Method for processing multi-degree-of-freedom data and electronic equipment

Abstract

The invention discloses a method for processing multi-degree-of-freedom data and electronic equipment. The method comprises the following steps: determining at least one collaborative computing power resource for data calculation from external computing power resources; receiving multi-degree-of-freedom original data collected by a plurality of sensors; constructing at least one calculation task carrying multi-degree-of-freedom task data on the basis of the multi-degree-of-freedom original data; distributing the computing task to the at least one collaborative computing power resource; receiving at least one calculation result obtained by calculating the multi-degree-of-freedom task data according to the calculation task from the collaborative computing power resource; and processing the calculation result to complete the calculation of the multi-degree-of-freedom original data. According to the method, external collaborative computing power resources can be utilized to assist in processing computing tasks of massive data of the equipment, and the performance problem caused by insufficient computing power resources of the equipment is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of extended reality devices, and in particular to a method and electronic device for processing multi-degree-of-freedom data. Background Art

[0002] Extended Reality (XR) is a combination of real and virtual environments that can be interacted with by humans and machines, created through computer technology and wearable devices. XR includes a variety of technologies, such as augmented reality (AR), virtual reality (VR), and mixed reality (MR). Augmented reality (AR) is the process of superimposing virtual digital information on the real world through computer image and video technology. Users can see virtual information through head-mounted devices or handheld devices and interact with the real environment. Virtual reality (VR) is the process of creating a completely computer-generated three-dimensional virtual environment through computer technology. Users can enter this virtual environment through head-mounted devices, gloves, motion trackers and other devices, and interact with objects in the virtual environment. Mixed reality (MR) combines the characteristics of AR and VR, integrating virtual information with the real environment. Users can see the superposition of virtual information and the real environment through head-mounted devices or handheld devices, and interact with both. XR technology can be applied to many fields, such as games, entertainment, education, medical care, industrial design, etc.

[0003] XR devices have a huge amount of image (4 to 8 color sensors) and sensor (1 to 2 sets of 3DOF sensors) calculations. The chips of XR devices are faced with a huge amount of calculations, and their system computing resources are stretched. Due to the strong constraints of system computing resources, XR devices have many performance problems that affect the user experience. For example, fluctuations in system computing power consumption cause fluctuations in the frame rate of the system calculating 6DOF (degrees of freedom in 6 directions), and frame rate fluctuations can easily cause visual fatigue in users. In addition, due to insufficient system computing power, the system balance experience can only choose the low resolution of the camera, low-resolution images, resulting in limited 6DOF calculation accuracy, which significantly affects the system sensitivity of users using XR devices. In addition, XR devices require portability and low power consumption. Increasing system computing power often increases the power consumption of the system and increases the weight of the entire machine. Summary of the invention

[0004] The present invention provides a method and electronic device for processing multi-degree-of-freedom data, which can utilize external collaborative computing resources to assist in processing computing tasks of massive data of the device and solve performance problems caused by insufficient computing resources of the device.

[0005] In one aspect of the present invention, a method for processing multi-degree-of-freedom data is provided. The method comprises: determining at least one collaborative computing resource for data calculation from external computing resources; receiving multi-degree-of-freedom raw data collected by multiple sensors; constructing at least one computing task each carrying multi-degree-of-freedom task data based on the multi-degree-of-freedom raw data; distributing the computing task to the at least one collaborative computing resource; receiving from the collaborative computing resource at least one computing result obtained by computing the multi-degree-of-freedom task data according to the computing task; and processing the computing result to complete the calculation of the multi-degree-of-freedom raw data.

[0006] In another aspect of the present invention, an electronic device is provided, comprising a memory configured to store a computer program and a processor configured to execute the computer program to perform the above method for processing multi-degree-of-freedom data.

[0007] According to the technical solution of the present invention, at least one collaborative computing resource is determined from the external computing resources, and the multi-degree-of-freedom raw data collected by the sensor is obtained at the same time. The corresponding computing task is constructed based on the multi-degree-of-freedom raw data, and the computing task is distributed to at least one collaborative computing resource for calculation to obtain the calculation result, and finally the calculation result is processed to respond to the operation request of the system or user to complete the calculation task of the multi-degree-of-freedom raw data. The present invention utilizes external collaborative computing resources and constructs distributed computing based on multi-degree-of-freedom raw data, so that multiple computing tasks can be processed in parallel through different collaborative computing resources, thereby assisting the device to complete the computing task of massive data, and solving the performance problem caused by insufficient computing resources of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flowchart of a method for processing multi-degree-of-freedom data according to an embodiment of the present invention;

[0009] Figure 2 Another flow chart of the method for processing multi-degree-of-freedom data according to an embodiment of the present invention;

[0010] Figure 3 A flowchart of the collaborative computing power management service according to an embodiment of the present invention;

[0011] Figure 4 A flowchart of the collaborative computing resources performing 6DOF computing tasks according to an embodiment of the present invention;

[0012] Figure 5 A flowchart of an XR device processing 6DOF data results according to an embodiment of the present invention;

[0013] Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention;

[0014] Figure 7 FIG. 4 is another structural schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0016] In existing technologies, the computing power resources of XR device systems cannot support large amounts of computing tasks, resulting in many performance issues that affect the user experience of XR devices. For example, fluctuations in system computing power consumption cause fluctuations in the system's 6DOF frame rate calculations, causing visual fatigue in users. Insufficient system computing power means that the system balance can only select low-resolution images from the camera, which limits the accuracy of 6DOF calculations and affects the system sensitivity of users using XR devices. If XR devices directly increase system computing power, it will often increase system power consumption and increase the weight of the entire device.

[0017] In order to solve at least the above technical problems, the present disclosure provides a method for processing multi-degree-of-freedom data. According to the present disclosure, at least one collaborative computing resource is determined from external computing resources, and the multi-degree-of-freedom raw data collected by the sensor is obtained at the same time. The corresponding computing task is constructed based on the multi-degree-of-freedom raw data, and the computing task is distributed to at least one collaborative computing resource for calculation to obtain the calculation result, and finally the calculation result is processed to respond to the operation request of the system or user to complete the calculation task of the multi-degree-of-freedom raw data. In this way, according to the embodiment of the present disclosure, it is possible to utilize external collaborative computing resources to construct distributed computing tasks based on multi-degree-of-freedom raw data, so that multiple computing tasks can be processed in parallel through different collaborative computing resources, thereby assisting the device to complete the computing task of massive data, and solving the performance problem caused by insufficient computing resources of the device.

[0018] Hereinafter, the technical solution according to the present disclosure will be described with reference to specific embodiments and in conjunction with the accompanying drawings.

[0019] Figure 1 1 is a flow chart showing a method 100 for processing multi-degree-of-freedom data according to an embodiment of the present disclosure. Figure 1 The method 100 includes the following steps 102 to 112.

[0020] In step 102, at least one collaborative computing resource for data computing is determined from external computing resources.

[0021] In some embodiments, all external computing resources with communication connection relationships are obtained, and the computing power of the external computing resources is determined through the collaborative computing service to determine whether the computing power meets the standard computing power requirements. If so, the external computing resources are determined to be collaborative computing resources, and the collaborative computing resources are added to the preset virtual computing resource pool. In this way, the external computing resources that meet the computing power requirements are determined through the collaborative computing management service, and the current computing resources of the device are flexibly expanded. At the same time, the external computing resources that meet the computing power requirements are added to the virtual computing resource pool for management, which can effectively monitor and manage the current collaborative computing resources and avoid errors in the distribution of subsequent computing tasks.

[0022] In step 104 , multi-degree-of-freedom raw data acquired by a plurality of sensors is received.

[0023] In some embodiments, image data and inertial measurement data for a total of six degrees of freedom are received. In some embodiments, the plurality of sensors includes image sensors and inertial sensors.

[0024] In step 106 , at least one computing task each carrying multi-degree-of-freedom task data is constructed based on the multi-degree-of-freedom raw data.

[0025] In some embodiments, the multi-degree-of-freedom raw data includes at least one set of multi-degree-of-freedom task data. In some embodiments, constructing at least one computing task includes: for each set of multi-degree-of-freedom task data, constructing a computing task containing the set of multi-degree-of-freedom task data based on the set of multi-degree-of-freedom task data, wherein the computing task is to calculate the set of multi-degree-of-freedom task data to obtain a computing result. In this way, constructing each set of multi-degree-of-freedom task data as a computing task facilitates the distribution of tasks for calculating target data of different multi-degree-of-freedom task data to different collaborative computing resources for execution, thereby effectively utilizing the resources of external computing resources to realize parallel computing of multiple sets of frame data and improve data processing efficiency.

[0026] In step 108, the computing task is distributed to the at least one collaborative computing resource.

[0027] In step 110, at least one calculation result obtained by calculating the multi-degree-of-freedom task data according to the computing task is received from the collaborative computing resource.

[0028] In some embodiments, receiving at least one calculation result from the collaborative computing power resource includes receiving the calculation result obtained from the collaborative computing power resource in the following manner: calculating the image data in the computing task by using an image position estimation algorithm preset in the collaborative computing power resource to obtain camera position data; performing fusion calculation on the camera position data and the inertial measurement data of three degrees of freedom in the computing task to obtain a calculation result of six degrees of freedom data; and updating the meta-information of the computing task. In this way, the inertial measurement data includes the rotation information of the object in space, and the image data can obtain the translation information of the object in space, that is, the camera position data, through the image position estimation algorithm. The six degrees of freedom data of the object can be determined based on the translation information and the rotation information. The present invention sends the calculation process originally executed in the XR device to the external collaborative computing power resource for execution, so that the data can be distributedly calculated and the calculation efficiency of the data can be improved.

[0029] In some embodiments, the meta-information includes a timestamp. In some embodiments, the method may further include: determining the validity of the calculation result according to the timestamp; if the calculation result is an invalid result, determining whether the number of other calculation tasks whose calculation result is a valid result is greater than a preset value; if so, sorting the calculation results of all calculation tasks according to the timestamp, and interpolating the invalid results according to the sorted valid results. In this way, since the collaborative computing resources will synchronously update the timestamp of the calculation task after obtaining the multi-degree-of-freedom data, the timestamp can be used to determine whether the current calculation result has timed out to verify the validity of the calculation result. When the calculation result times out, the current calculation result is an invalid result. However, when there are more valid results, the invalid results can be interpolated, so that the XR device can work normally and improve the fault tolerance and system robustness of distributed computing.

[0030] In step 112, the calculation results are processed to complete the calculation of the multi-degree-of-freedom raw data.

[0031] In some embodiments, processing the calculation result to complete the calculation of the multi-DOF raw data includes: performing post-processing and association processing on the calculation result to obtain the calculation result of the multi-DOF data. In this way, the calculation result of the external collaborative computing resource is returned to the original device for response.

[0032] In some embodiments, post-processing and associating the calculation result includes: constructing the calculation result as a system event to circulate and process in the operating system; and associating the calculation result with a user processing operation or a system processing operation. In this way, since the calculation result is a specific value, the calculation result is constructed as a system event so that the calculation result can be circulated and processed in the operating system of the device, thereby responding to the operation request of the user or the system, and realizing the application of the calculation result.

[0033] In some embodiments, after adding the collaborative computing resources to the preset virtual computing resource pool, the method may further include: detecting the working status and computing power of the collaborative computing resources in real time through the collaborative computing service; and updating the virtual computing resource pool according to the working status and computing power of the collaborative computing resources. In this way, since the device status and connection of the external computing resources are unstable, and the same external computing resource may correspond to multiple virtual computing resource pools, the working status and computing power of the collaborative computing resources are monitored and detected in real time after the collaborative computing resources are determined, thereby dynamically updating the virtual computing resource pool to ensure the reliability of the collaborative computing resources.

[0034] In some embodiments, updating the virtual computing power resource pool according to the working status and computing power of the collaborative computing power resource includes: if it is detected that the working status of the collaborative computing power resource is an exit state or an error state, or it is detected that the computing power of the collaborative computing power resource does not meet the computing power requirement, then the collaborative computing power resource is deleted from the virtual computing power resource pool. In this way, for collaborative computing power resources that have exited the working state or have errors, they are directly deleted from the virtual computing power resource pool to avoid affecting the validity of the calculation results. Since the collaborative computing power resources may be occupied by other devices, causing the computing power resources to change and unable to meet the computing power requirements of this device, in this case, the external computing power resources are actively deleted by this device to save management resources in the virtual device pool.

[0035] According to the embodiments of the present disclosure, by utilizing external collaborative computing resources, distributed computing tasks are constructed based on multi-degree-of-freedom original data, and multiple computing tasks are distributed to different collaborative computing resources for parallel processing, thereby assisting the device in completing computing tasks of massive data and solving performance problems caused by insufficient computing resources of the device.

[0036] The embodiments of the present disclosure relate to distributed computing of multi-degree-of-freedom (e.g., 6DOF) data, which is atypical / customized distributed computing due to reasons such as data granularity division and temporal correlation. The distributed computing according to the embodiments of the present disclosure does not require the complex concepts / mechanisms of general distributed computing (dynamic computing expansion, fault tolerance and dynamic migration, distributed parallel computing), but only needs to focus on how to distribute the multi-degree-of-freedom proprietary data. The distributed computing of the embodiments of the present disclosure mainly focuses on how to distribute the computing tasks of multi-degree-of-freedom data, how to calculate multiple collaborative computing resources, and how to use the computing results. The embodiments of the present disclosure abstract multi-degree-of-freedom data computing tasks and multi-degree-of-freedom data computing resources for customized distributed computing, that is, to separate abstract data and abstract computing capabilities, to facilitate data distribution and computing resource management, such as building a computing resource pool.

[0037] Figure 2 2 is a flow chart showing a method 200 for processing multi-degree-of-freedom data according to an embodiment of the present invention. Figure 2 The method 200 is applied to an XR device, and external computing resources are used to assist the XR device in processing computing tasks of massive 6DOF data. The method 200 includes the following steps 202 to 212.

[0038] In step 202, the XR device starts the collaborative computing power management service and determines at least one collaborative computing power resource for data computing from external computing power resources through the collaborative computing power management service.

[0039] Figure 3 2 is a flowchart showing a collaborative computing power management service according to an embodiment of the present invention. Figure 3 , the method includes the following steps 2022 to 2026.

[0040] In step 2022, external computing resources are collected through a network communication connection.

[0041] In some embodiments, the XR device is connected to multiple external computing resources through a network, and the external computing resources include BOX (computing box), PC (personal computer), MACBOOK, MID (mobile Internet device), etc.

[0042] In step 2024, determine whether the computing power of the external computing resources meets the computing power requirements. If so, add the external computing resources to the virtual computing resource pool to obtain collaborative computing resources; otherwise, continue to collect other external computing resources.

[0043] In step 2026, the working status and computing power of the collaborative computing resources in the virtual computing resource pool are detected in real time, and the virtual computing resource pool is dynamically adjusted according to the working status and computing power.

[0044] In some embodiments, if the collaborative computing resource exits the connection, the collaborative computing resource is deleted from the virtual computing resource pool. If an error occurs in the collaborative computing resource, the collaborative computing resource is deleted from the virtual computing resource pool. If the computing power of the collaborative computing resource does not meet the standard computing power, the collaborative computing resource is deleted from the virtual computing resource pool.

[0045] In some embodiments, one XR device may correspond to multiple external computing resources, and one external computing resource may also be added to the virtual computing resource pool of more than two XR devices at the same time. Therefore, when the collaborative computing resource management service detects that the computing power of the collaborative computing resource is insufficient, the collaborative computing resource will be actively deleted.

[0046] Next, in step 204, each time the XR device collects a set of multi-degree-of-freedom task data, a 6DOF calculation task is constructed that carries a set of multi-degree-of-freedom task data, and the 6DOF calculation task specifically calculates 6DOF data based on a set of multi-degree-of-freedom task data. The multi-degree-of-freedom task data includes image data and inertial measurement data.

[0047] In some embodiments, when constructing a computing task, meta information of the task is synchronously generated for task identification, task sorting, timeout discarding, error handling, etc. The meta information of the task includes a task number and a timestamp.

[0048] In some embodiments, the XR device includes 4 to 8 color sensors and 1 to 2 sets of 3DOF sensors. The 4 to 8 color sensors are generally used to stitch ultra-wide-angle (larger field of view FOV) wide-format images, so the XR device can collect massive amounts of image data and sensor data. Image data and inertial measurement data of 3DOF sensors can be used to calculate 6DOF data. Inertial measurement data is IMU data, which usually includes measurement values ​​of sensors such as accelerometers and gyroscopes.

[0049] In step 206 , the XR device distributes the 6DOF computing task to various collaborative computing resources.

[0050] In step 208, each collaborative computing resource executes the 6DOF computing task and returns the 6DOF data result of the DOF computing task to the XR device.

[0051] Figure 4 is a flowchart showing the collaborative computing resources executing 6DOF computing tasks according to an embodiment of the present invention. Figure 4 , the method includes the following steps 2082 to 2092.

[0052] In step 2082, collaborative computing resources load the image position estimation algorithm.

[0053] In step 2084, the collaborative computing resources synchronously load the corresponding 6DOF computing task according to the assigned task number and timestamp, wherein the 6DOF computing task load includes the image data and IMU data corresponding to the task.

[0054] In step 2086, the collaborative computing resources calculate the image data through an image position estimation algorithm to obtain camera position data.

[0055] In some embodiments, the image position estimation algorithm is a VSLAM algorithm or a binocular depth algorithm. Based on the image position estimation algorithm, the image data is calculated to obtain the translation information of the object in the space X-axis, Y-axis and Z-axis, that is, the camera position data.

[0056] In step 2088, the collaborative computing resources perform fusion calculations based on the camera position data and the IMU data to obtain 6DOF data.

[0057] In some embodiments, the IMU data is 3DOF data, including the rotation information of the object in the space X-axis, Y-axis and Z-axis. 6DOF data includes the rotation information and translation information of the object in the space X-axis, Y-axis and Z-axis. Therefore, through the translation information of the object in space and the rotation information of the object in space, multi-directional freedom data can be obtained.

[0058] In step 2090, if the collaborative computing resource completes the 6DOF computing task and obtains the corresponding 6DOF data result, the collaborative computing resource updates the task number and timestamp of the 6DOF computing task.

[0059] In step 2092, the collaborative computing resource returns the 6DOF data results to the XR device.

[0060] Next, in step 210 , the XR device detects and processes the 6DOF data results.

[0061] Figure 5 is a flow chart showing the result of processing 6DOF data by an XR device according to an embodiment of the present invention. Figure 5 The method includes the following steps 2102 to 2108.

[0062] In step 2102, the XR device monitors the working status of the current collaborative computing resources in real time through the collaborative computing management service, and dynamically updates the virtual computing resource pool.

[0063] In some embodiments, since the collaborative computing resources are external devices, their working status and connection are unreliable and may change at any time. Therefore, each time a 6DOF computing task is performed through the collaborative computing resources, the virtual 6DOF computing device pool needs to be dynamically updated.

[0064] In step 2104, the XR device dynamically receives 6DOF data results returned by the collaborative computing resources through a wired network or a wireless network.

[0065] In step 2106, the XR device determines whether the calculation result of each 6DOF calculation task is a timeout result or a null result according to the timestamp of the task. If so, it indicates that the calculation result is an invalid result; otherwise, the calculation result is a valid result.

[0066] In step 2108, when the XR device detects that the number of valid results is greater than 80% of the current number of 6DOF calculation tasks, all calculation results can be sorted based on the timestamp, and invalid results can be interpolated according to the valid results. Specifically, since each 6DOF calculation task is constructed by multiple sets of continuous multi-degree-of-freedom task data, the corresponding 6DOF data results also have a certain degree of continuity, so the data can be smoothed through interpolation processing to achieve data error correction, so that the XR device can work normally and improve the robustness of the system. Finally, all 6DOF data results are output in timestamp order.

[0067] Then, in step 212, the XR device performs post-processing and association processing on the 6DOF data results to complete the 6DOF calculation task.

[0068] In some embodiments, the XR device constructs the 6DOF data results as system events for circulation and processing in the operating system; and associates the 6DOF data results with user processing behaviors or system processing operations.

[0069] According to another aspect of the present invention, Figure 6 2 is a schematic diagram showing the structure of an electronic device 300 according to an embodiment of the present invention. Figure 6 The electronic device 300 includes a memory 302, a processor 304, and a computer program stored in the memory 302 and executable on the processor 304. When the processor 304 executes the computer program, each step of the method for processing multi-degree-of-freedom data as described above is implemented.

[0070] Figure 7 FIG. 2 is another schematic diagram showing the structure of an electronic device 300 according to an embodiment of the present invention. Figure 7In some embodiments, the electronic device 300 may further include a hardware acceleration unit 306 and a peripheral interface 308. The hardware acceleration unit 306 includes a neural network unit NPU and a digital signal processor DSP. The NPU unit hardware is suitable for accelerating the computing load of deep learning network types, and the DSP unit is suitable for floating-point intensive computing loads such as gyroscope IMU or accelerometer. The peripheral interface 308 includes a gyroscope interface, an accelerometer interface, an image sensor / Camera interface, a WIFI interface, an HDMI / MIPI display interface, and other IO interfaces.

[0071] In some embodiments, the electronic device 300 is connected to the external computing resource 400 via a wired or wireless data connection, the electronic device 300 distributes the 6DOF computing task to the external computing resource 400, and the external computing resource 400 returns the computing result to the electronic device 300. The external computing resource 400 generally refers to a consumer electronic device having a high-performance computing unit such as a GPU, a CPU, and a DSP.

[0072] In summary, the method and electronic device for processing multi-degree-of-freedom data provided by the present invention determine external collaborative computing resources through collaborative computing power management services, and construct computing tasks based on each group of multi-degree-of-freedom task data, so as to divide the massive data originally integrated in the current device into multiple small data for calculation, thereby facilitating the distribution of different data to multiple collaborative computing resources, realizing distributed parallel computing, effectively utilizing the computing power resources of external devices, and improving computing efficiency. At the same time, due to the instability of external collaborative computing resources, the state and computing power of collaborative computing resources are monitored and managed in real time through collaborative computing power management services, and collaborative computing resources are dynamically updated to ensure the reliability of computing power. In the case where the collaborative computing power resources calculate a large number of valid results, data error correction can be performed on timeout results or empty results based on the valid results to ensure the normal operation of the equipment, so as to improve the fault tolerance and system robustness of the calculation. Finally, the calculation results are constructed as system events to respond to the operation requests of the system or users to complete the calculation tasks, so as to realize the effective application of the calculation results.

[0073] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for processing multi-degree-of-freedom data, characterized in that: include: Determine at least one collaborative computing resource for data computing from external computing resources; Receiving multi-degree-of-freedom raw data collected by multiple sensors; constructing at least one computing task each carrying multi-degree-of-freedom task data based on the multi-degree-of-freedom raw data; Distributing the computing task to the at least one collaborative computing resource; receiving, from the collaborative computing resource, at least one calculation result obtained by calculating the multi-degree-of-freedom task data according to the computing task; as well as The calculation results are processed to complete the calculation of the multi-degree-of-freedom original data.

2. The method according to claim 1, characterized in that Determining at least one collaborative computing resource for data computing from the external computing resources includes: Obtain all external computing resources with communication connections; Determining whether the computing power of the external computing power resources meets the computing power requirements through the collaborative computing power service; and If so, the external computing power resource is determined to be a collaborative computing power resource, and the collaborative computing power resource is added to a preset virtual computing power resource pool.

3. The method according to claim 2, characterized in that After adding the collaborative computing power resources to the preset virtual computing power resource pool, the method further includes: Real-time detection of the working status and computing power of the collaborative computing resources through the collaborative computing service; and The virtual computing resource pool is updated according to the working status and computing power of the collaborative computing resources.

4. The method according to claim 3, characterized in that Updating the virtual computing resource pool according to the working status and computing power of the collaborative computing resource includes: If it is detected that the working state of the collaborative computing power resource is an exit state or an error state, or it is detected that the computing power of the collaborative computing power resource does not meet the computing power requirement, the collaborative computing power resource is deleted from the virtual computing power resource pool.

5. The method according to claim 1, characterized in that The multi-degree-of-freedom original data includes at least one set of multi-degree-of-freedom task data, wherein constructing at least one computing task each carrying the multi-degree-of-freedom task data based on the multi-degree-of-freedom original data includes: For each set of multi-degree-of-freedom task data, a computing task carrying the set of multi-degree-of-freedom task data is constructed based on the set of multi-degree-of-freedom task data, and the computing task is to obtain a computing result by computing the set of multi-degree-of-freedom task data.

6. The method according to claim 1, characterized in that Receiving multi-DOF raw data collected by multiple sensors includes: Receives image data and inertial measurement data for a total of six degrees of freedom.

7. The method according to claim 6, characterized in that Receiving, from the collaborative computing resource, at least one calculation result obtained by calculating the multi-degree-of-freedom task data according to the computing task comprises: Receive the calculation result obtained by the following method from the collaborative computing resource: Calculate the image data in the computing task by using an image position estimation algorithm preset in the collaborative computing resource to obtain camera position data; Performing a fusion calculation on the camera position data and the inertial measurement data of three degrees of freedom in the calculation task to obtain a calculation result of six degrees of freedom data; and Update the metadata of the computing task.

8. The method according to claim 7, characterized in that The meta information includes a timestamp, The method further comprises: determining the validity of the calculation result according to the timestamp; If the calculation result is an invalid result, determining whether the number of other calculation tasks whose calculation results are valid results is greater than a preset value; If so, the calculation results of all the calculation tasks are sorted according to the timestamps, and the invalid results are interpolated according to the sorted valid results.

9. The method according to claim 1, characterized in that: Processing the calculation results to complete the calculation of the multi-degree-of-freedom raw data includes: The calculation results are post-processed and associated to obtain calculation results of multi-degree-of-freedom data.

10. The method according to claim 9, characterized in that Post-processing and associating the calculation results include: constructing the calculation results into system events to be circulated and processed in the operating system; and The calculation result is associated with a user processing operation or a system processing operation.

11. An electronic device, characterized in that: include: a memory configured to store an executable program; as well as A processor configured to execute the program to perform the method according to any one of claims 1 to 10.

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