application;

[0021] Figure 2 It is a flowchart for determining the comprehensive evaluation levels of the device in various preset preloading comprehensive performance indicators and the comprehensive evaluation level of the device in the preset device comprehensive performance indicators based on the resource monitoring data provided by an embodiment of the present application;

[0022] Figure 3 It is a schematic structural diagram of a preloading classification model provided by an embodiment of the present application;

[0023] Figure 4 It is a flowchart of another video preloading method provided by an embodiment of the present application;

[0024] Figure 5 It is a schematic structural diagram of a video preloading apparatus provided by an embodiment of the present application;

[0025] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] In this application, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0028] Term Explanation:

[0029] APP: The full English name is Application, which represents an application program;

[0030] CPU: Central Processing Unit, which represents the central processing unit and is the operation and control core of a computer system;

[0031] GPU: Graphics Processing Unit, which represents a visual processing unit;

[0032] RAM: Random Access Memory, which represents random access memory;

[0033] ROM: Read-Only Memory, which represents read-only memory.

[0034] To solve the problem of resource competition between video preloading and the currently playing video in the background technology, which leads to video frame freezes and frequent caching, the video preloading method provided in this application determines the comprehensive evaluation levels of the video playback software in various preset preloading comprehensive performance indicators and the comprehensive evaluation level of the device in the device comprehensive performance indicators by collecting the resource monitoring data of the device. Then, it applies the comprehensive evaluation level of the device comprehensive performance indicators and the comprehensive evaluation levels of various preloading comprehensive performance indicators to generate a video preloading strategy corresponding to the remaining performance resources of the device currently. When the preloading timing of the video preloading strategy is immediate loading, the video is preloaded based on the video preloading strategy. Since the video preloading strategy is a video preloading strategy corresponding to the remaining performance resources of the device currently, when performing video preloading, it will not preempt the resources of the currently playing video of the device, avoiding problems such as video frame freezes and frequent loading of the currently playing video, ensuring that the current video is not affected during preloading of the video, and improving the user experience of watching videos.

[0035] This application can be used in many general or specific computing device environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or equipment, and so on.

[0036] Refer to Figure 1 , which is a flowchart of a video preloading method provided by an embodiment of this application, and is specifically described as follows:

[0037] S101. When the video playback software of the device is in the state of playing a video, collect the resource monitoring data of the device based on the various resource indicators of the preset performance monitoring dimensions.

[0038] Each performance monitoring dimension includes but is not limited to the device static performance monitoring dimension, the device dynamic performance monitoring dimension, and the APP dynamic performance monitoring dimension; different performance monitoring dimensions have different resource indicators. Refer to Table 1, which shows the various resource indicators of each performance monitoring dimension.

[0039] Table 1

[0040]

[0041] The APP dynamic performance monitoring dimension is used to monitor the real-time performance usage of the APP; the device dynamic performance monitoring dimension is used to monitor the real-time performance usage of the device; the device static performance monitoring dimension is used to monitor the total comprehensive performance of the device.

[0042] The resource monitoring data includes the collected data of each resource metric. Exemplarily, the collected data of the CPU static performance metric includes the parameters of the device's CPU; the collected data of the GPU static performance metric includes the parameters of the device's GPU; the collected data of the network static performance metric includes the parameters of the device's network card; the collected data of the energy consumption static performance metric includes the parameters of the device's battery; the collected data of the memory static performance metric includes the parameters of the device's RAM and ROM; the collected data of the screen static performance metric includes the parameters of the device's screen; and the collected data of other resource metrics will not be exemplified one by one here.

[0043] S102. Based on the resource monitoring data, determine the comprehensive evaluation levels of the device in each preset preloading comprehensive performance metric and the comprehensive evaluation level of the device in the preset device comprehensive performance metric.

[0044] Each preloading comprehensive performance metric includes, but is not limited to, the CPU comprehensive performance metric, the GPU comprehensive performance metric, the network comprehensive performance metric, the energy consumption comprehensive performance metric, the memory comprehensive performance metric, and the screen comprehensive performance metric. Different preloading comprehensive performance metrics can be used to evaluate the device's performance in different aspects.

[0045] This application determines the comprehensive evaluation levels of the device in each preloading comprehensive performance metric and the comprehensive evaluation level of the device in the device comprehensive performance metric by using the resource monitoring data; the comprehensive evaluation levels of each preloading comprehensive performance metric and the comprehensive evaluation level of the device comprehensive performance metric can represent the device's current performance from the side, thereby realizing the evaluation of the device's current remaining performance resources. Subsequently, a video preloading strategy corresponding to the device's current remaining performance resources can be generated according to the comprehensive evaluation levels of each preloading comprehensive performance metric and the comprehensive evaluation level of the device comprehensive performance metric.

[0046] S103. Based on the comprehensive evaluation level of the device comprehensive performance metric and the comprehensive evaluation levels of each preloading comprehensive performance metric, generate a video preloading strategy corresponding to the device's current remaining performance resources, and when the preloading timing in the video preloading strategy is immediate loading, preload the target video based on the video preloading strategy. The target video is the video that the video playback software needs to play after the current video ends.

[0047] The video preloading strategy provided by this application includes three aspects: preloading timing, preloading duration, and video processing nodes. Among them, the preloading timing is used to indicate the timing of preloading, and can also be understood as the timing of applying the video preloading strategy. The preloading duration is used to indicate the duration to be loaded during preloading, and the video processing stage is used to indicate the video processing stage to be achieved when preloading the video.

[0048] In the method provided by the embodiments of the present application, when the video playback software of the device is in the state of playing a video, resource monitoring data of the device is collected; based on the resource monitoring data, the comprehensive evaluation levels of the video playback software for each preloading comprehensive performance index and the comprehensive evaluation level of the device for the comprehensive performance index are determined, and based on the comprehensive evaluation level of the device for the comprehensive performance index and the comprehensive evaluation levels of each preloading comprehensive performance index, a video preloading strategy corresponding to the remaining performance resources of the device is generated, and when the preloading timing in the video preloading strategy is immediate loading, video preloading is performed based on the video preloading strategy. The video preloading strategy generated by the present application corresponds to the remaining performance resources of the device at present. Therefore, when video preloading is performed, the resources of the currently playing video are not preempted, and the effect of the currently playing video is not affected while video preloading, improving the user experience of watching videos.

[0049] In another embodiment provided by the present application, the process of determining the comprehensive evaluation levels of the device for each preset preloading comprehensive performance index and the comprehensive evaluation level of the device for the preset comprehensive performance index based on the resource monitoring data is described, and the detailed process is referred to Figure 2 , and the specific description is as follows:

[0050] S201. Obtain the evaluation parameters of each preloading comprehensive performance index from the resource monitoring data.

[0051] When obtaining the evaluation parameters of each preloading comprehensive performance index, determine the target index of each preloading comprehensive performance index among various resource indexes; for each preloading comprehensive performance index, obtain the monitoring parameters of each target index of the preloading comprehensive performance index from the resource monitoring data, and generate evaluation parameters including each monitoring parameter.

[0052] Exemplarily, when the preloading comprehensive performance index is the CPU comprehensive performance index, the respective target indexes of the CPU comprehensive performance index are: the CPU static performance index, the CPU dynamic performance index, and the CPU dynamic performance index of the APP. When the preloading comprehensive performance index is the GPU comprehensive performance index, the respective target indexes of the GPU comprehensive performance index are: the GPU static performance index and the GPU dynamic performance index. When the preloading comprehensive performance index is the network comprehensive performance index, the respective target indexes of the network comprehensive performance index are: the network static performance index, the network dynamic performance index, and the network dynamic performance index of the APP. When the preloading comprehensive performance index is the energy consumption comprehensive performance index, the respective target indexes of the energy consumption comprehensive performance index are: the energy consumption static performance index and the energy consumption dynamic performance index of the APP. When the preloading comprehensive performance index is the memory comprehensive performance index, the respective target indexes of the memory comprehensive performance index are: the memory static performance index, the RAM dynamic performance index, the ROM dynamic performance index, and the RAM dynamic performance index of the APP. When the preloading comprehensive performance index is the screen comprehensive performance index, the respective target indexes of the screen comprehensive performance index are: the screen static performance index and the frame rate dynamic performance index.

[0053] Each target index has corresponding monitoring parameters in the resource monitoring data. Preferably, the monitoring parameters here can be understood as the collected data of the resource indexes mentioned above.

[0054] For each preloading comprehensive performance index, after obtaining the monitoring parameters of each target index of the preloading comprehensive performance index from the resource monitoring data, an evaluation parameter of the preloading comprehensive performance index is generated, and the evaluation parameter includes the monitoring parameters of each target index of the preloading comprehensive performance index.

[0055] S202. Process the evaluation parameters of each preloading comprehensive performance index to determine the comprehensive evaluation score of each preloading comprehensive performance index.

[0056] For each target index of each preloading comprehensive performance index, process the monitoring parameter of the target index in the evaluation parameter of the preloading comprehensive performance index to obtain the first score of the target index, and perform an operation on the first score and the preset scoring weight of the target index to obtain the second score; for each preloading comprehensive performance index, perform a summation operation on the second scores of the respective target indexes of the preloading comprehensive performance index to obtain the comprehensive evaluation score of the preloading comprehensive performance index. The preset scoring weight of the target index is the scoring weight set in advance, and the specific value can be set according to actual needs.

[0057] The process of obtaining the first score for each target index is described as follows:

[0058] When the target metric is the CPU static performance metric: Match the preset score corresponding to the CPU model of the device in the preset CPU model score table, and determine this preset score as the first score of the CPU static performance metric; the CPU model score table contains the preset scores of multiple CPU models; at this time, the monitoring parameter of this metric is the CPU model of the device.

[0059] When the target metric is the GPU static performance metric: Match the preset score corresponding to the GPU model of the device in the preset GPU model score table, and determine this preset score as the first score of the GPU static performance metric; the GPU model score table contains the preset scores of multiple GPU models; at this time, the monitoring parameter of this metric is the GPU model of the device.

[0060] When the target metric is the memory static performance metric: The first score of the memory static performance metric = RAM score × RAM weight + ROM score × ROM weight; where the RAM weight and ROM weight can be set in advance according to actual needs.

[0061] Determine the RAM score based on the RAM parameters of the device, and determine the ROM score based on the ROM parameters of the device. Further, determine the RAM score of the RAM parameters of the device according to the preset RAM score table, and determine the ROM score of the ROM parameters of the device according to the preset ROM score table. The preset RAM score table sets the scores of multiple RAM parameters, and the preset ROM score sets the scores of multiple ROM parameters.

[0062] The monitoring parameters of this metric here include but are not limited to the RAM parameters and ROM parameters of the device; the RAM weight and ROM weight can be set according to actual needs. The RAM parameters of the device include but are not limited to storage capacity, storage speed, timing parameters, operating voltage, module scale, etc. The ROM parameters of the device include but are not limited to storage capacity, storage type, data persistence, programming / erasing method, interface and physical characteristics, etc.

[0063] When the target metric is the screen static performance metric: Traverse the screen model score table with the screen model of the device, so as to match the screen score corresponding to the screen model of the device in the screen model score table, and determine this screen score as the first score of this metric. The screen model score table includes the screen scores of multiple screen models. The monitoring parameter of this metric here is the screen model of the device.

[0064] When the target metric is a network static performance metric: Traverse the network card model score table preset for the network card model of the device, match the network card score corresponding to the network card model of the device in the network card model score table, and determine this network card score as the first score of this metric. The network card model score table includes the network card scores of multiple network card models. The monitoring parameter of the metric here is the network card model of the device.

[0065] When the target metric is an energy consumption static performance metric: Traverse the battery model score table preset for the battery model of the device, match the battery score corresponding to the battery model of the device in the battery model score table, and determine this battery score as the first score of this metric. The battery model score table includes the battery scores of multiple battery models. The monitoring parameter of the metric here is the battery model of the device.

[0066] When the target metric is a CPU dynamic performance metric: The first score of the CPU dynamic performance metric = CPU usage rate score × CPU usage rate weight + CPU usage rate volatility score × CPU usage rate volatility weight; among them, the CPU usage rate weight and the CPU usage rate volatility weight can be preset according to actual needs.

[0067] The process of determining the CPU usage rate score: During the statistical period, count the CPU usage rates at N time points, perform an averaging operation on the N CPU usage rates to obtain the average CPU usage rate during this statistical period, and obtain the CPU usage rate score corresponding to this average CPU usage rate according to the preset CPU usage rate scoring table.

[0068] The process of determining the CPU usage rate volatility score: During the statistical period, divide this statistical period into L sub-time periods, obtain the difference between the maximum CPU usage rate and the minimum CPU usage rate in each sub-time period, determine the number of differences that exceed the preset CPU usage rate, and determine the proportion of the number of differences that exceed the preset CPU usage rate in the total number of differences, and determine this proportion as the CPU usage rate volatility ratio, and determine the CPU usage rate volatility score corresponding to this CPU usage rate volatility ratio according to the CPU usage rate volatility scoring table.

[0069] It should be noted that the monitoring parameters of the CPU dynamic performance metric include the CPU usage rate data within the time period of the statistical period. Preferably, the statistical period can be set according to actual needs, such as set to 1 day, 1 hour or 5 hours, etc.

[0070] The CPU usage rate scoring table sets the CPU usage rate scores for multiple CPU usage rates; the CPU usage rate volatility scoring table sets the scores for multiple CPU usage rate volatility ratios; the preset CPU usage rate can be set according to actual needs; N and L can be set according to actual needs, and both N and L are positive integers.

[0071] GPU Dynamic Performance Metrics: The first score of GPU dynamic performance metrics = GPU utilization rate score × GPU utilization rate weight + GPU utilization rate volatility score × GPU utilization rate volatility weight; among them, the GPU utilization rate weight and the GPU utilization rate volatility weight can be set according to actual needs.

[0072] The process of determining the GPU utilization rate score is as follows: within the statistical period, the GPU utilization rates at M time points are counted, and the average value of the M GPU utilization rates is calculated to obtain the average GPU utilization rate within the statistical period. The GPU utilization rate score corresponding to the average GPU utilization rate is obtained according to the preset GPU utilization rate scoring table.

[0073] The process of determining the GPU utilization rate volatility score: within the statistical period, the statistical period is divided into K sub-time periods, the difference between the maximum GPU utilization rate and the minimum GPU utilization rate within each sub-time period is obtained, the number of differences exceeding the preset GPU utilization rate is determined, and the proportion of the number of differences exceeding the preset GPU utilization rate in the total number of differences is determined, and this proportion is determined as the GPU utilization rate volatility ratio. The GPU utilization rate volatility score corresponding to the GPU utilization rate volatility ratio is determined according to the GPU utilization rate volatility scoring table.

[0074] It should be noted that the monitoring parameters of the GPU dynamic performance metrics include the GPU utilization rate data within the time period of the statistical period. Preferably, the statistical period is the same as the statistical period for determining the first score of the GPU dynamic performance metrics above. Multiple GPU utilization rate scores are set in the GPU utilization rate scoring table; multiple scores of GPU utilization rate volatility ratios are set in the GPU utilization rate volatility scoring table; the preset GPU utilization rate can be set according to actual needs; M and K can be set according to actual needs, and both M and K are positive integers.

[0075] When the target metric is the network dynamic performance metric: The first score of the network dynamic performance metric = network speed score × network speed weight + network type score × network type weight; among them, the network speed weight and the network type weight can be set according to actual needs.

[0076] Network speed score = network transmission rate score × network transmission rate weight + network speed volatility score × network speed volatility weight; among them, the network transmission rate weight and the network speed volatility weight can be set according to actual needs.

[0077] Process for determining the network transmission rate score: During the statistical period, the network transmission rates at A time points are statistically counted, and the average value operation is performed on the A network transmission rates to obtain the average network transmission rate during this statistical period. According to the preset network transmission rate scoring table, the network transmission rate score corresponding to this average network transmission rate is obtained.

[0078] Process for determining the network speed volatility score: During the statistical period, the statistical period is divided into B sub - time periods, and the difference between the maximum network speed and the minimum network speed within each sub - time period is obtained. For each sub - time period, when the difference between the maximum network speed and the minimum network speed within this sub - time period exceeds the preset network speed, it is determined that network speed volatility occurs within this sub - time period; the total number of sub - time periods with network speed volatility is determined, and the total number of sub - time periods with network speed volatility is divided by the total number of sub - time periods to obtain the network speed volatility ratio. According to the network speed volatility scoring table, the network speed volatility score corresponding to this network speed volatility ratio is determined.

[0079] Among them, the network transmission rate scoring table sets the network transmission rate scores for multiple network transmission rates; the network speed volatility scoring table sets the scores for multiple network speed volatility ratios; the preset threshold can be set according to actual needs; A and B can be set according to actual needs, and both A and B are positive integers; the statistical period here refers to the description of the statistical period mentioned above.

[0080] Network type score = current network type score × network type weight + network type volatility score × network type volatility weight; among them, both the network type weight and the network type volatility weight can be set according to actual needs.

[0081] When determining the current network type score, determine the network type of the network to which the device is currently connected, and determine the network type score corresponding to the network type of the currently connected network in the preset network type scoring table; the network type scoring table sets the network type scores for multiple network types.

[0082] Process for determining the network type volatility score: During the statistical period, the statistical period is divided into C sub - time periods, and the network types at the start time and end time of each sub - time period are obtained; for each sub - time period, when the network types at the start time and end time of this sub - time period are inconsistent, it is determined that network type volatility occurs within this sub - time period; the total number of sub - time periods with network type volatility is determined, and the total number of sub - time periods with network type volatility is divided by the total number of sub - time periods to obtain the network type volatility ratio. According to the network type volatility scoring table, the network type volatility score corresponding to this network type volatility ratio is determined. The network type volatility scoring table pre - sets the network type volatility scores for multiple network type volatility ratios. C can be set according to actual needs, and C is a positive integer.

[0083] It should be noted that the monitoring parameters of the network dynamic performance indicators include the transmission rate, type, etc. of the network within the statistical period.

[0084] When the target indicator is the RAM dynamic performance indicator: The first score of the RAM dynamic performance indicator = RAM usage rate score × RAM usage rate weight + RAM usage rate volatility score × RAM usage rate volatility weight; among them, the RAM usage rate weight and the RAM usage rate volatility weight can be set according to actual needs.

[0085] The process of determining the RAM usage rate score: Within the statistical period, count the RAM usage rates at D time points, perform an averaging operation on the D RAM usage rates to obtain the average RAM usage rate within the statistical period, and obtain the RAM usage rate score corresponding to the average RAM usage rate according to the preset RAM usage rate scoring table. Multiple RAM usage rate scores are set in the RAM usage rate scoring table. D can be set according to actual needs, and D is a positive integer.

[0086] The process of determining the RAM usage rate volatility score: Divide the statistical period into E sub-time periods within the statistical period. For each sub-time period, when the difference between the maximum RAM usage rate and the minimum RAM usage rate within the sub-time period exceeds the preset RAM usage rate, it is determined that there is a RAM usage rate fluctuation within the sub-time period; divide the total number of sub-time periods with RAM usage rate fluctuations by the total number of sub-time periods to obtain the RAM usage rate volatility ratio; determine the RAM usage rate volatility score corresponding to the RAM usage rate volatility ratio according to the RAM usage rate volatility scoring table. Multiple RAM usage rate volatility scores are preset in the RAM usage rate volatility scoring table. E can be set according to actual needs, and E is a positive integer.

[0087] The monitoring parameters of the RAM dynamic performance indicator include but are not limited to the RAM usage rate data of the device within the statistical period.

[0088] When the target indicator is the ROM dynamic performance indicator: The first score of the ROM dynamic performance indicator = ROM transmission rate score × ROM transmission rate weight + ROM transmission rate volatility score × ROM transmission rate volatility weight; among them, the ROM transmission rate weight and the ROM transmission rate volatility weight can be set according to actual needs.

[0089] Process for determining the ROM transfer rate score: During the statistical period, the ROM transfer rates at F time points are statistically counted. An average operation is performed on the F ROM transfer rates to obtain the average ROM transfer rate during this statistical period. According to a preset ROM transfer rate scoring table, the ROM transfer rate score corresponding to this average ROM transfer rate is obtained. In the ROM transfer rate scoring table, the ROM transfer rate scores for multiple ROM transfer rates are set. F can be set according to actual requirements, and F is a positive integer.

[0090] Process for determining the ROM transfer rate volatility score: During the statistical period, the statistical period is divided into G sub-time periods. For each sub-time period, when the difference between the maximum ROM transfer rate and the minimum ROM transfer rate within this sub-time period exceeds the preset ROM transfer rate, it is determined that there is a ROM transfer rate fluctuation within this sub-time period; the total number of sub-time periods with ROM transfer rate fluctuations is divided by the total number of sub-time periods to obtain the ROM transfer rate volatility ratio; according to the ROM transfer rate volatility scoring table, the ROM transfer rate volatility score corresponding to this ROM transfer rate volatility ratio is determined. In the ROM transfer rate volatility scoring table, the ROM transfer rate volatility scores for multiple ROM transfer rate volatility ratios are preset. G can be set according to actual requirements, and G is a positive integer.

[0091] The monitoring parameters of the ROM dynamic performance index include, but are not limited to, the data of the transfer rate of the device's ROM during the statistical period.

[0092] When the target index is the frame rate dynamic performance index: The first score of the frame rate dynamic performance index = frame rate score × frame rate weight + frame rate volatility score × frame rate volatility weight; where the frame rate weight and the frame rate volatility weight can be set according to actual requirements.

[0093] Process for determining the frame rate score: During the statistical period, the frame rates at H time points are statistically counted. An average operation is performed on the H frame rates to obtain the average frame rate during this statistical period. According to a preset frame rate scoring table, the frame rate score corresponding to this average frame rate is obtained. In the frame rate scoring table, the frame rate scores for multiple frame rates are set; H can be set according to actual requirements and takes a positive integer value.

[0094] Process for determining the frame rate volatility score: During a statistical period, divide the statistical period into I sub-time periods, obtain the difference between the maximum frame rate and the minimum frame rate within each sub-time period. For each sub-time period, when the difference between the maximum frame rate and the minimum frame rate within the sub-time period exceeds a preset frame rate, it is determined that frame rate fluctuations occur within the sub-time period; determine the total number of sub-time periods with frame rate fluctuations, and divide the total number of sub-time periods with frame rate fluctuations by the total number of sub-time periods to obtain the frame rate volatility ratio. Determine the frame rate volatility score corresponding to this frame rate volatility ratio according to the frame rate volatility scoring table. The frame rate volatility scoring table sets the frame rate volatility scores for multiple frame rates. I can be set according to actual needs and takes positive integer values.

[0095] The monitoring parameters of the frame rate dynamic performance index include but are not limited to the frame rate data of the device during the statistical period.

[0096] When the target index is the CPU dynamic performance index of the APP: The first score of the CPU dynamic performance index of the APP = the GPU usage rate score of the APP × the GPU usage rate weight of the APP + the GPU usage rate volatility score of the APP × the GPU usage rate volatility weight of the APP; among them, the GPU usage rate weight and the GPU usage rate volatility weight of the APP can be set according to actual needs.

[0097] The process for determining the GPU usage rate score of the APP is as follows: During the statistical period, count the GPU usage rate of the APP at J time points, perform an averaging operation on the GPU usage rates of J APPs to obtain the average GPU usage rate of the APP during the statistical period, and obtain the GPU usage rate score of the APP corresponding to this average GPU usage rate of the APP according to the preset GPU usage rate scoring table of the APP. The GPU usage rate scoring table of the APP presets the scores for multiple GPU usage rates of the APP; J can be set according to actual needs and takes positive integer values.

[0098] The process for determining the GPU usage rate volatility score of the APP: During the statistical period, divide the statistical period into O sub-time periods, obtain the difference between the maximum GPU usage rate of the APP and the minimum GPU usage rate of the APP within each sub-time period, determine the number of differences that exceed the preset GPU usage rate of the APP, and determine the proportion of the number of differences that exceed the preset GPU usage rate of the APP in the total number of differences, and determine this proportion as the GPU usage rate volatility ratio of the APP. Determine the GPU usage rate volatility score of the APP corresponding to this GPU usage rate volatility ratio of the APP according to the GPU usage rate volatility scoring table of the APP. The GPU usage rate volatility scoring table of the APP sets the scores for multiple GPU usage rate volatility ratios of the APP; O can be set according to actual needs and takes positive integer values.

[0099] The monitoring parameters of the GPU dynamic performance metrics of the APP include the relevant data of the GPU applied by the APP during the device operation within the time period of the statistical cycle.

[0100] When the target metric is the network dynamic performance metric of the APP: The first score of the network dynamic performance metric of the APP = the network speed score of the APP × the network speed weight of the APP + the network type score of the APP × the network type weight of the APP; wherein, the network speed weight and the network type weight of the APP can be set according to actual requirements.

[0101] The network speed score of the APP = the network transmission rate score of the APP × the network transmission rate weight of the APP + the network speed volatility score of the APP × the network speed volatility weight of the APP; wherein, the network transmission rate weight and the network speed volatility weight of the APP can be set according to actual requirements.

[0102] The process of determining the network transmission rate score of the APP: Within the statistical cycle, the network transmission rates of the APP at P time points are statistically counted, the mean value operation is performed on the network transmission rates of the P APPs to obtain the network average transmission rate of the APP within the statistical cycle, and the network transmission rate score of the APP corresponding to the network average transmission rate of the APP is obtained according to the preset network transmission rate scoring table of the APP. Multiple scores of the network transmission rates of the APP are set in the network transmission rate scoring table of the APP; P can be set according to actual requirements and takes a positive integer value.

[0103] The process of determining the network speed volatility score of the APP: Within the statistical cycle, the statistical cycle is divided into R sub-time periods, the difference between the maximum network speed and the minimum network speed of the APP within each sub-time period is obtained. For each sub-time period, when the difference between the maximum network speed and the minimum network speed of the APP within the sub-time period exceeds the preset network speed of the APP, it is determined that the network speed fluctuation of the APP occurs within the sub-time period; the total number of sub-time periods in which the network speed fluctuation of the APP occurs is determined, and the total number of sub-time periods in which the network speed fluctuation of the APP occurs is divided by the total number of sub-time periods to obtain the network speed volatility ratio of the APP, and the network speed volatility score corresponding to the network speed volatility ratio of the APP is determined according to the network speed volatility scoring table of the APP. Multiple scores of the network speed volatility ratios of the APP are set in the network speed volatility scoring table of the APP; R can be set according to actual requirements and takes a positive integer value.

[0104] The network type score of the APP = the current network type score of the APP × the network type weight of the APP + the network type volatility score of the APP × the network type volatility weight of the APP; wherein, both the network type weight and the network type volatility weight of the APP can be set according to actual requirements.

[0105] When determining the network type score of the current APP, determine the network type of the network to which the APP is currently connected, and determine the network type score corresponding to the network type of the network to which the APP is currently connected in the preset network type scoring table of the APP; multiple scores of the network types of the APP are set in the network type scoring table of the APP.

[0106] The process of determining the network type volatility score of the APP: within the statistical period, divide the statistical period into Q sub-time periods, and obtain the network type of the APP at the start time and end time of each sub-time period; for each sub-time period, when the network type of the APP at the start time and end time of the sub-time period is inconsistent, determine that there is a network type fluctuation of the APP within the sub-time period; determine the total number of sub-time periods with network type fluctuations of the APP, and divide the total number of sub-time periods with network type fluctuations of the APP by the total number of sub-time periods to obtain the network type volatility ratio of the APP, and determine the network type volatility score corresponding to the network type volatility ratio of the APP according to the network type volatility scoring table of the APP. Multiple scores of the network type volatility ratios of the APP are preset in the network type volatility scoring table of the APP. Q can be set according to actual needs, and Q is a positive integer. Further, the APP runs on the device. When the network type of the device changes, the network type of the APP also changes, that is, it can be understood that the network type of the APP is the network type of the device.

[0107] The monitoring parameters of the network dynamic performance index of the APP include the transmission rate, type, etc. of the network of the APP within the statistical period.

[0108] When the target index is the energy consumption dynamic performance index of the APP: the first score of the energy consumption dynamic performance index of the APP = the power consumption rate score of the APP × the power consumption rate weight + the charging state score × the charging state weight.

[0109] The process of obtaining the power consumption rate score of the APP is as follows: within the statistical period, count the power consumption rate of the APP at R time points, perform an averaging operation on the power consumption rates of the R APPs to obtain the average power consumption rate of the APP within the statistical period, and obtain the power consumption rate score of the APP corresponding to the average power consumption rate of the APP according to the preset power consumption rate scoring table of the APP. Multiple scores of the power consumption rates of the APP are set in the power consumption rate scoring table of the APP. R can be set according to actual needs, and R is a positive integer.

[0110] The process of obtaining the charging status score is as follows: determine the charging status of the device, and determine the charging status score corresponding to the charging status of the device in a preset charging status scoring table; the preset charging status scoring table includes the scores of various charging statuses of the device. Preferably, when determining the charging status of the device, it can be determined by parameters such as the voltage, current, and temperature of the device.

[0111] When the target metric is the RAM dynamic performance metric of the APP: The first score of the RAM dynamic performance metric of the APP = the RAM usage rate score of the APP × the RAM usage rate weight of the APP + the RAM usage rate volatility score of the APP × the RAM usage rate volatility weight of the APP; among them, the RAM usage rate weight of the APP and the RAM usage rate volatility weight of the APP can be set according to actual needs.

[0112] The process of determining the RAM usage rate score of the APP: During the statistical period, count the RAM usage rate of the APP at R time points, perform an averaging operation on the RAM usage rates of the R APPs to obtain the average RAM usage rate of the APP during this statistical period, and obtain the RAM usage rate score of the APP corresponding to the average RAM usage rate of the APP according to the preset RAM usage rate scoring table of the APP. Multiple RAM usage rate scores of the APP are set in the RAM usage rate scoring table of the APP. R can be set according to actual needs, and R is a positive integer.

[0113] The process of determining the RAM usage rate volatility score of the APP: Divide the statistical period into S sub-time periods during the statistical period. For each sub-time period, when the difference between the maximum RAM usage rate and the minimum RAM usage rate of the APP within this sub-time period exceeds the preset RAM usage rate of the APP, it is determined that there is a RAM usage rate fluctuation of the APP within this sub-time period; divide the total number of sub-time periods with RAM usage rate fluctuations of the APP by the total number of sub-time periods to obtain the RAM usage rate volatility ratio of the APP; determine the RAM usage rate volatility score of the APP corresponding to the RAM usage rate volatility ratio of the APP according to the RAM usage rate volatility scoring table. Multiple RAM usage rate volatility scores corresponding to the RAM usage rate volatility ratios of the APP are preset in the RAM usage rate volatility scoring table of the APP. R can be set according to actual needs, and R is a positive integer.

[0114] The monitoring parameters of the RAM dynamic performance metric of the APP include but are not limited to the data of the APP's use of RAM during the statistical period.

[0115] In the embodiments provided in this application, when determining the scores of different metrics, the number of sub-time periods divided in the statistical period mentioned can be the same, and the time points counted can also be the same.

[0116] When the preloading comprehensive performance index is the CPU comprehensive performance index, the comprehensive evaluation score of the CPU comprehensive performance index = the first score of the CPU static performance index × the scoring weight of the CPU static performance index + the first score of the CPU dynamic performance index × the scoring weight of the CPU dynamic performance index + the first score of the CPU dynamic performance index of the APP × the scoring weight of the CPU dynamic performance index of the APP.

[0117] When the preloading comprehensive performance index is the GPU comprehensive performance index, the comprehensive evaluation score of the GPU comprehensive performance index = the first score of the GPU static performance index × the scoring weight of the GPU static performance index + the first score of the GPU dynamic performance index × the scoring weight of the GPU dynamic performance index.

[0118] When the preloading comprehensive performance index is the network comprehensive performance index, the comprehensive evaluation score of the network comprehensive performance index = the first score of the network static performance index × the scoring weight of the network static performance index + the first score of the network dynamic performance index × the scoring weight of the network dynamic performance index + the first score of the network dynamic performance index of the APP × the scoring weight of the network dynamic performance index of the APP.

[0119] When the preloading comprehensive performance index is the energy consumption comprehensive performance index, the comprehensive evaluation score of the energy consumption comprehensive performance index = the first score of the energy consumption static performance index × the scoring weight of the energy consumption static performance index + the first score of the energy consumption dynamic performance index of the APP × the scoring weight of the energy consumption dynamic performance index of the APP.

[0120] When the preloading comprehensive performance index is the memory comprehensive performance index, the comprehensive evaluation score of the memory comprehensive performance index = the first score of the memory static performance index × the scoring weight of the memory static performance index + the first score of the RAM dynamic performance index × the scoring weight of the RAM dynamic performance index + the first score of the ROM dynamic performance index × the scoring weight of the ROM dynamic performance index + the first score of the RAM dynamic performance index of the APP × the scoring weight of the RAM dynamic performance index of the APP.

[0121] When the preloading comprehensive performance index is the screen comprehensive performance index, the comprehensive evaluation score of the screen comprehensive performance index = the first score of the screen static performance index × the scoring weight of the screen static performance index + the first score of the frame rate dynamic performance index × the scoring weight of the frame rate dynamic performance index.

[0122] The scoring weights of different performance indicators can be set according to actual needs, and they can be the same or different.

[0123] S203. Determine the comprehensive evaluation level of each preloading comprehensive performance index based on the comprehensive evaluation score of each preloading comprehensive performance index.

[0124] Use a preset grade scoring table to determine the comprehensive evaluation grade of each preloading viewing ability index. Multiple evaluation score intervals are set in the grade scoring table, and there is a corresponding grade for each evaluation score interval. For each preloading comprehensive performance index, the grade of the evaluation score interval where the comprehensive evaluation score of this preloading comprehensive performance index is located is used as the comprehensive evaluation grade of this preloading comprehensive performance index.

[0125] Referring to Table 2, it is an example table of the grade scoring table provided by the embodiments of the present application.

[0126] Table 2

[0127]

[0128] As shown in Table 2, the full score of the comprehensive evaluation score is 80 points. The evaluation score intervals and the grades corresponding to the evaluation score intervals in the grade scoring table shown in Table 2 are exemplary contents, and the evaluation score intervals and grades can be changed and configured according to actual needs.

[0129] S204. Based on the comprehensive evaluation scores of each preloading comprehensive performance index, determine the index score of the device comprehensive performance index, and based on the index score of the device comprehensive performance index, determine the comprehensive evaluation grade of the device comprehensive performance index.

[0130] Perform an operation on the comprehensive evaluation score of each preloading comprehensive performance index and the preset index weight to obtain the third score of each preloading comprehensive performance index; perform a summation operation on each third score to obtain the index score of the device comprehensive performance index.

[0131] Index score of device comprehensive performance index = Comprehensive evaluation score of CPU comprehensive performance index × Index weight of CPU comprehensive performance index + Comprehensive evaluation score of GPU comprehensive performance index × Index weight of GPU comprehensive performance index + Comprehensive evaluation score of network comprehensive performance index × Index weight of network comprehensive performance index + Comprehensive evaluation score of energy consumption comprehensive performance index × Index weight of energy consumption comprehensive performance index + Comprehensive evaluation score of memory comprehensive performance index × Index weight of memory comprehensive performance index + Comprehensive evaluation score of screen comprehensive performance index × Index weight of screen comprehensive performance index.

[0132] After determining the index score of the device comprehensive performance index, use the grade scoring table described in S203 to determine the comprehensive evaluation grade of the device comprehensive performance index. Preferably, the device comprehensive performance index is used to identify the current real-time comprehensive performance margin of the device.

[0133] In the embodiments provided in this application, after determining the comprehensive evaluation level of the device comprehensive performance indicators, based on the comprehensive evaluation level of the device comprehensive performance indicators and the comprehensive evaluation levels of each preloading comprehensive performance indicator, determine the preloading timing, preloading duration, and video processing stage that match the remaining performance resources of the device in a pre-defined video preloading classification model; generate a video preloading strategy based on the preloading timing, preloading duration, and video processing stage. The video processing node can be understood as the loading stage to be achieved when preloading a video.

[0134] It should be noted that a preloading classification strategy is pre-configured in the preloading classification model, and this strategy table includes the comprehensive evaluation levels of the device comprehensive performance indicators corresponding to each classification sub-item and the comprehensive evaluation levels of each preloading comprehensive performance indicator.

[0135] Refer to Figure 3 , which is a schematic structural diagram of the preloading classification model provided in the embodiments of this application. The figure shows that the classification dimension of the preloading classification model is the preloading ability standard, and the preloading ability standard is divided into three classification items: preloading timing, preloading duration, and video processing stage. Each classification item has corresponding classification sub-items. The preloading timing includes three classification sub-items: immediate preloading, preloading during idle time, and no preloading. The preloading duration includes two classification sub-items: one piece and multiple pieces. The video processing stage includes three classification sub-items: pulling the stream, decoding, and rendering.

[0136] One piece in the preloading duration represents the duration of one video segment, and multiple pieces represent the duration of multiple video segments. When the preloading duration is multiple pieces, the specific number of video segments can be pre-set or calculated according to the preloading cache queue. For example, the total duration of the preloaded video segments is less than or equal to the preloading cache queue.

[0137] In Figure 3 In the preloading classification model shown, each classification sub-item of each classification item has a corresponding level matching condition, and this level matching condition records the comprehensive evaluation level that each comprehensive performance indicator needs to meet when matching this classification sub-item. Preferably, the comprehensive evaluation levels that different comprehensive performance indicators need to meet can be different. Exemplarily, for the classification sub-item with an immediate preloading timing, the level matching condition for this classification sub-item is: the comprehensive evaluation level of each comprehensive performance indicator needs to be greater than or equal to level B; that is, when the comprehensive evaluation of any one comprehensive performance indicator is less than B, the level matching condition of this classification sub-item is not met. The level matching conditions of other classification sub-items will not be exemplified here.

[0138] Further, according to the level matching conditions of each sub - classification item of each classification item, various level combinations corresponding to each sub - classification item of each classification item can be obtained. The level combinations include the comprehensive evaluation levels of each comprehensive performance index. For each classification item, after summarizing the various level combinations of each sub - classification item of this classification item, it covers various combinations formed by each comprehensive performance index under various comprehensive evaluation levels. Regardless of the comprehensive evaluation levels of each comprehensive performance index, corresponding sub - classification items can be matched in the classification item.

[0139] Exemplarily, after obtaining the comprehensive evaluation levels of each comprehensive performance index, when matching the sub - classification item of the pre - loading timing, it is preferentially matched with the "immediate" sub - classification item. When it fails to match with the "immediate" sub - classification item, it is then matched with the "idle time" sub - classification item. When it fails to match with the "idle time" sub - classification item, it can directly determine that the comprehensive evaluation levels of each comprehensive performance index match the "no pre - loading" sub - classification item.

[0140] After determining the current comprehensive evaluation levels of each comprehensive performance index, when determining the sub - classification items of each classification item that match the current comprehensive evaluation levels of each comprehensive performance index, for each classification item, the comprehensive evaluation levels of each comprehensive performance index are successively matched with the level matching conditions of each sub - classification item of this classification item, and the matching conditions satisfied by the current comprehensive evaluation levels of each comprehensive performance index are determined as the target matching conditions, and the sub - classification item corresponding to the target matching conditions is determined as the target sub - classification item. Thus, the target sub - classification items in each classification item can be determined, and then the video pre - loading strategy is generated using each target sub - classification item.

[0141] Preferably, the process of pre - loading the target video based on the video pre - loading strategy includes: determining the number of video segments based on the pre - loading duration in the video pre - loading strategy; determining the target video segments corresponding to the number of video segments in the target video; and pre - loading each target video segment to the pre - loading processing stage in the video pre - loading strategy.

[0142] This application defines the current device comprehensive performance indicators, combines with the video pre - loading classification model, generates the video pre - loading strategy, completes the pre - loading of the next video, realizes the comprehensive control of the device system resources, solves the problems such as freezing, slow response, overheating, and increased power consumption caused by the APP not considering the current device system resource status for video pre - loading, and improves the user experience during the video playback of the APP.

[0143] Refer to Figure 4 , which is another flowchart of video pre - loading provided by the embodiment of this application, and is specifically described as follows:

[0144] (1) The APP on the device plays a video.

[0145] The APP on the device is started, and then the video selected by the user is played. It should be noted that when the APP on the device is started, the request interface obtains the configuration related to preloading monitoring and the configuration of the video preloading classification model.

[0146] The device encapsulates the performance monitoring module before startup, and encapsulates it in combination with the preloading monitoring related configuration when encapsulating the performance monitoring mode. The encapsulated performance monitoring module includes the monitoring and statistical tools involved in executing the video preloading solution provided in this application. Different monitoring and statistical tools are used to collect parameters of indicators in different monitoring dimensions.

[0147] The performance monitoring module includes a static performance monitoring and statistics tool for devices. The static performance monitoring and statistics tool for devices is used to collect parameters of various resource indicators in the static monitoring dimension of devices. Since the device hardware parameters that need to be counted in the current static resource indicators of the device will not change due to the use of the device, the device hardware parameters are obtained once through this tool when the performance monitoring module is started, and the parameters of the static resource indicators of the device are processed according to the configured processing method.

[0148] The performance monitoring module also includes a device dynamic performance monitoring statistics tool, which is used to collect parameters of various resource indicators in the device dynamic monitoring dimension. Because the device parameters that need to be counted in the current device dynamic resource indicators will continue to change with the device usage, it is necessary to continuously monitor parameter changes and update indicators during the APP operation. During the APP operation, the real-time operating parameters of the device are periodically obtained, and the collected parameters are processed according to the preset processing method to obtain the scores of various resource indicators in the device dynamic performance monitoring dimension.

[0149] The performance monitoring module also includes an APP dynamic performance monitoring statistics tool, which is used to collect parameters of various resource indicators in the APP dynamic performance monitoring dimension. Because the system resources occupied by the current APP will continue to change during the use of scenarios such as foreground and background switching and page switching, it is necessary to continuously monitor parameter changes and update indicators during the operation of the APP. During the operation of the APP, the real-time operating parameters of the APP are periodically obtained, and the collected parameters are processed according to the preset processing method to obtain the scores of various resource indicators in the device dynamic performance monitoring dimension.

[0150] The performance monitoring module also includes a comprehensive performance monitoring statistics tool, which is used to collect parameters related to each comprehensive performance indicator, and then process the parameters to obtain the scores of each comprehensive performance indicator. During the operation of the APP, the scores of each resource indicator in each monitoring dimension are obtained, and the collected scores are processed according to the preset processing method to obtain the scores of each comprehensive performance indicator.

[0151] (2)Monitor each resource metric for each performance dimension.

[0152] When the APP starts, start the performance monitoring module, use the performance monitoring module to collect the parameters of each resource metric for each performance dimension, then process the collected parameters, and then generate a video preloading strategy.

[0153] It should be noted that the device also encapsulates a video preloading module, obtains the scores of each comprehensive performance metric through the performance monitoring module, and generates a video preloading strategy based on the scores of each comprehensive performance metric and in combination with the video preloading grading model.

[0154] Furthermore, a video preloading timing tool is also encapsulated in the device. This tool is used to determine the preloading timing in the video preloading strategy according to the scores of each comprehensive performance metric and the video preloading grading model. Furthermore, when the preloading timing in the generated video preloading strategy is immediate loading, the preloading is triggered immediately.

[0155] A video preloading duration tool is also encapsulated in the device. This tool is used to determine the preloading duration in the preloading strategy according to the scores of each comprehensive performance metric and the video preloading grading model. Furthermore, when the preloading is triggered, preload the corresponding video segment according to the preloading duration in the video preloading strategy.

[0156] A video processing stage tool is also encapsulated in the device. This tool is used to determine the video processing stage in the video preloading strategy according to the scores of each comprehensive performance metric and the video preloading grading model. During video preloading, process the preloaded video segment to the stage corresponding to the video processing stage in the video preloading strategy.

[0157] (3)Generate a video preloading strategy.

[0158] (4)Judge whether the preloading timing in the video preloading strategy is immediate preloading.

[0159] When the preloading timing in the video preloading strategy is immediate preloading, execute the preloading. When the preloading timing in the video preloading strategy is not immediate preloading, return to the step of monitoring each resource metric for each performance dimension; that is, when the preloading timing in the video preloading strategy is idle preloading or no preloading, return to the step of monitoring each resource metric for each performance dimension.

[0160] (5)Execute the preloading.

[0161] (6) Determine whether the preloading duration in the video preloading strategy is for multiple segments; when the preloading duration in the video preloading strategy is for multiple segments, set the preloading duration parameter to multiple segments, and when the preloading duration in the video preloading strategy is for one segment, set the preloading duration parameter to one segment.

[0162] (7) Load video segments according to the set preloading duration parameter.

[0163] (8) Process the preloaded video segments to the corresponding stage according to the video processing stage in the video preloading strategy.

[0164] Exemplarily, if the video processing stage in the video preloading strategy is pulling the stream, then the video segments are processed to the pulling the stream stage and it ends; if the video processing stage in the video preloading strategy is decoding, then the video is processed to the decoding stage and it ends, that is, the video segments are first pulled the stream and then decoded; if the video processing stage in the video preloading strategy is rendering, then the video segments are processed to the rendering stage and it ends, that is, first pull the stream for the video, then decode it, and finally render it.

[0165] It should be noted that these three stages of pulling the stream, decoding, and rendering are three necessary stages for the normal start of playing the video. In the video preloading of the present application, the preloaded video segments are processed to the corresponding stage, so that the preloaded video reduces the processing of the video when starting to play, speeds up the start time of the video, and the present application preloads according to the remaining performance resources of the device at present during video preloading, without affecting the video being played during preloading, so that the video being played does not freeze frames and the picture is smooth, avoiding the system resource contention between preloading and the video being played, and avoiding the increase in device heating and power consumption.

[0166] Corresponding to Figure 1 the method shown, the present application also provides a video preloading device, which is used to support Figure 1 the implementation of the method shown, referring to Figure 5 which is a schematic structural diagram of a video preloading device provided by an embodiment of the present application, and is specifically described as follows:

[0167] An acquisition unit 401, configured to collect resource monitoring data corresponding to a target video based on each resource index of each preset monitoring dimension when the video playback software is in a state of playing a video, where the target video is the video that needs to be played after the current video played by the video playback software ends;

[0168] A determination unit 402, configured to determine the comprehensive evaluation level of the device in each preset preloading comprehensive performance index and the comprehensive evaluation level of the device in the preset device comprehensive performance index based on the resource monitoring data;

[0169] A preloading unit 403, configured to generate a video preloading policy corresponding to the remaining performance resources of the device based on the comprehensive evaluation level of the comprehensive performance metrics of the device and the comprehensive evaluation levels of the comprehensive performance metrics of each preloading, and when the preloading timing in the video preloading policy is immediate loading, preload the target video based on the video preloading policy.

[0170] In the method provided by the embodiments of the present application, when the video playback software of the device is in the state of playing a video, resource monitoring data of the device is collected; based on the resource monitoring data, the comprehensive evaluation levels of the video playback software in the comprehensive performance metrics of each preloading and the comprehensive evaluation level of the comprehensive performance metrics of the device are determined, and a video preloading policy corresponding to the remaining performance resources of the device is generated based on the comprehensive evaluation level of the comprehensive performance metrics of the device and the comprehensive evaluation levels of the comprehensive performance metrics of each preloading, and when the preloading timing in the video preloading policy is immediate loading, video preloading is performed based on the video preloading policy. The video preloading policy generated by the present application corresponds to the remaining performance resources of the device. Therefore, when video preloading is performed, it does not preempt the resources of the currently playing video, and the effect of the currently playing video is not affected while video preloading is performed, improving the user experience of watching videos.

[0171] In another embodiment provided by the present application, the determining unit 402 of the device executes the process of determining the comprehensive evaluation levels of the device in the comprehensive performance metrics of each preset preloading and the comprehensive evaluation level of the preset comprehensive performance metrics of the device based on the resource monitoring data, including:

[0172] Obtain the evaluation parameters of each preloading comprehensive performance metric from the resource monitoring data;

[0173] Process the evaluation parameters of each preloading comprehensive performance metric to determine the comprehensive evaluation score of each preloading comprehensive performance metric;

[0174] Based on the comprehensive evaluation scores of each preloading comprehensive performance metric, determine the comprehensive evaluation level of each preloading comprehensive performance metric;

[0175] Based on the comprehensive evaluation scores of each preloading comprehensive performance metric, determine the metric score of the comprehensive performance metric of the device, and determine the comprehensive evaluation level of the comprehensive performance metric of the device based on the metric score of the comprehensive performance metric of the device.

[0176] In another embodiment provided by the present application, the determining unit 402 of the device executes the process of obtaining the evaluation parameters of each preloading comprehensive performance metric from the resource monitoring data, including:

[0177] Determine the target index of each preloading comprehensive performance index among the various resource indexes;

[0178] For each preloading comprehensive performance index, obtain the monitoring parameters of each target index of the preloading comprehensive performance index from the resource monitoring data, and generate evaluation parameters including the various monitoring parameters.

[0179] In another embodiment provided by the present application, the determining unit 402 of the device executes the process of processing the evaluation parameters of each preloading comprehensive performance index to determine the comprehensive evaluation score of each preloading comprehensive performance index, including:

[0180] For each target index of each preloading comprehensive performance index, process the monitoring parameters of the target index in the evaluation parameters of the preloading comprehensive performance index to obtain the first score of the target index, and perform an operation on the first score and the preset scoring weight of the target index to obtain the second score;

[0181] For each preloading comprehensive performance index, perform a summation operation on the second scores of the various target indexes of the preloading comprehensive performance index to obtain the comprehensive evaluation score of the preloading comprehensive performance index.

[0182] In another embodiment provided by the present application, the determining unit 402 of the device executes the process of determining the index score of the device comprehensive performance index based on the comprehensive evaluation score of each preloading comprehensive performance index, including:

[0183] Perform an operation on the comprehensive evaluation score of each preloading comprehensive performance index and the preset index weight to obtain the third score of each preloading comprehensive performance index;

[0184] Perform a summation operation on the various third scores to obtain the index score of the device comprehensive performance index.

[0185] In another embodiment provided by the present application, the preloading unit 403 of the device executes the process of generating a video preloading strategy corresponding to the performance resources remaining for the device currently based on the comprehensive evaluation level of the device comprehensive performance index and the comprehensive evaluation levels of the various preloading comprehensive performance indexes, including:

[0186] Based on the comprehensive evaluation level of the device comprehensive performance index and the comprehensive evaluation levels of the various preloading comprehensive performance indexes, determine the preloading timing, preloading duration, and video processing stage matched by the performance resources remaining for the device currently in a pre-defined video preloading classification model;

[0187] Generate a video preloading strategy based on the preloading timing, preloading duration, and video processing stage.

[0188] In another embodiment provided by the present application, the preloading unit 403 of the device executes the process of preloading the target video based on the video preloading strategy, including:

[0189] Determine the number of video segments based on the preloading duration in the video preloading strategy;

[0190] Determine target video segments corresponding to the number of video segments in the target video;

[0191] Preload each of the target video segments to the preloading processing stage in the video preloading strategy.

[0192] Although the present invention depicts the operations in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain circumstances, multitasking and parallel processing may be advantageous.

[0193] The embodiment of the present invention also provides a storage medium, which includes stored instructions. When the instructions run, they control the device where the storage medium is located to execute the above video preloading method.

[0194] The embodiment of the present invention also provides an electronic device, and its structural schematic diagram is as Figure 6 shown. Specifically, it includes a memory 601 and one or more instructions 602. One or more instructions 602 are stored in the memory 601 and are configured to be executed by one or more processors 603 to perform the following operations:

[0195] When the video playback software of the device is in the state of playing a video, collect resource monitoring data of the device based on various resource indicators of preset performance monitoring dimensions;

[0196] Based on the resource monitoring data, determine the comprehensive evaluation level of the device in various preset preloading comprehensive performance indicators and the comprehensive evaluation level of the device in preset device comprehensive performance indicators;

[0197] Based on the comprehensive evaluation level of the device comprehensive performance indicators and the comprehensive evaluation levels of the various preloading comprehensive performance indicators, generate a video preloading strategy corresponding to the remaining performance resources of the device currently. When the preloading timing in the video preloading strategy is immediate loading, preload the target video based on the video preloading strategy. The target video is the video that needs to be played after the current video played by the video playback software ends.

[0198] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards in the relevant regions.

[0199] The specific implementation processes and their derivative methods of the above various embodiments are all within the protection scope of the present invention.

[0200] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other, and each embodiment focuses on the differences from other embodiments. In particular, for a system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the corresponding part of the method embodiment for the relevant content. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0201] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0202] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A video preloading method, characterized in that: include: When the video playback software of the device is in a state of playing a video, based on various resource indicators of various preset performance monitoring dimensions, resource monitoring data of the device is collected; Based on the resource monitoring data, determining the comprehensive evaluation level of the device in each preset preloaded comprehensive performance indicator and the comprehensive evaluation level of the preset device comprehensive performance indicator; Based on the comprehensive evaluation level of the comprehensive performance indicators of the device and the comprehensive evaluation levels of each of the preloading comprehensive performance indicators, a video preloading strategy corresponding to the currently remaining performance resources of the device is generated, and when the preloading timing in the video preloading strategy is immediate loading, the target video is preloaded based on the video preloading strategy, and the target video is a video that needs to be played after the video playback software finishes playing the current video.

2. The method according to claim 1, characterized in that The determining, based on the resource monitoring data, the comprehensive evaluation level of the device in each preset preload comprehensive performance indicator and the comprehensive evaluation level of the preset device comprehensive performance indicator comprises: Acquire evaluation parameters of each of the preloading comprehensive performance indicators from the resource monitoring data; Processing the evaluation parameters of each of the preloading comprehensive performance indicators to determine a comprehensive evaluation score of each of the preloading comprehensive performance indicators; Determining a comprehensive evaluation grade of each of the preloading comprehensive performance indicators based on the comprehensive evaluation score of each of the preloading comprehensive performance indicators; Based on the comprehensive evaluation score of each of the preloaded comprehensive performance indicators, the index score of the device comprehensive performance indicator is determined, and based on the index score of the device comprehensive performance indicator, the comprehensive evaluation level of the device comprehensive performance indicator is determined.

3. The method according to claim 2, characterized in that The obtaining of the evaluation parameters of each preloading comprehensive performance indicator from the resource monitoring data includes: Determining a target indicator for each of the preloading comprehensive performance indicators among the various resource indicators; For each of the preloading comprehensive performance indicators, a monitoring parameter of each target indicator of the preloading comprehensive performance indicator is obtained from the resource monitoring data, and an evaluation parameter including each of the monitoring parameters is generated.

4. The method according to claim 2, characterized in that: The step of processing the evaluation parameters of each preloading comprehensive performance indicator to determine the comprehensive evaluation score of each preloading comprehensive performance indicator includes: For each target indicator of each preloaded comprehensive performance indicator, the monitoring parameters of the target indicator in the evaluation parameters of the preloaded comprehensive performance indicator are processed to obtain a first score of the target indicator, and the first score is calculated with a preset score weight of the target indicator to obtain a second score; For each of the preloading comprehensive performance indicators, the second scores of the target indicators of the preloading comprehensive performance indicator are summed to obtain a comprehensive evaluation score of the preloading comprehensive performance indicator.

5. The method according to claim 2, characterized in that: The step of determining the index score of the device comprehensive performance index based on the comprehensive evaluation score of each preloaded comprehensive performance index includes: Calculating the comprehensive evaluation score of each of the preloaded comprehensive performance indicators and the preset indicator weight to obtain a third score of each of the preloaded comprehensive performance indicators; The third scores are summed to obtain the index score of the comprehensive performance index of the equipment.

6. The method according to claim 1, characterized in that The generating of a video preloading strategy corresponding to the currently remaining performance resources of the device based on the comprehensive evaluation level of the device comprehensive performance indicator and the comprehensive evaluation levels of each preloading comprehensive performance indicator includes: Based on the comprehensive evaluation level of the device comprehensive performance indicators and the comprehensive evaluation levels of each of the preloading comprehensive performance indicators, determining the preloading timing, preloading duration, and video processing stage matched by the currently remaining performance resources of the device in a predefined video preloading classification model; A video preloading strategy is generated based on the preloading timing, preloading duration, and video processing stage.

7. The method according to claim 1, characterized in that The preloading of the target video based on the video preloading strategy includes: Determining the number of video clips based on the preloading duration in the video preloading strategy; Determining target video segments corresponding to the number of video segments in the target video; Each of the target video segments is preloaded into the preloading processing stage in the video preloading strategy.

8. A video preloading device, characterized in that: include: The acquisition unit is used to acquire resource monitoring data corresponding to a target video based on various resource indicators of preset monitoring dimensions when the video playback software is in a state of playing a video, wherein the target video is a video to be played after the video playback software finishes playing the current video; A determination unit, configured to determine, based on the resource monitoring data, a comprehensive evaluation level of the device in each preset preloaded comprehensive performance indicator and a comprehensive evaluation level of the preset device comprehensive performance indicator; A preloading unit is used to generate a video preloading strategy corresponding to the current remaining performance resources of the device based on the comprehensive evaluation level of the device comprehensive performance indicators and the comprehensive evaluation levels of each of the preloading comprehensive performance indicators, and when the preloading timing in the video preloading strategy is immediate loading, preloading the target video based on the video preloading strategy.

9. A storage medium, characterized in that: The storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the video preloading method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a memory and one or more instructions, wherein the one or more instructions are stored in the memory and are configured to be executed by one or more processors to implement the video preloading method as described in any one of claims 1-7.

Images