Method of processing multi-degree of freedom data, electronic device

Abstract

The application discloses a method for processing multi-degree-of-freedom data and an electronic device, the method comprising: determining at least one cooperative computing resource for data calculation from external computing resources; receiving multi-degree-of-freedom original data collected by a plurality of sensors; constructing at least one computing task carrying multi-degree-of-freedom task data based on the multi-degree-of-freedom original data; distributing the computing task to the at least one cooperative computing resource; receiving at least one computing result obtained by calculating the multi-degree-of-freedom task data according to the computing task from the cooperative computing resource; and processing the computing result to complete the calculation of the multi-degree-of-freedom original data. The application can utilize external cooperative computing resources to assist in processing the computing task of massive data of a device, and solve the performance problem caused by insufficient computing resources of the device.

Description

Technical Field

[0001] This invention relates to the field of extended reality devices, and more particularly to methods and electronic devices for processing multi-degree-of-freedom data. Background Technology

[0002] Extended Reality (XR) devices create an interactive environment that combines real and virtual elements, using computer technology and wearable devices. XR encompasses various technologies such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR). Augmented Reality (AR) overlays virtual digital information onto the real world using computer graphics and video technology. Users can view virtual information and interact with the real environment through head-mounted or handheld devices. Virtual Reality (VR) creates a completely computer-generated three-dimensional virtual environment. Users can enter this virtual environment through head-mounted devices, gloves, motion trackers, etc., and interact with objects within the virtual environment. Mixed Reality (MR) combines the features of AR and VR, merging virtual information with the real environment. Users can see the overlay of virtual information and the real environment through head-mounted or handheld devices and interact with both. XR technology can be applied in many fields, such as gaming, entertainment, education, healthcare, and industrial design.

[0003] XR devices involve massive image processing (4-8 color sensors) and sensor computation (1-2 sets of 3DOF sensors). Faced with this immense computational load, the chip power of XR devices is severely limited. This constraint on system computing power leads to numerous performance issues affecting user experience. For example, fluctuations in system computing power consumption cause fluctuations in the 6DOF (6 degrees of freedom) frame rate, which can easily cause visual fatigue for users. Furthermore, due to insufficient system computing power, the system can only achieve a balanced experience by using lower camera resolution. Lower resolution images limit the accuracy of 6DOF calculations, significantly impacting the user's sensitivity to the XR device's system. In addition, XR devices require portability and low power consumption; increasing system computing power often increases power consumption and overall weight. Summary of the Invention

[0004] This 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 the computational tasks of massive amounts of data on the device, and solve the performance problems caused by insufficient computing resources on the device.

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

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

[0007] According to the technical solution of this invention, at least one collaborative computing resource is determined from external computing resources, and multi-degree-of-freedom raw data collected by sensors is acquired simultaneously. Based on the multi-degree-of-freedom raw data, a corresponding computing task is constructed, and the computing task is distributed to at least one collaborative computing resource for computation to obtain the computation result. Finally, the computation result is processed to respond to system or user operation requests and complete the computation task of the multi-degree-of-freedom raw data. This invention utilizes external collaborative computing resources to construct distributed computing based on multi-degree-of-freedom raw data, thereby enabling multiple computing tasks to be processed in parallel through different collaborative computing resources. This assists the device in completing the computation task of massive amounts of data and solves the performance problem caused by insufficient device computing resources. Attached Figure Description

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

[0009] Figure 2 This is another flowchart of a method for processing multi-degree-of-freedom data according to an embodiment of the present invention;

[0010] Figure 3 This is a flowchart illustrating the collaborative computing power management service according to an embodiment of the present invention.

[0011] Figure 4 This is a flowchart illustrating the workflow of collaborative computing resources executing 6DOF computation tasks according to an embodiment of the present invention.

[0012] Figure 5 This is a flowchart illustrating the processing of 6DOF data results by an XR device according to an embodiment of the present invention.

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

[0014] Figure 7 This is another structural schematic diagram of the electronic device according to an embodiment of the present invention. Detailed Implementation

[0015] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0016] In existing technologies, the computing power of XR device systems is insufficient to support large-scale data processing tasks, leading to numerous performance issues that negatively impact user experience. For example, fluctuations in system computing power consumption cause fluctuations in the 6DOF frame rate, resulting in visual fatigue for users. Insufficient system computing power forces the system to select low-resolution images from the camera for balancing, limiting the accuracy of 6DOF calculations and affecting the system's sensitivity when using the XR device. Directly increasing the computing power of an XR device often increases power consumption and overall weight.

[0017] To address at least the aforementioned technical problems, this disclosure provides a method for processing multi-degree-of-freedom (DOF) data. According to this disclosure, at least one collaborative computing resource is determined from external computing resources. Simultaneously, raw multi-degree-of-freedom data collected by sensors is acquired. A corresponding computational task is constructed based on the raw multi-degree-of-freedom data, and the computational task is distributed to the at least one collaborative computing resource for computation to obtain the computational result. Finally, the computational result is processed to respond to system or user operation requests and complete the computational task of the raw multi-degree-of-freedom data. In this manner, embodiments of this disclosure can utilize external collaborative computing resources to construct distributed computing tasks based on raw multi-degree-of-freedom data, thereby enabling parallel processing of multiple computational tasks through different collaborative computing resources. This assists the device in completing computational tasks involving massive amounts of data, solving the performance problems caused by insufficient device computing resources.

[0018] In the following, the technical solutions according to this disclosure will be described with reference to specific embodiments and in conjunction with the accompanying drawings.

[0019] Figure 1 This is a flowchart illustrating a method 100 for processing multi-degree-of-freedom data according to an embodiment of the present disclosure. (Refer to...) Figure 1 The method 100 includes the following steps 102 to 112.

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

[0021] In some embodiments, all external computing resources with communication connections are acquired, and a collaborative computing service is used to determine whether the computing power of these external computing resources meets the standard computing power requirements. If so, the external computing resources are identified as collaborative computing resources, and they are added to a preset virtual computing resource pool. In this way, the collaborative computing power management service identifies external computing resources that meet the computing power requirements, flexibly expanding the device's current computing resources. Simultaneously, adding external computing resources that meet the requirements to the virtual computing resource pool for management effectively monitors and manages the current collaborative computing resources, preventing errors in subsequent task distribution.

[0022] In step 104, raw multi-degree-of-freedom data collected by multiple sensors is received.

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

[0024] In step 106, at least one computational task, each carrying multi-degree-of-freedom task data, is constructed based on the original multi-degree-of-freedom 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 computational task includes: for each set of multi-degree-of-freedom task data, constructing a computational task carrying the set of multi-degree-of-freedom task data based on the set of multi-degree-of-freedom task data, wherein the computational task performs calculations on the set of multi-degree-of-freedom task data to obtain a computational result. In this way, constructing each set of multi-degree-of-freedom task data as a computational task facilitates the distribution of tasks for calculating target data from different multi-degree-of-freedom task data to different collaborative computing resources for execution, thereby effectively utilizing external computing resources to achieve parallel computation of multiple sets of frame data and improving 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 is received from the collaborative computing resources to obtain the multi-degree-of-freedom task data calculated based on the computing task.

[0028] In some embodiments, receiving at least one computation result from the collaborative computing resource includes receiving the computation result obtained from the collaborative computing resource in the following manner: calculating the image data in the computation task using a preset image position estimation algorithm in the collaborative computing resource to obtain camera position data; performing a fusion calculation on the camera position data and the three-degree-of-freedom inertial measurement data in the computation task to obtain a computation result of six-degree-of-freedom data; and updating the metadata of the computation task. In this manner, the inertial measurement data includes the rotation information of the object in space, while the image data can obtain the translation information of the object in space, i.e., camera position data, through the image position estimation algorithm. Based on the translation and rotation information, the six-degree-of-freedom data of the object can be determined. This invention offloads the computation process originally executed in the XR device to an external collaborative computing resource for execution, enabling distributed computing of data and improving the computational efficiency of the data.

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

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

[0031] In some embodiments, processing the calculation results to complete the calculation of the multi-degree-of-freedom raw data includes: performing post-processing and correlation processing on the calculation results to obtain the calculation results of the multi-degree-of-freedom data. In this way, the calculation results of external collaborative computing resources are returned to the original device for response.

[0032] In some embodiments, post-processing and association processing of the calculation result includes: constructing the calculation result as a system event for flow and processing in the operating system; and associating the calculation result with user processing operations or system processing operations. In this way, since the calculation result is a specific numerical value, by constructing the calculation result as a system event, the calculation result can be flowed and processed in the device's operating system, thereby responding to user or system operation requests and realizing the application of the calculation result.

[0033] In some embodiments, after adding the collaborative computing resources to a preset virtual computing resource pool, the method may further include: real-time detection of the working status and computing power of the collaborative computing resources through the collaborative computing service; and updating the virtual computing resource pool based on the working status and computing power of the collaborative computing resources. In this way, since the device status and connection of external computing resources are unstable, and the same external computing resource may correspond to multiple virtual computing resource pools, real-time monitoring and detection of the working status and computing power of the collaborative computing resources after their identification dynamically updates the virtual computing resource pool, ensuring the reliability of the collaborative computing resources.

[0034] In some embodiments, updating the virtual computing resource pool based on the working status and computing power of the collaborative computing resource includes: if the working status of the collaborative computing resource is detected to be in an exited or error state, or if the computing power of the collaborative computing resource is detected to be insufficient to meet the computing power requirements, then the collaborative computing resource is deleted from the virtual computing resource pool. In this way, collaborative computing resources that have exited the working state or are experiencing errors are directly deleted from the virtual computing resource pool, avoiding any impact on the validity of the computation results. Since the collaborative computing resource may be occupied by other devices, causing changes in computing power resources that may not meet the computing power requirements of this device, in this case, the device actively deletes the external computing resource to conserve management resources in the virtual device pool.

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

[0036] The embodiments of this disclosure relate to distributed computing of multi-degree-of-freedom (e.g., 6DOF) data. Due to factors such as data granularity and temporal correlation, this type of distributed computing is atypical / customized. The distributed computing according to the embodiments of this 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 multi-degree-of-freedom proprietary data is distributed for computing. The main focus of the distributed computing embodiments of this disclosure is on how computing tasks for multi-degree-of-freedom data are distributed, how multiple collaborative computing resources are used for computing, and how the computing results are used. The embodiments of this disclosure abstract multi-degree-of-freedom data computing tasks and multi-degree-of-freedom data computing resources to prepare for customized distributed computing, that is, separating abstract data and abstract computing capabilities to facilitate data distribution and computing resource management, such as building computing resource pools.

[0037] Figure 2 This is a flowchart illustrating a method 200 for processing multi-degree-of-freedom data according to an embodiment of the present invention. (Refer to...) Figure 2 This method 200 is applied to XR devices to assist XR devices in processing massive 6DOF data computation tasks through external computing resources. This 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 from external computing power resources for data computation.

[0039] Figure 3 This is a flowchart illustrating the workflow of a collaborative computing power management service according to an embodiment of the present invention. (Refer to...) Figure 3 The method includes steps 2022 to 2026.

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

[0041] In some embodiments, the XR device connects to multiple external computing resources via a network, including BOX (computing box), PC (personal computer), MacBook, MID (mobile internet device), etc.

[0042] In step 2024, it is determined whether the computing power of the external computing power resources meets the computing power requirements. If so, the external computing power resources are added to the virtual computing power resource pool to obtain collaborative computing power resources; otherwise, other external computing power resources are collected.

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

[0044] In some embodiments, if a collaborative computing resource disconnects from the connection, it is removed from the virtual computing resource pool. If an error occurs with a collaborative computing resource, it is removed from the virtual computing resource pool. If the computing power of a collaborative computing resource does not meet the standard computing power requirement, it is removed from the virtual computing resource pool.

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

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

[0047] In some embodiments, when constructing a computing task, task metadata is generated synchronously for tasks such as task identification, task sorting, timeout discarding, and error handling. The task metadata 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 together a wide-angle (large field of view) image, thus the XR device can acquire massive amounts of image and sensor data. The image data and inertial measurement data from the 3DOF sensors can be used to calculate 6DOF data. Inertial measurement data, also known as IMU data, typically includes measurements from sensors such as accelerometers and gyroscopes.

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

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

[0051] Figure 4 This is a flowchart illustrating the workflow of collaborative computing resources performing 6DOF computation tasks according to an embodiment of the present invention. (Refer to...) Figure 4 The method includes steps 2082 to 2092.

[0052] In step 2082, the collaborative computing resources are loaded into the image location 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 loads the image data and IMU data corresponding to the task.

[0054] In step 2086, the collaborative computing resources calculate the image data using 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 stereo depth algorithm. Calculating the image data based on the image position estimation algorithm yields the translation information of the object along the X, Y, and Z axes in space, i.e., the camera position data.

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

[0057] In some embodiments, the IMU data is 3DOF data, including rotational information of the object along the X, Y, and Z axes in space. 6DOF data includes rotational and translational information of the object along the X, Y, and Z axes in space. Therefore, multi-degree-of-freedom data can be obtained by using the translational and rotational information of the object in space.

[0058] In step 2090, if the collaborative computing resources complete the 6DOF calculation task and obtain the corresponding 6DOF data results, the collaborative computing resources update the task number and timestamp of the 6DOF calculation task.

[0059] In step 2092, the collaborative computing resources return 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 This is a flowchart illustrating the results of an XR device processing 6DOF data according to an embodiment of the present invention. (Refer to...) Figure 5 The method includes 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 power management service and dynamically updates the virtual computing power resource pool.

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

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

[0065] In step 2106, the XR device determines whether the calculation result of each 6DOF calculation task is a timeout result or an empty result based on the timestamp of each task. If so, the calculation result is invalid; otherwise, the calculation result is valid.

[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 computation tasks, all computation results can be sorted based on timestamps, and invalid results can be interpolated according to the valid results. Specifically, since each 6DOF computation task is constructed from multiple consecutive sets of multi-DOF task data, its corresponding 6DOF data results also have a certain continuity. Therefore, interpolation can smooth the data, achieve data error correction, and thus allow the XR device to work normally, improving 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 correlation processing on the 6DOF data results to complete the 6DOF calculation task.

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

[0069] According to another aspect of the invention, Figure 6 This is a schematic diagram illustrating the structure of an electronic device 300 according to an embodiment of the present invention. (Refer to...) 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, it implements the various steps of the method for processing multi-degree-of-freedom data as described above.

[0070] Figure 7 This is another structural schematic diagram illustrating an electronic device 300 according to an embodiment of the present invention. (Refer to...) 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 is suitable for accelerating computational loads of deep learning networks, while the DSP unit is suitable for floating-point intensive computational loads such as gyroscopes (IMUs) or accelerometers. The peripheral interface 308 includes a gyroscope interface, an accelerometer interface, an image sensor / camera interface, a Wi-Fi interface, an HDMI / MIPI display interface, and other I / O interfaces.

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

[0072] In summary, the method and electronic device for processing multi-degree-of-freedom data provided by this invention determine external collaborative computing resources through a collaborative computing power management service. Simultaneously, it constructs computing tasks based on each set of multi-degree-of-freedom task data, dividing the massive data originally integrated into the current device into multiple smaller datasets for computation. This facilitates the distribution of different data to multiple collaborative computing resources, achieving distributed parallel computing, effectively utilizing the computing resources of external devices, and improving computational efficiency. Furthermore, due to the instability of external collaborative computing resources, the collaborative computing power management service monitors and manages the status and computing power of these resources in real time, dynamically updating them to ensure reliability. When a large number of valid results are obtained from the collaborative computing resources, data correction can be performed on timeout or empty results based on these valid results, ensuring normal device operation and improving computational fault tolerance and system robustness. Finally, the computation results are constructed as system events to respond to system or user operation requests, completing the computation task and enabling effective application of the computation results.

[0073] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for processing multi-degree-of-freedom data, characterized in that, include: Identify at least one collaborative computing resource from external computing resources for data computation; Receives raw multi-degree-of-freedom data collected by multiple sensors, including image data and inertial measurement data with a total of six degrees of freedom; Based on the original multi-degree-of-freedom data, construct at least one computational task, each carrying multi-degree-of-freedom task data; Distribute the computing tasks to the at least one collaborative computing resource; Receive at least one calculation result obtained from the collaborative computing resources for calculating the multi-degree-of-freedom task data based on the computing task; as well as The calculation results are processed to complete the calculation of the original multi-degree-of-freedom data. Receiving at least one calculation result from the collaborative computing resources to calculate the multi-degree-of-freedom task data based on the computing task includes: receiving the calculation result from the collaborative computing resources in the following manner: calculating the image data in the computing task using a preset image position estimation algorithm in the collaborative computing resources to obtain camera position data; performing a fusion calculation on the camera position data and the three-degree-of-freedom inertial measurement data in the computing task to obtain a calculation result for six-degree-of-freedom data; and updating the metadata of the computing task, the metadata including a timestamp. The method further includes: determining the validity of the calculation result based on the timestamp; if the calculation result is invalid, determining whether the number of other calculation tasks with valid results is greater than a preset value; if so, sorting the calculation results of all calculation tasks according to the timestamp, and interpolating the invalid result according to the sorted valid results.

2. The method according to claim 1, characterized in that, Identifying at least one collaborative computing resource from external computing resources for data computation includes: Acquire all external computing resources with which there is a communication connection; The collaborative computing service determines whether the computing power of the external computing resources meets the computing power requirements; 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 the preset virtual computing power resource pool.

3. The method according to claim 2, characterized in that, After adding the collaborative computing resources to the preset virtual computing resource pool, the method further includes: The collaborative computing power service monitors the working status and computing power of the collaborative computing power resources in real time; and The virtual computing resource pool is updated based on 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 power resource pool based on the working status and computing power of the collaborative computing power resources includes: If the collaborative computing power resource is detected to be in an exited or error state, or if the computing power of the collaborative computing power resource is detected to be insufficient to meet the computing power requirements, then the collaborative computing power resource will be removed from the virtual computing power resource pool.

5. The method according to claim 1, characterized in that, The multi-degree-of-freedom raw data includes at least one set of multi-degree-of-freedom task data, wherein constructing at least one computational task, each carrying multi-degree-of-freedom task data, based on the multi-degree-of-freedom raw data includes: For each set of multi-degree-of-freedom task data, a computation task containing the set of multi-degree-of-freedom task data is constructed based on the set of multi-degree-of-freedom task data. The computation task is to perform calculations on the set of multi-degree-of-freedom task data to obtain a computation result.

6. 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 then post-processed and correlated to obtain the calculation results for multi-degree-of-freedom data.

7. The method according to claim 6, characterized in that, Post-processing and correlation processing of the calculation results include: The calculation results are constructed into system events for circulation and processing within the operating system; and The calculation results are associated with user processing operations or system processing operations.

8. An electronic device, characterized in that, include: The memory is configured to store executable programs; as well as A processor is configured to execute the program to perform the method according to any one of claims 1 to 7.

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