Computing collaboration method, electronic device, and storage medium

Abstract

A computing cooperation method, an electronic device and a storage medium. The method is used for an electronic device, and the method comprises the following steps: a plurality of computing devices connected to a same near field network are formed into a computing power sharing network, the computing power sharing network comprises a master node and at least one slave node, the master node is the electronic device, and the slave node is a computing device other than the electronic device in the plurality of computing devices; it is confirmed that the computing power of a first computing device in the computing power sharing network meets a preset condition; and the master node distributes to-be-processed data of the first computing device to at least one second computing device in the computing power sharing network based on the computing power states of the nodes in the computing power sharing network, so as to provide computing power support for the first computing device through the computing power sharing network. The method provides a brand-new network combination mode and can solve the problem of insufficient device computing power.

Description

Technical Field

[0001] Embodiments of this disclosure relate to a computing collaboration method, electronic device, and storage medium. Background Technology

[0002] In computing network technologies, there are various computing methods, such as "cloud computing" and "fog computing." Fog computing is an extension of the cloud computing concept. It primarily uses devices in edge networks, resulting in extremely low latency data transmission. Fog computing features a large number of network nodes, excellent mobility, and allows mobile devices to communicate directly with each other without signal relay through the cloud. Compared to cloud computing, fog computing employs a more distributed architecture, closer to the network edge. Fog computing centralizes data, data processing, and applications on devices at the network edge, unlike cloud computing which stores almost entirely in the cloud. In fog computing, data storage and processing rely more on local devices than servers. Fog computing is a new generation of distributed computing, consistent with the "decentralized" nature of the internet. Summary of the Invention

[0003] At least one embodiment of this disclosure provides a computing collaboration method for an electronic device, wherein the method includes: forming a computing power sharing network by assembling multiple computing devices connected to the same near-field network, wherein the computing power sharing network includes a master node and at least one slave node, the master node being the electronic device, and the slave node being a computing device other than the electronic device among the multiple computing devices; confirming that the computing power of a first computing device in the computing power sharing network meets preset conditions; the master node allocating the data to be processed by the first computing device to at least one second computing device in the computing power sharing network based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to the first computing device through the computing power sharing network.

[0004] For example, in a method provided in one embodiment of this disclosure, the plurality of computing devices connected to the same near-field network are organized into a computing power sharing network, including: determining the master node and the at least one slave node according to the computing power of the plurality of computing devices; installing and running a master collaboration service component in the master node, and installing and running a client component in each of the at least one slave node; establishing the computing power sharing network using the interaction between the master collaboration service component and the client component, and notifying the first computing device of the node organization status; wherein, determining the master node and the at least one slave node according to the computing power of the plurality of computing devices includes: determining a performance score for each of the plurality of computing devices, wherein the performance score represents the computing power of each computing device; determining a performance ranking list of the plurality of computing devices based on the performance score; confirming that the electronic device is ranked first in the performance ranking list, and determining the electronic device as the master node, and determining the computing devices other than the first one in the performance ranking list as the at least one slave node.

[0005] For example, in a method provided in one embodiment of this disclosure, determining the performance score of each of the plurality of computing devices includes: causing each of the plurality of computing devices to obtain its own performance score from a cloud server; or, causing the plurality of computing devices to obtain their own performance evaluation applications from the cloud server and run the performance evaluation applications to obtain their own performance scores.

[0006] For example, in one embodiment of the method provided in this disclosure, determining a performance ranking list of the plurality of computing devices based on the performance scores includes: the electronic device receiving performance scores broadcast by other computing devices in the near-field network; establishing an initial list, calculating the unique values ​​of the list information of the initial list, and broadcasting its own unique values ​​of the list information in the near-field network; receiving the unique values ​​of the list information broadcast by other computing devices in the near-field network, determining that they are consistent with its own unique values ​​of the list information, and broadcasting the confirmation result in the near-field network; and using the initial list corresponding to the unique values ​​of the list information that have been confirmed correctly by a preset number of computing devices as the performance ranking list.

[0007] For example, in a method provided in one embodiment of this disclosure, the main collaboration service component is installed and run in the master node, and each of the at least one slave node installs and runs the client component, including: the master node obtains the main collaboration service component and the client component from a cloud server, and transmits the client component to each slave node through the near-field network; the main collaboration service component is installed and run in the master node, and each slave node installs and runs the client component.

[0008] For example, in a method provided in one embodiment of this disclosure, the master node allocates the data to be processed by the first computing device to at least one second computing device in the computing power sharing network based on the computing power status of each node in the computing power sharing network. This includes: selecting at least one second computing device in the computing power sharing network according to the computing power status of each node; obtaining information about the data to be processed from the first computing device and synchronizing the data to be processed to the at least one second computing device through the near-field network; and notifying the first computing device of the information of the data transmission channel of the data to be processed, so that the first computing device establishes a link with the at least one second computing device and performs data interaction based on the information of the data transmission channel.

[0009] For example, in a method provided in one embodiment of this disclosure, the preset conditions include: the processor utilization rate of the first computing device is greater than or equal to a first preset threshold; the method further includes: periodically acquiring the computing power status of each node in the computing power sharing network, and using devices with processor utilization rates less than a second preset threshold as candidate devices for the next allocation; wherein, the multiple computing devices connected to the same near-field network are combined to form the computing power sharing network, and the method further includes: performing security authentication on the multiple computing devices, wherein the multiple computing devices include at least one of mobile devices, vehicle-mounted devices, and wearable devices.

[0010] At least one embodiment of this disclosure also provides a computing collaboration method for an electronic device, wherein the method includes: the electronic device being configured as a slave node in a computing power sharing network, wherein the computing power sharing network includes multiple computing devices connected to the same near-field network, the computing power sharing network includes a master node and at least one slave node, one of the at least one slave node being the electronic device, and the master node being one of the multiple computing devices other than the electronic device; receiving and processing unprocessed data allocated by the master node based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to a first computing device in the computing power sharing network through the computing power sharing network.

[0011] At least one embodiment of this disclosure also provides an electronic device, including: a processor; a memory including one or more computer program modules; wherein the one or more computer program modules are stored in the memory and configured to be executed by the processor, the one or more computer program modules being used to implement the computational collaboration method provided in any embodiment of this disclosure.

[0012] At least one embodiment of this disclosure also provides a non-volatile storage medium storing non-transitory computer-readable instructions that, when executed by a computer, implement the computational collaboration method provided in any embodiment of this disclosure. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments will be briefly described below. Obviously, the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure.

[0014] Figure 1 This is a schematic diagram illustrating an application scenario of a computational collaboration method provided in some embodiments of this disclosure;

[0015] Figure 2 A flowchart illustrating a computational collaboration method provided in some embodiments of this disclosure;

[0016] Figure 3 for Figure 2 An exemplary flowchart of step S10;

[0017] Figure 4 for Figure 3 An exemplary flowchart of step S11;

[0018] Figure 5 for Figure 4 An exemplary flowchart of step S112;

[0019] Figure 6 for Figure 3 An exemplary flowchart of step S12;

[0020] Figure 7 for Figure 2 An exemplary flowchart of step S30;

[0021] Figure 8 for Figure 7 An exemplary flowchart of step S32;

[0022] Figure 9 A flowchart illustrating a computational collaboration method provided for some embodiments of this disclosure;

[0023] Figure 10 A flowchart illustrating another computational collaboration method provided in some embodiments of this disclosure;

[0024] Figure 11 A schematic block diagram of an electronic device provided for some embodiments of this disclosure;

[0025] Figure 12 A schematic block diagram of another electronic device provided for some embodiments of this disclosure; and

[0026] Figure 13 This is a schematic diagram of a storage medium provided for some embodiments of this disclosure. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0028] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “including,” “comprising,” or “containing,” and similar terms mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms “upper,” “lower,” “left,” and “right,” etc., are used only to indicate relative positional relationships, and these relative positional relationships may change accordingly when the absolute position of the described objects changes.

[0029] In extended reality (XR) scenarios, various applications consume significant device resources. If cloud computing is used to provide computing power, all information would need to be processed and forwarded through the cloud, resulting in substantial latency and network resource consumption.

[0030] Embodiments of this disclosure provide a computing collaboration method, an electronic device, and a storage medium. This computing collaboration method offers a novel network combination approach, integrating the advantages of fog computing and cloud computing. It can solve the problem of insufficient device computing power (e.g., insufficient computing power in extended reality devices), extend device availability, achieve the purpose of computing power collaboration, and enable dynamic allocation of node tasks, providing great flexibility. This computing collaboration method can improve the computing power of extended reality scenarios, further meeting the needs of extended reality device application scenarios.

[0031] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the same reference numerals will be used to refer to the same elements described in different drawings.

[0032] At least one embodiment of this disclosure provides a computing collaboration method for an electronic device. The computing collaboration method includes: forming a computing power sharing network by connecting multiple computing devices connected to the same near-field network, the computing power sharing network including a master node and at least one slave node, the master node being the electronic device, and the slave nodes being computing devices other than the electronic device among the multiple computing devices; confirming that the computing power of a first computing device in the computing power sharing network meets preset conditions; and the master node allocating the data to be processed by the first computing device to at least one second computing device in the computing power sharing network based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to the first computing device through the computing power sharing network.

[0033] Figure 1 This diagram illustrates an application scenario of a computational collaboration method provided in some embodiments of this disclosure. For example... Figure 1 As shown, the computational collaboration method provided in this disclosure can be applied to scenarios including extended reality devices (XR devices). For example, in-vehicle devices, mobile devices, and XR devices are all connected to the same near-field network and can communicate with each other through the near-field network. In XR scenarios, each application consumes a significant amount of device performance, therefore... Figure 1 The devices shown can be combined to form a computing power sharing network. Relying on the internal computing power negotiation of this computing power sharing network, master nodes and slave nodes are autonomously allocated. The master node devices are responsible for establishing the cloud network, and the slave node devices form the fog network, thereby coordinating and cooperating to provide computing power support for XR devices.

[0034] For example, XR devices may include Virtual Reality (VR) devices, Augmented Reality (AR) devices, Mixed Reality (MR) devices, or other types of extended reality devices, and the embodiments of this disclosure are not limited thereto. For example, XR devices may be wearable devices, such as XR glasses, XR helmets, etc., and may be of any device form. For example, in-vehicle devices may include electronic devices, electronic devices, processors, etc., with computing capabilities installed on vehicles, such as electronic devices or electronic devices installed in automobiles, electronic devices or electronic devices installed on ships, etc. The embodiments of this disclosure are not limited to the type of vehicle or the type of device installed on the vehicle, as long as the device is installed on the vehicle and has computing capabilities. For example, mobile devices may include smartphones, tablets, laptops, and any type of mobile device, and the embodiments of this disclosure are not limited thereto. For example, XR devices, in-vehicle devices, and mobile devices all have near-field communication capabilities and can access the same near-field network.

[0035] Figure 2 This is a flowchart illustrating a computational collaboration method provided for some embodiments of this disclosure. For example, this computational collaboration method is used in an electronic device. Figure 2 As shown, the computational collaboration method provided in this embodiment may include the following operations.

[0036] Step S10: Multiple computing devices connected to the same near-field network are organized into a computing power sharing network, wherein the computing power sharing network includes a master node and at least one slave node, the master node is the electronic device, and the slave node is the computing device other than the electronic device among the multiple computing devices;

[0037] Step S20: Confirm that the computing power of the first computing device in the computing power sharing network meets the preset conditions;

[0038] Step S30: The master node allocates the data to be processed by the first computing device to at least one second computing device in the computing power sharing network based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to the first computing device through the computing power sharing network.

[0039] For example, in step S10, in some examples, when multiple computing devices include at least one extended reality device, that is, when the extended reality device accesses the near-field network, multiple computing devices connected to the same near-field network are configured into a computing power sharing network. For example, the aforementioned electronic device is one of the multiple computing devices. The computing power sharing network includes a master node and at least one slave node, where the master node is the electronic device and the slave nodes are computing devices other than the electronic device among the multiple computing devices. In this embodiment, to distinguish between the master node and slave nodes among the multiple computing devices, the device acting as the master node is referred to as the electronic device, which is one of the multiple computing devices and also a device with computing capabilities. For example, multiple computing devices may include at least one extended reality device, and multiple computing devices may further include mobile devices and / or in-vehicle devices, etc., which are not limited in the embodiments of this disclosure. For example, the near-field network can be a network based on any type of near-field communication technology, such as WiFi (Wireless Fidelity) networks, Bluetooth networks, Near Field Communication (NFC) networks, etc., which are not limited in the embodiments of this disclosure. In this example, all computing devices connected to the near-field network can be configured into a computing power sharing network. Of course, the embodiments of this disclosure are not limited to this; in other examples, a subset of computing devices connected to the near-field network can also be configured into a computing power sharing network.

[0040] For example, a computing power sharing network can achieve the purpose of computing power collaboration, combining the advantages of cloud computing and fog computing. A computing power sharing network includes one master node and at least one slave node. For example, the number of master nodes is one, that is, only one master node exists. The master node is one of multiple computing devices, referred to as an electronic device in this embodiment. This master node is used to communicate with a remote server (e.g., a cloud server) to establish a cloud network. The number of slave nodes is greater than or equal to one, that is, there can be one or more slave nodes. Slave nodes are computing devices other than the master node (i.e., computing devices other than the aforementioned electronic device). Slave nodes are used to establish a fog network, thereby collaboratively realizing the functions of the fog network under the control of the master node. For example, the aforementioned first computing device can be either a master node device or a slave node device. The first computing device can be an extended reality device in the computing power sharing network. For example, the aforementioned second computing device can be either a master node device or a slave node device.

[0041] For example, in some examples, the access of an extended reality device to a near-field network (NFC) can serve as a trigger condition for establishing a computing power sharing network. When the access of an extended reality device to the NFC is detected, the operation of establishing a computing power sharing network is executed. Of course, the embodiments of this disclosure are not limited to this. In other embodiments, the access of an extended reality device to the NFC can be one of the trigger conditions for establishing a computing power sharing network, and other trigger conditions may also exist. The operation of establishing a computing power sharing network will only be triggered when multiple conditions are met. For example, in some examples, the trigger condition may also include successful security authentication of the in-vehicle device. The operation of establishing a computing power sharing network will only be executed when the extended reality device accesses the NFC and the in-vehicle device's security authentication is successful. For example, in-vehicle device security authentication may refer to security authentication of computing devices connected to the same NFC as the in-vehicle device through a cloud server. It should be noted that when multiple computing devices do not include extended reality devices, the trigger condition for establishing a computing power sharing network may be other conditions, and the embodiments of this disclosure do not limit this.

[0042] For example, in some examples, N computing devices are connected to the same near-field network, where N is an integer greater than 1. At least one of these N computing devices is an extended reality (XR) device, and the remaining devices can be in-vehicle devices and / or mobile devices. One of these N computing devices acts as the master node; for ease of explanation and distinction, in this embodiment, the device acting as the master node is referred to as an electronic device. The remaining N-1 computing devices act as slave nodes, and the master node and slave nodes together form a computing power sharing network. The master node can communicate with a cloud server to realize the functions of a cloud network. The slave nodes coordinate and cooperate under the control of the master node to realize the functions of a fog network.

[0043] Figure 3 for Figure 2 An exemplary flowchart of step S10. In some embodiments, such as Figure 3 As shown, step S10 may further include the following operations.

[0044] Step S11: Determine the master node and at least one slave node based on the computing power of multiple computing devices;

[0045] Step S12: Install and run the master collaboration service component on the master node, and install and run the client component on each of the at least one slave node;

[0046] Step S13: Establish a computing power sharing network by utilizing the interaction between the main collaborative service component and the client component, and notify the first computing device of the node formation status.

[0047] For example, in step S11, a master node and at least one slave node are first determined based on the computing power of multiple computing devices. For instance, the computing device with the strongest computing power can be designated as the master node, and the remaining computing devices as slave nodes. In this embodiment, the device serving as the master node is referred to as an electronic device.

[0048] For example, before determining the master and slave nodes, the following operations can be performed: secure authentication of multiple computing devices. For instance, security authentication can be performed between the vehicle's security authentication information and a remote server (such as a cloud server), and authentication can be achieved through security authorization by the cloud server, thereby ensuring the security of the access devices.

[0049] Figure 4 for Figure 3 An exemplary flowchart of step S11. In some embodiments, such as Figure 4 As shown, step S11 may further include the following operations.

[0050] Step S111: Determine the performance score of each computing device among the multiple computing devices, wherein the performance score represents the computing power of each computing device;

[0051] Step S112: Determine a performance ranking list of multiple computing devices based on performance scores;

[0052] Step S113: Confirm that the electronic device is ranked first in the performance ranking list, and determine the electronic device as the master node, and determine the computing devices other than the first one in the performance ranking list as at least one slave node.

[0053] For example, in step S111, a performance score is first determined for each of the multiple computing devices. For example, the performance score represents the computing power of the computing device; that is, each computing device corresponds to a performance score, which indicates the strength of its computing power. For example, a higher performance score indicates stronger computing power, and a lower performance score indicates weaker computing power. For example, the computing power of a computing device is related to factors such as the performance of its processor, hardware configuration (e.g., memory, disk configuration), and software configuration; different computing devices may have different computing capabilities.

[0054] For example, in some examples, assuming a performance rating is based on a 100-point scale, the Central Processing Unit (CPU) accounts for 30 points, the Graphics Processing Unit (GPU) for 40 points, memory for 20 points, and the disk for 10 points. The better the performance of each hardware component, the higher the score. The sum of the scores for each hardware component is the performance rating of the computing device. For instance, for a computing device with a CPU score of 25, a GPU score of 30, memory for 18, and disk for 5, its performance rating is 78. For another computing device with a CPU score of 30, a GPU score of 35, memory for 15, and disk for 6, its performance rating is 86. Therefore, a computing device with a score of 86 performs better than a computing device with a score of 78. The performance rating reflects the computing power of the device.

[0055] For example, in some examples, step S111 may include: enabling multiple computing devices to each obtain their own performance scores from a cloud server. In this example, the cloud server stores the performance scores of each computing device. When each computing device sends a request to the cloud server, the cloud server queries based on the computing device information carried in the request to obtain the performance score, and then sends the performance score to each computing device, thereby enabling each computing device to obtain its performance score.

[0056] For example, in other examples, step S111 may include: enabling multiple computing devices to obtain their respective performance evaluation applications from a cloud server and running the performance evaluation applications to obtain their respective performance scores. In this example, the cloud server stores the performance evaluation applications. When each computing device sends a request to the cloud server, the cloud server distributes the performance evaluation applications. After receiving the performance evaluation applications, each computing device can obtain its own performance score by running them. For example, the performance evaluation applications may have the function of obtaining device information, such as detecting device performance, such as detecting hardware configuration, software configuration, etc., and can give corresponding performance scores based on the device's hardware and software configuration. For example, when there are multiple computing devices, each computing device can receive the performance evaluation applications distributed by the cloud server separately, or one device can receive the performance evaluation applications distributed by the cloud server and forward them to other computing devices through a near-field network, thereby reducing bandwidth consumption and improving transmission efficiency.

[0057] For example, in the first method described above, the computing device directly receives the performance score; in the second method, the computing device receives a performance evaluation application and obtains the performance score by running the application. These two methods can be combined: the computing device sends a request to the cloud server; if the cloud server can find the performance score, it directly issues the score; if it cannot find the score, it issues a performance evaluation application, which the computing device then runs to obtain the score. In a computing power sharing network, all computing devices can directly receive the performance score; all computing devices can receive the performance evaluation application and obtain the score by running it individually; or some computing devices can directly receive the performance score, while others receive and run the application to obtain the score. The embodiments disclosed herein do not limit this.

[0058] For example, in step S112, a performance ranking list of multiple computing devices is determined based on performance scores. Figure 5 for Figure 4 An exemplary flowchart of step S112. In some embodiments, such as Figure 5 As shown, step S112 may further include the following operations.

[0059] Step S1121: The electronic device receives performance scores broadcast by other computing devices within the near-field network;

[0060] Step S1122: Establish an initial list, calculate the unique value of the list information of the initial list, and broadcast the unique value of its own list information within the near-field network;

[0061] Step S1123: Receive the unique value of the list information broadcast by other computing devices in the near field network, determine that it is consistent with the unique value of its own list information, and broadcast the confirmation result in the near field network;

[0062] Step S1124: Use the initial list corresponding to the unique values ​​of the list information that have been confirmed to be correct by all the preset number of computing devices as the performance sorting list.

[0063] For example, in step S1121, the electronic device (in this embodiment, the computing device acting as the master node is referred to as the electronic device) can receive performance scores broadcast by other computing devices within the near-field network. For instance, each computing device broadcasts its own performance score within the near-field network, that is, it notifies other computing devices within the near-field network of its performance score. During the broadcast, not only the performance score is broadcast, but device information is also broadcast, so that the computing devices receiving the performance scores know the correspondence between the broadcasting device and the performance score.

[0064] For example, in step S1122, since performance scores broadcast by other computing devices can be received, the electronic device can establish an initial list. For instance, each computing device in the near-field network establishes an initial list based on the received performance scores; that is, each computing device establishes an initial list based on its received performance scores and device information, reflecting the ranking of performance scores across all computing devices. For example, the ranking can be from highest to lowest performance score. For example, the initial lists of multiple computing devices may be the same or different due to incorrect received information. Then, each computing device calculates a unique list information value for its own initial list. The unique list information value represents the uniqueness of the corresponding list, and can be represented by a hash value, for example. Each list has a specific unique list information value, and the unique list information values ​​of different lists are different. This unique list information value can be an MD5 value (or a hash value), where the MD5 value represents the uniqueness of the list, and the MD5 value is a value calculated using a hash function. Each device broadcasts its calculated unique list information value within the near-field network, that is, it notifies other computing devices in the near-field network of its calculated unique list information value.

[0065] For example, in step S1123, the electronic device (in this embodiment, the computing device acting as the master node is referred to as the electronic device) receives the unique value of the list information broadcast by other computing devices in the near-field network, determines whether it matches its own unique value, and broadcasts the confirmation result within the near-field network. For instance, each computing device must determine whether its own unique value matches the received unique value, and if they match, broadcasts the confirmation result within the near-field network. That is, when a computing device receives the unique value of the list information broadcast by another computing device, it compares the received unique value with its own unique value to determine if they match. If they match, the unique value is confirmed as correct, and the confirmation result is broadcast within the near-field network; in other words, the unique value of the list information is confirmed as correct within the near-field network.

[0066] For example, in step S1124, the initial list corresponding to the unique values ​​of the list information that have been confirmed as correct by a preset number of computing devices is used as the performance ranking list. That is, each computing device compares the received unique value of the list information with its own unique value of the list information. If they match, the device broadcasts the confirmation result within the near-field network to confirm that the received unique value of the list information is correct. If the number of computing devices that have confirmed the correct value reaches a preset number, the initial list corresponding to the confirmed unique values ​​of the list information is used as the performance ranking list. For example, the preset number can be determined according to actual needs; it can be a specific number or a percentage, and the embodiments of this disclosure do not limit this. For example, in some examples, assuming the total number of computing devices in the computing power sharing network is M, where M is a positive integer, when the number of computing devices that have confirmed the correct value reaches M / 2, the initial list corresponding to the confirmed unique values ​​of the list information is used as the performance ranking list. Since it has been confirmed by multiple computing devices, the correctness of the performance ranking list can be guaranteed.

[0067] For example, in some implementations, each device in the near-field network first broadcasts its current score and device information. The remaining devices receive the scores, sort them, convert the sorted list into a string, calculate the MD5 hash of the string, and then broadcast the MD5 hash again. The remaining devices then verify that the received list matches their locally stored order. If they do, the list is broadcast again to confirm its correctness. If, for example, more than half of the devices in the network confirm the list is correct, then all devices use this list.

[0068] Return to Figure 4 For example, in step S113, it is confirmed that the electronic device is ranked first in the performance ranking list, and the electronic device is designated as the master node. The computing devices in the performance ranking list other than the first-ranked device are designated as at least one slave node. For instance, in the performance ranking list, multiple computing devices are arranged in descending order of performance score. The computing device ranked first has the highest performance score, indicating its strongest computing power. Therefore, the computing device with the strongest computing power is designated as the master node, also known as the master node, thereby ensuring the performance of the fog network. In this embodiment, the computing device serving as the master node is called an electronic device. The computing devices in the performance ranking list other than the master node are designated as slave nodes, also known as slave nodes. For example, the number of master nodes is 1, and the number of slave nodes is greater than or equal to 1.

[0069] Return to Figure 3 For example, in step S12, the master collaboration service component is installed and run in the master node, and the client component is installed and run in each of the at least one slave node. Figure 6 for Figure 3An exemplary flowchart of step S12. In some embodiments, such as Figure 6 As shown, step S12 may further include the following operations.

[0070] Step S121: The master node obtains the master collaboration service component and client component from the cloud server, and transmits the client component to each slave node through the near-field network;

[0071] Step S122: Install and run the main collaboration service component on the master node, and install and run the client component on each slave node.

[0072] For example, in step S121, the master node can obtain a fog cloud component (e.g., an XR scene fog cloud component) from a cloud server via the Internet. This fog cloud component includes a master collaborative service component (server component) and a client component (client component). The master node transmits the client component to each slave node via a near-field network, thereby avoiding each slave node from individually obtaining the client component from the cloud server, thus avoiding bandwidth consumption and improving transmission efficiency.

[0073] For example, in step S122, the main coordination service component of the Fog Cloud component is installed and run on the master node, and the client component of the Fog Cloud component is installed and run on each slave node. For example, by running the main coordination service component on the master node and the client component on the slave nodes, the nodes can communicate and cooperate according to preset functions, thereby realizing the function of the computing power sharing network.

[0074] Return to Figure 3 For example, in step S13, a computing power sharing network is established using the interaction between the main collaborative service component and the client component, and the node formation status is notified to the first computing device. For example, the first computing device may be a device with insufficient computing power that needs computing power support. For example, the first computing device may be an extended reality device. Thus, the construction of the computing power sharing network is completed, and the first computing device (e.g., the extended reality device) also learns about the node formation status, and can start the computing power sharing network at any time during subsequent operation.

[0075] Return to Figure 2After establishing the computing power sharing network, in step S20, it is determined whether the computing power of the first computing device (e.g., an extended reality device) in the computing power sharing network meets a preset condition. If so, it is confirmed that the computing power of the first computing device (e.g., an extended reality device) in the computing power sharing network meets the preset condition. For example, the preset condition may include: the processor utilization rate of the first computing device is greater than or equal to a first preset threshold. That is, when the processor utilization rate of the first computing device reaches a certain set threshold (e.g., 85%), it can be considered that the computing power of the first computing device is insufficient. Therefore, in this case, it is necessary to provide computing power support to the first computing device through the computing power sharing network. For example, the processor may include CPU, GPU, etc., and the embodiments of this disclosure do not limit this. The value of the first preset threshold is not limited to 85%, and may be any other arbitrary value, which can be determined according to actual needs, and the embodiments of this disclosure do not limit this. The first preset threshold may be set in advance, or it may be dynamically adjusted during the operation of the computing power sharing network, and the embodiments of this disclosure do not limit this.

[0076] For example, in step S30, when it is confirmed that the computing power of the first computing device (e.g., an extended reality device) meets the preset conditions, the master node allocates the data to be processed by the first computing device to at least one second computing device in the computing power sharing network based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to the first computing device (e.g., the extended reality device) through the computing power sharing network. For example, the first computing device can be a device acting as a master node or a device acting as a slave node, and the embodiments of this disclosure do not limit this. For example, the first computing device can be an extended reality device in the computing power sharing network. For example, the second computing device can be a device acting as a master node or a device acting as a slave node, and the embodiments of this disclosure do not limit this.

[0077] Figure 7 for Figure 2 An exemplary flowchart of step S30. In some embodiments, such as Figure 7 As shown, step S30 may further include the following operations.

[0078] Step S31: Select at least one second computing device in the computing power sharing network according to the computing power status of each node in the computing power sharing network;

[0079] Step S32: Obtain information about the data to be processed from the first computing device, and synchronize the data to be processed to at least one second computing device through a near-field network;

[0080] Step S33: Notify the first computing device of the information of the data transmission channel of the data to be processed, so that the first computing device can establish a link with at least one second computing device and perform data interaction based on the information of the data transmission channel.

[0081] For example, in step S31, when the computing power of the first computing device (e.g., an extended reality device) meets a preset condition (e.g., insufficient computing power), the master node selects at least one second computing device based on the computing power status of each node in the computing power sharing network. The selected second computing device is the device that will undertake the computing task. For example, the master node can select a device with surplus computing power as the second computing device, instead of selecting a device whose computing power is close to saturation, thereby achieving dynamic task allocation. For example, the selected second computing device can be a master node or a slave node; the embodiments of this disclosure do not limit this. For example, a threshold for processor utilization can be set. When the processor utilization of a device reaches the threshold, it indicates that the computing power of the device is close to saturation, and the device is not selected. When the processor utilization of a device has not yet reached the threshold, it indicates that the device has surplus computing power, and therefore the device can be selected.

[0082] For example, in some embodiments, the computing collaboration method provided in this disclosure may further include the following operations: the master node periodically acquires the computing power status of each node in the computing power sharing network, and selects devices with processor utilization rates less than a second preset threshold as candidate devices for the next allocation. For example, the master node may acquire the processor utilization rate of each node every 10 seconds, and thus select nodes based on this data, selecting devices with processor utilization rates less than the second preset threshold as candidate devices for the next allocation. This allows for the selection of devices with surplus computing power as the second computing device in the next allocation, thereby achieving dynamic task allocation, effectively improving the overall system performance, and providing great flexibility. For example, the period interval is not limited to 10 seconds and can be any time interval. The value of the second preset threshold is, for example, 85%, and can also be any other arbitrary value, which can be determined according to actual needs. The embodiments of this disclosure do not impose any limitations on this.

[0083] For example, in step S32, the master node obtains information about the data to be processed from the first computing device (e.g., an extended reality device) and synchronizes the data to be processed to the selected second computing device via a near-field network. After selecting the second computing device, the master node obtains information about the data to be processed from the first computing device (e.g., an extended reality device), that is, obtains task information, and synchronizes the data to be processed to the selected second computing device via a near-field network, thereby enabling the selected second computing device to process the received data to be processed.

[0084] Figure 8 for Figure 7 An exemplary flowchart of step S32. In some embodiments, such as Figure 8 As shown, step S32 may further include the following operations.

[0085] Step S321: The master node obtains information about the data to be processed from the first computing device;

[0086] Step S322: In response to the selected second computing device not having the corresponding program, the master node obtains the corresponding program from the cloud server based on the information of the data to be processed, transmits the corresponding program to the second computing device through the near-field network, and notifies the second computing device to process the data to be processed.

[0087] Step S323: In response to the selected second computing device having the corresponding program, the master node notifies the second computing device to process the data to be processed.

[0088] For example, in step S321, the master node first obtains information about the data to be processed from the first computing device (e.g., an extended reality device), that is, the first computing device notifies the master node of the information about the data to be processed.

[0089] For example, in step S322, if the selected second computing device does not have the corresponding program (and cannot process the data to be processed), the master node obtains the corresponding program from the cloud server based on the information of the data to be processed, transmits the obtained program to the second computing device through the near-field network, and notifies the second computing device to use the program to process the data to be processed. Thus, the second computing device can process the data to be processed.

[0090] For example, in step S323, if the second computing device has a corresponding program (which can process the data to be processed), the master node notifies the second computing device to directly process the data to be processed.

[0091] For example, the corresponding program described above can be any applicable form such as an installation package or a dynamic library, and the program can be used to process the data to be processed. The embodiments disclosed herein do not limit this.

[0092] Return to Figure 7For example, in step S33, the master node notifies the first computing device (e.g., an extended reality device) of the information of the data transmission channel for the data to be processed, enabling the first computing device to establish a link with the second computing device and perform data interaction based on the information of the data transmission channel. That is, the master node informs the first computing device (e.g., the extended reality device) of the transmission network channels for the corresponding running program data, graphics, frame data, and audio data, such as User Datagram Protocol (UDP), Transmission Control Protocol (TCP), Bluetooth protocol, etc. The extended reality device establishes a link with the assigned second computing device, which can be a near-field communication method such as Bluetooth or WIFI, and receives the relevant data transmitted by these devices and displays it on the first computing device (e.g., the extended reality device). Furthermore, the first computing device (e.g., the extended reality device) transmits the events that the application displayed on the interface needs to respond to to the corresponding second computing device through the near-field network. The second computing device responds to the events, and each second computing device transmits the results back to the first computing device (e.g., the extended reality device) after completing the event response, thereby forming a complete multi-node computing power allocation network. For example, the extended reality device can be XR glasses or other suitable devices.

[0093] The embodiments disclosed herein provide a novel method for constructing a computing power sharing network, and sorts devices according to performance, offering a new network combination approach. This method, applied to near-field networks for computing power collaboration, addresses the problem of insufficient computing power in XR devices (such as AR, VR, and MR devices), extending device availability.

[0094] Figure 9 This is a flowchart illustrating a computational collaboration method provided in some embodiments of this disclosure. In this example, near-field network devices dynamically allocate nodes for program execution based on the computing power ranking of devices in a computing power sharing network, thereby achieving the purpose of computational power collaboration. The following is in conjunction with... Figure 9 A brief overview of the workflow of computational collaborative methods is provided.

[0095] First, the computing power assessment process is triggered when the AR device (or other types of XR device) connects to the vehicle-mounted device or mobile device network via a near-field network. Alternatively, the computing power assessment process is triggered after the AR device connects to the vehicle-mounted device or mobile device network via a near-field network and passes the vehicle-mounted device's security authentication information.

[0096] Each device requests a performance evaluation application from a remote server (such as a cloud server) via the Internet. This process may involve security authentication with the remote server using vehicle safety authentication information, and then the remote server retrieves the performance score based on the device information reported by the device.

[0097] If no specific rating item is found on the remote server, the performance evaluation application is deployed; if the corresponding device rating item is found, the corresponding rating is deployed, which is the aforementioned performance rating.

[0098] If the AR device obtains a performance evaluation application, it will start running the application. For example, the accuracy of the calculation results can be improved by averaging the results from several (or many) runs. If the AR device obtains a score from a remote server, it does not need to obtain a performance evaluation application.

[0099] After the AR device receives a score (e.g., after a performance evaluation application finishes running, or when the score result is obtained directly), the AR device broadcasts its corresponding score within the near-field network. Other devices, upon hearing the broadcast, save the corresponding ordered list, which may be sorted, for example, by score from highest to lowest.

[0100] After all devices in the near-field network (NFC) have completed their broadcasts, each device calculates a unique value (e.g., an MD5 hash) for its saved list information. The MD5 hash represents the uniqueness of the list. Each device broadcasts its own MD5 hash across the NFC network via UDP or other applicable protocols. Each device confirms that the received MD5 hash matches its own calculated MD5 hash, thus verifying the list's correctness. Once all devices have confirmed the hash, a performance ranking list is generated, which is a list confirmed correct by a majority of devices. For example, after completing a UDP multi-device broadcast, NFC devices can monitor other devices in the network. Each device can then save a list of all device information and add itself to that list. During broadcasting, each device includes its device type (e.g., automotive device, mobile device, XR device, etc.) and its performance score in the broadcast data to determine its ranking. For example, mobile devices can include smartphones, and XR devices can include XR glasses.

[0101] After the devices are ranked by computing power, the top-ranked devices form a computing power sharing network. For example, node attributes are assigned starting from the first ranked device, with the first-ranked device serving as the master node and the second-ranked and subsequent devices serving as slave nodes. Alternatively, the computing power sharing network can be formed by only the top-ranked devices or by all devices in the list. In some examples, non-XR devices in the entire network can be used as nodes; in others, all devices in the network, including XR devices, can be used as nodes. Here, "node" refers to the aforementioned master and slave nodes. For example, a master node can be a mobile device, an in-vehicle device, or an XR device; a slave node can also be a mobile device, an in-vehicle device, or an XR device.

[0102] Then, the master node device downloads the XR scene fog cloud component from the remote server via the Internet. The XR scene fog cloud component includes a main collaboration service component (server component) and a client component (client component). The master node installs the main collaboration service component from the XR scene fog cloud component, while the slave nodes receive the client component transmitted by the master node within the near-field network and install it.

[0103] After the master and slave nodes complete the component installation, the master node notifies the slave nodes to start the corresponding fog cloud component. Furthermore, the master node informs the AR device of the XR scene node allocation and installation status.

[0104] When an AR device detects that the CPU and GPU usage exceeds 85% during operation, it determines that the current computing power is insufficient. In this scenario, the AR device establishes a communication connection with the master node, informs the master node of the data to be processed, and the master node synchronizes the corresponding program through a remote server and then synchronizes the program to each slave node in the near field.

[0105] The master node acquires CPU and GPU utilization data from each slave node, allocates the data to be processed on each node accordingly, and informs each device of the target device connection information (e.g., UDP / TCP / Bluetooth channel) for data transmission. For example, the amount of data to be processed assigned to the master and slave nodes can be dynamically adjusted based on usage data. For instance, the percentage of CPU and GPU utilization for each device can be periodically (e.g., every 10 seconds), with devices with high utilization not receiving new tasks and those with low utilization receiving new tasks. For example, the CPU and GPU utilization threshold can be set to 85%. The master and slave nodes notify the AR device to start processing the data to be processed, and inform it of the corresponding network channels (e.g., UDP / TCP / Bluetooth channel) for transmitting the running program data, graphics, frame data, and audio data.

[0106] Then, the AR device establishes links with each node (e.g., via near-field communication methods such as Bluetooth or Wi-Fi), receives relevant data transmitted from each device, and displays the data that needs to be shown on the AR device. Furthermore, the AR device transmits events that the application displays on the interface needs to respond to to the corresponding nodes via the near-field network. The corresponding nodes then respond to the events, and after completing their event responses, each node transmits the response results back to the AR device, thus forming a complete computing power sharing network capable of allocating computing power.

[0107] In this example, mobile devices, in-vehicle devices, and AR devices (such as XR glasses) are linked together to form a near-field network. Each device obtains a performance score and broadcasts it within the near-field network. Each device within the near-field network maintains a list of performance scores, confirming the device with the best performance as the master node and the remaining devices as slave nodes. The relevant devices can be sorted using a Java / C (or a relevant application conforming to the platform's coding model) version of a GPU / CPU benchmarking application (i.e., a performance evaluation application) prepared by a remote server, or by directly obtaining the device performance scores. After the devices are combined into a network, the master node downloads the XR scene fog cloud component. The XR scene fog cloud component includes a server component and a client component; the server component represents the cloud component, and the client component represents the fog component. The server component running on the master node is responsible for downloading the XR scene program to the device with the best performance in the near-field network (i.e., downloading it to the master node), and allocating some of the other XR scene programs to the client components on the slave nodes for them to run, thus forming a computing power sharing network. When the server component on the master node starts, it notifies the slave nodes to start as well. Based on the CPU and GPU load of each device, the XR scene dynamically allocates multi-tasking and multi-window running scenarios, dynamically distributing single-machine running tasks to each device. Different program running data, screen frames, and audio data are transmitted from the master node and slave node to the XR glasses, which then aggregate and display the data.

[0108] This example uses remote server security authentication to combine the vehicle and its in-vehicle mobile devices into a near-field network. Relying on internal device computing power negotiation, the devices autonomously allocate master and slave nodes. The master node device downloads the remote server's XR scene server component to its local machine to establish a cloud network; the remaining slave node devices form a fog network to run client components, ultimately providing computing power support for XR glasses and in-vehicle devices.

[0109] This allows for the formation of a near-field collaborative network, thereby enhancing the computing power for XR scenarios and enabling the expansion of computing power. It facilitates the evolution of individual devices into collaborative networks, comprehensively addressing the insufficient computing power of XR devices (such as AR, VR, and MR devices) and better meeting the application scenarios of XR devices. Furthermore, it enables dynamic allocation of node tasks, providing significant flexibility.

[0110] It should be noted that, Figure 9The workflow shown is merely exemplary and not restrictive. The computational collaboration method provided by the embodiments of this disclosure is not limited to the workflow described above, and other applicable workflows may also be used. The embodiments of this disclosure do not limit this.

[0111] At least one embodiment of this disclosure also provides another computational collaboration method for electronic devices. Figure 10 This is a flowchart illustrating another computational collaboration method provided in some embodiments of this disclosure. Figure 10 In the illustrated embodiment, the computing device acting as a slave node is referred to as an electronic device. For example... Figure 10 As shown, the computational collaboration method includes the following operations.

[0112] Step S1000: The electronic device is configured as a slave node in a computing power sharing network, wherein the computing power sharing network includes multiple computing devices connected to the same near-field network, the computing power sharing network includes a master node and at least one slave node, one of the slave nodes is the electronic device, and the master node is one of the computing devices other than the electronic device among the multiple computing devices;

[0113] Step S2000: Receive and process the data to be processed allocated by the master node based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to the first computing device in the computing power sharing network through the computing power sharing network.

[0114] The computational collaboration method provided in this embodiment is Figure 2 The computational collaboration method shown is similar, except that in this embodiment, the electronic device refers to a device acting as a slave node. For example, in step S1000, the electronic device is configured as a slave node in a computing power sharing network. In step S2000, the electronic device receives and processes the data to be processed allocated by the master node based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to the first computing device (e.g., an extended reality device) through the computing power sharing network. For a detailed explanation of the above steps S1000 and S2000, please refer to the section above regarding... Figure 2 The explanation of the computational co-operation method shown will not be repeated here.

[0115] At least one embodiment of this disclosure also provides an electronic device. This electronic device offers a novel network combination method, integrating the advantages of fog computing and cloud computing. It can solve the problem of insufficient device computing power (e.g., insufficient computing power of extended reality devices), extend device availability, achieve computing power collaboration, and enable dynamic allocation of node tasks, providing great flexibility. This electronic device can enhance the computing power for extended reality scenarios, further meeting the needs of extended reality device application scenarios.

[0116] Figure 11 This is a schematic block diagram of an electronic device provided for some embodiments of this disclosure. For example... Figure 11 As shown, the electronic device 300 includes a processor 310 and a memory 320. The memory 320 stores computer-executable instructions (e.g., one or more computer program modules) non-transitoryly. The processor 310 executes the computer-executable instructions, which, when run by the processor 310, can perform one or more steps of the computational coordination method described above, thereby implementing the computational coordination method described above. The memory 320 and the processor 310 can be interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0117] For example, processor 310 may be a central processing unit (CPU), a graphics processing unit (GPU), or other form of processing unit with data processing and / or program execution capabilities. For example, the central processing unit (CPU) may be an x86 or ARM architecture. Processor 310 may be a general-purpose processor or a special-purpose processor, capable of controlling other components in electronic device 300 to perform desired functions.

[0118] For example, memory 320 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer program modules may be stored on the computer-readable storage medium, and processor 310 may run one or more computer program modules to implement various functions of electronic device 300. Various application programs and various data, as well as various data used and / or generated by the application programs, may also be stored in the computer-readable storage medium.

[0119] It should be noted that, in the embodiments of this disclosure, the specific functions and technical effects of the electronic device 300 can be referred to the description of the computing collaboration method above, and will not be repeated here.

[0120] Figure 12 This is a schematic block diagram of another electronic device provided for some embodiments of this disclosure. (See diagram below.) Figure 12 As shown, the electronic device 400 is, for example, suitable for implementing the computational collaboration method provided in the embodiments of this disclosure. The electronic device 400 may be a terminal device or a server, etc. It should be noted that... Figure 12The electronic device 400 shown is merely an example and does not impose any limitation on the functionality and scope of use of the embodiments of this disclosure.

[0121] like Figure 12 As shown, electronic device 400 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 410, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 420 or a program loaded from storage device 480 into random access memory (RAM) 430. RAM 430 also stores various programs and data required for the operation of electronic device 400. Processing unit 410, ROM 420, and RAM 430 are interconnected via bus 440. Input / output (I / O) interface 450 is also connected to bus 440.

[0122] Typically, the following devices can be connected to I / O interface 450: input devices 460 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 470 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 480 including, for example, magnetic tapes, hard disks, etc.; and communication devices 490. Communication device 490 allows electronic device 400 to communicate wirelessly or wiredly with other electronic devices to exchange data. Although Figure 12 An electronic device 400 with various modules is shown, but it should be understood that it is not required to implement or have all of the modules shown, and the electronic device 400 may alternatively implement or have more or fewer devices.

[0123] For example, according to embodiments of this disclosure, the above-described computational collaboration method can be implemented as a computer software program. For instance, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program including program code for performing the above-described computational collaboration method. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 490, or installed from a storage device 480, or installed from a ROM 420. When the computer program is executed by the processing device 410, the functions defined in the computational collaboration method provided by embodiments of this disclosure can be implemented.

[0124] At least one embodiment of this disclosure also provides a storage medium. This storage medium can provide a novel network combination method, integrating the advantages of fog computing and cloud computing. It can solve the problem of insufficient device computing power (e.g., insufficient computing power of extended reality devices), extend device availability, achieve computing power collaboration, and enable dynamic allocation of node tasks, providing great flexibility. Using this storage medium can improve the computing power for extended reality scenarios, further meeting the needs of extended reality device application scenarios.

[0125] Figure 13 This is a schematic diagram of a storage medium provided for some embodiments of this disclosure. For example, such as... Figure 13 As shown, the storage medium 500 can be a non-transitory computer-readable storage medium storing non-transitory computer-readable instructions 510. When the non-transitory computer-readable instructions 510 are executed by the processor, the computational coordination method provided in the embodiments of this disclosure can be implemented. For example, when the non-transitory computer-readable instructions 510 are executed by the processor, one or more steps in the computational coordination method described above can be performed.

[0126] For example, the storage medium 500 can be used in the aforementioned electronic device; for example, the storage medium 500 can be... Figure 11 The memory 320 in the electronic device 300 shown.

[0127] For a description of the storage medium 500, please refer to the description of the memory in the embodiments of the electronic device; details that are repeated will not be repeated here. The specific functions and technical effects of the storage medium 500 can be referred to the description of the computing collaboration method above; they will not be repeated here.

[0128] The following points need to be explained:

[0129] (1) The accompanying drawings of the embodiments of this disclosure only involve the structures involved in the embodiments of this disclosure. Other structures can be referred to the general design.

[0130] (2) Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

[0131] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. The scope of protection of this disclosure should be determined by the scope of protection of the claims.

Claims

1. A computational collaboration method applied to scenarios including extended reality devices, for electronic devices, wherein, The method includes: Multiple computing devices connected to the same near-field network are organized into a computing power sharing network, wherein the computing power sharing network includes a master node and at least one slave node, the master node is the electronic device, and the slave node is a computing device other than the electronic device among the multiple computing devices. The master node is used to communicate with a remote server to establish a cloud network, and the slave node is used to establish a fog network. Confirm that the computing power of the first computing device in the computing power sharing network meets the preset conditions, wherein the first computing device includes the extended reality device; The master node allocates the data to be processed by the first computing device to at least one second computing device in the computing power sharing network based on the computing power status of each node in the computing power sharing network, so as to provide computing power support to the first computing device through the computing power sharing network. The step of forming the computing power sharing network by connecting the multiple computing devices connected to the same near-field network includes: The master node and the at least one slave node are determined based on the computing power of the plurality of computing devices; Install and run the main collaboration service component in the master node, and install and run the client component in each of the at least one slave node; The computing power sharing network is established by utilizing the interaction between the main collaborative service component and the client component, and the node formation status is notified to the first computing device.

2. The method according to claim 1, wherein, Determining the master node and the at least one slave node based on the computing power of the plurality of computing devices includes: Determine a performance score for each of the plurality of computing devices, wherein the performance score represents the computing power of each computing device; A performance ranking list of the plurality of computing devices is determined based on the performance scores; The electronic device is confirmed to be first in the performance ranking list and is identified as the master node. The computing devices other than the first one in the performance ranking list are identified as the at least one slave node.

3. The method according to claim 2, wherein, Determining the performance score of each of the plurality of computing devices includes: Each of the multiple computing devices obtains its own performance score from the cloud server; or The plurality of computing devices obtain their respective performance evaluation applications from the cloud server and run the performance evaluation applications to obtain their respective performance scores.

4. The method according to claim 2, wherein, Based on the performance scores, a performance ranking list of the plurality of computing devices is determined, including: The electronic device receives performance scores broadcast by other computing devices within the near-field network; An initial list is established, the unique value of the list information of the initial list is calculated, and the unique value of its own list information is broadcast within the near-field network; Receive the unique value of the list information broadcast by other computing devices in the near-field network, determine that it is consistent with the unique value of its own list information, and broadcast the confirmation result in the near-field network; The initial list corresponding to the unique values ​​of the list information that have been confirmed to be correct by a preset number of computing devices is used as the performance sorting list.

5. The method according to claim 2, wherein, Installing and running the master collaboration service component on the master node, and installing and running the client component on each of the at least one slave node, including: The master node obtains the master collaboration service component and the client component from the cloud server, and transmits the client component to each slave node through the near-field network; The master collaboration service component is installed and run on the master node, and the client component is installed and run on each slave node.

6. The method according to claim 1, wherein, The master node allocates the data to be processed by the first computing device to at least one second computing device in the computing power sharing network based on the computing power status of each node in the computing power sharing network, including: Based on the computing power status of each node in the computing power sharing network, at least one second computing device is selected in the computing power sharing network; The system obtains information about the data to be processed from the first computing device and synchronizes the data to be processed to the at least one second computing device via the near-field network. The first computing device is notified of the information of the data transmission channel of the data to be processed, so that the first computing device can establish a link with the at least one second computing device and perform data interaction based on the information of the data transmission channel.

7. The method according to claim 1, wherein, The preset conditions include: the processor utilization rate of the first computing device is greater than or equal to a first preset threshold. The method further includes: The computing power status of each node in the computing power sharing network is periodically acquired, and devices with processor utilization rates less than a second preset threshold are selected as candidate devices for the next allocation. The computing power sharing network is formed by connecting the multiple computing devices connected to the same near-field network. Security authentication is performed on the plurality of computing devices, wherein the plurality of computing devices include at least one of mobile devices, in-vehicle devices, and wearable devices.

8. A computational collaboration method applied to scenarios including extended reality devices, for electronic devices, wherein, The method includes: The electronic device is configured as a slave node in a computing power sharing network, wherein the computing power sharing network includes multiple computing devices connected to the same near-field network, the computing power sharing network includes a master node and at least one slave node, one of the slave nodes being the electronic device, the master node being one of the multiple computing devices other than the electronic device, the master node being used to communicate with a remote server to establish a cloud network, and the slave node being used to establish a fog network; The master node receives and processes the data to be processed, allocated based on the computing power status of each node in the computing power sharing network, to provide computing power support to a first computing device in the computing power sharing network, wherein the first computing device includes the extended reality device. The computing power sharing network is obtained in the following way: The master node and the at least one slave node are determined based on the computing power of the plurality of computing devices; Install and run the main collaboration service component in the master node, and install and run the client component in each of the at least one slave node; The computing power sharing network is established by utilizing the interaction between the main collaborative service component and the client component, and the node formation status is notified to the first computing device.

9. An electronic device comprising: processor; Memory, including one or more computer program modules; The one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules are used to implement the computational cooperative method according to any one of claims 1-8.

10. A non-volatile storage medium storing non-transitory computer-readable instructions that, when executed by a computer, implement the computational cooperative method according to any one of claims 1-8.

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