Processor module, MR device and electronic device

Abstract

The utility model provides a processor module, MR equipment and electronic equipment. The processor module comprises a main processor and a coprocessor; the coprocessor is electrically connected with the main processor through a communication bus; the coprocessor is electrically connected with a plurality of equipment devices; the coprocessor is used for acquiring equipment data acquired by any one of the plurality of equipment devices and forwarding the processed equipment data to the main processor; and the processing module is also used for processing the first rendering data returned by the main processor. According to the embodiment, the coprocessor can be utilized to interact with a plurality of equipment devices, and the main processor is utilized to process the rendering data, so that the data processing efficiency can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of control technology, and in particular to a processor module, an MR device, and an electronic device. Background Art

[0002] Mixed Reality (MR) devices can merge the real and virtual worlds to create a new visual environment. They can build an interactive feedback information loop between the real world, the virtual world and the user to enhance the realism of the user experience. Utility Model Content

[0003] The present disclosure provides a processor module, an MR device, and an electronic device.

[0004] According to a first aspect of the present disclosure, there is provided a processor module, comprising a main processor and a coprocessor; the coprocessor is electrically connected to the main processor via a communication bus; the coprocessor is electrically connected to a plurality of equipment components;

[0005] The coprocessor is used to obtain device data collected by any one of the multiple device devices and forward the processed device data to the main processor; and is also used to process the first rendering data returned by the main processor.

[0006] Optionally, the coprocessor is further configured to forward the processed device data to a target device among the plurality of device components.

[0007] Optionally, the communication bus includes at least one of a PCIE bus, a DP bus and a USB bus.

[0008] Optionally, the multiple device components include image acquisition sensors, and the coprocessor includes an ISP processing module and a deep learning processing module; the ISP processing module is electrically connected to the deep learning processing module and the image acquisition sensors in the multiple external devices, respectively;

[0009] The ISP processing module is used to process the image data collected by the image acquisition sensor and send the processed image data to the deep learning processing module;

[0010] The deep learning processing module is used to perform reprojection processing and geometric correction processing on the processed image data to obtain second rendering data; and send the second rendering data to a display in the multiple external devices.

[0011] Optionally, the communication bus includes a first communication bus, and the deep learning processing module is electrically connected to the main processor through the first communication bus in the communication bus;

[0012] The deep learning processing module is also used to perform reprojection processing and / or geometric correction processing on the first rendering data returned by the main processor and forward it to the display.

[0013] Optionally, the communication bus includes a second communication bus, the coprocessor includes a processing module and a neural network module, and the processing module is electrically connected to the neural network module and the second communication bus respectively;

[0014] The processing module is used to forward the device data to the neural network module;

[0015] The neural network module is used to forward the perception data of the device data to the processing module;

[0016] The processing module is used to forward the perception data to the main processor through the second communication bus.

[0017] Optionally, the coprocessor further includes a cache module, which is electrically connected to the processing module; the cache module is used to store processing data and intermediate data generated by the processing module.

[0018] Optionally, the main processor includes a computing module, and the computing module is electrically connected to the coprocessor via the communication bus;

[0019] The calculation module is used to obtain the posture feature data and / or physiological feature data of the target object according to the device data.

[0020] Optionally, the calculation module is further used to generate first rendering data according to the posture feature data and / or the physiological feature data or generate first rendering data according to local image data, and send the first rendering data to the deep learning processing module of the coprocessor through the first communication bus;

[0021] The deep learning processing module is used to perform reprojection processing on the first rendering data and forward the reprojected first rendering data to a display screen.

[0022] According to a second aspect of the present disclosure, there is provided an MR device, the MR device comprising a plurality of device components and a processor module as described in any one of the first aspects;

[0023] The sensors in the plurality of equipment components are used to obtain equipment data and send the data to the processor module;

[0024] The processor module is used for adjusting rendering data of displays in the plurality of device components according to the device data.

[0025] According to a third aspect of the present disclosure, an electronic device is provided, comprising a processor module as described in any one of the first aspects.

[0026] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0027] The processor module provided in this embodiment includes a main processor and a coprocessor; the coprocessor is electrically connected to the main processor via a communication bus; the coprocessor is electrically connected to multiple devices; the coprocessor is used to obtain device data collected by any one of the multiple devices, and forward the processed device data to the main processor; and is also used to process the first rendering data returned by the main processor. In this way, this embodiment can use the coprocessor to interact with multiple devices, and use the main processor to process rendering data, which can improve data processing efficiency.

[0028] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a block diagram of a processor module according to an embodiment of the present invention.

[0030] Figure 2 FIG. 4 is a block diagram of another processor module according to an embodiment of the present disclosure.

[0031] Figure 3 The present invention is a block diagram of an MR device according to an embodiment of the present invention.

[0032] Figure 4 The present invention is a block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices consistent with some aspects of the present disclosure as detailed in the appended claims.

[0034] To solve the above technical problems, the embodiments of the present disclosure provide a processor module, an MR device, and an electronic device. The MR device may include smart glasses, a smart helmet, a smart display screen, etc. The electronic device may include but is not limited to a smart phone, a computer, a digital broadcast terminal, a tablet device, a smart bracelet, a smart watch, etc.

[0035] Figure 1FIG. 1 is a block diagram of a processor module according to an embodiment of the present disclosure. Figure 1 , a processor module 100 includes a main processor 20 and a coprocessor 10; the coprocessor 10 is electrically connected to the main processor 20 via a communication bus 30; the coprocessor 10 is electrically connected to a plurality of device components 40;

[0036] The coprocessor 10 is used to obtain device data collected by any device device of the plurality of device devices 40 and forward the processed device data to the main processor 20 ; and is also used to correct the first rendering data returned by the main processor 20 .

[0037] In one embodiment, the communication bus 30 may include at least one of a PCIE bus, a DP bus, and a USB bus. It is understandable that the above communication bus 30 can be selected according to a specific scenario. In one example, the processor module 100 includes two communication buses, namely a first communication bus and a second communication bus. The first communication bus is a DP bus or a USB bus, and the second communication bus is a PCIE bus.

[0038] In one embodiment, in addition to forwarding the device data or the processed device data to the main processor 20, the coprocessor 10 can also forward the device data or the processed device data to a target device in a plurality of device devices. For example, the processed device data is image data, and the target device is a display. At this time, the coprocessor can forward the image data to the display to display it on the display, and this process does not need to pass through the main processor 20. For another example, the coprocessor can obtain the posture data collected by the posture sensor (such as a gravity sensor, an acceleration sensor, etc.), and then send the processed posture data to the display to display the direction information on the display. For another example, the coprocessor can obtain the posture data collected by the posture sensor (such as a gravity sensor, an acceleration sensor, etc.), and then send the processed posture data to the image acquisition sensor (such as a camera and / or a camera), so that the image acquisition sensor adjusts the lens device according to the above posture data. It is understandable that the technician can select the type of data forwarded by the coprocessor, the target device, etc. according to the specific scenario, and the corresponding scheme falls within the protection scope of the present disclosure.

[0039] In one embodiment, the plurality of devices include an image acquisition sensor, which may be one or more of a camera, a webcam, or a camera array. In one example, the image acquisition sensor is implemented using two video see-through (VST) cameras.

[0040] In this embodiment, see Figure 2The coprocessor 10 includes an ISP processing module (Low Latency ISP) and a deep learning processing module DPU; the ISP processing module is electrically connected to the deep learning processing module and the image acquisition sensors in the multiple external devices respectively. The ISP processing module is used to process the image data acquired by the image acquisition sensor and send the processed image data to the deep learning processing module; the deep learning processing module is used to perform reprojection processing and geometric correction processing on the processed image data to obtain second rendering data; and send the second rendering data to the display in the multiple external devices.

[0041] It should be noted that the above-mentioned deep learning processing module can be implemented by a deep learning chip, or the deep learning algorithm can be set on a device with processing functions such as a processor, a single-chip microcomputer, an ARM chip, etc., and the above-mentioned device executes the above-mentioned deep learning algorithm. The above-mentioned deep learning algorithm may include but is not limited to convolutional neural network (CNN), fully connected neural network (FCN), generative adversarial network (GAN), recurrent neural network (RNN), deep belief network (DBN) and restricted Boltzmann machine (RBM) and Transformer network, etc., which can be selected according to the specific scenario.

[0042] In one embodiment, the communication bus 30 includes a first communication bus, and the deep learning processing module is electrically connected to the main processor 20 via the first communication bus. The first communication bus is used to transmit the rendering data returned by the main processor 20, which is hereinafter referred to as the first rendering data; and performs reprojection processing and / or geometric correction processing on the first rendering data, and forwards the processing results to the display.

[0043] Among them, the reprojection processing is used to convert the plane image and the stereoscopic image, and the geometric correction processing is used to scale, distort and correct the brightness of the image, or in other words, the deep learning processing module is used to adapt the first rendering data from the main processor so that the display can be displayed accurately. It should be noted that in this embodiment, the reprojection processing and / or geometric correction processing can eliminate the influence of the posture change during the main processor processing the image data and the coprocessor transmitting the image data, thereby improving the accuracy of the displayed image.

[0044] In one embodiment, see Figure 2, the communication bus includes a second communication bus, the coprocessor includes a processing module and a neural network module NPU, the processing module is electrically connected to the neural network module and the second communication bus respectively; the processing module is used to forward the device data to the neural network module; the neural network module is used to forward the perception data of the device data to the processing module; the processing module is used to forward the perception data to the main processor through the second communication bus. In this way, the main processor can process the above-mentioned perception data, for example, render the image data according to the above-mentioned perception data to obtain the first rendering data. In this way, the neural network module can first perceive the device data, filter out the perception data and forward it to the main processor; in addition, the coprocessor can process other device data; in other words, the coprocessor can classify the device data, process part of it by itself, and forward the other part to the main processor for processing, which is conducive to improving data processing efficiency and reducing the data processing volume of the main processor.

[0045] It should be noted that the neural network module can be implemented using a neural network chip, or the neural network algorithm can be set on a device with processing functions such as a processor, a single-chip microcomputer, an ARM chip, etc., and the neural network algorithm is implemented by the device. Among them, the neural network algorithm can include but is not limited to convolutional neural network (CNN), fully connected neural network (FCN), generative adversarial network (GAN), etc., which can be selected according to the specific scenario.

[0046] In one embodiment, the processing module can perform 6dof calculation, gesture and body posture calculation, face perception calculation, eye movement calculation, depth calculation and spatial audio calculation, etc. In one example, the processing module can be implemented by a single chip microcomputer, DSP or ARM processor.

[0047] In one embodiment, see Figure 2 , the coprocessor also includes a cache module (cache). The cache module is electrically connected to the processing module; the cache module is used to store the processing data and intermediate data generated by the processing module. Among them, the processing data may include the above-mentioned device data, rendering data, and intermediate data generated during the calculation process, etc. In one example, the capacity of the above-mentioned cache module is greater than or equal to the preset capacity threshold, wherein the value range of the preset capacity threshold is 512MB~10GB, which can be set according to the specific scenario. Under the condition of ensuring the cache required for the coprocessor calculation process, the corresponding scheme falls within the protection scope of this disclosure. In this embodiment, the real-time calculation and the real-time data transmission can be guaranteed by setting the cache module.

[0048] In one embodiment, see Figure 2The main processor is responsible for interactive operations with the user, such as APP installation management, generating rendering data, real-time operating system, SDK and application framework layer of 3D space management, etc. In one example, the main processor can generate rendering data, i.e., first rendering data, based on the perception data forwarded by the coprocessor, and transmit it back to the coprocessor.

[0049] In some possible examples, the main processor includes a computing module, and the computing module of the main processor is electrically connected to the coprocessor via a communication bus; the computing module is used to obtain the posture feature data and / or physiological feature data of the target object according to the device data. The posture feature data may include but is not limited to hand gesture features, body posture, etc. The physiological feature data may include but is not limited to eye movement features, facial features, or audio features, etc.

[0050] In some possible examples, the computing module of the main processor is also used to generate first rendering data based on the posture feature data and / or the physiological feature data or generate first rendering data based on local image data, and send it to the deep learning processing module of the coprocessor through the first communication bus; the deep learning processing module is used to reproject the first rendering data and forward the reprojected first rendering data to the display screen.

[0051] In one embodiment, the main processor may include a GPU device, a CPU device, a memory device, and an NPU device; the main processor is provided with an operating system (Core OS), a real-time subsystem (Real-Time Subsystem), a foveated renderer (foveatedrenderer), a multi-application 3D application (Multi-APP 3D Engine), a system framework (OS Frameworks), a spatial framework (Spatial Frameworks), a spatial audio engine (Spatial Audio Engine), an interface layer (Accessibility) and an application layer. The application layer includes scene recognition (Scene Recognition), interaction (interaction, gesture and / or sound), optical recognition (Optic ID), positioning (Location), real-time mapping (real time mapping), audio input (Voice Input), 3D image / audio (3D photo & video), computer screen projection (pc screensprojection), immersion adjustable (immersion adjustable) and video retrieval (Visual Search).

[0052] In one embodiment, the main processor can also obtain local image data, which can come from the Internet, local memory, etc., and generate first rendering data from the image data, and then send it to the deep learning processing module of the coprocessor through the first communication bus. In this way, the main processor and the coprocessor in this embodiment can establish a rendering data transmission channel, which can reduce transmission delay and achieve real-time display effect.

[0053] At this point, this embodiment can use a coprocessor and a main processor to form a processor module architecture, wherein the coprocessor processes part of the data and sends the perception data to the main processor; the main processor is responsible for interacting with the user and processing perception data and generating rendering data, etc. The two work together to improve data processing efficiency.

[0054] The present disclosure also provides an MR device, see Figure 3 The MR device includes a plurality of device components and Figure 1 The processor module; the sensors in the multiple equipment devices are used to obtain equipment data and send it to the processor module; the processor module is used to adjust the rendering data of the display in the multiple equipment devices based on the equipment data.

[0055] In one embodiment, see Figure 3 , multiple devices and components may include but are not limited to displays, VST cameras, gesture sensors (head 6DOF+gesture+body), face recognition devices (face capture), eye tracking sensors (eyetracking), structured light devices (structure light), spatial audio devices (spatial audio), and low-latency LPDDR. It is understandable that those skilled in the art can select devices and components according to specific scenarios, and the corresponding solutions fall within the protection scope of this disclosure.

[0056] In one example, see Figure 3 Two displays can be set up, and each display is electrically connected to the deep learning processing module of the coprocessor through a communication interface. The rendering data of the deep learning processing module can be sent to each display for display.

[0057] In one example, see Figure 3 Two VST cameras can be set up, and each VST camera can be electrically connected to the ISP processing module of the coprocessor through a communication interface, and can send image data to the ISP processing module, so that the ISP processing module performs low-latency processing on the above image data to ensure that the quality of the processed image is normal and available.

[0058] In one example, see Figure 3The posture sensor can obtain the posture data of the MR device. For example, when the MR device is MR glasses or a MR helmet, the above posture data can be used to calculate the 6DOF data of the head using the device data of the posture sensor of the MR device.

[0059] In one example, see Figure 3 The face recognition device can be electrically connected to the processing module of the coprocessor through a communication interface or an API interface, thereby sending the face recognition data to the processing module. The above face recognition data constitutes a part of the physiological feature data.

[0060] In one example, see Figure 3 , 4 eye movement sensors can be provided, and each eye movement sensor can be electrically connected to the processing module of the coprocessor through a communication interface or an eye movement API interface, so that the eye movement feature data is sent to the processing module. Among them, the above eye movement feature data constitutes a part of the physiological feature data.

[0061] In one example, see Figure 3 , two structured light devices can be set, such as infrared sensors, distance sensors, etc., each structured light device can be electrically connected to the processing module of the coprocessor through a communication interface, and the above structured light device can send depth data to the processing module of the coprocessor. The above depth data constitutes a part of the posture feature data.

[0062] In one example, see Figure 3 , 8 spatial audio devices can be provided, and the above-mentioned spatial audio devices can be electrically connected to the processing module of the coprocessor through a communication interface. Among them, 6 audio acquisition devices are provided and 2 speaker devices are provided. The audio acquisition device can transmit the acquired audio data to the processing module. The processing module can also send the audio data to the speaker device.

[0063] Figure 4 4 is a block diagram of an electronic device according to an exemplary embodiment. For example, the electronic device 400 may be a smart phone, a computer, a digital broadcast terminal, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0064] Reference Figure 4 The electronic device 400 may include one or more of the following components: a processing component 402 , a memory 404 , a power component 406 , a multimedia component 408 , an audio component 410 , an input / output (I / O) interface 412 , a sensor component 414 , a communication component 416 , and an image acquisition component 418 .

[0065] The processing component 402 generally controls the overall operation of the electronic device 400, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 402 may include one or more processors 420 to execute computer programs. In addition, the processing component 402 may include one or more modules to facilitate interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402. In an example, the processor 420 may implement the scheme of the above-mentioned power supply control method.

[0066] The memory 404 is configured to store various types of data to support operations on the electronic device 400. Examples of such data include computer programs for any application or method operating on the electronic device 400, contact data, phone book data, messages, pictures, videos, etc. The memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0067] The power supply component 406 provides power to various components of the electronic device 400. The power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 400. The power supply component 406 may include a power supply chip, and the controller may communicate with the power supply chip to control the power supply chip to turn on or off the first switching device, so that the battery supplies power to the mainboard circuit or does not supply power.

[0068] The multimedia component 408 includes a screen that provides an output interface between the electronic device 400 and the target object.

[0069] In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input information from a target object. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0070] The audio component 410 is configured to output and / or input audio file information. For example, the audio component 410 includes a microphone (MIC), and when the electronic device 400 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive external audio file information. The received audio file information can be further stored in the memory 404 or sent via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio file information.

[0071] The I / O interface 412 provides an interface between the processing component 402 and a peripheral interface module, such as a keyboard, a click wheel, a button, etc.

[0072] The sensor component 414 includes one or more sensors for providing various aspects of status assessment for the electronic device 400. For example, the sensor component 414 can detect the open / closed state of the electronic device 400, the relative positioning of components, such as the display screen and keypad of the electronic device 400, and the sensor component 414 can also detect the position change of the electronic device 400 or a component, the presence or absence of contact between the target object and the electronic device 400, the orientation or acceleration / deceleration of the electronic device 400, and the temperature change of the electronic device 400. In this example, the sensor component 414 can include a magnetic sensor, a gyroscope, and a magnetic field sensor, and can also include an inertial sensor, an image sensor, etc., wherein the magnetic field sensor includes at least one of the following: a Hall sensor, a thin film magnetoresistive sensor, and a magnetic liquid acceleration sensor.

[0073] The communication component 416 is configured to facilitate wired or wireless communication between the electronic device 400 and other devices. The electronic device 400 can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In an exemplary embodiment, the communication component 416 receives broadcast information or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0074] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components.

[0075] In an exemplary embodiment, the present disclosure also provides a non-transitory computer-readable storage medium, and when an executable computer program in the storage medium is executed by a processor, the above-mentioned method can be implemented.

[0076] In an exemplary embodiment, a chip is also provided, the chip including a processor and an interface for reading a computer program through the interface to implement the above method. The chip may be a conventional CPU (central processing unit) chip, a GPU (graphics processing unit) chip, etc., or an acceleration chip dedicated to artificial intelligence technology, such as an AI (Artificial Intelligence) accelerator, etc.

[0077] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0078] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A processor module, characterized in that: It includes a main processor and a coprocessor; the coprocessor is electrically connected to the main processor via a communication bus; the coprocessor is electrically connected to a plurality of equipment components; The coprocessor is used to obtain device data collected by any one of the multiple device devices and forward the processed device data to the main processor; and is also used to process the first rendering data returned by the main processor.

2. The processor module according to claim 1, characterized in that: The coprocessor is further configured to forward the processed device data to a target device among the plurality of device components.

3. The processor module according to claim 1, characterized in that: The communication bus includes at least one of a PCIE bus, a DP bus and a USB bus.

4. The processor module according to claim 1, characterized in that: The multiple devices include image acquisition sensors, and the coprocessor includes an ISP processing module and a deep learning processing module; the ISP processing module is electrically connected to the deep learning processing module and the image acquisition sensors in the multiple devices, respectively; The ISP processing module is used to process the image data collected by the image acquisition sensor and send the processed image data to the deep learning processing module; The deep learning processing module is used to perform reprojection processing and geometric correction processing on the processed image data to obtain second rendering data; and send the second rendering data to the display in the multiple device devices.

5. The processor module according to claim 4, characterized in that: The communication bus includes a first communication bus, and the deep learning processing module is electrically connected to the main processor through the first communication bus in the communication bus; The deep learning processing module is also used to perform reprojection processing and / or geometric correction processing on the first rendering data returned by the main processor and forward it to the display.

6. The processor module according to claim 1, characterized in that: The communication bus includes a second communication bus, the coprocessor includes a processing module and a neural network module, and the processing module is electrically connected to the neural network module and the second communication bus respectively; The processing module is used to forward the device data to the neural network module; The neural network module is used to forward the perception data of the device data to the processing module; The processing module is used to forward the perception data to the main processor through the second communication bus.

7. The processor module according to claim 6, characterized in that: The coprocessor further includes a buffer module, which is electrically connected to the processing module; the buffer module is used to store processing data and intermediate data generated by the processing module.

8. The processor module according to claim 1, characterized in that: The main processor includes a computing module, and the computing module is electrically connected to the coprocessor via the communication bus; The calculation module is used to obtain the posture feature data and / or physiological feature data of the target object according to the device data.

9. The processor module according to claim 8, characterized in that: The computing module is further used to generate first rendering data according to the posture feature data and / or the physiological feature data or generate first rendering data according to local image data, and send the data to the deep learning processing module of the coprocessor through the first communication bus; The deep learning processing module is used to perform reprojection processing on the first rendering data and forward the reprojected first rendering data to a display screen.

10. An MR device, characterized in that: The MR device comprises a plurality of device components and a processor module as claimed in any one of claims 1 to 9; The sensors in the plurality of equipment components are used to obtain equipment data and send the data to the processor module; The processor module is used for adjusting rendering data of displays in the plurality of device components according to the device data.

11. An electronic device, characterized in that: Comprising the processor module as claimed in any one of claims 1 to 9.

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