A virtual reality resource hardware adaptation analysis method and VR device

Abstract

The present invention relates to the field of data processing technology, and in particular to a method for hardware adaptation analysis of virtual reality resources and a VR device, the method comprising: obtaining virtual content in a virtual reality device test process in real time, and calculating the average value of the picture quality coefficient of the test video according to the virtual content; obtaining hardware detection information in the virtual reality device test process, and calculating the stability coefficient of the virtual reality device according to the hardware detection information; obtaining user feedback data in the virtual reality device test process, and calculating the user feedback coefficient according to the user feedback data; calculating the hardware adaptation coefficient of the virtual reality device according to the average value of the picture quality coefficient, the stability coefficient and the user feedback coefficient; and performing adaptation analysis on the virtual reality device according to the hardware adaptation coefficient. The method avoids the technical problem that the hardware adaptation analysis in the prior art only focuses on the correlation response between hardware, resulting in inaccurate calculation results and affecting the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a virtual reality resource hardware adaptation analysis method and a VR device. Background Art

[0002] Virtual Reality (VR) technology is developing rapidly and is widely used in many fields. VR devices can provide a highly immersive experience, allowing users to feel as if they are in a virtual environment. With the advancement of VR technology, the performance and functions of devices are also constantly improving, bringing users a more realistic and interactive experience. This development has promoted the demand for more intelligent and efficient hardware adaptation analysis methods and device designs to further optimize users' VR experience.

[0003] For VR devices or equipment, since they need to provide users with an excellent immersive experience during operation, the amount of data required for calculation and processing is significantly increased compared to conventional video data. In order to ensure that massive amounts of data can be processed quickly and accurately, the hardware of the device must have good adaptability and response speed. However, traditional hardware adaptation analysis only focuses on the correlation response between hardware, and does not fully consider the potential impact of real-time computing changes caused by content changes on the overall picture processing level and the actual user experience. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a virtual reality resource hardware adaptation analysis method and a VR device, and the technical solutions adopted are as follows:

[0005] A virtual reality resource hardware adaptation analysis method, the method comprising:

[0006] Acquire virtual content during the test of the virtual reality device in real time, and calculate the average value of the picture quality coefficient of the test video according to the virtual content;

[0007] Acquiring hardware detection information during the virtual reality device testing process, and calculating a stability coefficient of the virtual reality device according to the hardware detection information;

[0008] Acquire user feedback data during the virtual reality device testing process, and calculate a user feedback coefficient based on the user feedback data;

[0009] Calculating a hardware adaptation coefficient of the virtual reality device according to the average value of the picture quality coefficient, the stability coefficient and the user feedback coefficient;

[0010] Perform adaptation analysis on the virtual reality device according to the hardware adaptation coefficient.

[0011] Furthermore, the real-time acquisition of virtual content during the virtual reality device testing process further includes:

[0012] Real-time acquisition of multiple virtual units existing in virtual content during the virtual reality device testing process;

[0013] Therein, an object existing in the virtual environment of the virtual reality device is regarded as a virtual unit;

[0014] For each virtual unit, the virtual content of each frame of the virtual unit is obtained.

[0015] Furthermore, the calculating the mean value of the picture quality coefficient of the test video according to the virtual content further includes:

[0016] For each virtual content of each frame of each virtual unit, the virtual content of each frame of the virtual unit is divided into multiple segmentation modules by using an edge segmentation algorithm;

[0017] Determine the number of edges for each segmentation module;

[0018] Obtaining the rendering level, the number of segmentation modules, and the number of edges of each segmentation module of the virtual unit to calculate the rendering complexity of the virtual content of each frame of each virtual unit;

[0019] The average value of the picture quality coefficient of the test video is calculated according to the rendering complexity of the virtual content of each frame of each virtual unit and the multiple virtual units existing in the virtual content during the test process of the virtual reality device.

[0020] Furthermore, the hardware detection information includes at least: resource utilization of the acquisition processor, resource utilization of the graphics processor, and resource utilization of the memory.

[0021] Furthermore, the obtaining of hardware detection information during the virtual reality device testing process further includes:

[0022] A full battery life test is performed on the virtual reality device, and a preset time interval is used as a unit time to obtain the resource utilization rate of the acquisition processor, the resource utilization rate of the graphics processor, and the resource utilization rate of the memory during the full battery life test.

[0023] Furthermore, the calculating the stability coefficient of the virtual reality device according to the hardware detection information further includes:

[0024] Collect the screen display content during the full battery life test;

[0025] Taking a preset time interval as a unit time, calculating an average value of a picture quality coefficient within a corresponding unit time according to the picture display content;

[0026] Calculating the frame number data of the played pictures within a plurality of unit times according to the picture display content;

[0027] The stability coefficient of the virtual reality device is calculated according to the average value of the picture quality coefficient within the unit time, the number of picture frames played within the unit time, and the hardware detection information.

[0028] Furthermore, obtaining user feedback data during the virtual reality device testing process further includes:

[0029] Conduct user testing on the virtual reality device and obtain user feedback data during the testing process;

[0030] The user feedback data includes at least: eye tracking feedback data and user heart rate data corresponding to the user during the test.

[0031] Furthermore, the calculating the user feedback coefficient according to the user feedback data further includes:

[0032] Determining the corresponding eye movement response time variance according to the eye movement tracking feedback data of the user corresponding to the test process;

[0033] determining the skewness of the user's heart rate based on the user's heart rate data;

[0034] A user feedback coefficient is calculated according to the eye movement response time variance and the skewness of the user's heart rate.

[0035] Furthermore, the preset time interval is 1 second.

[0036] A VR device, the VR device comprising at least a VR device housing, a processor and a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0037] The VR device includes at least a processor and a memory, wherein the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned virtual reality resource hardware adaptation analysis method.

[0038] The present invention has the following beneficial effects: the present invention obtains virtual content in a virtual reality device test process in real time, and calculates a mean value of a picture quality coefficient of a test video according to the virtual content; obtains hardware detection information in a virtual reality device test process, and calculates a stability coefficient of the virtual reality device according to the hardware detection information; obtains user feedback data in a virtual reality device test process, and calculates a user feedback coefficient according to the user feedback data; calculates a hardware adaptation coefficient of the virtual reality device according to the mean value of the picture quality coefficient, the stability coefficient and the user feedback coefficient; and performs adaptation analysis on the virtual reality device according to the hardware adaptation coefficient. The present invention firstly conducts a detailed analysis of the picture content of each virtual unit during use, and accurately obtains the picture quality coefficient of the virtual unit in each frame; and uses the full battery life test technology to comprehensively track and analyze the number of picture frames and the corresponding hardware equipment detection data during continuous use to evaluate the stability of the hardware; at the same time, collects user feedback data during use, and uses it as an important reference for measuring the hardware adaptability of VR equipment, and integrates relevant data in the real-time application process of the VR device to obtain an objective and accurate VR equipment hardware adaptation coefficient; the present invention fully considers the potential impact of the real-time computing amount changes brought about by the changes in virtual content on the overall picture processing level and the actual user experience, and avoids the technical problem that the hardware adaptation analysis in the prior art only focuses on the correlation response between hardware, resulting in inaccurate calculation results and affecting the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A flowchart of a virtual reality resource hardware adaptation analysis method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the virtual reality resource hardware adaptation analysis method and VR device proposed by the present invention, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0043] Virtual reality technology (VR technology) can bring users a more realistic viewing experience, mainly due to the high-resolution display screen and wide field of view equipped with its equipment. These devices can track the user's head movement in real time and adjust the displayed image accordingly according to the movement, thereby ensuring the continuity and realism of the picture. In addition, in order to create a more three-dimensional and formal environment experience, VR technology also uses the Law of Demeter (LoD) software design principle and various advanced rendering techniques to finely process the image content. However, this high-quality image rendering has high requirements for device hardware such as graphics processing unit (GPU), and requires the device hardware to have high performance and adaptability. If the hardware adaptation is not good, it may cause problems such as picture delay and frame drop, thus affecting the user's experience. Therefore, in the application of virtual reality technology, it is very important to ensure the high adaptability of device hardware.

[0044] The following specifically describes a method for analyzing hardware adaptation of virtual reality resources provided by the present invention in conjunction with the accompanying drawings.

[0045] See also Figure 1 , which shows a virtual reality resource hardware adaptation analysis method provided by an embodiment of the present invention, the method comprising:

[0046] Step S110: acquiring virtual content during the virtual reality device test in real time, and calculating the average value of the picture quality coefficient of the test video based on the virtual content.

[0047] Compared with traditional videos, VR technology provides 360° panoramic view, and users can move in the virtual environment by themselves to feel the distance and layout between objects. For this, VR needs to use higher rendering costs to process the corresponding image content, and in order to reduce the dizziness of users during the experience, the corresponding VR headset must maintain a frame rate of at least 90hz to render the image. For this, the GPU needs to process each frame quickly to avoid visual delays and errors. At the same time, when the user's head and body turn, it is necessary to provide more accurate spatial positioning and interactive experience, so the GPU also needs to process more data.

[0048] In this embodiment, firstly, the hardware parameter data of the relevant VR device, the relevant parameters of playing the VR virtual video and the effect data of the actual playing video are collected.

[0049] In an optional implementation, S110 further includes: acquiring in real time a plurality of virtual units existing in the virtual content during the virtual reality device testing process; wherein an object existing in the virtual environment of the virtual reality device is regarded as a virtual unit; and for each virtual unit, acquiring the virtual content of each frame of the virtual unit.

[0050] In an optional embodiment, S110 further includes: for each virtual unit, dividing the virtual content of each frame of the virtual unit into multiple segmentation modules through an edge segmentation algorithm; determining the number of edges of each segmentation module; obtaining the rendering level, the number of segmentation modules, and the number of edges of each segmentation module of the virtual unit to calculate the rendering complexity of the virtual content of each frame of each virtual unit; and calculating the average picture quality coefficient of the test video according to the rendering complexity of the virtual content of each frame of each virtual unit and the multiple virtual units existing in the virtual content during the virtual reality device test.

[0051] In order to better analyze the corresponding picture quality, multiple virtual units in the corresponding VR virtual content are obtained in real time, that is, an object in the virtual environment is regarded as a virtual unit, and the entire VR virtual space is composed of different virtual units. The more virtual units appear in the corresponding real content, the greater the pressure to render. The richness of the details of the corresponding virtual unit will decrease with the distance of the user in the virtual space. At the same time, the richness of the detail texture of the virtual unit and the resolution of the relevant equipment are also related factors affecting the picture quality. For this, the virtual unit of each frame of content is analyzed to obtain the corresponding picture quality coefficient. The formula for calculating the picture quality coefficient of each frame of content is as follows (1):

[0052]

[0053] In the above formula, JV i Indicates the rendering complexity of the i-th virtual unit; LOD i Indicates the rendering level of the virtual unit; XM i represents the number of segmentation modules after the i-th virtual unit is divided into multiple segmentation modules by the edge segmentation algorithm; B i,z Indicates the number of edges of the z-segmentation module; It represents the color difference gradient mean between the z segmentation module and the surrounding modules. The more rendering levels the corresponding single virtual unit has, the higher the complexity. The more complex the shape of the corresponding single segmentation module is and the higher the contrast with the surrounding content is, the more hardware resources are needed to process the corresponding details.

[0054] The rendering complexity of a single virtual unit is obtained, and the amount of data that needs to be processed for a corresponding frame is obtained by analyzing the virtual units existing in the single frame content. n is the picture quality coefficient corresponding to the nth frame content; res indicates the corresponding resolution size. When the value is larger, a frame of content needs to spend more resources to process the relevant details and textures; MV i Indicates the number of virtual units present; IT i represents the distance between the i-th virtual unit and the corresponding target in the corresponding virtual space; ANG i Represents the field of view angle of the ith virtual unit in the virtual space; when the field of view angle of the virtual unit is smaller, the object needs higher clarity to process the best gaze area, but when the angle is larger, it does not need to spend a large amount of resources to enrich the collective details of the virtual unit. Among them, the size of the field of view angle determines the current field of view of the VR device, which can be taken as the vertex of the lens of the VR device. Specifically, the field of view angle can be measured by the angle formed by the two edges of the maximum range of the lens; the field of view angle in the above formula is determined according to the distribution position of each virtual unit in the current test video. It can be considered that the closer to the middle, the better the recognition effect.

[0055] Furthermore, the rendering complexity of a single virtual unit is obtained by analyzing the single virtual unit, and then the picture quality coefficient of the corresponding frame is obtained by analyzing the relevant virtual units in each frame. The average picture quality coefficient μ of the test video is calculated according to the rendering complexity of the virtual content of each virtual unit in each frame and the multiple virtual units in the virtual content during the virtual reality device test. JY .

[0056] By analyzing the virtual units that appear in the virtual images displayed in each frame, and analyzing the corresponding detail complexity and the relevant LOD rendering levels, the picture quality coefficient of the corresponding frame of the virtual image is obtained; in the actual use and operation process, the compatibility of the relevant hardware of the device will highlight the relevant differences as the use time increases. For devices with good compatibility, the changes in the picture quality coefficient are small during a period of continuous use, and the relevant frame rate changes and the corresponding resolution and motion response will remain at a relatively stable level. At the same time, the relevant detection parameters of each hardware will also remain in a relatively stable state; for devices with poor compatibility, after a period of continuous use, the frame rate will drop, freeze, and respond too slowly, which will bring a poor experience to users. At the same time, the data of the corresponding hardware detection information will also be abnormal to a large extent.

[0057] Step S120: Acquire hardware detection information during the virtual reality device test process, and calculate the stability coefficient of the virtual reality device based on the hardware detection information.

[0058] Specifically, we first collect detailed hardware parameter information of VR devices and related software optimization versions, aiming to analyze hardware compatibility more accurately, and conduct actual usage tests on VR devices to obtain test data to ensure that the basic hardware requirements of the selected VR video are lower than the device specifications, so as to ensure the comprehensiveness and accuracy of the test. During the test, the playback quality of VR videos is detected in real time, and the hardware usage, i.e., hardware detection information, is continuously monitored to ensure the accuracy and reliability of the test data.

[0059] In an optional implementation, the hardware detection information includes at least: a resource utilization rate of a central processing unit (CPU), a resource utilization rate of a graphics processing unit (GPU), and a resource utilization rate of a random access memory (RAM).

[0060] In this step, a full battery life test is performed on the virtual VR device. It is run from the beginning to the end after it is fully charged, and the screen display content is collected. Because the screen display during actual use is affected by the hardware parameter information, and when the corresponding picture quality changes significantly within a continuous time range, the corresponding hardware needs to increase the load capacity and processing power to keep the corresponding picture frame rate relatively stable. For this, it is necessary to combine the corresponding hardware stability with the corresponding screen playback quality to analyze the corresponding overall operation stability.

[0061] In an optional implementation, S120 further includes: performing a full battery life test on the virtual reality device, taking a preset time interval as a unit time, and obtaining a resource usage rate of the acquisition processor, a resource usage rate of the graphics processor, and a resource usage rate of the memory during the full battery life test.

[0062] Taking the preset time interval of every second as an example, take every second as the unit time, first obtain the average value of the picture quality coefficient within the unit time to form it into a continuous signal, and at the same time obtain the average number of frames within the unit time to construct a continuous data information, and for the three main types of data processing hardware, namely CPU, GPU and RAM, detect their corresponding resource utilization rates. Similarly, take every second as the unit time, detect the corresponding average resource usage within the unit time, and construct a continuous data signal according to the corresponding time series. According to the fluctuations of these data signals, analyze the actual picture reality and hardware detection data during continuous use to obtain the corresponding device stability.

[0063] In an optional implementation, S120 further includes: collecting screen display content during the full battery life test; taking a preset time interval as a unit time, calculating the average of the screen quality coefficients in the corresponding unit time according to the screen display content; calculating the number of screen frames played in multiple unit times according to the screen display content; calculating the stability coefficient of the virtual reality device according to the average of the screen quality coefficients in the unit time, the number of screen frames played in the unit time, and the hardware detection information. The formula for calculating the stability coefficient of the VR device is as follows (2):

[0064]

[0065] In the above formula, BH represents the stability coefficient of the corresponding VR device; MBH represents the number of corresponding signals, that is, the number per unit time; ΔJY p Indicates the offset of the picture quality coefficient at the pth second; μ JY Indicates the corresponding mean value of the picture quality coefficient; ΔHY p Indicates the difference between the frame rate of the corresponding time and the standard frame rate; HY indicates the corresponding standard frame rate, i.e. the preset frame rate; MU indicates the number of hardware detected; ΔZU p,d The load fluctuation degree of the d-th hardware corresponding to the p-th second (determined according to the fluctuation of the data signal); ZK represents the load rate value of the previous second (unit time).

[0066] Above Indicates the difference between the fluctuation degree of the corresponding picture quality and the fluctuation degree of the corresponding picture playback frame rate per unit time. Indicates the degree of fluctuation of the corresponding hardware in a unit time. When the corresponding video quality fluctuates greatly in a unit time, the corresponding hardware needs to call more resources to process and calculate to meet the corresponding picture requirements. At this time, when the corresponding picture frame rate changes are not different, the corresponding related stability is higher. The greater the video quality fluctuation, the greater the overall fluctuation of the picture frame rate and the corresponding hardware load fluctuation. The larger the corresponding value of the calculated hardware fluctuation degree per unit time is, the worse the stability is.

[0067] Step S130: Obtain user feedback data during the virtual reality device testing process, and calculate a user feedback coefficient based on the user feedback data.

[0068] In an optional implementation, step S130 further includes: performing user testing on the virtual reality device to obtain user feedback data during the testing process; wherein the user feedback data includes at least: eye tracking feedback data and user heart rate data corresponding to the user during the testing process.

[0069] In an optional embodiment, step S130 further includes: determining the corresponding eye movement response time variance based on the eye tracking feedback data corresponding to the user during the test; determining the skewness of the user's heart rate based on the user's heart rate data; and calculating the user feedback coefficient based on the eye movement response time variance and the skewness of the user's heart rate.

[0070] In order to more intuitively detect the actual usage of the corresponding VR devices, actual user tests are conducted. By detecting the changes in the user's heart rate during use and the corresponding eye tracking feedback data, the hardware adaptability of the corresponding VR devices is analyzed. For VR devices with higher adaptability, the user will be provided with a clear and smooth picture during use, and the user will feel comfortable and immersed. The heart rate changes are small, and the corresponding picture response time when the user's eye movement changes is fast. For VR devices with poor adaptability, users may experience freezes and delays during use, which increases the user's tension and discomfort, thereby causing the heart rate to rise. At the same time, the picture response time when the user's eye movement changes is slow. In this regard, the corresponding user feedback coefficient is analyzed based on the user's feedback data, and the calculation formula is as follows (3):

[0071]

[0072] In the above formula, BU represents the corresponding user feedback coefficient; MUP represents the number of user detection data; represents the corresponding eye movement response time variance; Indicates the skewness of the heart rate; norm indicates the normalization of the result.

[0073] It indicates that the corresponding user feedback is obtained from the analysis of the eye movement response time and the corresponding heart rate skewness. When the corresponding eye movement response time variance is smaller and the heart rate skewness is smaller, it means that the overall hardware adaptation is better. When the corresponding eye movement response time variance is larger and the heart rate skewness is larger, the corresponding hardware adaptation is worse.

[0074] Step S140: Calculate the hardware adaptation coefficient of the virtual reality device according to the average value of the picture quality coefficient, the stability coefficient and the user feedback coefficient.

[0075] The above steps S110-S130 analyze the stability coefficient and user feedback coefficient of the device to evaluate the hardware adaptability. After rigorous data analysis, it is found that when the stability coefficient and user feedback coefficient of the device are both large values, it means that the hardware adaptability is poor; on the contrary, when the stability coefficient and user feedback coefficient of the device are both at a low level, it indicates that the hardware adaptability is relatively ideal.

[0076] In addition, the mean value of the picture quality coefficient of the test video was evaluated. The results showed that the larger the mean value of the picture coefficient, the larger the amount of relevant data that the device hardware can process, which reflects that the hardware performs better in video processing. Combining the above analysis, the hardware adaptation coefficient of the virtual reality device is calculated as follows (4):

[0077]

[0078] In the above formula, LK represents the hardware adaptation coefficient of the virtual reality device; μ JY It represents the mean value of the picture quality coefficient of the test video; BU represents the corresponding user feedback coefficient; BH represents the stability coefficient of the corresponding VR device.

[0079] Step S150: performing adaptation analysis on the virtual reality device according to the hardware adaptation coefficient.

[0080] In this step, for VR devices, since the amount of data they process is relatively large, the adaptation between hardware will greatly affect the response speed and quality of the picture. Therefore, providing high-quality picture response speed and quality based on the hardware adaptation coefficient of the device can bring users a more outstanding immersive experience. If the picture response speed is not good, it may make it difficult for users to integrate into the virtual environment, and even cause adverse symptoms such as dizziness.

[0081] According to the method of this embodiment, the virtual content in the virtual reality device test process is obtained in real time, and the average picture quality coefficient of the test video is calculated according to the virtual content; the hardware detection information in the virtual reality device test process is obtained, and the stability coefficient of the virtual reality device is calculated according to the hardware detection information; the user feedback data in the virtual reality device test process is obtained, and the user feedback coefficient is calculated according to the user feedback data; the hardware adaptation coefficient of the virtual reality device is calculated according to the average picture quality coefficient, the stability coefficient and the user feedback coefficient; and the virtual reality device is adapted and analyzed according to the hardware adaptation coefficient. This method firstly conducts a detailed analysis of the image content of each virtual unit during use, and accurately obtains the image quality coefficient of the virtual unit in each frame; and uses the full battery life test technology to comprehensively track and analyze the number of image frames during continuous use and the corresponding hardware device detection data to evaluate the stability of the hardware; at the same time, the user feedback data during use is collected and used as an important reference for measuring the hardware adaptability of the VR device, and the relevant data in the real-time application process of the VR device is integrated to obtain an objective and accurate VR device hardware adaptation coefficient; this method fully considers the potential impact of the real-time computing changes brought about by the changes in virtual content on the overall image processing level and the actual user experience, and avoids the technical problem that the hardware adaptation analysis in the prior art only focuses on the correlation response between hardware, resulting in inaccurate calculation results and affecting the user experience.

[0082] An embodiment of the present invention provides a VR device, which includes at least a VR device housing, a processor and a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0083] The VR device includes at least a processor and a memory, and the memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the above-mentioned virtual reality resource hardware adaptation analysis method.

[0084] Specifically, the processor may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the server may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0085] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0086] The program can be specifically used to cause the processor to perform the following operations:

[0087] Acquire the virtual content of the virtual reality device in real time during the test, and calculate the average value of the picture quality coefficient of the test video based on the virtual content;

[0088] Acquire hardware detection information during the virtual reality device testing process, and calculate the stability coefficient of the virtual reality device based on the hardware detection information;

[0089] Acquire user feedback data during the virtual reality device testing process, and calculate a user feedback coefficient based on the user feedback data;

[0090] The hardware adaptation coefficient of the virtual reality device is calculated based on the average value of the picture quality coefficient, the stability coefficient and the user feedback coefficient;

[0091] Adaptation analysis of virtual reality devices is performed based on hardware adaptation coefficients.

[0092] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A virtual reality resource hardware adaptation analysis method, characterized in that: The method comprises: Acquire virtual content during the test of the virtual reality device in real time, and calculate the average value of the picture quality coefficient of the test video according to the virtual content; Acquiring hardware detection information during the virtual reality device testing process, and calculating a stability coefficient of the virtual reality device according to the hardware detection information; Acquire user feedback data during the virtual reality device testing process, and calculate a user feedback coefficient based on the user feedback data; Calculating a hardware adaptation coefficient of the virtual reality device according to the average value of the picture quality coefficient, the stability coefficient and the user feedback coefficient; Performing adaptation analysis on the virtual reality device according to the hardware adaptation coefficient; The real-time acquisition of virtual content during the virtual reality device testing process further includes: Real-time acquisition of multiple virtual units existing in virtual content during the virtual reality device testing process; Therein, an object existing in the virtual environment of the virtual reality device is regarded as a virtual unit; For each virtual unit, obtain the virtual content of each frame of the virtual unit; The step of calculating the average value of the picture quality coefficient of the test video according to the virtual content further includes: For each virtual content of each frame of each virtual unit, the virtual content of each frame of the virtual unit is divided into multiple segmentation modules by using an edge segmentation algorithm; Determine the number of edges for each segmentation module; Obtaining the rendering level, the number of segmentation modules, and the number of edges of each segmentation module of the virtual unit to calculate the rendering complexity of the virtual content of each frame of each virtual unit; The average value of the picture quality coefficient of the test video is calculated according to the rendering complexity of the virtual content of each frame of each virtual unit and the multiple virtual units existing in the virtual content during the test of the virtual reality device; The hardware detection information includes at least: the resource utilization rate of the acquisition processor, the resource utilization rate of the graphics processor, and the resource utilization rate of the memory; The obtaining of hardware detection information during the virtual reality device testing process further includes: Performing a full battery life test on the virtual reality device, taking a preset time interval as a unit time, and obtaining a resource usage rate of an acquisition processor, a resource usage rate of a graphics processor, and a resource usage rate of a memory during the full battery life test; The calculating the stability coefficient of the virtual reality device according to the hardware detection information further includes: Collect the screen display content during the full battery life test; Taking a preset time interval as a unit time, calculating an average value of a picture quality coefficient within a corresponding unit time according to the picture display content; Calculating the frame number data of the played pictures within a plurality of unit times according to the picture display content; The stability coefficient of the virtual reality device is calculated according to the average value of the picture quality coefficient within the unit time, the number of picture frames played within the unit time, and the hardware detection information; The obtaining of user feedback data during the virtual reality device testing process further includes: Conduct user testing on the virtual reality device and obtain user feedback data during the testing process; The user feedback data includes at least: eye tracking feedback data and user heart rate data corresponding to the user during the test; The calculating the user feedback coefficient according to the user feedback data further comprises: Determining the corresponding eye movement response time variance according to the eye movement tracking feedback data of the user corresponding to the test process; determining the skewness of the user's heart rate based on the user's heart rate data; A user feedback coefficient is calculated according to the eye movement response time variance and the skewness of the user's heart rate.

2. A virtual reality resource hardware adaptation analysis method according to claim 1, characterized in that: The preset time interval is 1 second.

3. A VR device, characterized in that: The VR device at least includes a VR device housing, a processor and a memory, a communication interface and a communication bus, and the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the virtual reality resource hardware adaptation analysis method according to any one of claims 1-2.

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