CN112684873B

CN112684873B - VR resource hardware adaptation method

Info

Publication number: CN112684873B
Application number: CN202110268860.3A
Authority: CN
Inventors: 罗涛; 王志远; 陈美松
Original assignee: Beijing Rainier Network Technology Co ltd
Current assignee: Beijing Runneier Technology Co ltd
Priority date: 2021-03-12
Filing date: 2021-03-12
Publication date: 2021-06-25
Anticipated expiration: 2041-03-12
Also published as: CN112684873A

Abstract

The invention provides a VR resource hardware adaptation method, which comprises the following steps: step a: integrating a plurality of VR helmet interfaces into a unified VR interface; step b: collecting test data of the plurality of VR helmets, establishing a temperature prediction model according to the test data, and predicting the temperature trend of a processor of the VR helmets through the temperature prediction model; step c: correcting images output by the screens of the VR helmets according to the screen characteristics of the VR helmets; step d: collecting controller position data in a VR experience process, and filtering the position data; according to the method, any VR helmet can be deployed through one-time VR resource development, the workload of a developer for testing and adapting the VR helmet is greatly reduced, the VR resource development speed is increased, and the quality of VR experiment resources is improved.

Description

VR resource hardware adaptation method

Technical Field

The invention relates to the technical field of VR equipment, in particular to a VR resource hardware adaptation method.

Background

At present, VR helmet brand on the market is various, leads to VR experimental resources to be difficult compatible because hardware equipment is different, causes VR resource preparation and a great deal of inconvenience of use, needs the compatible instrument of the multiple helmet equipment of once preparation. Different VR helmets have great difference in tracking mode, handle interaction mode, screen resolution and field angle, and in the VR experimental resource development stage, rapid test and preview can not be carried out on specific VR helmets, and rapid verification can not be carried out on interaction prototypes. Because of VR helmet adaptation problem, test problem lets the developer hardly put the development of function and interaction on. Developers often spend a large amount of experience on the adaptation and the test of different VR helmets, often even account for more than two thirds of the whole resource development cycle, and the real function and content part of the VR experiment are made in a very short time, so that the VR experiment resource has fewer contents, poor quality, simple experiment and effective experiment time compared with the traditional virtual simulation resource, and the popularization and the promotion of VR experiment teaching are restricted. Moreover, the processor of the VR helmet cannot be effectively controlled in temperature in the prior art, so that the VR helmet cannot run efficiently for a long time.

Disclosure of Invention

In view of this, the invention provides a method for adapting hardware of a VR resource, which aims to solve the problem of how to effectively control the temperature of a processor of a VR helmet when the VR helmet is used, so that the VR helmet can run efficiently for a long time.

In one aspect, the present invention provides a VR resource hardware adaptation method, including the following steps:

step a: integrating a plurality of VR helmet interfaces into a unified VR interface;

step b: collecting test data of the plurality of VR helmets, establishing a temperature prediction model according to the test data, and predicting the temperature trend of a processor of the VR helmets through the temperature prediction model;

step c: correcting images output by the screens of the VR helmets according to the screen characteristics of the VR helmets;

step d: collecting controller position data in a VR experience process, and filtering the position data;

step e: presetting a plurality of visual guidance modes in a VR resource, repeatedly testing the preset visual guidance modes through a VR helmet with an eye tracker, determining the suitable visual guidance modes in each scene through analyzing whether the sight of a VR experiencer is successfully guided and whether the sight is successfully far away from a collision area in the test, and formulating a guidance strategy according to the determined visual guidance modes;

step f: collecting interface data of development software commonly used by VR developers, and designing and manufacturing an interface with a proper style by using the collected interface data; collecting VR resource interaction cases with good user experience, and sorting and concluding the VR resource interaction cases into a plurality of interaction schemes to be provided for developers;

when the temperature trend of the processor is predicted, the temperature, the frequency and the screen refresh rate data of the processor are acquired through a performance self-adaptive module, the data change amplitude of the processor is analyzed by combining with state data of an interactive preset module, whether the data change of the processor accords with a preset growth model is judged, if not, intervention is not carried out, if yes, whether the data change of the processor continues to be judged, if so, the time point when the processor reaches the temperature control intervention temperature is predicted, and the image quality with different amplitudes is reduced or improved according to the predicted time length when the processor reaches the temperature control intervention temperature, so that the frequency of the processor is changed, and the processor is prevented from reaching the temperature control intervention temperature.

Further, the performance self-adapting module records the three-dimensional information of the VR scene and the temperature change trend at the time when the temperature of the processor is greatly changed, intervenes the frequency of the processor in advance before the VR scene reaches the position of the three-dimensional information again, and increases or decreases the processor frequency according to the temperature change trend recorded before.

Furthermore, a first preset processor temperature T1, a second preset processor temperature T2 and a third preset processor temperature T3 are set in the performance self-adaptive module, and T1 is greater than T2 and is less than T3, a first preset processor frequency H1, a second preset processor frequency H2, a third preset processor frequency H3 and a fourth preset processor frequency H4 are set in the performance self-adaptive module, and H1 is greater than H2 is greater than H3 and is less than H4, and the performance self-adaptive module acquires the real-time temperature Δ T of the processor in real time;

the performance self-adaptive module selects an endpoint value of a set range of the processor frequency from each preset processor frequency according to the size relation between the real-time temperature delta T of the processor and the temperature of each preset processor, and takes a frequency value range between two adjacent endpoint values as a value range when the processor frequency is set:

when the delta T is less than T3, selecting H1 as a lower limit value when the processor frequency is taken, and selecting H2 as an upper limit value obtained when the processor frequency is taken, wherein the value range of the processor frequency is set to be H1-H2;

when T2 is not more than or equal to and delta T is less than T3, selecting H2 as a lower limit value when the processor frequency is taken, and selecting H3 as an upper limit value obtained when the processor frequency is taken, wherein the value range of the processor frequency is set to be H2-H3;

when T1 is not less than or equal to Δ T which is less than T2, H3 is selected as a lower limit value when the processor frequency is taken, and H4 is selected as an upper limit value when the processor frequency is taken, and the value range of the processor frequency is set to be H3-H4 at the moment.

Furthermore, a first preset screen refresh rate L1, a second preset screen refresh rate L2 and a third preset screen refresh rate L3 are set in the performance self-adaptive module, and L1 is larger than or equal to 30Hz and smaller than L2 and smaller than L3;

the performance self-adapting module also collects the real-time frequency delta H of the processor in real time, and sets the screen refresh rate of the processor according to the size relation between the real-time frequency delta H of the processor and the frequency of each preset processor:

setting the screen refresh rate of the processor to the third preset screen refresh rate L3 when Δ H < H1;

when H1 is less than or equal to Δ H < H2, setting the screen refresh rate of the processor as the second preset screen refresh rate L2;

when H2 is less than or equal to Δ H < H3, setting the screen refresh rate of the processor as the first preset screen refresh rate L1;

when H3 ≦ Δ H < H4, the screen refresh rate of the processor is set to 2L1- (L1+ L2+ L3)/3.

Further, a first preset image quality A1, a second preset image quality A2, a third preset image quality A3 and a fourth preset image quality A4 are set in the performance adaptive module, and A1 < A2 < A3 < A4;

the performance self-adapting module is further configured to obtain a real-time data throughput U0 of the processor within the preset time period t in real time, and the performance self-adapting module adjusts the quality of the image output by the processor in real time according to the state data of the interaction preset module and the change of the data throughput of the processor:

when the interaction preset module is in a state of starting a main menu, performing VR interaction and being in an indoor scene, judging whether the change condition of the real-time data processing amount U0 accords with the growth model or not, setting the image quality output by the processor according to the judgment result,

when U0 is a linear decrease, setting the image quality of the processor output to the first preset image quality A1;

when U0 fluctuates greatly, setting the image quality output by the processor to be the second preset image quality A2;

setting the image quality of the processor output to the third preset image quality A3 when U0 is linearly increasing;

when U0 is exponentially growing, setting the image quality of the processor output to the fourth preset image quality A4.

Furthermore, a first preset image quality correction coefficient a1, a second preset image quality correction coefficient a2, a third preset image quality correction coefficient a3 and a fourth preset image quality correction coefficient a4 are set in the performance adaptive module, and a1 is more than a2 and more than a3 is more than 1 and more than a 4;

when the interaction preset module is in a state of continuously starting a main menu, performing VR interaction and being in an indoor scene, the performance self-adaptive module is further used for calculating a time point delta t when the processor reaches a temperature control intervention temperature and acquiring a time difference t0 when the time point delta t is reached, and a first preset time difference t1 and a second preset time difference t2 are further set in the performance self-adaptive module, wherein t1 is less than t 2;

the performance self-adapting module determines an image quality correction coefficient according to the magnitude relation between t0 and each preset time difference to correct the image quality output by the processor:

when t0 < t1,

when U0 is in linear descending, selecting a4 to correct A1, wherein the corrected image quality is A1 × a 4;

when U0 fluctuates greatly, A3 is selected to correct A2, and the corrected image quality is A2 × A3;

when U0 is linearly increased, selecting a2 to correct A3, wherein the corrected image quality is A3 × a 2;

when U0 is exponentially increased, selecting a1 to correct A4, wherein the corrected image quality is A4 × a 1;

when t2 < t0,

when U0 is in linear decline, 1.4 × a4 is selected to correct A1, and the corrected image quality is A1 × 1.2 × a 4;

when U0 fluctuates greatly, 1.3 × A3 is selected to correct A2, and the corrected image quality is A2 × 1.3 × A3;

when U0 is linearly increasing, 1.2 × a2 is selected to correct A3, and the corrected image quality is A3 × 1.2 × a 2;

when U0 increased exponentially, 1.1 × a1 was selected to correct a4, and the corrected image quality was a4 × 1.1 × a 1.

Furthermore, a first preset data throughput U1, a second preset data throughput U2, and a third preset data throughput U3 of the processor within the preset time period t are also set in the performance adaptive module, and U1 is greater than U2 and less than U3, a first preset processor temperature correction coefficient b1, a second preset processor temperature correction coefficient b2, and a third preset processor temperature correction coefficient b3 are also set in the performance adaptive module, and b1 is greater than b2 and greater than b 3;

the performance self-adapting module determines a temperature correction coefficient according to the relation between the real-time data processing amount U0 of the processor and each preset data processing amount so as to correct the preset processor temperature:

when U0 is less than U1, selecting the first preset processor temperature correction coefficient b1 to correct the first preset processor temperature T1, wherein the corrected preset processor temperature is T1 × b 1;

when U1 is not less than U0 and is less than U2, selecting the second preset processor temperature correction coefficient b2 to correct the second preset processor temperature T2, wherein the corrected preset processor temperature is T2 x b 2;

when U2 is not less than U0 and is less than U3, selecting the third preset processor temperature correction coefficient b3 to correct the third preset processor temperature T3, wherein the corrected preset processor temperature is T3 x b 3;

further, after the temperature of each preset processor is corrected, the value range of the processor frequency is determined again according to the corrected temperature of each preset processor.

Furthermore, a first preset processor frequency correction coefficient h1, a second preset processor frequency correction coefficient h2, a third preset processor frequency correction coefficient h3 and a fourth preset processor frequency correction coefficient h4 are set in the performance self-adaptive module, and h1 is more than h2 and more than h3 and more than h 4;

the performance self-adaptive module determines a preset processor frequency correction coefficient according to the relation between the real-time temperature delta T of the processor and the temperature of each corrected preset processor so as to correct the frequency of each preset processor:

when Δ T < T1 × b1, selecting the first preset processor frequency correction coefficient H1 to correct the first preset processor frequency H1, wherein the corrected processor frequency is H1 × H1;

when T1 × b1 is not less than Δ T < T2 × b2, selecting the second preset processor frequency correction coefficient H2 to correct the second preset processor frequency H2, wherein the corrected processor frequency is H2 × H2;

when T2 × b2 is not less than Δ T < T3 × b3, selecting the third pre-processor frequency correction coefficient H3 to correct the third pre-processor frequency H3, wherein the corrected processor frequency is H3 × H3;

and when T3 × b3 is not more than Δ T, selecting the fourth preset processor frequency correction coefficient H4 to correct the fourth preset processor frequency H4, wherein the corrected processor frequency is H4 × H4.

Compared with the prior art, the invention has the advantages that the invention adopts a temperature trend prediction algorithm, the algorithm adopts the data of the temperature, the frequency, the screen refresh rate and the like of the processor, combines the state data (whether a main menu is started, whether VR interaction is carried out, whether the data is in an indoor scene and the like) of the interaction preset module as input data, analyzes the change amplitude of the data of the processor, judges whether the data change accords with a linear increase model, a large fluctuation model, a linear decrease model, an exponential increase model and the like, if the data accords with the increase model, judges whether the change continues by combining the state data of the interaction preset module, calculates the time point of reaching the temperature control intervention temperature by the existing data, reduces or improves the image quality with different amplitudes according to the predicted time length of reaching the temperature control intervention temperature, indirectly change the frequency of the processor, thereby avoiding the temperature from reaching the temperature control intervention temperature of the processor. If the above does not match, no intervention is performed. The performance self-adaptive module records three-dimensional information of the VR scene and a current change trend when the temperature changes greatly each time, intervenes the frequency of the processor in advance after the VR scene reaches the vicinity of the same position again, and increases or decreases the frequency of the processor according to the previously recorded change trend. Because the change of the temperature generally lags behind the change of the frequency of the processor, the temperature change trend is predicted through an algorithm, the dynamic adjustment of image quality such as light and shadow quality, model rendering precision, special effect quality, anti-aliasing quality and the like is dynamically adjusted, the frequency of the processor is changed, the temperature control of the processor can be prevented from being triggered, and a VR resource experiencer is guaranteed to have the optimal game experience for a longer time.

Furthermore, by one-time VR resource development, the method can be deployed for any VR helmet, so that the workload of a developer for testing and adapting the VR helmet is greatly reduced, the VR resource development speed is increased, and the quality of VR experiment resources is improved. VR resource designer can carry out the quick verification to the performance of mutual prototype on different VR helmets before the development stage begins, avoids because of the difference of mutual experience on different VR helmets, causes unnecessary reworking, delays the development progress of VR resource. Adopt performance self-adaptation technique, according to different VR helmets automatically regulated image quality, guarantee that the user all has smooth experience on different VR helmets. The realization adopts computer vision technique, carries out color, luminance correction according to the screen characteristics of different VR helmets, guarantees that VR experience person sees rich, the high-quality VR picture of color. Adopt the safety box as supplementary development instrument, let the VR developer in the development stage, can carry out suitable scene according to the scope suggestion of safety box and arrange and the setting of visual guide, promote the user experience of VR resource.

Furthermore, according to the three-dimensional information of the VR scene when the temperature of the processor changes greatly each time and the current temperature change trend, the frequency of the processor is interfered in advance before the VR scene reaches the position of the three-dimensional information again, so that the temperature of the processor can be effectively controlled in time, and the phenomenon that the processor cannot normally run due to overhigh temperature of the processor to influence user experience is avoided.

Further, the performance self-adaptive module selects an endpoint value of a set range of the processor frequency from the preset processor frequencies according to a magnitude relation between the real-time temperature delta T of the processor and the temperature of each preset processor, and takes a frequency value range between two adjacent endpoint values as a value range when the processor frequency is set, so that the frequency of the processor can be adjusted according to the temperature of the processor in real time, and the heat productivity of the processor can be timely controlled by adjusting the frequency of the processor, thereby greatly improving the temperature control efficiency of the processor.

Furthermore, by setting the screen refresh rate of the processor according to the magnitude relation between the real-time frequency Δ H of the processor and the frequency of each preset processor, the screen refresh rate data of the processor can be timely adjusted when the frequency of the processor changes, so that the load of the processor can be effectively reduced, the temperature of the processor can be effectively reduced, and the normal operation of the processor can be ensured.

Further, the performance self-adapting module is further configured to obtain a real-time data throughput U0 of the processor within the preset time period t in real time, and the performance self-adapting module adjusts the image quality output by the processor in real time according to the state data of the interaction preset module and the change of the data throughput of the processor, and can reduce the frequency of the processor by adjusting the data throughput of the image quality reduction processor in real time, so as to effectively adjust the temperature of the processor.

Further, the time point delta t when the processor reaches the temperature control intervention temperature is calculated, the time difference t0 when the time point delta t reaches is obtained, an image quality correction coefficient is determined according to the magnitude relation between t0 and each preset time difference to correct the image quality output by the processor, and the image quality can be improved to the maximum extent under the condition that the processor does not trigger the temperature control intervention temperature by correcting the image quality.

Further, the performance self-adapting module determines a temperature correction coefficient according to a relationship between the real-time data throughput U0 of the processor and each of the preset data throughputs to correct the preset processor temperature, and can more accurately determine a value range of the processor by correcting the preset processor temperature, and determine the frequency of the processor according to the determined value range, thereby more accurately selecting the frequency of the processor.

Furthermore, the performance self-adaptive module determines a preset processor frequency correction coefficient according to the relationship between the real-time temperature delta T of the processor and the temperature of each corrected preset processor, so as to correct the frequency of each preset processor, and the screen refresh rate data in the processor can be accurately determined by correcting the frequency of the preset processor.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 is a functional block diagram of a VR resource hardware adaptation system according to an embodiment of the present invention;

fig. 2 is a flowchart of a VR resource hardware adaptation method according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.

In the prior art, different VR helmets have some differences in the adopted technology due to the hardware configuration, and the experience of actual use is greatly different, which is mainly reflected in the following aspects:

displaying a screen: early VR helmet mainly adopts the LCD screen, LCD itself can not give out light, need be with the help of extra light source, when every frame pixel is luminous always, afterglow will appear, when showing fortune animation face, can appear the smear phenomenon, compare in the aspect of the color in human eyes, the color is not bright-colored enough, the visual angle of screen is little, VR picture edge has the whitening of certain degree or even color to lack, in the aspect of the biggest screen refresh rate, be 60Hz mostly, there is stronger delay, dizzy sense, in the aspect of the screen resolution ratio, because early technical reason, be less than 2K usually. The VR helmet in the middle period mainly adopts an AMOLED screen, the AMOLED luminescent material is an organic luminescent material, self luminescence can be realized by applying correct voltage, the color range is larger compared with that of an LCD screen in the aspect of color, the color is more gorgeous, the visual angle of the screen is large, the colors of the edge and the central part of a VR picture are basically consistent, the maximum screen refresh rate is mostly 90Hz, delay and dizziness are hardly caused, and the screen resolution ratio is usually 2.5K due to the reasons of technology, price and the like. The recent VR helmet mainly adopts the LCD screen, because of the continuous development and the optimization of LCD technique in recent years, afterglow phenomenon has obtained very big improvement, and the screen refresh rate also can reach 90Hz, and screen resolution ratio can reach 4K, 5K usually, still is not bright-colored in the aspect of the color, and it has great gap to compare in AMOLED screen.

The field angle: at present, the field angle of a mainstream VR helmet is generally 110 degrees, a part of high-end VR helmets adopt a 200-degree field angle, a part of cheap VR helmets adopt a 90-degree field angle, great difference of the field angles brings great difficulty to VR resource creators in making visual guidance, if the actual field angle is much smaller than the field angle for development and use, cutting of images can be caused, and the actual field angle is much larger than the field angle for development and use, so that experience persons can see much irrelevant content, attention of the experience persons is dispersed, and difficulty in visual guidance is increased.

Hand tracking: what the high-end VR helmet hand of present mainstream was tracked and is adopted is brake valve lever, carry out position tracking through optics location brake valve lever, touch pad through the handle, the rocker, the button is mutual, mainly divide on the locate mode Inside-out location technique and Lighting House location technique, Lighting House location technique needs extra location base station to erect around VR experience person, this has just restricted the mobility range of VR experience person in actual space, need be with the help of the conveying mobile mode in the VR resource, realize the removal on a large scale in VR resource space, simultaneously owing to adopt optical sensor's reason, receive easily and shelter from causing the location confusion. The Inside-out positioning technology positions the control handle through various sensors around the VR helmet, is not easily influenced by shielding, is not limited by the range of an external positioner, and can move in a real space in a large range.

Processor performance: the current mainstream VR helmet can be divided into an all-in-one type and a PC type according to whether a processor and a system are arranged in the helmet. The all-in-one machine generally adopts an android system, adopts a high-pass cellcell processor close to the mobile equipment, and approaches the performance of the mainstream mobile phone in performance. The PC type is connected with the PC through the multimedia interface and the data interface, and the processor of the PC is used for rendering and calculating.

The embodiment of the invention realizes the adaptation of the same VR resource in different VR helmets through the VR resource adaptation framework.

Referring to fig. 1, the present disclosure provides a VR resource hardware adaptation system in real time, which aims to reduce the adaptation difficulty of a developer to a VR helmet, improve the development efficiency of VR resources, and reduce the test workload in the VR resource development process. To achieve the above object, the present embodiment achieves the following:

the VR resource hardware adaptation system of this embodiment includes:

a performance self-adaptive module: the performance self-adaptive module adopts a temperature trend prediction algorithm, the algorithm adopts the data of the temperature, the frequency, the screen refresh rate and the like of the processor, and combines the state data (whether a main menu is started, whether VR interaction is carried out, whether an indoor scene is in and the like) of the interaction preset module as input data, analyzing the variation range of the processor data, judging whether the data variation accords with linear growth, large-amplitude fluctuation, linear decline, exponential growth and other growth models, if the data is matched with the preset interaction module, judging whether the change continues or not by combining the state data of the preset interaction module, if so, calculating the time point at which the temperature control intervention temperature is to be reached from the existing data, and reducing or improving the image quality with different amplitudes according to the predicted time for reaching the temperature control intervention temperature, and indirectly changing the frequency of the processor, thereby avoiding the temperature from reaching the temperature control intervention temperature of the processor. If the above does not match, no intervention is performed. The performance self-adaptive module records three-dimensional information of the VR scene and a current change trend when the temperature changes greatly each time, intervenes the frequency of the processor in advance after the VR scene reaches the vicinity of the same position again, and increases or decreases the frequency of the processor according to the previously recorded change trend. Because the change of the temperature generally lags behind the change of the frequency of the processor, the temperature change trend is predicted through an algorithm, the dynamic adjustment of image quality such as light and shadow quality, model rendering precision, special effect quality, anti-aliasing quality and the like is dynamically adjusted, the frequency of the processor is changed, the temperature control of the processor can be prevented from being triggered, and a VR resource experiencer is guaranteed to have the optimal game experience for a longer time.

Screen post-processing module: and processing the image after the scene rendering is finished by adopting a computer vision technology. By reading parameters of a VR helmet screen, the color and brightness of an output image are corrected by adopting an LUT technology, the color band phenomenon in the output image is reduced by adopting a Dithering technology, the color of an LCD screen image is enriched, the color loss of the edge of a screen is reduced, and the color cast of an AMOLED screen image is corrected.

A controller tracking module: the position data of the VR helmet and the control handle are read, the data are analyzed, abnormal position and direction data are automatically corrected, the change trend of the position and direction data is more gentle, and the position change exceeding the physiological limit of a human body is marked. And the processed azimuth data is transmitted to an interaction preset module, and the interaction preset module updates the position of the controller in the VR resource according to an interaction rule set by a developer.

The interactive adaptation module: interactive keys and interactive events on different VR helmet control handles are mapped to a set of uniform key values in an input key value mapping mode, an interactive adaptation module analyzes input data, abnormal data are automatically filtered, the processed input data are transmitted to an interactive preset module, and an interactive template makes corresponding response.

An environment monitoring module: read surrounding environment perception data, to sensor data analysis such as the outside infrared inductor of VR helmet, camera, combine the motion trend data of controller tracking module, warn the collision danger that will take place, the event classification strategy that the developer set up in the environmental monitoring module according to mutual preset module, with warning incident classification, when serious collision risk, transmit warning incident for real-time feedback module, the vibrations of trigger handle and the sound warning of immersive earphone. When the collision probability is small or the collision predicted by the distance is long, the warning event is transmitted to the sight guiding module, and the VR experiencer is guided to be far away from objects such as walls, tables and chairs in reality through visual changes such as a fluttering photosphere and a running animal in a virtual world in VR resources, so that VR immersion experience of the VR experiencer is not damaged.

A real-time feedback module: receiving feedback data sent by the environment monitoring module and the interaction preset module, calling a RainierVR API corresponding interface after the real-time feedback module analyzes the data, triggering a vibration module and a light module switch in the control handle, and automatically sending an end event to the environment monitoring module and the interaction preset module after vibration and light change are finished.

An interaction preset module: the interaction presetting module comprises a set of interaction interfaces designed for developers and corresponding data tables. And the developer presets an interaction strategy for interaction behaviors in the VR resources in the interaction interface, the interaction strategy is stored in the context in the form of a lookup table, and other modules can read the table through the context.

The implementation steps of the system of the embodiment are as follows:

integrate each VR helmet interface: firstly, collecting and sorting information of mainstream VR helmets in the market, finding APIs of language versions adopted by a VR development engine, and integrating the APIs into a virtual simulation development engine; and then, further packaging each API on the basis, and integrating into a unified VR interface.

And (3) constructing a performance self-adaptive module: firstly, collecting official test data of each VR helmet, establishing a temperature expectation model according to the official data, and developing a basic self-adaptive algorithm; secondly, correcting the model and the algorithm through a large number of real machine tests; and then, performing iterative training by using AI deep learning, optimizing a model and an algorithm and improving the accuracy.

Construction of a screen post-processing module: firstly, according to the screen characteristics of each VR helmet, an LUT (look up table) graph is made for correcting brightness and color; and then capturing the corrected images, comparing the corrected images through image analysis processing software, continuously optimizing a post-processing algorithm, and reducing the difference of final output images of different VR helmets.

Construction of the controller tracking module: firstly, collecting actual controller position data in a VR experience process, and developing a position data filtering algorithm based on the data; and then developing a sample using a data filtering algorithm, carrying out a large number of tests, and continuously optimizing the algorithm by using newly-added data to improve the filtering effect.

Construction of an environment monitoring module: firstly, making a plurality of visual guidance modes in VR resources; secondly, a large number of tests are carried out by using the VR helmet with the eye tracker, and the proper visual guidance mode in each scene is summarized by analyzing whether the sight of a VR experiencer is successfully guided and whether the sight of the VR experiencer is successfully far away from the area where collision occurs in the tests; and then different guiding strategies are formulated and provided for the interactive preset module, and after the environment monitoring module is on line, the guiding mode is continuously optimized, so that the safety and immersion experience of the user are protected.

Constructing an interactive preset module: firstly, collecting interface data of development software commonly used by VR developers, and designing and manufacturing an interface with a proper style by using the collected interface data; secondly, collecting VR resource interaction cases with good user experience on the market, and sorting and summarizing the VR resource interaction cases into a plurality of sets of interaction schemes for developers; and finally, carrying out a large number of tests, collecting feedback information of developers, and continuously adjusting and optimizing the interaction scheme.

The system of this embodiment realizes the adaptation of same VR resource at different VR helmets through the VR resource adaptation frame, has following advantage at least:

once VR resource development, arbitrary VR helmet deployment reduces the work load of developer's test, adaptation VR helmet by a wide margin for VR resource development speed promotes VR experiment resource quality.

VR resource designer can carry out the quick verification to the performance of mutual prototype on different VR helmets before the development stage begins, avoids because of the difference of mutual experience on different VR helmets, causes unnecessary reworking, delays the development progress of VR resource.

Adopt performance self-adaptation technique, according to different VR helmets automatically regulated image quality, guarantee that the user all has smooth experience on different VR helmets.

The realization adopts computer vision technique, carries out color, luminance correction according to the screen characteristics of different VR helmets, guarantees that VR experience person sees rich, the high-quality VR picture of color.

Adopt the safety box as supplementary development instrument, let the VR developer in the development stage, can carry out suitable scene according to the scope suggestion of safety box and arrange and the setting of visual guide, promote the user experience of VR resource.

Referring to fig. 2, in another preferred implementation based on the foregoing embodiment, this implementation provides a VR resource hardware adaptation method, including the following steps:

Specifically, the performance self-adapting module records three-dimensional information of a VR scene and a temperature change trend at the moment of each time when the temperature of the processor is greatly changed, intervenes the frequency of the processor in advance before the VR scene reaches the position of the three-dimensional information again, and increases or decreases the frequency of the processor according to the previously recorded temperature change trend.

According to the three-dimensional information of the VR scene when the temperature of the processor changes greatly each time and the current temperature change trend, the frequency of the processor is interfered in advance before the VR scene reaches the position of the three-dimensional information again, so that the temperature of the processor can be effectively controlled in time, and the phenomenon that the processor cannot normally run due to overhigh temperature of the processor to influence user experience is avoided.

Specifically, a first preset processor temperature T1, a second preset processor temperature T2 and a third preset processor temperature T3 are set in the performance self-adaptive module, T1 is greater than T2 and is less than T3, a first preset processor frequency H1, a second preset processor frequency H2, a third preset processor frequency H3 and a fourth preset processor frequency H4 are set in the performance self-adaptive module, H1 is greater than H2 is greater than H3 and is less than H4, and the performance self-adaptive module acquires the real-time temperature Δ T of the processor in real time;

It can be seen that, the performance self-adaptive module selects the endpoint value of the setting range of the processor frequency from the preset processor frequencies according to the magnitude relation between the real-time temperature Δ T of the processor and the preset processor temperatures, and uses the frequency value range between two adjacent endpoint values as the value range when the processor frequency is set, so that the frequency of the processor can be adjusted according to the processor temperature in real time, and the heat productivity of the processor can be timely controlled by adjusting the processor frequency, thereby greatly improving the temperature control efficiency of the processor.

Specifically, a first preset screen refresh rate L1, a second preset screen refresh rate L2 and a third preset screen refresh rate L3 are also set in the performance self-adaptive module, and L1 is larger than or equal to 30Hz and smaller than L2 and smaller than L3;

It can be seen that by setting the screen refresh rate of the processor according to the magnitude relationship between the real-time frequency Δ H of the processor and the frequency of each preset processor, the screen refresh rate data of the processor can be timely adjusted when the frequency of the processor changes, so that the load of the processor can be effectively reduced, the temperature of the processor can be effectively reduced, and the normal operation of the processor can be ensured.

Specifically, a first preset image quality A1, a second preset image quality A2, a third preset image quality A3 and a fourth preset image quality A4 are set in the performance adaptive module, and A1 < A2 < A3 < A4;

It can be seen that the performance adaptive module is further configured to obtain a real-time data throughput U0 of the processor within the preset time period t in real time, and the performance adaptive module adjusts the image quality output by the processor in real time according to the state data of the interaction preset module and the change of the data throughput of the processor, and can reduce the frequency of the processor by adjusting the data throughput of the image quality reduction processor in real time, so as to effectively adjust the temperature of the processor.

Specifically, a first preset image quality correction coefficient a1, a second preset image quality correction coefficient a2, a third preset image quality correction coefficient a3 and a fourth preset image quality correction coefficient a4 are set in the performance adaptive module, and a1 < a2 < a3 < 1 < a 4;

when t0 < t1,

when t2 < t0,

It can be seen that by calculating the time point Δ t when the processor reaches the temperature control intervention temperature and acquiring the time difference t0 when the time point Δ t is reached, the image quality correction coefficient is determined according to the magnitude relation between t0 and each preset time difference to correct the image quality output by the processor, and by correcting the image quality, the image quality can be improved to the maximum extent under the condition that the processor does not trigger the temperature control.

Specifically, a first preset data processing amount U1, a second preset data processing amount U2, and a third preset data processing amount U3 of the processor within the preset time period t are further set in the performance adaptive module, and U1 is greater than U2 and less than U3, a first preset processor temperature correction coefficient b1, a second preset processor temperature correction coefficient b2, and a third preset processor temperature correction coefficient b3 are further set in the performance adaptive module, and b1 is greater than b2 and greater than b 3;

specifically, after the temperature of each preset processor is corrected, the value range of the processor frequency is determined again according to the corrected temperature of each preset processor.

It can be seen that the performance self-adapting module determines a temperature correction coefficient according to a relationship between the real-time data throughput U0 of the processor and each of the preset data throughputs to correct the preset processor temperature, and can determine the value range of the processor more accurately by correcting the preset processor temperature, and determine the frequency of the processor according to the determined value range, thereby being able to select the frequency of the processor more accurately.

Specifically, a first preset processor frequency correction coefficient h1, a second preset processor frequency correction coefficient h2, a third preset processor frequency correction coefficient h3 and a fourth preset processor frequency correction coefficient h4 are set in the performance self-adaptive module, and h1 is more than h2 and more than h3 and more than h 4;

It can be seen that the performance self-adaptive module determines a preset processor frequency correction coefficient according to a relationship between the real-time temperature Δ T of the processor and each corrected preset processor temperature, so as to correct each preset processor frequency, and by correcting the preset processor frequency, screen refresh rate data in the processor can be accurately determined.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims

1. A VR resource hardware adaptation method is characterized by comprising the following steps:

step f: collecting interface data of development software commonly used by VR developers, and designing and manufacturing style interfaces by using the collected interface data; collecting other VR resource interaction cases, and sorting and inducing the cases into a plurality of interaction schemes to be provided for developers;

when the temperature trend of the processor is predicted, acquiring temperature, frequency and screen refresh rate data of the processor through a performance self-adaptive module, analyzing the data change amplitude of the processor by combining state data of an interactive preset module to judge whether the data change of the processor accords with a preset growth model, if not, not intervening, if so, continuously judging whether the data change of the processor continues, if so, predicting the time point when the processor reaches the temperature control intervening temperature, and reducing or improving the image quality with different amplitudes according to the predicted time length when the processor reaches the temperature control intervening temperature so as to change the frequency of the processor; wherein the content of the first and second substances,

a first preset image quality A1, a second preset image quality A2, a third preset image quality A3 and a fourth preset image quality A4 are set in the performance self-adaptive module, and A1 is more than A2 and more than A3 and more than A4; the performance self-adaptive module is also used for acquiring the real-time data processing amount U0 of the processor within a preset time length t in real time;

a first preset image quality correction coefficient a1, a second preset image quality correction coefficient a2, a third preset image quality correction coefficient a3 and a fourth preset image quality correction coefficient a4 are also set in the performance adaptive module, and a1 is more than a2 and more than a3 and more than 1 and more than a 4;

the performance self-adapting module determines an image quality correction coefficient according to the size relation between t0 and each preset time difference to correct the image quality output by the processor;

a first preset processor temperature T1, a second preset processor temperature T2 and a third preset processor temperature T3 are set in the performance self-adaptive module, T1 is more than T2 and more than T3, a first preset processor frequency H1, a second preset processor frequency H2, a third preset processor frequency H3 and a fourth preset processor frequency H4 are also set in the performance self-adaptive module, H1 is more than H2 is more than H3 and more than H4, and the performance self-adaptive module acquires the real-time temperature delta T of the processor in real time;

the performance self-adapting module selects an endpoint value of a set range of a processor frequency from the first preset processor frequency H1, the second preset processor frequency H2, the third preset processor frequency H3 and the fourth preset processor frequency H4 according to a magnitude relation between a real-time temperature Δ T of the processor and the first preset processor temperature T1, the second preset processor temperature T2 and the third preset processor temperature T3, and sets a frequency range between two adjacent endpoint values as a value range of the processor frequency:

when the delta T is greater than T3, selecting H1 as a lower limit value when the processor frequency is taken, and selecting H2 as an upper limit value when the processor frequency is taken, wherein the taking range of the processor frequency is set to be H1-H2;

when T2 is not less than or equal to Δ T which is less than T3, selecting H2 as a lower limit value when the processor frequency is taken, and selecting H3 as an upper limit value when the processor frequency is taken, wherein the value range of the processor frequency is set to be H2-H3;

when T1 is not less than Δ T and less than T2, H3 is selected as a lower limit value when the processor frequency is taken, and H4 is selected as an upper limit value when the processor frequency is taken, and the value range of the processor frequency is set to be H3-H4 at the moment.

2. The VR resource hardware adaptation method of claim 1 wherein the performance adaptation module records three-dimensional information of a VR scene and a current temperature change trend of the processor each time the temperature changes greatly, and intervenes in advance on the frequency of the processor before arriving at a three-dimensional information location again after the VR scene, and increases or decreases the frequency of the processor according to the previously recorded temperature change trend.

3. The VR resource hardware adaptation method of claim 1 wherein a first preset screen refresh rate L1, a second preset screen refresh rate L2 and a third preset screen refresh rate L3 are further set within the performance adaptation module, and 30Hz L1 < L2 < L3;

4. The VR resource hardware adaptation method of claim 3 wherein the performance adaptation module adjusts the quality of the image output by the processor in real-time based on the state data of the interactive preset module and the change in the data throughput of the processor:

when the interaction preset module is in a state of starting a main menu, performing VR interaction and being in an indoor scene, judging the change condition of the real-time data processing amount U0, setting the image quality output by the processor according to the judgment result,

5. The VR resource hardware adaptation method of claim 4,

when the performance self-adapting module determines an image quality correction coefficient according to the magnitude relation between t0 and each preset time difference to correct the image quality output by the processor:

when t0 < t1,

if U0 is linearly decreased, selecting a4 to correct A1, and the corrected image quality is A1 × a 4;

if U0 fluctuates greatly, A3 is selected to correct A2, and the corrected image quality is A2 × A3;

if U0 is increased linearly, a2 is selected to correct A3, and the corrected image quality is A3 × a 2;

if U0 is exponentially increased, selecting a1 to correct A4, wherein the corrected image quality is A4 × a 1;

when t2 < t0,

if U0 is linearly decreased, 1.4 × a4 is selected to correct A1, and the corrected image quality is A1 × 1.4 × a 4;

if U0 fluctuates greatly, 1.3 × A3 is selected to correct A2, and the corrected image quality is A2 × 1.3 × A3;

if U0 is increased linearly, 1.2 × a2 is selected to correct A3, and the corrected image quality is A3 × 1.2 × a 2;

if U0 is exponentially increasing, 1.1 × a1 is selected to correct a4, and the corrected image quality is a4 × 1.1 × a 1.

6. The VR resource hardware adaptation method of claim 4, wherein a first preset data throughput U1, a second preset data throughput U2 and a third preset data throughput U3 of the processor within the preset time period t are further set in the performance adaptation module, and U1 is greater than U2 and less than U3, and a first preset processor temperature correction coefficient b1, a second preset processor temperature correction coefficient b2 and a third preset processor temperature correction coefficient b3 are further set in the performance adaptation module, and b1 > b2 > b 3;

the performance self-adapting module determines a temperature correction coefficient according to the relation between the real-time data throughput U0 of the processor and the first preset data throughput U1, the second preset data throughput U2 and the third preset data throughput U3 so as to correct the preset processor temperature:

and when U2 is not less than U0 and less than U3, selecting the third preset processor temperature correction coefficient b3 to correct the third preset processor temperature T3, wherein the corrected preset processor temperature is T3 x b 3.

7. The VR resource hardware adaptation method of claim 6 wherein after each pre-determined processor temperature is modified, a range of processor frequencies is re-determined based on the modified pre-determined processor temperatures.

8. The VR resource hardware adaptation method of claim 6 wherein a first pre-processor frequency correction coefficient h1, a second pre-processor frequency correction coefficient h2, a third pre-processor frequency correction coefficient h3 and a fourth pre-processor frequency correction coefficient h4 are further configured in the performance adaptation module, and h1 < h2 < h3 < h 4;

the performance self-adaptive module determines a preset processor frequency correction coefficient according to the relation between the real-time temperature of the processor and the temperature of each corrected preset processor so as to correct the frequency of each preset processor: