CN116668440A

CN116668440A - Computation cooperation method, electronic device and storage medium

Info

Publication number: CN116668440A
Application number: CN202310551958.9A
Authority: CN
Inventors: 杨焜; 谢鑫; 王勇
Original assignee: Hubei Xingji Meizu Technology Co ltd
Current assignee: Hubei Xingji Meizu Technology Co ltd
Priority date: 2023-05-16
Filing date: 2023-05-16
Publication date: 2023-08-29

Abstract

Provided are a computing cooperation method, an electronic device and a storage medium. The method is used for the electronic equipment, and comprises the following steps: constructing a plurality of computing devices connected to the same near-field network as a computing power sharing network, wherein 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 except the electronic device in the plurality of computing devices; confirming that the computing power of a first computing device in the computing power sharing network meets a preset condition; the master node distributes data to be processed of the first computing device to at least one second computing device in the computing force sharing network based on the computing force states of the respective nodes in the computing force sharing network to provide computing force support to the first computing device through the computing force sharing network. The method provides a novel network combination mode and can solve the problem of insufficient calculation power of equipment.

Description

Computation cooperation method, electronic device and storage medium

Technical Field

Embodiments of the present disclosure relate to a computing cooperation method, an electronic device, and a storage medium.

Background

In the computing network related art, there are various computing manners, such as "cloud computing", "fog computing", and the like. Fog computing is an extension to the concept of cloud computing, which uses mainly devices in edge networks, with very low latency for data transfer. The fog calculation has a large number of network nodes, the mobility is good, all mobile devices can communicate with each other directly, and signals do not need to be forwarded through a cloud. Compared with cloud computing, the architecture adopted by fog computing is more distributed and is closer to the edge of a network. Fog computing concentrates data, data processing and applications in devices at the edge of the network, rather than storing them almost entirely in the cloud as in cloud computing, where data storage and processing is more dependent on local devices than servers. Fog computing is a new generation of distributed computing, conforming to the "decentralization" feature of the internet.

Disclosure of Invention

At least one embodiment of the present disclosure provides a computational collaboration method for an electronic device, wherein the method includes: constructing a plurality of computing devices connected to the same near-field network into a computing force sharing network, wherein the computing force 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 except the electronic device in the plurality of computing devices; confirming that the computing power of the first computing device in the computing power sharing network meets a preset condition; the master node distributes data to be processed of the first computing device to at least one second computing device in the computing force sharing network based on computing force states of all nodes in the computing force sharing network so as to provide computing force support for the first computing device through the computing force sharing network.

For example, in a method provided by an embodiment of the present disclosure, constructing the plurality of computing devices connected to the same near-field network as the computing force sharing network includes: determining the master node and the at least one slave node according to the computing capabilities of the plurality of computing devices; installing and running a master collaboration service component in the master node, causing each slave node of the at least one slave node to install and run a client component; establishing the computing force sharing network by utilizing interaction between the main cooperative service component and the client component, and notifying the first computing device of node establishment; wherein determining the master node and the at least one slave node according to the computing capabilities of the plurality of computing devices comprises: determining a performance score for each computing device of the plurality of computing devices, wherein the performance score represents an operational capability of the each computing device; determining a ranked list of performance of the plurality of computing devices based on the performance scores; confirming that the electronic device is located at the first position in the performance ordering list, determining the electronic device as the master node, and determining computing devices except the first position in the performance ordering list as the at least one slave node.

For example, in a method provided by an embodiment of the present disclosure, determining a performance score for each of the plurality of computing devices includes: causing each of the plurality of computing devices to obtain a respective of the performance scores from a cloud server; or, the computing devices are caused to acquire respective performance evaluation applications from the cloud server and run the respective performance evaluation applications to obtain the respective performance scores.

For example, in a method provided by an embodiment of the present disclosure, determining a ranked list of performance of the plurality of computing devices based on the performance scores includes: the electronic device receives respective performance scores broadcast by other computing devices in the near-field network; establishing an initial list, calculating a list information unique value of the initial list, and broadcasting a list information unique value of the initial list in the near-field network; receiving list information unique values broadcasted by other computing devices in the near-field network, judging that the list information unique values are consistent with the list information unique values of the other computing devices, and broadcasting a confirmation result in the near-field network; and taking the initial list corresponding to the unique list information value confirmed to be correct by the preset number of computing devices as the performance sorting list.

For example, in a method provided by an embodiment of the present disclosure, installing and running the master collaboration service component in the master node, causing each of the at least one slave node to install and run the client component includes: the master node acquires the master cooperative 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 master collaboration service component is installed and operated in the master node, with each slave node installing and operating the client component.

For example, in a method provided by an embodiment of the present disclosure, the master node distributes the data to be processed of the first computing device to at least one second computing device in the computing force sharing network based on a computing force state of each node in the computing force sharing network, including: selecting at least one second computing device in the computing force sharing network according to the computing force states of all nodes in the computing force sharing network; obtaining information of 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, and enabling the first computing device to establish a link with the at least one second computing device and conduct data interaction based on the information of the data transmission channel.

For example, in a method provided in an embodiment of the present disclosure, the preset condition includes: the processor usage of the first computing device is greater than or equal to a first preset threshold; the method further comprises the steps of: periodically acquiring the computing power state of each node in the computing power sharing network, and taking the equipment with the processor utilization rate smaller than a second preset threshold value as the next-allocated alternative equipment; wherein the plurality of computing devices connected to the same near-field network are configured as the computing force sharing network, further comprising: the plurality of computing devices are securely authenticated, wherein the plurality of computing devices include at least one of a mobile device, an in-vehicle device, and a wearable device.

At least one embodiment of the present disclosure also provides a computational collaboration method for an electronic device, wherein the method includes: the electronic device is configured as a slave node in a computing force sharing network, wherein the computing force sharing network comprises a plurality of computing devices connected to the same near field network, the computing force sharing network comprises a master node and at least one slave node, one slave node in the at least one slave node is the electronic device, and the master node is one of the computing devices except the electronic device; and receiving and processing the data to be processed distributed by the master node based on the computing power states of all nodes in the computing power sharing network so as to provide computing power support for first computing equipment in the computing power sharing network through the computing power sharing network.

At least one embodiment of the present 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 for implementing the computational synergy methods provided by any of the embodiments of the present disclosure.

At least one embodiment of the present disclosure also provides a non-transitory storage medium storing non-transitory computer readable instructions that, when executed by a computer, implement the computational synergy method provided by any of the embodiments of the present disclosure.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below, and it is apparent that the drawings in the following description relate only to some embodiments of the present disclosure, not to limit the present disclosure.

Fig. 1 is a schematic view of an application scenario of a computational collaboration method according to some embodiments of the present disclosure;

FIG. 2 is a flow chart of a computational collaboration method according to some embodiments of the present disclosure;

FIG. 3 is an exemplary flowchart of step S10 of FIG. 2;

FIG. 4 is an exemplary flowchart of step S11 in FIG. 3;

FIG. 5 is an exemplary flowchart of step S112 of FIG. 4;

FIG. 6 is an exemplary flow chart of step S12 of FIG. 3;

fig. 7 is an exemplary flowchart of step S30 in fig. 2;

FIG. 8 is an exemplary flowchart of step S32 of FIG. 7;

FIG. 9 is a workflow diagram of a computational collaboration method provided by some embodiments of the present disclosure;

FIG. 10 is a flow chart of another computational collaboration method provided by some embodiments of the present disclosure;

FIG. 11 is a schematic block diagram of an electronic device provided by some embodiments of the present disclosure;

FIG. 12 is a schematic block diagram of another electronic device provided by some embodiments of the present disclosure; and

fig. 13 is a schematic diagram of a storage medium according to some embodiments of the present disclosure.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without the need for inventive faculty, are within the scope of the present disclosure, based on the described embodiments of the present disclosure.

Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Likewise, the terms "a," "an," or "the" and similar terms do not denote a limitation of quantity, but rather denote the presence of at least one. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.

In an Extended Reality (XR) scenario, the consumption of device performance by each application is relatively large. If cloud computing is fully employed to provide computing power support, all information needs to be processed and forwarded through the cloud, which can result in significant latency and network resource consumption.

The embodiment of the disclosure provides a computing cooperation method, an electronic device and a storage medium. The calculation collaboration method provides a brand new network combination mode, integrates the advantages of fog calculation and cloud calculation, can solve the problem of insufficient calculation power of equipment (such as insufficient calculation power of augmented reality equipment), extends the usability of the equipment, achieves the purpose of calculation power collaboration, can realize dynamic allocation of node tasks, and has great flexibility. The computing cooperation method can improve the operation computing power of the augmented reality scene, and further meets the requirements of the application scene of the augmented reality equipment.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that the same reference numerals in different drawings will be used to refer to the same elements already described.

At least one embodiment of the present disclosure provides a computational collaboration method for an electronic device. The computational synergy method comprises the following steps: constructing a plurality of computing devices connected to the same near-field network as a computing power sharing network, wherein 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 except the electronic device in the plurality of computing devices; confirming that the computing power of a first computing device in the computing power sharing network meets a preset condition; the master node distributes data to be processed of the first computing device to at least one second computing device in the computing force sharing network based on the computing force states of the respective nodes in the computing force sharing network to provide computing force support to the first computing device through the computing force sharing network.

Fig. 1 is an application scenario schematic diagram of a computing cooperation method according to some embodiments of the present disclosure. As shown in fig. 1, the computational collaboration method provided by the embodiments of the present disclosure may be applied to a scene containing an augmented reality device (XR device). For example, the vehicle-mounted device, the mobile device and the XR device are all connected in the same near-field network, and can communicate with each other through the near-field network. The individual applications consume relatively large amounts of device performance in the XR scenario, so the individual devices shown in fig. 1 can be organized into a computing force sharing network. Depending on the calculation force negotiation of the internal equipment of the calculation force sharing network, the master node and the slave nodes are distributed autonomously, the master node equipment is responsible for establishing a cloud network, and the slave node equipment forms a fog network, so that calculation force support is provided for XR equipment by coordination and cooperation.

For example, the XR device may comprise a Virtual Reality (VR) device, an augmented Reality (Augmented Reality, AR) device, a Mixed Reality (MR) device, or other type of augmented Reality device, as embodiments of the disclosure are not limited in this respect. For example, the XR device may be a wearable device, such as XR glasses, an XR helmet, etc., and may be in any device configuration. For example, the in-vehicle apparatus may include an electronic device having a computing capability, an electronic apparatus, a processor, or the like, mounted on a vehicle, such as an electronic device or an electronic apparatus mounted on an automobile, an electronic device or an electronic apparatus mounted on a ship, or the like, and the embodiments of the present disclosure do not limit the type of vehicle and the type of apparatus mounted on a vehicle as long as the apparatus is mounted on a vehicle and has a computing capability. For example, the mobile device may include any type of mobile device, including a smart phone, tablet, notebook, etc., as embodiments of the present disclosure are not limited in this respect. For example, the XR device, the vehicle-mounted device, and the mobile device all have near field communication functions and can access the same near field network.

Fig. 2 is a flow chart of a computational collaboration method according to some embodiments of the present disclosure. For example, the computational synergy method is used for the electronic device. As shown in fig. 2, the computational collaboration method provided by the embodiments of the present disclosure may include the following operations.

Step S10: constructing a plurality of computing devices connected to the same near-field network as a computing force sharing network, wherein the computing force 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 except the electronic device in the plurality of computing devices;

step S20: confirming that the computing power of a first computing device in the computing power sharing network meets a preset condition;

step S30: the master node distributes data to be processed of the first computing device to at least one second computing device in the computing force sharing network based on the computing force states of the respective nodes in the computing force sharing network to provide computing force support to the first computing device through the computing force sharing network.

For example, in step S10, in some examples, when the plurality of computing devices includes at least one augmented reality device, that is, when the augmented reality device accesses a near field network, the plurality of computing devices connected to the same near field network are built as a computing force sharing network. For example, the electronic device is one of a plurality of computing devices. The computing force sharing network comprises a master node and at least one slave node, wherein the master node is the electronic device, and the slave node is a computing device except the electronic device in a plurality of computing devices. In this embodiment, in order to distinguish a master node from a slave node among a plurality of computing devices, a device that is a master node is referred to as an electronic device, which is one of the plurality of computing devices and also a device having computing power. For example, the plurality of computing devices may include at least one augmented reality device, the plurality of computing devices may further include a mobile device and/or an in-vehicle device, etc., as embodiments of the present disclosure are not limited in this regard. For example, the near field network may be a network based on any type of near field communication technology, including, for example, wiFi (Wireless Fidelity) networks, bluetooth (Bluetooth) networks, near field communication (Near Field Communication, NFC) networks, etc., as embodiments of the present disclosure are not limited in this regard. In this example, all computing devices accessing the near field network may be built as a computing force sharing network. Of course, embodiments of the present disclosure are not limited thereto, and in other examples, a portion of the computing devices accessing the near field network may also be configured as a computing force sharing network.

For example, a computing force sharing network may achieve the goal of computing force coordination, combining the advantages of cloud computing and fog computing. The computing force sharing network includes a master node and at least one slave node. For example, the number of master nodes is 1, i.e., there is only one master node, which is one of a plurality of computing devices, referred to in this embodiment as an electronic device, that is to communicate with a remote server (e.g., cloud server) to establish a cloud network. The number of slave nodes is greater than or equal to 1, that is, there may be one or more slave nodes, which are computing devices other than the computing device as the master node (that is, computing devices other than the above-described electronic device) among the plurality of computing devices, and the slave nodes are used to establish the fog network, so that the functions of the fog network are cooperatively implemented under the control of the master node. For example, the first computing device may be a device that is a master node or a device that is a slave node. The first computing device may be an augmented reality device in a computing force sharing network. For example, the aforementioned second computing device may be a device that is a master node or a device that is a slave node.

For example, in some examples, the access of the augmented reality device to the near field network may be used as a trigger to build the computing force sharing network, which is performed when the access of the augmented reality device to the near field network is detected. Of course, embodiments of the present disclosure are not limited thereto, and in other embodiments, the access of the augmented reality device to the near field network may be one of the triggering conditions for building the computing power sharing network, and other triggering conditions may exist, where the operations for building the computing power sharing network are triggered when the multiple conditions meet the requirements. For example, in some examples, the trigger condition may further include the vehicle device security authentication passing, the operation of building the computing force sharing network being performed when the augmented reality device accesses the near field network and the vehicle device security authentication passes. For example, the vehicle-mounted device security authentication may refer to security authentication through a cloud server, and security authentication is performed on a computing device that accesses the same near-field network as the vehicle-mounted device. It should be noted that, when the plurality of computing devices do not include an augmented reality device, the trigger condition for constructing the computing force sharing network may be other conditions, which embodiments of the present disclosure do not limit.

For example, in some examples, a total of N computing devices access the same near field network, N being an integer greater than 1. At least one augmented reality device (XR device) exists in the N computing devices, and the rest of the computing devices can be vehicle-mounted devices and/or mobile devices. One of the N computing devices serves as a master node, and for ease of illustration and distinction, the device that serves as the master node is referred to as an electronic device in this embodiment. The remaining N-1 computing devices act as slave nodes, and the master node and the slave nodes are jointly built into a computing force sharing network. The master node can communicate with the cloud server to realize the function of the cloud network. The slave nodes coordinate under the control of the master node, thereby realizing the function of the fog network.

Fig. 3 is an exemplary flowchart of step S10 in fig. 2. In some embodiments, as shown in fig. 3, step S10 may further include the following operations.

Step S11: determining a master node and at least one slave node according to the computing capabilities of the plurality of computing devices;

step S12: installing and operating a master collaboration service component in a master node, such that each slave node of the at least one slave node installs and operates a client component;

Step S13: the method comprises the steps of establishing a computing force sharing network by utilizing interaction between a main cooperative service component and a client component, and notifying the first computing device of node establishment.

For example, in step S11, a master node and at least one slave node are first determined according to the computing capabilities of the plurality of computing devices. For example, the most computationally powerful computing device may be determined as the master node and the remaining computing devices as slave nodes. In this embodiment, a device that is a master node is referred to as an electronic device.

For example, before determining the master node and the slave node, the following operations may also be performed: secure authentication is performed for a plurality of computing devices. For example, the security authentication can be performed through the vehicle-mounted security authentication information and a remote server (such as a cloud server), and the authentication can be realized through the security authentication of the cloud server, so that the security of the access equipment is ensured.

Fig. 4 is an exemplary flowchart of step S11 in fig. 3. In some embodiments, as shown in fig. 4, step S11 may further include the following operations.

Step S111: determining a performance score for each of the plurality of computing devices, wherein the performance score represents the computing capabilities of each computing device;

Step S112: determining a ranked list of capabilities of the plurality of computing devices based on the performance scores;

step S113: confirming that the electronic device is located at the first position in the performance ordering list, determining the electronic device as a master node, and determining computing devices except the first position in the performance ordering list as at least one slave node.

For example, in step S111, a performance score for each of a plurality of computing devices is first determined. For example, a performance score represents the computing capabilities of a computing device, i.e., each computing device corresponds to a performance score that represents the strength of the computing device's computing capabilities. For example, the higher the performance score, the more computing power the computing device is represented; the lower the performance score, the weaker the computing power of the computing device. For example, the computing capabilities of a computing device may be related to the performance of the processor of the computing device, hardware configuration (e.g., memory, disk, etc. configuration), software configuration, etc., and different computing devices may have different computing capabilities.

For example, in some examples, assuming performance scores are percentile, the central processing unit (Central Processing Unit, CPU) score is 30 points, the graphics processor (Graphics Processing Unit, GPU) score is 40 points, the memory score is 20 points, and the disk score is 10 points, among 100 points. The better the performance of the individual hardware, the higher the score. The sum of the scores of the individual hardware is then the performance score of the computing device. For example, for a certain computing device, the CPU score is 25 points, the GPU score is 30 points, the memory score is 18 points, and the disk score is 5 points, then the performance score is 78 points; for another computing device, the CPU score is 30 points, the GPU score is 35 points, the memory score is 15 points, and the disk score is 6 points, then the performance score is 86 points. For example, a computing device scored 86 may perform better than a computing device scored 78, and the performance score may reflect the computing capabilities of the computing device.

For example, in some examples, step S111 may include: causing each of the plurality of computing devices to obtain a respective performance score from the cloud server. In this example, the performance score of each computing device is stored in the cloud server, when each computing device sends an acquisition request to the cloud server, the cloud server queries according to the computing device information carried in the request, so as to obtain the performance score, and sends the performance score to each computing device, so that each computing device obtains the performance score.

For example, in other examples, step S111 may include: and enabling the computing devices to acquire respective performance evaluation applications from the cloud server and run the respective performance evaluation applications to obtain respective performance scores. In this example, the cloud server stores a performance evaluation application, and when each computing device sends an acquisition request to the cloud server, the cloud server issues the performance evaluation application, and after each computing device receives the performance evaluation application, the performance evaluation application is run to obtain respective performance scores. For example, the performance evaluation application has a function of acquiring device information, e.g., a function of detecting device performance, e.g., a hardware configuration, a software configuration, etc., may be detected, and a corresponding performance score may be given according to the hardware configuration, the software configuration, etc., of the device. For example, when there are multiple computing devices, each computing device may receive the performance evaluation application issued by the cloud server, or may receive, by a certain device, the performance evaluation application issued by the cloud server and forward the performance evaluation application to other computing devices through the near-field network, so that bandwidth consumption may be reduced, and transmission efficiency may be improved.

For example, in the first manner described above, the computing device receives the performance score directly, and in the second manner described above, the computing device receives the performance assessment application, and the performance score is obtained by running the performance assessment application. The two modes can be combined, namely, the computing device sends an acquisition request to the cloud server, if the cloud server can inquire the performance score, the performance score is directly issued, if the cloud server can not inquire the performance score, the performance evaluation application is issued, and the computing device runs the performance evaluation application to obtain the performance score. In a computing force sharing network, all computing devices may directly receive performance scores; all computing devices can also receive the performance evaluation application, and the performance scores are obtained by respectively running the performance evaluation application; it is also possible that some of the computing devices directly receive the performance scores and another part of the computing devices receive and run the performance assessment application to obtain the performance scores. Embodiments of the present disclosure are not limited in this regard.

For example, in step S112, a ranked list of performance of the plurality of computing devices is determined based on the performance scores. Fig. 5 is an exemplary flowchart of step S112 in fig. 4. In some embodiments, as shown in fig. 5, step S112 may further include the following operations.

Step S1121: the electronic device receives respective performance scores broadcast by other computing devices in the near-field network;

step S1122: establishing an initial list, calculating a list information unique value of the initial list, and broadcasting the own list information unique value in a near-field network;

step S1123: receiving list information unique values broadcast by other computing devices in the near-field network, judging that the list information unique values are consistent with the list information unique values, and broadcasting a confirmation result in the near-field network;

step S1124: and taking the initial list corresponding to the unique list information value confirmed to be correct by the preset number of computing devices as a performance sorting list.

For example, in step S1121, an electronic device (in this embodiment, a computing device that is the master node is referred to as an electronic device) may receive respective performance scores broadcast by other computing devices within the near-field network. For example, each computing device broadcasts a respective performance score within the near-field network, i.e., informs other computing devices within the near-field network of its own performance score. In broadcasting, not only the performance score is broadcasted, but also the equipment information is broadcasted together, so that the computing equipment receiving the performance score can acquire the corresponding relation between the equipment for broadcasting and the performance score.

For example, in step S1122, the electronic device may build an initial list as performance scores broadcast by other computing devices may be received. For example, each computing device within the near field network builds an initial list based on the received performance scores, i.e., each computing device builds an initial list based on its own received performance scores and device information and its own performance scores and device information, the initial list reflecting the ordering of the performance scores of all computing devices. For example, the ranking may be in order of high to low performance scores. For example, the initial list of each of the plurality of computing devices may be the same or may be different due to errors in the received information. Each computing device then calculates a list information unique value for its own initial list. The list information unique value indicates the uniqueness of the corresponding list, and may be represented by a hash value, for example. Each list has a particular list information unique value, the list information unique values of different lists being different. The list information unique value may be an MD5 value (or hash value), the MD5 value representing the uniqueness of the list, the MD5 value being a value calculated by a hash function. Each device broadcasts its own calculated list information unique value within the near field network, i.e., informs other computing devices within the near field network of its own calculated list information unique value.

For example, in step S1123, the electronic device (in this embodiment, the computing device as the master node is referred to as an electronic device) receives the list information unique value broadcast by other computing devices in the near-field network, determines that the list information unique value is consistent with itself, and broadcasts the confirmation result in the near-field network. For example, each computing device determines whether the unique value of the list information of the computing device is consistent with the received unique value of the list information, and broadcasts a confirmation result in the near field network in case of the consistency. That is, when a certain computing device receives the unique list information value broadcast by other computing devices, the received unique list information value is compared with the unique list information value of the computing device, and whether the two values are consistent is determined. If the two match, the list information unique value is confirmed to be correct and the confirmation result is broadcast in the near field network, that is, the list information unique value is confirmed to be correct by broadcast in the near field network.

For example, in step S1124, an initial list corresponding to the unique value of the list information, which is confirmed to be correct by the preset number of computing devices, is used as the performance ranking list. That is, each computing device compares the received unique list information value with its own unique list information value, if the two values are identical, a confirmation result is broadcast in the near-field network to confirm that the received unique list information value is correct, and if the number of computing devices confirmed to be correct reaches a preset number, the initial list corresponding to the confirmed correct unique list information value is used as the performance ranking list. For example, the preset number may be a specific number or a percentage according to actual requirements, and the embodiments of the present disclosure are not limited thereto. For example, in some examples, assuming that the total number of computing devices in the computing power sharing network is M, where M is a positive integer, when the number of correct computing devices is confirmed to reach M/2, the initial list to which the correct list information unique value is confirmed corresponds is taken as the performance ranking list. The correctness of the ranked list of capabilities can be guaranteed due to confirmation by multiple computing devices.

For example, in some examples, each device in the near field network broadcasts the score of the current device and the device information first, and the remaining devices receive the score and sort the devices, convert the sorting into a string, calculate the string as an MD5 value, and then broadcast the MD5 value again. After receiving the message, the other devices confirm whether the message is consistent with the locally stored sequence, and if so, the message is broadcast again to inform the other devices that the message is correct. In case, for example, more than half of the devices in the current network confirm error-free, then all devices employ the list.

Returning to fig. 4, for example, in step S113, it is confirmed that the electronic device is located at the first position in the performance ranking list, and the electronic device is determined as the master node, and the computing devices other than the first position in the performance ranking list are determined as at least one slave node. For example, in the performance ranking list, the plurality of computing devices are arranged in order of performance score from high to low, the performance score of the computing device of the first rank is highest, which means that its computing power is the strongest, and thus the computing device with the strongest computing power is determined as the master node, also referred to as a master node, whereby the performance of the fog network can be ensured. In this embodiment, the computing device as the master node is referred to as an electronic device. Other computing devices in the performance ordered list than the master node are determined to be slave nodes, also referred to 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.

Returning to fig. 3, for example, in step S12, a master collaboration service component is installed and run in the master node, causing each of the at least one slave nodes to install and run a client component. Fig. 6 is an exemplary flowchart of step S12 in fig. 3. In some embodiments, as shown in fig. 6, step S12 may further include the following operations.

Step S121: the master node acquires a master cooperative service component and a client component from a cloud server, and transmits the client component to each slave node through a near-field network;

step S122: a master collaboration service component is installed and operated in the master node, with each slave node installing and operating a client component.

For example, in step S121, the master node may obtain a cloud component (e.g., an XR scene cloud component) from the cloud server through the internet, where the cloud component includes a master collaboration service component (server component) and a client component (client component). The master node transmits the client assembly to each slave node through the near-field network, so that each slave node is prevented from acquiring the client assembly from the cloud server independently, bandwidth consumption can be avoided, and transmission efficiency is improved.

For example, in step S122, a master collaboration service component of the cloud components is installed and operated in the master node, causing each slave node to install and operate a client component of the cloud components. For example, by running a master coordination service component in a master node and a client component in a slave node, each node can communicate and cooperate according to a preset function, thereby realizing the function of the computing power sharing network.

Returning to fig. 3, for example, in step S13, a computing force sharing network is established using interactions between the primary collaboration service component and the client component, and the first computing device is notified of the node establishment. For example, the first computing device may be a device that is under-powered and requires computing power support. For example, the first computing device may be an augmented reality device. Thus, the construction of the computing force sharing network is completed, and the first computing device (such as an augmented reality device) also knows the node construction condition, and can be started at any time in the working process.

Returning to fig. 2, after the computing power sharing network is built, in step S20, it is determined whether the computing power of the first computing device (for example, the augmented reality device) in the computing power sharing network meets the preset condition, and if so, it is determined that the computing power of the first computing device (for example, the augmented reality device) in the computing power sharing network meets the preset condition. For example, the preset conditions may include: the processor usage of the first computing device is greater than or equal to a first preset threshold. That is, when the processor usage of the first computing device reaches a certain set threshold (e.g., 85%), then the first computing device may be considered to be under-powered, and thus, in this case, computing power support needs to be provided to the first computing device through the computing power sharing network. For example, the processor may include a CPU, GPU, etc., to which embodiments of the present disclosure are not limited. The value of the first preset threshold is not limited to 85%, but may be any other value, which may be determined according to actual requirements, and the embodiments of the present disclosure are not limited thereto. The first preset threshold may be set in advance, or may be dynamically adjusted during operation of the computing power sharing network, which embodiments of the present disclosure are not limited in this respect.

For example, in step S30, when it is confirmed that the computing power of the first computing device (e.g., the augmented reality device) satisfies the preset condition, the master node distributes the data to be processed 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 respective nodes in the computing power sharing network, so as to provide computing power support to the first computing device (e.g., the augmented reality device) through the computing power sharing network. For example, the first computing device may be a device that is a master node or a device that is a slave node, as embodiments of the present disclosure are not limited in this respect. For example, the first computing device may be an augmented reality device in a computing force sharing network. For example, the second computing device may be a device that is a master node or a device that is a slave node, as embodiments of the present disclosure are not limited in this respect.

Fig. 7 is an exemplary flowchart of step S30 in fig. 2. In some embodiments, as shown in fig. 7, step S30 may further include the following operations.

Step S31: selecting at least one second computing device in the computing force sharing network according to the computing force states of the nodes in the computing force sharing network;

Step S32: obtaining information of data to be processed from a first computing device and synchronizing the data to be processed to at least one second computing device through a near field network;

step S33: and notifying the first computing device of the information of the data transmission channel of the data to be processed, and enabling the first computing device to establish a link with at least one second computing device and conduct data interaction based on the information of the data transmission channel.

For example, in step S31, when the computing power of the first computing device (for example, the augmented reality device) satisfies a preset condition (for example, when the computing power is insufficient), the master node selects at least one second computing device according to the computing power states of the respective nodes in the computing power sharing network, and the selected second computing device is a device to be subjected to the computing task. For example, the master node may select a device with spare computing power as the second computing device, and not select a device whose computing power is already near saturation, thereby enabling dynamic allocation of tasks. For example, the selected second computing device may be a master node or a slave node, as embodiments of the present disclosure are not limited in this regard. For example, a threshold value for processor usage may be set, and when the processor usage of a device reaches the threshold value, it indicates that the computing power of the device is already near saturation, and the device is not selected. When the processor utilization of a device has not reached a threshold, it is indicated that the device has spare computing power, and the device may be selected accordingly.

For example, in some embodiments, the computing cooperation method provided by the embodiments of the present disclosure may further include the following operations: and periodically acquiring the computing power state of each node in the computing power sharing network by the master node, and taking the equipment with the processor utilization rate smaller than the second preset threshold value as the next allocated alternative equipment. For example, the master node may acquire the processor usage rate of each node every 10s, so that node selection may be performed based on the data, and a device with the processor usage rate smaller than the second preset threshold is used as an alternative device for next allocation, so that a device with surplus computing capability is selected as a second computing device when the device is selected next time, so as to realize dynamic allocation of tasks, effectively improve overall performance of the system, and have great flexibility. For example, the interval of the period is not limited to 10s, and may be an arbitrary time interval. The value of the second preset threshold is, for example, 85%, but may be any other value, which may be determined according to practical requirements, and embodiments of the present disclosure are not limited thereto.

For example, in step S32, the master node obtains information of the data to be processed from the first computing device (e.g., the augmented reality device) and synchronizes the data to be processed to the selected second computing device through the near field network. After the second computing device is selected, the master node acquires information of the data to be processed, i.e., acquires task information, from the first computing device (e.g., an augmented reality device), and synchronizes the data to be processed to the selected second computing device through the near-field network, so that the selected second computing device processes the received data to be processed.

Fig. 8 is an exemplary flowchart of step S32 in fig. 7. In some embodiments, as shown in fig. 8, step S32 may further include the following operations.

Step S321: the method comprises the steps that a master node obtains information of data to be processed from first computing equipment;

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 according to 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;

step S323: in response to the selected second computing device having a corresponding program, the master node notifies the second computing device to process the pending data.

For example, in step S321, the master node first acquires information of data to be processed from the first computing device (e.g., an augmented reality device), that is, the first computing device notifies the master node of information of data to be processed.

For example, in step S322, if the selected second computing device does not have a corresponding program (cannot process the data to be processed), the host node obtains the corresponding program from the cloud server according to 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 process the data to be processed by using the program. Thereby, the second computing device can be caused to process the data to be processed.

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

For example, the corresponding program may be in any suitable form, such as an installation package, a dynamic library, etc., and may be used to process data to be processed, which is not limited in this embodiment of the disclosure.

Returning to fig. 7, for example, in step S33, the master node notifies the first computing device (e.g., an augmented reality device) of information of a data transfer channel of the data to be processed, causes the first computing device to establish a link with the second computing device based on the information of the data transfer channel and performs data interaction. That is, the host node informs the first computing device (e.g., an augmented reality device) of the corresponding running program data, graphics, per-frame data, transport network channels for audio data, such as user datagram protocol (User Datagram Protocol, UDP), transmission control protocol (Transmission Control Protocol, TCP), bluetooth protocol, etc. The augmented reality device establishes a link with the allocated second computing device, which may be in a near field communication manner such as bluetooth, WIFI, etc., and receives relevant data transmitted by these devices, and displays the relevant data on the first computing device (e.g., the augmented reality device). And the first computing device (such as the augmented reality device) transmits the event which needs to be responded by the application displayed on the interface to the corresponding second computing device through the near-field network, the second computing device responds to the event, and each second computing device transmits the result to the first computing device (such as the augmented reality device) again after completing the event response, so that a complete multi-node computing force distribution network is formed. For example, the augmented reality device may be XR glasses or other suitable device.

The embodiment of the disclosure provides a brand-new method for constructing a computing power sharing network, and provides a brand-new network combination mode by sequencing devices according to performances. The method is applied to the scene of computational power coordination of a near-field network, solves the problem of insufficient computational power of XR equipment (such as AR equipment, VR equipment, MR equipment and the like), and extends the usability of the equipment.

Fig. 9 is a workflow diagram of a computational collaboration method provided by some embodiments of the present disclosure. In this example, the near field network related device dynamically allocates nodes for program operation according to device computational ordering in the computational force sharing network, thereby achieving the goal of computational force coordination. The workflow of the computational synergy is briefly described below in conjunction with fig. 9.

First, when an AR device (or other type of XR device) accesses a vehicle-mounted device or a mobile device network through a near field network, a calculation power evaluation flow is triggered. Or triggering the calculation power evaluation flow after the AR device accesses the vehicle-mounted device and the mobile device through the near-field network and passes through the safety authentication information of the vehicle-mounted device.

Each device requests a performance evaluation application from a remote server (e.g., a cloud server) through the internet, the process performs security authentication with the remote server through, for example, vehicle-mounted security authentication information, and then the remote server searches performance scores according to device information reported by the devices.

If the remote server does not find a specific scoring item, issuing a performance evaluation application; if the corresponding equipment score item is queried, the corresponding score, namely the performance score, is issued.

The AR device starts running the performance assessment application if it acquires the performance assessment application. For example, the calculation result may be averaged over several (or many) runs, thereby improving the accuracy of the calculation result. If the AR device obtains the score issued by the remote server, it is not necessary to obtain the performance evaluation application any more.

After the AR device obtains the score (e.g., the performance evaluation application runs, or directly obtains the score result), the AR device broadcasts the corresponding score of the device in the near-field network, and after the other devices monitor the broadcast, the AR device stores a corresponding ordered list (list), which is sorted from high to low according to the score, for example.

After all devices in the near field network complete the broadcast, each device calculates a list information unique value (e.g., MD5 value) for the saved list, the MD5 value representing the list's uniqueness. Each device broadcasts the respective MD5 value in the near field network through UDP protocol or other applicable protocols, and each device broadcasts to confirm whether the received MD5 value is consistent with the MD5 value calculated by itself, thereby confirming the correctness of the list. After all device acknowledgements are completed, a performance ordered list is obtained, which is a list that is correctly acknowledged by most devices. For example, after the device in the near field network completes the UDP multi-device broadcast, the near field network device may monitor the rest of the devices in the current network, in this way, each device stores a list of all device information, and adds the device to the list information. At the time of broadcast, each device will take in the broadcast data the device type (e.g., type as in-vehicle device, mobile device, XR device, etc.) and its performance score, thereby determining the ranking. For example, the mobile device may comprise a cell phone, the XR device may comprise XR glasses, and the like.

After the equipment finishes the calculation force sequencing, the equipment of the first few bits of calculation force sequencing constructs a calculation force sharing network. For example, node attributes are assigned from the first rank, the first rank device acts as a master node (master node), and the second and subsequent ranks devices act as slave nodes (slave nodes). For example, the computing force sharing network may be built up only by the devices that are the first few in the computing force row, or by all the devices in the list. For example, in some examples, a device other than an XR device in the entire network may be considered a node; for example, in other examples, all devices in the entire network may be considered nodes, as well as XR devices. Here, the nodes refer to the master node and slave node described above. For example, the master node may be a mobile device, an in-vehicle device, or an XR device; the slave node may also be a mobile device, an in-vehicle device, or an XR device.

The master node device then downloads an XR scene cloud component from a remote server over the internet, the XR scene cloud component comprising a master collaboration service component (server component) and a client component (client component). The master node installs a main cooperative service component in the XR scene cloud component, and the slave node receives and installs a client component transmitted by the master node in the near-field network.

After the master node and the slave node finish component installation, the master node notifies the slave node to start the corresponding cloud component. And, master node informs AR equipment of XR scene node allocation and installation conditions.

When the AR equipment finds that the CPU and GPU use ratio exceeds 85%, the AR equipment determines that the current calculation force is insufficient, under the situation, the AR equipment and a master node acquire communication connection, the master node is informed of data to be processed, the master node synchronizes corresponding programs required by remote server, and the programs are near-field synchronized to each slave node.

The master node acquires the related data of the CPU and GPU utilization rate of each save node, sequentially distributes the data to be processed on each node, and informs each device of the link information (such as UDP\TCP\Bluetooth channels) of the target device to be transmitted when the data are processed. For example, the data to be processed to which the master node and slave node are assigned may be dynamically adjusted according to the usage data. For example, the percentage value of the CPU and GPU usage of each device may be periodically (e.g., every 10 seconds), and devices with high usage may not be assigned new tasks, and devices with low usage may be assigned new tasks. For example, the CPU and GPU usage threshold may be set to 85%. The data to be processed is notified to the master node and the slave node to start processing, and the corresponding running program data, graphics, each frame of data and the transmission network channel (such as UDP\TCP\Bluetooth channel) of the audio data are notified to the AR device.

Then, the AR device establishes a link with each node (for example, may be in a near field communication manner such as bluetooth, WIFI, etc.), receives relevant data transmitted by each device, and displays the data to be displayed on the AR device. In addition, the AR equipment transmits the event which needs to be responded by the application displayed on the interface to the corresponding node through the near-field network, the corresponding node responds the event, and each node transmits the response result to the AR equipment again after completing the event response, so that a complete computing power sharing network capable of distributing computing power is formed.

In this example, the mobile device, the in-vehicle device, the AR device (e.g., XR glasses) are linked to combine one near field network; each device obtains a performance score and broadcasts within the near field network; and each device in the near-field network stores a performance grading list, and the device with the best performance is confirmed to be used as a master node, and the rest devices are used as slave nodes. The relevant device may prepare a java/C (or related applications that meet the platform coding model) version of the GPU/CPU running rule application (i.e., performance assessment application) by the remote server or directly obtain the device performance scores for ranking. After the equipment is combined into a network, the master node downloads an XR scene fog cloud component, wherein the XR scene fog cloud component comprises 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 near-field network performance (i.e. to the master node), and distributing part of other XR scene programs to the client component on the slave node for running, thereby combining the computing power sharing network. And when the server component of the master node is started, notifying the slave node to start. And dynamically distributing the multi-task and multi-window operation scenes in the XR scene according to the load conditions of the CPU and the GPU of each device, and dynamically distributing the single-machine operation task to each device. Different program running data, picture frames and audio data are transmitted to XR glasses on the master node and the slave node, and the XR glasses gather the data and display the data.

The example can combine the vehicle and the mobile equipment in the vehicle into a near-field network through the security authentication of a remote server, rely on the calculation force negotiation of the internal equipment, and the equipment autonomously distributes a master node and a slave node. The master node equipment downloads a remote server XR scene server component to a local area, and a cloud network is established; the rest slave node devices form a fog network to run the client component, and finally provide calculation force support for the XR glasses and the vehicle-mounted devices.

Therefore, a near-field cooperative network can be formed, so that the operating computing power of an XR scene is improved, the computing power expansion is realized, the evolution of single equipment to the cooperative network can be expanded, the situation that the computing power of XR equipment (such as AR equipment, VR equipment, MR equipment and the like) is insufficient is comprehensively solved, and the application scene of the XR equipment is better met. And moreover, the dynamic allocation of the node tasks can be realized, and the flexibility is very high.

It should be noted that the workflow shown in fig. 9 is only exemplary, and not limiting, and the calculation collaboration method provided by the embodiments of the present disclosure is not limited to the above-described flow, and other applicable flows may also be adopted, which are not limited by the embodiments of the present disclosure.

At least one embodiment of the present disclosure also provides another computational collaboration method for an electronic device. Fig. 10 is a flow chart of another method of computing cooperation provided in some embodiments of the present disclosure, and in the embodiment shown in fig. 10, a computing device as a slave node is referred to as an electronic device. As shown in fig. 10, the calculation synergy method includes the following operations.

Step S1000: the electronic device is configured as a slave node in a computing force sharing network, wherein the computing force sharing network comprises a plurality of computing devices connected to the same near field network, the computing force sharing network comprises a master node and at least one slave node, one slave node in the at least one slave node is the electronic device, and the master node is one of the plurality of computing devices except the electronic device;

step S2000: the method comprises the steps of receiving and processing data to be processed, which are distributed by a master node based on the computing power states of all nodes in the computing power sharing network, so as to provide computing power support for first computing equipment in the computing power sharing network through the computing power sharing network.

The calculation cooperation method provided by this embodiment is similar to that shown in fig. 2, except that the electronic device in this embodiment refers to a device as a slave node. For example, in step S1000, the electronic device is built as a slave node in a computing force sharing network. In step S2000, the electronic device receives and processes data to be processed allocated by the master node based on the computing power states of the respective nodes in the computing power sharing network to provide computing power support to a first computing device (e.g., an augmented reality device) through the computing power sharing network. For the detailed description of the above steps S1000 and S2000, reference may be made to the description of the calculation collaboration method shown in fig. 2, which is not repeated here.

At least one embodiment of the present disclosure also provides an electronic device. The electronic device provides a brand new network combination mode, integrates the advantages of fog calculation and cloud calculation, can solve the problem of insufficient calculation power of equipment (such as insufficient calculation power of an augmented reality device), extends the usability of the equipment, achieves the aim of calculation power coordination, can realize dynamic allocation of node tasks, and has great flexibility. The electronic device can improve the operation computing power of the augmented reality scene, and further meets the requirements of the application scene of the augmented reality equipment.

Fig. 11 is a schematic block diagram of an electronic device provided in some embodiments of the present disclosure. As shown in fig. 11, the electronic device 300 includes a processor 310 and a memory 320. Memory 320 is used to store computer-executable instructions (e.g., one or more computer program modules) non-transitory. The processor 310 is configured to execute the computer-executable instructions that, when executed by the processor 310, perform one or more of the steps of the computational collaboration methods described above, thereby implementing the computational collaboration methods described above. The memory 320 and the processor 310 may be interconnected by a bus system and/or other forms of connection mechanisms (not shown).

For example, the processor 310 may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or other form of processing unit having data processing capabilities and/or program execution capabilities. For example, the Central Processing Unit (CPU) may be an X86 or ARM architecture, or the like. The processor 310 may be a general purpose processor or a special purpose processor that may control other components in the electronic device 300 to perform the desired functions.

For example, memory 320 may comprise 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 can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The 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, and the like. One or more computer program modules may be stored on the computer readable storage medium and executed by the processor 310 to implement various functions of the electronic device 300. Various applications and various data, as well as various data used and/or generated by the applications, etc., may also be stored in the computer readable storage medium.

It should be noted that, in the embodiment of the present disclosure, specific functions and technical effects of the electronic device 300 may refer to the above description about the calculation collaboration method, which is not repeated herein.

Fig. 12 is a schematic block diagram of another electronic device provided by some embodiments of the present disclosure. As shown in fig. 12, the electronic device 400 is suitable for implementing, for example, a computing cooperation method provided by an embodiment of the present disclosure. The electronic apparatus 400 may be a terminal device or a server, etc. It should be noted that the electronic device 400 shown in fig. 12 is only an example, and does not impose any limitation on the functionality and scope of use of the embodiments of the present disclosure.

As shown in fig. 12, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 410 that may perform various suitable actions and processes according to programs stored in a Read Only Memory (ROM) 420 or loaded from a storage device 480 into a Random Access Memory (RAM) 430. In the RAM 430, various programs and data required for the operation of the electronic apparatus 400 are also stored. The processing device 410, ROM 420, and RAM 430 are connected to each other by a bus 440. An input/output (I/O) interface 450 is also connected to bus 440.

In general, the following devices may be connected to the I/O interface 450: input devices 460 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 470 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, etc.; storage 480 including, for example, magnetic tape, hard disk, etc.; and communication device 490. The communication device 490 may allow the electronic device 400 to communicate wirelessly or by wire with other electronic devices to exchange data. While fig. 12 shows the electronic device 400 with various modules, it is to be understood that not all of the illustrated modules are required to be implemented or provided, and that the electronic device 400 may alternatively be implemented or provided with more or fewer devices.

For example, according to embodiments of the present disclosure, the above-described computational collaboration methods may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the above-described computational synergy method. In such an embodiment, the computer program may be downloaded and installed from a network via communications device 490, or from storage 480, or from ROM 420. The functions defined in the computational synergy methods provided by the embodiments of the present disclosure may be implemented when the computer program is executed by the processing device 410.

At least one embodiment of the present disclosure also provides a storage medium. By utilizing the storage medium, a brand new network combination mode can be provided, the advantages of fog calculation and cloud calculation are integrated, the problem of insufficient equipment calculation (such as insufficient calculation power of an augmented reality device) can be solved, the usability of the equipment is extended, the purpose of calculation power coordination is achieved, the dynamic allocation of node tasks can be realized, and the node task management method has great flexibility. By utilizing the storage medium, the operation computing power of the augmented reality scene can be improved, and the requirements of the application scene of the augmented reality equipment can be further met.

Fig. 13 is a schematic diagram of a storage medium according to some embodiments of the present disclosure. For example, as shown in fig. 13, the storage medium 500 may be a non-transitory computer-readable storage medium, storing non-transitory computer-readable instructions 510. The computational synergy methods provided by embodiments of the present disclosure may be implemented when the non-transitory computer readable instructions 510 are executed by a processor, e.g., one or more steps of the computational synergy methods described above may be performed when the non-transitory computer readable instructions 510 are executed by a processor.

For example, the storage medium 500 may be applied to the above-described electronic device, and for example, the storage medium 500 may be the memory 320 in the electronic device 300 shown in fig. 11.

The description of the storage medium 500 may refer to the description of the memory in the embodiment of the electronic device, and the repetition is omitted. The specific functions and technical effects of the storage medium 500 may refer to the above description of the computational synergy method, and will not be repeated here.

The following points need to be described:

(1) The drawings of the embodiments of the present disclosure relate only to the structures to which the embodiments of the present disclosure relate, and reference may be made to the general design for other structures.

(2) The embodiments of the present disclosure and features in the embodiments may be combined with each other to arrive at a new embodiment without conflict.

The foregoing is merely specific embodiments of the disclosure, but the scope of the disclosure is not limited thereto, and the scope of the disclosure should be determined by the claims.

Claims

1. A computational collaboration method for an electronic device, wherein the method comprises:

constructing a plurality of computing devices connected to the same near-field network into a computing force sharing network, wherein the computing force 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 except the electronic device in the plurality of computing devices;

Confirming that the computing power of the first computing device in the computing power sharing network meets a preset condition;

the master node distributes data to be processed of the first computing device to at least one second computing device in the computing force sharing network based on computing force states of all nodes in the computing force sharing network so as to provide computing force support for the first computing device through the computing force sharing network.

2. The method of claim 1, wherein building the plurality of computing devices connected to the same near field network as the computing force sharing network comprises:

determining the master node and the at least one slave node according to the computing capabilities of the plurality of computing devices;

installing and running a master collaboration service component in the master node, causing each slave node of the at least one slave node to install and run a client component;

establishing the computing force sharing network by utilizing interaction between the main cooperative service component and the client component, and notifying the first computing device of node establishment;

wherein determining the master node and the at least one slave node according to the computing capabilities of the plurality of computing devices comprises:

Determining a performance score for each computing device of the plurality of computing devices, wherein the performance score represents an operational capability of the each computing device;

determining a ranked list of performance of the plurality of computing devices based on the performance scores;

confirming that the electronic device is located at the first position in the performance ordering list, determining the electronic device as the master node, and determining computing devices except the first position in the performance ordering list as the at least one slave node.

3. The method of claim 2, wherein determining a performance score for the each of the plurality of computing devices comprises:

causing each of the plurality of computing devices to obtain a respective of the performance scores from a cloud server; or alternatively

And enabling the computing devices to acquire respective performance evaluation applications from the cloud server and respectively run the performance evaluation applications to obtain the respective performance scores.

4. The method of claim 2, wherein determining a ranked list of performance of the plurality of computing devices based on the performance scores comprises:

the electronic device receives respective performance scores broadcast by other computing devices in the near-field network;

Establishing an initial list, calculating a list information unique value of the initial list, and broadcasting a list information unique value of the initial list in the near-field network;

receiving list information unique values broadcasted by other computing devices in the near-field network, judging that the list information unique values are consistent with the list information unique values of the other computing devices, and broadcasting a confirmation result in the near-field network;

and taking the initial list corresponding to the unique list information value confirmed to be correct by the preset number of computing devices as the performance sorting list.

5. The method of claim 2, wherein installing and running the master collaboration service component in the master node, causing each of the at least one slave nodes to install and run the client component comprises:

the master node acquires the master cooperative 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 master collaboration service component is installed and operated in the master node, with each slave node installing and operating the client component.

6. The method of claim 1, wherein the master node distributes the pending data of the first computing device to at least one second computing device in the computing force sharing network based on the computing force status of each node in the computing force sharing network, comprising:

Selecting at least one second computing device in the computing force sharing network according to the computing force states of all nodes in the computing force sharing network;

obtaining information of 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, and enabling the first computing device to establish a link with the at least one second computing device and conduct data interaction based on the information of the data transmission channel.

7. The method of claim 1, wherein the preset condition comprises: the processor usage of the first computing device is greater than or equal to a first preset threshold;

the method further comprises the steps of:

periodically acquiring the computing power state of each node in the computing power sharing network, and taking the equipment with the processor utilization rate smaller than a second preset threshold value as the next-allocated alternative equipment;

wherein the plurality of computing devices connected to the same near-field network are configured as the computing force sharing network, further comprising:

the plurality of computing devices are securely authenticated, wherein the plurality of computing devices include at least one of a mobile device, an in-vehicle device, and a wearable device.

8. A computational collaboration method for an electronic device, wherein the method comprises:

the electronic device is configured as a slave node in a computing force sharing network, wherein the computing force sharing network comprises a plurality of computing devices connected to the same near field network, the computing force sharing network comprises a master node and at least one slave node, one slave node in the at least one slave node is the electronic device, and the master node is one of the computing devices except the electronic device;

and receiving and processing the data to be processed distributed by the master node based on the computing power states of all nodes in the computing power sharing network so as to provide computing power support for first computing equipment in the computing power sharing network through the computing power sharing network.

9. An electronic device, comprising:

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 for implementing the computational collaboration method of any of claims 1-8.

10. A non-transitory storage medium storing non-transitory computer readable instructions which, when executed by a computer, implement the computational collaboration method of any of claims 1-8.