Communication method and device, storage medium and electronic equipment

Abstract

The invention provides a communication method and device, a storage medium and electronic equipment, and relates to the technical field of communication. The method is applied to a general computing resource scheduling function, the general computing resource scheduling function belongs to a wireless intelligent management and arrangement function layer, and the method comprises the following steps: receiving a service request forwarded by a first node of an intelligent wireless access network; the first node of the intelligent wireless access network is connected with target equipment, and the service request is sent to the first node of the intelligent wireless access network by the target equipment; according to the service request, deploying a target artificial intelligence model to a second node of the intelligent wireless access network, so that the second node of the intelligent wireless access network completes reasoning of the target artificial intelligence model and sends a reasoning result to a first node of the intelligent wireless access network; and the first node of the intelligent wireless access network is also used for sending the reasoning result to the target equipment. The problems of high cost, high power consumption and non-uniform computing power resource utilization rate are solved.

Description

Background Art

[0002] In the current technology field, when an intelligent robot is running, its main intelligent functions, such as environment recognition, path planning, target tracking, etc., are usually completed by the robot itself. These functions rely on high-performance embedded computing units (such as GPU or TPU).

[0003] Since intelligent robots need to be equipped with high-performance computing units, this significantly increases the manufacturing cost of the robots; high-performance computing units and complex intelligent processing tasks will greatly increase the energy consumption of the robots, which not only shortens the battery life of the robots, but may also cause heating problems, further affecting the stability and life of the hardware. The internal computing resources of the robot need to be reserved for the most complex scenarios, but in daily operations, these computing powers may be idle, resulting in a waste of resources. At the same time, in complex task scenarios that require greater computing power, the internal computing power of the robot may be insufficient, resulting in task execution failure or performance degradation.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The present disclosure provides a communication method and device, a storage medium and an electronic device, which at least to a certain extent overcome the problems of high cost, high power consumption and uneven utilization of computing resources due to related technologies.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a communication method is provided, which is applied to a general computing resource scheduling function, wherein the general computing resource scheduling function belongs to a wireless intelligent management and orchestration function layer, and the method includes:

[0008] receiving a service request forwarded by a first node of an intelligent wireless access network; the first node of the intelligent wireless access network is connected to a target device, and the service request is sent by the target device to the first node of the intelligent wireless access network;

[0009] According to the service request, the target artificial intelligence model is deployed to the second node of the intelligent wireless access network, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

[0010] In some embodiments, the target device is in a business scenario, and the business request is determined by the target device according to the business instructions in the business scenario. The business request includes: the requirements of the target device for the network and the requirements for the artificial intelligence model when executing the business instructions in the business scenario.

[0011] In some embodiments, before forwarding the service request, the first node of the intelligent wireless access network further includes:

[0012] The first node of the intelligent wireless access network determines, based on the network requirement in the service request, that the current network status meets the network requirement.

[0013] In some embodiments, according to the service request, deploying the target artificial intelligence model to the second node of the intelligent wireless access network includes:

[0014] Determine, according to the network requirements in the service request, a second node of the intelligent wireless access network registered with the general computing resource scheduling function;

[0015] Determine a target artificial intelligence model according to the demand for the artificial intelligence model in the business request;

[0016] Deploy the target artificial intelligence model to the second node of the intelligent wireless access network.

[0017] In some embodiments, it also includes: the target device is also used to complete the execution of the business instruction in the business scenario according to the inference result.

[0018] In some embodiments, the first intelligent wireless access network node is an intelligent wireless access network node adjacent to the target device in the service scenario.

[0019] According to another aspect of the present disclosure, a communication method is also provided, which is applied to a first node of an intelligent wireless access network, and the method includes:

[0020] Receiving a service request sent by a target device; the target device is connected to the first node of the intelligent wireless access network;

[0021] Forwarding the service request to the general computing resource scheduling function, so that the general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request; the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer; the second node of the intelligent wireless access network is used to complete the reasoning of the target artificial intelligence model and send the reasoning result to the first node of the intelligent wireless access network;

[0022] The inference result is received and forwarded to the target device.

[0023] According to another aspect of the present disclosure, a communication device is provided, which is applied to a general computing resource scheduling function, wherein the general computing resource scheduling function belongs to a wireless intelligent management and orchestration function layer, and the device includes:

[0024] A service request receiving module, configured to receive a service request forwarded by a first node of an intelligent wireless access network; the first node of the intelligent wireless access network is connected to a target device, and the service request is sent by the target device to the first node of the intelligent wireless access network;

[0025] The target artificial intelligence model deployment module is used to deploy the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

[0026] According to another aspect of the present disclosure, a communication device is further provided, which is applied to a first node of an intelligent wireless access network, and the device includes:

[0027] A service request receiving module, configured to receive a service request sent by a target device; the target device is connected to the first node of the intelligent wireless access network;

[0028] A service request forwarding module, used to forward the service request to the general computing resource scheduling function, so that the general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request; the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer; the second node of the intelligent wireless access network is used to complete the reasoning of the target artificial intelligence model and send the reasoning result to the first node of the intelligent wireless access network;

[0029] The inference result forwarding module is used to receive the inference result and forward it to the target device.

[0030] According to another aspect of the present disclosure, an electronic device is also provided, which includes: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the communication methods described above by executing the executable instructions.

[0031] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the communication method described above is implemented.

[0032] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the above communication methods.

[0033] The communication method and device, storage medium and electronic device provided in the embodiments of the present invention are applied to the general computing resource scheduling function, which belongs to the wireless intelligent management and orchestration function layer. The method includes: receiving a service request forwarded by the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is connected to the target device, and the service request is sent by the target device to the first node of the intelligent wireless access network; according to the service request, the target artificial intelligence model is deployed to the second node of the intelligent wireless access network, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

[0034] In the disclosed embodiment, the target device sends a service request to the first node of the intelligent wireless access network, and the first node of the intelligent wireless access network forwards the service request to the general computing resource scheduling function. The general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request, completes the reasoning of the target artificial intelligence model in the second node of the intelligent wireless access network and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network sends the reasoning result to the target device to complete the operation of the target device. Through the general computing resource scheduling function of the wireless intelligent management and orchestration function layer, the fragmented computing power resources in the wireless intelligent management and orchestration function layer are uniformly managed and dynamically scheduled. There is no need to configure excess computing power and high-performance computing units for the target device to cope with complex scenarios. Instead, on-demand allocation is achieved through networked collaboration, which significantly improves resource utilization and reduces costs. The intelligent processing functions that require high-performance computing units to process are offloaded from the target device to the intelligent wireless access network locally, and the demand for local high-performance hardware is reduced by sharing computing resources, which can reduce hardware costs. Offloading high-function intelligent processing functions from the target device to the intelligent wireless access network can reduce the load of the local computing unit, significantly reduce power consumption, and thus extend the battery life of the target device. Based on the low-latency communication capability of the intelligent wireless access network, combined with the optimized deployment of delay-sensitive tasks by the wireless intelligent management and orchestration function layer, the real-time and reliability of task execution of the target device in a complex environment can be ensured. Compared with edge computing solutions for intelligent processing tasks, it can better meet real-time requirements, especially in high-reliability task scenarios in dynamic environments. The wireless intelligent management and orchestration function layer can achieve efficient integration and scheduling of fragmented computing power, reducing the waste of computing power resources. The wireless intelligent management and orchestration function layer can realize dynamic update and on-demand deployment of intelligent models, without the need for hardware upgrades on the target device, and achieve functional expansion through software updates and network scheduling, greatly improving the adaptability of the system. The wireless intelligent management and orchestration function layer can be used to schedule and control computing power sharing and task division among multiple robots. Compared with the traditional mode of single robot operation, it can support multi-robot collaborative operation, improve overall efficiency and reduce the pressure of single machine operation. Dynamic deployment capabilities based on the wireless intelligent management and orchestration functional layer support rapid iteration and expansion of new intelligent models and algorithms.

[0035] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0037] Figure 1 A schematic diagram showing the system structure of a communication method in an embodiment of the present disclosure.

[0038] Figure 2 A schematic diagram of a communication method in an embodiment of the present disclosure is shown.

[0039] Figure 3 A schematic diagram of the wireless intelligent management and orchestration function layer of a communication method in an embodiment of the present disclosure is shown.

[0040] Figure 4 A first interaction schematic diagram of a communication method in an embodiment of the present disclosure is shown.

[0041] Figure 5 A second interaction schematic diagram of a communication method in an embodiment of the present disclosure is shown.

[0042] Figure 6 Another schematic diagram of a communication method in an embodiment of the present disclosure is shown.

[0043] Figure 7 A schematic diagram of a communication device in an embodiment of the present disclosure is shown.

[0044] Figure 8 Another schematic diagram of a communication device in an embodiment of the present disclosure is shown.

[0045] Fig. 9 A structural block diagram of a computer device showing a communication method in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0047] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0048] For ease of understanding, before introducing the embodiments of the present disclosure, several terms involved in the embodiments of the present disclosure are first explained as follows:

[0049] Artificial Intelligence Radio Access Network (AI RAN) is a new architecture that integrates artificial intelligence technology into the radio access network (RAN). By leveraging AI and machine learning algorithms, AI RAN aims to optimize network performance, improve resource management efficiency, enhance user experience, and achieve more automated and intelligent network operation and maintenance. This architecture represents an important trend in the development of traditional RAN towards a more efficient, flexible and intelligent direction. Intelligent Radio Access Network (AI RAN) refers to a network architecture that introduces AI technology to improve network management and operation based on the existing radio access network. It not only focuses on data transmission itself, but also emphasizes analyzing the large amount of data generated in the network to predict and solve possible problems, thereby improving overall network performance and service quality.

[0050] The wireless intelligent management and orchestration function layer (RAN AI Layer) refers to a layer specially designed in the radio access network (RAN) to integrate and apply artificial intelligence (AI) technology to achieve more efficient network management and optimization. This layer focuses on leveraging the capabilities of AI to improve network performance, automate operation and maintenance, and provide personalized user experience. The RAN AI Layer is a logical-level structure that aims to manage and coordinate network resources and services in an intelligent manner by introducing advanced AI algorithms and technologies into the wireless access network. This is not only limited to optimizing existing network operations, but also includes functions such as predictive maintenance and automatic fault recovery, thereby achieving a higher level of automation and intelligence.

[0051] The specific implementation of the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0052] like Figure 1As shown, the system architecture includes a terminal device 101, a network 102 and a network side device 103. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the network side device 103, and can be a wired network or a wireless network.

[0053] Optionally, the wireless network or wired network described above uses standard communication technology and / or protocol. The network is usually the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or any combination of a virtual private network). In some embodiments, the data exchanged through the network is represented by technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPSec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above data communication technologies.

[0054] Optionally, the terminal device in the embodiment of the present disclosure may also be referred to as UE (User Equipment). In a specific implementation, the terminal device may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a personal digital assistant (Personal Digital Assistant, PDA), a mobile Internet device (Mobile Internet Device, MID), a wearable device (Wearable Device) or a vehicle-mounted device, etc. It should be noted that the specific type of the terminal device is not limited in the embodiment of the present invention.

[0055] The network side device may be a base station, a relay or an access point, etc. The base station may be a base station of 5G or later versions (e.g., 5G NR NB), or a base station in other communication systems (e.g., an eNB base station). It should be noted that the specific type of the network side device is not limited in the embodiments of the present disclosure.

[0056] Those skilled in the art will know that Figure 1 The number of terminals, networks, and network-side devices in the example is only illustrative, and any number of terminals, networks, and network-side devices may be provided according to actual needs. The embodiments of the present disclosure are not limited to this.

[0057] Under the above system architecture, a communication method is provided in an embodiment of the present disclosure, and the method can be executed by any electronic device with computing and processing capabilities.

[0058] In some embodiments, the communication method provided in the embodiments of the present disclosure may be executed by a terminal device of the above-mentioned system architecture; in other embodiments, the communication method provided in the embodiments of the present disclosure may be executed by a server in the above-mentioned system architecture; in other embodiments, the communication method provided in the embodiments of the present disclosure may be implemented by a terminal device and a server in the above-mentioned system architecture through interaction.

[0059] When an intelligent robot is running, its main intelligent functions, such as environmental recognition, path planning, target tracking, etc., are usually completed by the robot itself. These functions rely on high-performance embedded computing units, such as GPU (graphics processing unit), TPU (tensor processing unit) or high-end CPU. The main features of this technical architecture are: computing power is concentrated in the robot body: the robot is equipped with high-performance computing hardware, collects surrounding environment data through built-in cameras, radars and other sensors, and then processes and analyzes the data locally to complete environmental perception, decision-making and motion control. Application of edge computing: Some existing solutions attempt to partially collaborate between the robot and the edge server, and hand over some non-real-time tasks, such as historical data analysis or complex model training, to the edge server. However, tasks with high real-time requirements still need to be completed locally by the robot. High-dependence hardware architecture: Current intelligent robots must rely on expensive and high-power computing hardware to achieve high-level intelligent functions. For example, some commercial intelligent service robots usually have a built-in high-performance series of computing platforms, which have powerful AI computing capabilities and can run deep learning models in real time for image recognition and voice interaction. However, this solution requires the robot to be equipped with a high-performance battery to support the high power consumption requirements of the computing module.

[0060] In the embodied intelligence scenario, hardware equipment is installed in the robot to process intelligent algorithms such as robot balance and visual recognition. Due to the limitation of terminal computing power, the embodied intelligent robot can only process small models. Through AI RAN, the intelligent module can be placed in AI RAN for processing, which reduces the cost of embodied intelligent robots and can process intelligent applications with greater computing power requirements. Although the above technologies have achieved the autonomous operation of intelligent robots to a certain extent, there are still the following significant technical problems:

[0061] High cost: Since intelligent robots need to be equipped with high-performance computing units (such as GPU or TPU), this significantly increases the manufacturing cost of the robot, resulting in a higher market price, which limits its large-scale popularization and commercial application.

[0062] Excessive power consumption: High-performance computing units and complex intelligent processing tasks will greatly increase the energy consumption of the robot, which not only shortens the battery life of the robot, but may also cause heating problems, further affecting the stability and life of the hardware.

[0063] Uneven utilization of computing resources: The computing resources inside the robot need to be reserved for the most complex scenarios, but in daily operation, these computing resources may be idle, resulting in resource waste. At the same time, in complex task scenarios that require greater computing power, the robot's internal computing power may be insufficient, resulting in task execution failure or performance degradation.

[0064] Real-time and reliability challenges: Although the edge computing solutions in the existing technology attempt to offload some tasks to the edge server, in scenarios with high real-time requirements, they are limited by the delay and instability of network transmission and cannot fully meet the needs of intelligent robots.

[0065] Poor system scalability: The current intelligent robot architecture is highly dependent on local hardware. Upgrading or replacing algorithms usually requires replacing or upgrading the entire computing module. This architecture limits the flexibility and scalability of the system.

[0066] Figure 2 A schematic diagram of a communication method in an embodiment of the present disclosure is shown. Figure 2 As shown, the communication method provided in the embodiment of the present disclosure is applied to the general computing resource scheduling function, which belongs to the wireless intelligent management and orchestration function layer. The method includes:

[0067] Step S202: receiving a service request forwarded by the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is connected to a target device, and the service request is sent by the target device to the first node of the intelligent wireless access network;

[0068] Step S204: According to the service request, the target artificial intelligence model is deployed to the second node of the intelligent wireless access network, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

[0069] In the disclosed embodiment, the target device sends a service request to the first node of the intelligent wireless access network, and the first node of the intelligent wireless access network forwards the service request to the general computing resource scheduling function. The general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request, completes the reasoning of the target artificial intelligence model in the second node of the intelligent wireless access network and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network sends the reasoning result to the target device to complete the operation of the target device. Through the general computing resource scheduling function of the wireless intelligent management and orchestration function layer, the fragmented computing power resources in the wireless intelligent management and orchestration function layer are uniformly managed and dynamically scheduled. There is no need to configure excess computing power and high-performance computing units for the target device to cope with complex scenarios. Instead, on-demand allocation is achieved through networked collaboration, which significantly improves resource utilization and reduces costs. The intelligent processing functions that require high-performance computing units to process are offloaded from the target device to the intelligent wireless access network locally, and the demand for local high-performance hardware is reduced by sharing computing resources, which can reduce hardware costs. Offloading high-function intelligent processing functions from the target device to the intelligent wireless access network can reduce the load of the local computing unit, significantly reduce power consumption, and thus extend the battery life of the target device. Based on the low-latency communication capability of the intelligent wireless access network, combined with the optimized deployment of delay-sensitive tasks by the wireless intelligent management and orchestration function layer, the real-time and reliability of task execution of the target device in a complex environment can be ensured. Compared with edge computing solutions for intelligent processing tasks, it can better meet real-time requirements, especially in high-reliability task scenarios in dynamic environments. The wireless intelligent management and orchestration function layer can achieve efficient integration and scheduling of fragmented computing power, reducing the waste of computing power resources. The wireless intelligent management and orchestration function layer can realize dynamic update and on-demand deployment of intelligent models, without the need for hardware upgrades on the target device, and achieve functional expansion through software updates and network scheduling, greatly improving the adaptability of the system. The wireless intelligent management and orchestration function layer can be used to schedule and control computing power sharing and task division among multiple robots. Compared with the traditional mode of single robot operation, it can support multi-robot collaborative operation, improve overall efficiency and reduce the pressure of single machine operation. Dynamic deployment capabilities based on the wireless intelligent management and orchestration functional layer support rapid iteration and expansion of new intelligent models and algorithms.

[0070] Figure 3 A schematic diagram of the wireless intelligent management and orchestration function layer of a communication method in an embodiment of the present disclosure is shown. Figure 3 As shown, the wireless intelligent management and orchestration function layer includes at least: model management function, data management service, business management orchestration and general computing resource scheduling function. The wireless intelligent management and orchestration function layer is connected to various types of wireless access networks, including at least: intelligent wireless access network (AI RAN), 3GPP RAN and other RANs. The wireless intelligent management and orchestration function layer realizes cross-domain coordination of computing power between different RANs through the general computing resource scheduling function, improves the efficiency of computing power utilization, and optimizes the overall performance of the target terminal operation.

[0071] In the embodiment, the target device can be an intelligent robot, which is a device that integrates advanced artificial intelligence (AI), machine learning, sensor technology and mechanical engineering, and is designed to perform a variety of tasks, from simple repetitive work to complex decision-making and interaction. Intelligent robots are able to perceive the environment, understand instructions, make decisions and perform actions to complete specific goals or tasks. Intelligent robots have a certain degree of autonomous decision-making ability and can perform tasks without direct human intervention. By integrating a variety of sensors (such as cameras, lidar, microphones, etc.), intelligent robots can "sense" the surrounding environment, collect information for navigation, identify objects and people, avoid obstacles, etc. Using machine learning algorithms, intelligent robots can learn from experience, improve their performance, and adapt to new situations and environmental changes. Intelligent robots can communicate with humans, including understanding natural language, speech recognition, facial expression recognition, and responding to human commands and questions. It can adjust behavior patterns according to different scenarios and is suitable for a variety of application scenarios, such as home assistants, medical care, industrial manufacturing and other fields.

[0072] Figure 4 A first interaction schematic diagram of a communication method in an embodiment of the present disclosure is shown. Figure 4 As shown, in the embodiment, the target device is in a business scenario, and the business request is determined by the target device according to the business instructions in the business scenario. The business request includes: the requirements of the target device for the network and the requirements for the artificial intelligence model when executing the business instructions in the business scenario.

[0073] The target device is in a business scenario, which can be an embodied intelligence scenario. Among them, embodied intelligence scenarios refer to application scenarios that realize intelligent behavior and interaction through physical entities (such as robots or other types of automated equipment). These entities can not only perceive the surrounding environment, but also make decisions and perform corresponding actions based on the information obtained, so as to effectively interact with the environment and other entities. For example, a home assistant robot can automatically perform a variety of tasks at home, such as cleaning, managing daily affairs reminders, etc. In hospitals or nursing homes, medical care robots can assist medical staff in completing intelligent accompanying work and reduce their burden. Robots in the field of industrial manufacturing are usually used for repetitive work on the production line to improve production efficiency and product quality. Agricultural robots are designed to perform various field operations, such as sowing, weeding, harvesting, etc., aiming to improve agricultural production efficiency. Educational and entertainment robots can be used as teaching tools or game partners to help children learn new knowledge or provide entertainment experience. In the business scenario, the target device determines the business request according to specific business instructions. Taking the smart companion with embodied intelligent scenario as an example, the business instruction is for the medical care robot to measure the patient's temperature. The business request is to determine the corresponding requirements for the network and the artificial intelligence model based on the network and artificial intelligence model required by the medical care robot to measure the patient's temperature in the business instruction, and obtain the corresponding business request.

[0074] The target device sends a service request to the first node of the intelligent wireless access network. In the embodiment, the first node of the intelligent wireless access network is the first node of the intelligent wireless access network adjacent to the target device in the service scenario. In the service scenario, the target device selects and communicates with the closest intelligent wireless access network node to achieve connection communication with the network side. The intelligent wireless access network node is the closest node in the service scenario, so a communication connection is established with the target device.

[0075] In an embodiment, before forwarding the service request, the first node of the intelligent wireless access network further includes: the first node of the intelligent wireless access network determines that the current network status meets the network requirements according to the network requirements in the service request. After the first node of the intelligent wireless access network receives the service request, it is necessary to analyze and determine whether the current network status can meet the network requirements in the service request. The network requirements in the service request include: the computing performance of the intelligent wireless access network needs to meet the computing requirements required by the service request, the delay value of the intelligent wireless access network needs to meet the delay requirements required by the service request, and the communication bandwidth of the intelligent wireless access network needs to meet the service bandwidth required by the service request.

[0076] If the first node of the intelligent wireless access network determines that the current network status cannot meet the network requirements in the service demand, then the current service request is rejected and a service rejection response is sent to the target device. The target device stops executing the service instruction.

[0077] If the first node of the intelligent wireless access network determines that the current network status meets the network requirements in the service demand, then the service demand is forwarded to the general computing resource scheduling function of the wireless intelligent management and orchestration function layer.

[0078] In the embodiment, according to the service request, the target artificial intelligence model is deployed to the second node of the intelligent wireless access network, including:

[0079] Determine the second node of the intelligent wireless access network registered with the general computing resource scheduling function according to the network requirements in the service request;

[0080] Determine the target AI model based on the requirements for AI models in business requests;

[0081] Deploy the target artificial intelligence model to the second node of the intelligent wireless access network.

[0082] The general computing resource scheduling function receives the service request forwarded by the first node of the intelligent wireless access network. The second node of the intelligent wireless access network is registered with the general computing resource scheduling function. The second node of the intelligent wireless access network can be a single node or multiple nodes.

[0083] When one node can meet the network requirements in the service request, then one intelligent wireless access network second node is used. When multiple nodes are required to meet the network requirements in the service request, multiple intelligent wireless access network second nodes are required to be used to achieve the joint implementation.

[0084] According to the network requirements in the service request, a second intelligent wireless access network node is searched and determined among multiple intelligent wireless access network nodes registered with the general computing resource scheduling function; wherein the second intelligent wireless access network node can meet the network requirements in the service request.

[0085] Further, according to the demand for the artificial intelligence model in the service request, the corresponding target artificial intelligence model is determined; for example, if the demand for the artificial intelligence model in the service request is to perform environment recognition and action judgment, then the corresponding target artificial intelligence model is the environment recognition artificial intelligence model and the intelligent judgment artificial intelligence model. The target artificial intelligence model is deployed to the second node of the intelligent wireless access network. The specific deployment process includes:

[0086] The general computing resource scheduling function sends a model deployment instruction to the second node of the intelligent wireless access network. The second node of the intelligent wireless access network receives the model deployment instruction, obtains the corresponding target artificial intelligence model from the model management function of the wireless intelligent management and orchestration function layer according to the model deployment instruction, and deploys the target artificial intelligence model in the second node of the intelligent wireless access network.

[0087] After the target artificial intelligence model is deployed in the second node of the intelligent wireless access network, the model reasoning task is executed in the second node of the intelligent wireless access network to complete the reasoning of the target artificial intelligence model and generate the reasoning result.

[0088] The second node of the intelligent wireless access network sends the inference result to the first node of the intelligent wireless access network. After receiving the inference result, the first node of the intelligent wireless access network sends the inference result to the target device.

[0089] In the embodiment, it also includes: the target device is also used to complete the execution of the business instruction in the business scenario according to the reasoning result.

[0090] After receiving the inference result, the target device completes the execution of the business instruction in the business scenario according to the inference result.

[0091] The communication method provided by the embodiment of the present disclosure can realize dynamic computing power scheduling to achieve efficient resource utilization, reduce the manufacturing cost of robots, improve the endurance of robots, ensure real-time and reliability, enhance system flexibility and scalability, and support the collaborative operation of multiple robots. Among them, dynamic computing power scheduling realizes efficient resource utilization by uniformly managing and dynamically scheduling fragmented computing power resources in AI RAN through RAN AILayer. There is no need to configure excess computing power for robots to cope with complex scenarios, but to achieve on-demand allocation through networked collaboration, significantly improving resource utilization and reducing costs. The manufacturing cost of robots is reduced by offloading intelligent processing functions from the robot body to AI RAN and reducing the demand for local high-performance hardware by sharing computing resources. Traditional solutions require the use of expensive embedded computing units; this embodiment adopts a shared computing power mode to reduce hardware costs. Improving the endurance of robots is to reduce the load of local computing modules by offloading high-power tasks to AIRAN. Compared with traditional solutions that require continuous operation of high-performance computing units, this embodiment can significantly reduce power consumption, thereby extending the robot's endurance by more than 30%. Ensuring real-time and reliability is based on the low-latency communication capability of AI RAN, combined with the optimized deployment of RAN AI Layer for latency-sensitive tasks. Compared with existing edge computing solutions, this embodiment can better meet real-time requirements, especially in high-reliability task scenarios in dynamic environments. Enhancing system flexibility and scalability is to achieve dynamic updates and on-demand deployment of intelligent models through RAN AI Layer. Existing technologies usually require hardware upgrades to adapt to new task requirements, while this embodiment achieves functional expansion through software updates and network scheduling, greatly improving system adaptability. Supporting multi-robot collaborative operations is to achieve computing power sharing and task division among multiple robots through RAN AI Layer scheduling and control. Compared with the traditional mode of single robot operation, this embodiment supports multi-robot collaborative operations, improves overall efficiency and reduces the pressure of single-machine operation.

[0092] Figure 5 A second interaction schematic diagram of a communication method according to an embodiment of the present disclosure is shown. Figure 5 As shown, in this embodiment, the second node of the intelligent wireless access network includes two: the second node of the intelligent wireless access network-1 and the second node of the intelligent wireless access network-2; the specific process includes:

[0093] In a business scenario, the target device determines the business request according to the specific business instruction.

[0094] The target device sends a service request to the first node of the intelligent wireless access network.

[0095] The first node of the intelligent wireless access network determines whether the current network status meets the network requirements in the service request. If not, the service request is rejected; if satisfied, the service demand is forwarded to the general computing resource scheduling function of the wireless intelligent management and orchestration function layer.

[0096] The general computing resource scheduling function receives a service request forwarded by the first node of the intelligent wireless access network. According to the network requirements in the service request, the second intelligent wireless access network node-1 and the second intelligent wireless access network node-2 registered with the general computing resource scheduling function are determined; according to the requirements for the artificial intelligence model in the service request, the target artificial intelligence model-1 and the target artificial intelligence model-2 are determined; the target artificial intelligence model-1 is deployed to the second intelligent wireless access network node-1, and the target artificial intelligence model-2 is deployed to the second intelligent wireless access network node-2.

[0097] The model reasoning task is performed in the second node-1 of the intelligent wireless access network to complete the reasoning of the target artificial intelligence model-1 and generate the reasoning result-1. The second node-1 of the intelligent wireless access network sends the reasoning result-1 to the first node of the intelligent wireless access network.

[0098] The model reasoning task is performed in the second node-2 of the intelligent wireless access network to complete the reasoning of the target artificial intelligence model-2 and generate the reasoning result-2. The second node-2 of the intelligent wireless access network sends the reasoning result-2 to the first node of the intelligent wireless access network.

[0099] The first node of the intelligent wireless access network receives the inference result-1 and the inference result-2, and sends the inference result-1 and the inference result-2 to the target device.

[0100] After receiving the inference result-1 and the inference result-2, the target device completes the execution of the business instruction in the business scenario according to the inference results.

[0101] The disclosed embodiments achieve cost reduction and reduce the robot's dependence on high-performance hardware. By sharing computing resources through AIRAN, manufacturing costs can be significantly reduced. Reduce power consumption: By offloading high-energy intelligent processing tasks to the AIRAN side, the power consumption of the robot body is greatly reduced and endurance is improved. Improve computing power utilization: Through the RAN AI Layer, efficient integration and scheduling of fragmented computing power is achieved to reduce the waste of computing resources. Ensure real-time and reliability: Combined with the low-latency communication capabilities of AI RAN, ensure the real-time and reliability of the robot's task execution in complex environments. Improve system flexibility and scalability: Based on the dynamic deployment capabilities of the RAN AI Layer, support rapid iteration and expansion of new intelligent models and algorithms.

[0102] Figure 6Another schematic diagram of a communication method in an embodiment of the present disclosure is shown. Figure 6 As shown, the embodiment of the present disclosure further provides a communication method, which is applied to a first node of an intelligent wireless access network, and the method includes:

[0103] Step S602: receiving a service request sent by a target device; the target device connects to a first node of the intelligent wireless access network;

[0104] Step S604: forwarding the service request to the general computing resource scheduling function, so that the general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request; the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer; the second node of the intelligent wireless access network is used to complete the reasoning of the target artificial intelligence model and send the reasoning result to the first node of the intelligent wireless access network;

[0105] Step S606: Receive the inference result and forward it to the target device.

[0106] It should be noted that the acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of national laws and regulations, and various types of data such as personal identity data, operation data, behavioral data, etc. related to individuals, customers, and groups obtained in the embodiments of this disclosure have all been authorized.

[0107] Based on the same inventive concept, the present disclosure also provides a communication device in the following embodiments. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0108] Figure 7 A schematic diagram of a communication device in an embodiment of the present disclosure is shown. Figure 7 As shown, the device comprises:

[0109] The service request receiving module 701 is used to receive a service request forwarded by a first node of an intelligent wireless access network; the first node of the intelligent wireless access network is connected to a target device, and the service request is sent by the target device to the first node of the intelligent wireless access network;

[0110] The target artificial intelligence model deployment module 702 is used to deploy the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

[0111] It should be noted that the business request receiving module 701 and the target artificial intelligence model deployment module 702 correspond to S202 to S204 in the method embodiment, and the examples and application scenarios implemented by the modules and corresponding steps are the same, but are not limited to the contents disclosed in the method embodiment. It should be noted that the modules as part of the device can be executed in a computer system such as a set of computer executable instructions.

[0112] Based on the same inventive concept, the present disclosure also provides a communication device in the following embodiments. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0113] Figure 8 Another schematic diagram of a communication device in an embodiment of the present disclosure is shown. Figure 8 As shown, the device comprises:

[0114] The service request receiving module 801 is used to receive a service request sent by a target device; the target device is connected to the first node of the intelligent wireless access network;

[0115] The service request forwarding module 802 is used to forward the service request to the general computing resource scheduling function, so that the general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request; the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer; the second node of the intelligent wireless access network is used to complete the reasoning of the target artificial intelligence model and send the reasoning result to the first node of the intelligent wireless access network;

[0116] The inference result forwarding module 803 is used to receive the inference result and forward it to the target device.

[0117] It should be noted that the business request receiving module 801, the business request forwarding module 802 and the inference result forwarding module 803 correspond to S602 to S606 in the method embodiment, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above method embodiment. It should be noted that the above modules as part of the device can be executed in a computer system such as a set of computer executable instructions.

[0118] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0119] Refer to the following Fig. 9 The electronic device 900 according to this embodiment of the present disclosure is described. Fig. 9 The electronic device 900 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0120] like Fig. 9 As shown, the electronic device 900 is in the form of a general computing device. The components of the electronic device 900 may include but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910).

[0121] Among them, the storage unit stores a program code, and the program code can be executed by the processing unit 910, so that the processing unit 910 executes the steps described in the above "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 910 can execute the following steps of the above method embodiment: applied to the general computing resource scheduling function, the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer, the method includes: receiving a service request forwarded by a first node of an intelligent wireless access network; the first node of the intelligent wireless access network is connected to a target device, and the service request is sent by the target device to the first node of the intelligent wireless access network; according to the service request, the target artificial intelligence model is deployed to the second node of the intelligent wireless access network, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

[0122] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 9201 and / or a cache storage unit 9202 , and may further include a read-only storage unit (ROM) 9203 .

[0123] The storage unit 920 may also include a program / utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0124] Bus 930 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0125] The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or may communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 950. Furthermore, the electronic device 900 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 via a bus 930. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0126] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0127] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer program product, which includes: a computer program, which implements the above communication method when executed by a processor.

[0128] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the above method of the present disclosure is stored thereon. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above “Exemplary Method” section of this specification.

[0129] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] In the present disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0132] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0133] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0134] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0135] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0136] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A communication method, characterized in that: Applied to the general computing resource scheduling function, the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer, the method includes: receiving a service request forwarded by a first node of an intelligent wireless access network; the first node of the intelligent wireless access network is connected to a target device, and the service request is sent by the target device to the first node of the intelligent wireless access network; According to the service request, the target artificial intelligence model is deployed to the second node of the intelligent wireless access network, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

2. The communication method according to claim 1, characterized in that: The target device is in a business scenario, and the business request is determined by the target device according to the business instructions in the business scenario. The business request includes: the requirements of the target device for the network and the requirements for the artificial intelligence model when executing the business instructions in the business scenario.

3. The communication method according to claim 2, characterized in that: Before forwarding the service request, the first node of the intelligent wireless access network further includes: The first node of the intelligent wireless access network determines, based on the network requirement in the service request, that the current network status meets the network requirement.

4. The communication method according to claim 3, characterized in that: According to the service request, deploying the target artificial intelligence model to the second node of the intelligent wireless access network includes: Determine, according to the network requirements in the service request, a second node of the intelligent wireless access network registered with the general computing resource scheduling function; Determine a target artificial intelligence model according to the demand for the artificial intelligence model in the business request; Deploy the target artificial intelligence model to the second node of the intelligent wireless access network.

5. The communication method according to claim 2, characterized in that: Also includes: The target device is also used to complete the execution of the business instruction in the business scenario according to the inference result.

6. The communication method according to claim 2, characterized in that: The first intelligent wireless access network node is an intelligent wireless access network node adjacent to the target device in the service scenario.

7. A communication method, characterized in that: Applied to a first node of an intelligent wireless access network, the method comprises: Receiving a service request sent by a target device; the target device is connected to the first node of the intelligent wireless access network; Forwarding the service request to the general computing resource scheduling function, so that the general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request; the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer; the second node of the intelligent wireless access network is used to complete the reasoning of the target artificial intelligence model and send the reasoning result to the first node of the intelligent wireless access network; The inference result is received and forwarded to the target device.

8. A communication device, characterized in that: Applied to the general computing resource scheduling function, the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer, and the device includes: A service request receiving module, configured to receive a service request forwarded by a first node of an intelligent wireless access network; the first node of the intelligent wireless access network is connected to a target device, and the service request is sent by the target device to the first node of the intelligent wireless access network; The target artificial intelligence model deployment module is used to deploy the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request, so that the second node of the intelligent wireless access network completes the reasoning of the target artificial intelligence model and sends the reasoning result to the first node of the intelligent wireless access network; the first node of the intelligent wireless access network is also used to send the reasoning result to the target device.

9. A communication device, characterized in that: Applied to a first node of an intelligent wireless access network, the device comprises: A service request receiving module, configured to receive a service request sent by a target device; the target device is connected to the first node of the intelligent wireless access network; A service request forwarding module, used to forward the service request to the general computing resource scheduling function, so that the general computing resource scheduling function deploys the target artificial intelligence model to the second node of the intelligent wireless access network according to the service request; the general computing resource scheduling function belongs to the wireless intelligent management and orchestration function layer; the second node of the intelligent wireless access network is used to complete the reasoning of the target artificial intelligence model and send the reasoning result to the first node of the intelligent wireless access network; The inference result forwarding module is used to receive the inference result and forward it to the target device.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the communication method according to any one of claims 1 to 6 or claim 7 by executing the executable instructions.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the communication method according to any one of claims 1 to 6 or claim 7 is implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the communication method according to any one of claims 1 to 6 or claim 7 is implemented.

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