A demand scheme recommendation method and device for a computing power network

Abstract

The application provides a demand scheme recommendation method and device for a computing power network, comprising: obtaining demand information of a target user for the computing power network; determining a demand scheme set and an application demand set corresponding to the demand information based on a pre-established digital twin model corresponding to an infrastructure of the computing power network; constructing a virtual-real combined immersive three-dimensional scene matched with the demand scheme set and the application scene set respectively, and recommending the virtual-real combined immersive three-dimensional scene to the target user. The application constructs a digital twin model accessible by the target user, so that the target user can see and touch the computing power network based on the digital twin model with naked eyes or through a wearable device. The demand scheme set and the application scene set of the application can be presented to the target user in a three-dimensional scene mode, so that the target user can see and touch the immersive demand scheme under the demand of the target user more intuitively, and the target user's understanding of the computing power network is further deepened.

Description

Technical Field

[0001] This application relates to the field of computing power network technology, and in particular to a method and apparatus for recommending demand schemes for computing power networks. Background Technology

[0002] Computing power networks are a new type of ICT (information and communications technology) infrastructure convergence solution. By distributing the computing power, storage, and algorithms of nodes through computing power networks, and combining them with network information (such as bandwidth and latency), they provide the best computing power network allocation and computing-network convergence solutions for different types of customer needs, thereby achieving optimal network utilization.

[0003] Currently, computing power networks are typically displayed by looping images on the network operator's system homepage, playing videos, or pinning text at the top. This increases users' understanding of computing power networks to some extent, but users cannot see or access the solutions that computing power networks can provide for their specific needs. Summary of the Invention

[0004] In view of this, this application provides a method and apparatus for recommending solutions for computing power networks, which enables users to intuitively see and access the solutions that computing power networks can provide for their own needs, thereby deepening users' understanding of computing power networks.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A method for recommending demand solutions for computing power networks includes:

[0007] Obtain information on the target users' needs for computing power networks;

[0008] Based on the digital twin model corresponding to the pre-established computing power network infrastructure, the set of demand solutions and the set of application scenarios corresponding to the demand information are determined. The digital twin model refers to a digital three-dimensional immersive virtual entity model constructed based on the computing power of the computing power network and the operation data of each infrastructure in the computing power network. The set of demand solutions includes demand solutions corresponding to at least one supplier. Each demand solution contains all computing power network resources that the corresponding supplier can provide in the process of solving the demand information.

[0009] Construct immersive 3D scenes that combine virtual and real elements, matching the sets of demand solutions and application scenarios respectively, and recommend these immersive 3D scenes to target users.

[0010] Optionally, based on a pre-established digital twin model, determine the set of demand solutions and application scenarios corresponding to the demand information, including:

[0011] The demand information is parsed to obtain the corresponding feature data.

[0012] Based on the digital twin model, the set of demand solutions and application scenarios corresponding to the feature data are determined.

[0013] Optionally, the demand information can be parsed to obtain the corresponding feature data, including:

[0014] Input the demand information into a pre-built demand model to obtain the feature data output by the demand model.

[0015] Optionally, construct an immersive 3D scene that combines virtual and real elements to match the set of required solutions, including:

[0016] For each requirement solution in the requirement solution set, a pre-built 3D visualization model is used to determine the matching virtual and real immersive 3D scene for that requirement solution.

[0017] Optionally, after recommending the immersive 3D scene combining virtual and real elements to the target users, it also includes:

[0018] Receive human body information fed back by the target user through wearable devices;

[0019] Determine the actual evaluation effect of each demand solution in the demand solution set for the target user based on human body information;

[0020] Based on the actual evaluation effect of each requirement solution in the requirement solution set, the target requirement solution is determined from the requirement solution set;

[0021] Recommend immersive 3D scenes that combine virtual and real elements to the target users, matching the solutions to their needs.

[0022] Optionally, wearable devices include one or more of the following: virtual reality (VR) devices, augmented reality (AR) devices, and mixed reality (MR) devices.

[0023] Optionally, the human body information includes one or more of the following: pose information, eye movement information, gesture information, speech-to-text information, text-to-speech information, facial expression information, and question information.

[0024] Optionally, the requirements information includes edge computing requirements and basic requirements. Basic requirements refer to the basic requirements for computing power network resources and application scenarios, while edge computing requirements refer to the requirements for providing solutions based on the location, mobility, latency requirements and jitter requirements of the target users.

[0025] A demand scheme recommendation device for computing power networks includes:

[0026] The demand acquisition module is used to acquire information about the target users' demand for computing power networks;

[0027] The demand solution determination module is used to determine the demand solution set and application scenario set corresponding to the demand information based on the digital twin model corresponding to the infrastructure of the pre-established computing power network. The digital twin model refers to a digital three-dimensional virtual entity model constructed based on the computing power of the computing power network and the operation data of each infrastructure in the computing power network. The demand solution set includes the demand solution corresponding to at least one supplier. Each demand solution contains all the computing power network resources that the corresponding supplier can provide in the process of solving the demand information.

[0028] The 3D scene construction module is used to build immersive 3D scenes that combine virtual and real elements, matching the sets of demand solutions and application scenarios respectively, and recommend these immersive 3D scenes to target users.

[0029] Optionally, it also includes: a requirement evaluation module, which is used for:

[0030] After recommending an immersive 3D scene that combines virtual and real elements to the target user, the system receives human body information fed back by the target user through wearable devices.

[0031] Determine the actual evaluation effect of each demand solution in the demand solution set for the target user based on human body information;

[0032] Based on the actual evaluation effect of each requirement solution in the requirement solution set, the target requirement solution is determined from the requirement solution set;

[0033] Recommend immersive 3D scenes that combine virtual and real elements to the target users, matching the solutions to their needs.

[0034] Compared to existing technologies, this application provides a method and apparatus for recommending demand solutions for computing power networks, including: acquiring target users' demand information for computing power networks; determining a set of demand solutions and a set of application scenarios corresponding to the demand information based on a pre-established digital twin model corresponding to the infrastructure of the computing power network; constructing virtual-real immersive 3D scenes that match the set of demand solutions and the set of application scenarios respectively, and recommending the virtual-real immersive 3D scenes to the target users. Considering that target users cannot directly access the infrastructure of computing power networks, this application constructs a digital twin model that target users can access, enabling target users to see and interact with the computing power network based on the digital twin model with their naked eyes or through wearable devices. Furthermore, the digital twin model provided by this application can obtain the set of demand solutions and the set of application scenarios corresponding to each supplier based on the target user's demand information. This set of demand solutions and the set of application scenarios can be presented to the target user in a three-dimensional way in a virtual-real immersive 3D scene, allowing the target user to see and interact with immersive demand solutions under their own needs more intuitively, further deepening the target user's concrete understanding of the computing power network. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0036] Figure 1 A flowchart illustrating the demand scheme recommendation method for computing power networks provided in this application embodiment;

[0037] Figure 2 A functional framework diagram of a computing power network intelligent advertising system provided in this application embodiment;

[0038] Figure 3 A flowchart illustrating a demand solution recommendation method provided in this application embodiment;

[0039] Figure 4 A schematic diagram of the structure of the demand scheme recommendation device for computing power networks provided in the embodiments of this application;

[0040] Figure 5 This is a hardware structure block diagram of a recommended device for a computing network requirement scheme provided in the embodiments of this application. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] To facilitate the explanation of the recommended solution for computing power networks provided in this application, the relevant terms used in this application are explained below.

[0043] Mobile Edge Computing (MEC) technology migrates computing / storage capabilities and service capabilities to the network edge, enabling applications, services, and content to be deployed locally, close-range, and in a distributed manner. This addresses, to some extent, the service requirements of 5G networks in scenarios such as high capacity, low power consumption, massive connectivity, low latency, and high reliability. Simultaneously, MEC fully leverages data and information from multiple access networks, including 5G, to perceive and analyze network context information and make it available to third-party business applications. This effectively enhances the network's intelligence level and promotes deep integration of the network and services, making it one of the key technologies of 5G networks.

[0044] 5G: A new generation of cellular technology, characterized by high bandwidth, low latency, and massive connectivity.

[0045] Computing power network: This is a new type of ICT (information and communications technology) infrastructure integration solution. It distributes the computing power, storage, and algorithms of nodes through the network, and combines this with network information (such as bandwidth and latency) to provide the best allocation and network connection solutions for different types of customer needs, thereby achieving optimal use of the network.

[0046] "East Data, West Computing": In "East Data, West Computing," "data" refers to data, and "computing power" refers to computing power, i.e., the ability to process data. "East Data, West Computing" aims to guide the computing power demand of the east to the west in an orderly manner by building a new computing power network system that integrates data centers, cloud computing, and big data, thereby optimizing the layout of data center construction and promoting collaborative development between the east and west.

[0047] Digital twin: A digital twin is a virtual entity created from a physical entity using digital methods. It utilizes historical data, real-time data, and algorithmic models to simulate, verify, predict, and control the entire lifecycle of the physical entity. Digital twins fully leverage data from physical models, sensor updates, and operational history, integrating multi-disciplinary, multi-physical-quantity, multi-scale, and multi-probabilistic simulation processes to complete mapping in virtual space, thereby reflecting the entire lifecycle of the corresponding physical equipment. It possesses characteristics that transcend reality and can be considered a digital mapping system of one or more important, interdependent equipment systems. Digital twin technology fully utilizes technologies such as the Internet of Things, big data, artificial intelligence, and 3D visualization to create a virtual entity—a digital twin—of a physical entity based on historical and real-time data.

[0048] This application provides a method for recommending solutions for computing power networks. It uses digital twin technology to map the physical entity of the computing power network into a three-dimensional, digital virtual entity, obtaining a digital twin model. Then, the target user's demand information is input into the digital twin model to obtain a set of computing power network solutions and application scenarios corresponding to the demand information provided by various suppliers. This set of computing power network solutions and application scenarios is then presented to the target user as a three-dimensional scene solution. The target user can see, hear, and interact with various computing power network solutions and application scenarios through wearable devices or the naked eye. After experiencing and comparing them, they can easily choose a suitable computing power network solution. At the same time, the three-dimensional presentation solution also deepens the target user's concrete understanding of computing power networks.

[0049] Please see Figure 1 The diagram illustrates a flowchart of a demand scheme recommendation method for computing power networks provided in an embodiment of this application. This demand scheme recommendation method for computing power networks may include:

[0050] Step S101: Obtain the target user's demand information for computing power network.

[0051] Here, the target user refers to a consumer with a need to purchase computing power. In one possible scenario, the target user may need to perform data calculations, but their own computing resources are limited and cannot support high-speed computation. In this case, the target user may have a need to purchase computing power from a computing network. For example, the target user may need to purchase 3GB of bandwidth for video data analysis. The target user can then input their demand for computing power into the intelligent advertising system for computing networks provided in this application, thereby obtaining the target user's demand information for computing power networks.

[0052] Optionally, the demand information includes edge computing requirements and basic requirements. Basic requirements refer to the fundamental needs for computing network resources and application scenarios, while edge computing requirements refer to the needs for solutions tailored to the target user's location, mobility, latency requirements, and jitter requirements (i.e., jitter limitation requirements). For example, if the target user's demand for computing network resources is that they need to purchase 3GB of bandwidth in both Beijing and Shanghai for video data analysis, then the basic requirement is the 3GB bandwidth requirement, and the edge computing requirement is the computing needs in both Shanghai and Beijing. For instance, if the target user can select edge computing services in the computing network intelligent advertising system, then the computing network intelligent advertising system can determine that the target user has edge computing needs.

[0053] Step S102: Based on the digital twin model corresponding to the pre-established computing power network infrastructure, determine the set of demand solutions and the set of application scenarios corresponding to the demand information.

[0054] Among them, the digital twin model refers to a digital three-dimensional virtual entity model constructed based on the computing power of the computing power network and the operational data of each infrastructure in the computing power network. The demand solution set includes at least one demand solution corresponding to a supplier, and each demand solution contains all computing power network resources that the corresponding supplier can provide in the process of solving the demand information.

[0055] The aforementioned set of application scenarios refers to a set of scenarios related to the current needs of the target user, which includes the computing power and network resources required by each scenario related to the current needs of the target user.

[0056] The aforementioned infrastructure includes, but is not limited to, the following facilities: data centers, cloud equipment, hosts, switches, routers, and gateways.

[0057] Those skilled in the art should understand that, for security and other considerations, data centers of various computing power networks typically do not allow target users to access them directly. In order to obtain the demand solutions corresponding to the demand information, this embodiment adopts digital twin technology and pre-establishes a digital twin model corresponding to the infrastructure of the computing power network. Specifically, this embodiment can collect the computing power and infrastructure operation data (such as server operation status) of computing power network resources based on the location of the data center and application service mechanism to realize a real-time data network digital twin, that is, a digital twin model.

[0058] In this step, the digital twin model can be used to determine the set of demand solutions and application scenarios corresponding to the demand information. This set of demand solutions includes a demand solution corresponding to at least one supplier. This demand solution refers to a computing power network solution that meets the needs of the target user, derived from a comprehensive evaluation of location, equipment, and service mechanisms. Location refers to the geographical location information of the computing power network resource entity, which is related to application latency and cost. Equipment can be information about the computing power network resource entity, related to computing power, network, and cost. Of course, equipment can also be virtual, but it must be associated with a physical device and needs to be presented simultaneously. Services include one-stop application services, resource services, AI and big data analysis services, and software development services.

[0059] In this embodiment, by establishing a digital twin model, different services and effects provided by different suppliers can be superimposed with virtual and real data. For example, real-time data of video analysis applications of users in the east (i.e. target users) and AI model training scenarios of data centers in the west can be combined with virtual data to construct video quality analysis and monitoring applications in intelligent manufacturing.

[0060] In one optional embodiment, the process of "determining the set of demand solutions and the set of application scenarios corresponding to the demand information based on the digital twin model corresponding to the pre-established computing network infrastructure" includes: parsing the demand information to obtain feature data corresponding to the demand information; and determining the set of demand solutions and the set of application scenarios corresponding to the feature data based on the digital twin model. The digital twin model is trained using historical feature data corresponding to historical demand information as training samples, and the set of demand solutions and the set of application scenarios corresponding to the labeled historical feature data as sample labels.

[0061] Optionally, the process of "analyzing the demand information and obtaining the corresponding feature data" can be implemented based on a pre-built demand model. That is, the demand information can be input into the pre-built demand model to obtain the feature data output by the demand model.

[0062] Here, the demand model is obtained by constructing a computing power network, a deep learning neural network, and a reinforcement learning algorithm. Specifically, the training process of the demand model includes: using historical demand information as training samples and using the feature data corresponding to the labeled historical demand information as sample labels to train the demand model.

[0063] Step S103: Construct virtual-real immersive 3D scenes that match the demand solution set and application scenario set respectively, and recommend the virtual-real immersive 3D scenes to the target users.

[0064] In this step, a virtual-real immersive 3D scene matching each demand solution in the demand solution set (note that this 3D scene is a virtual simulation scene) can be constructed, along with a virtual-real immersive 3D scene matching the application scenario set. These constructed virtual-real immersive 3D scenes are then recommended to target users. Target users can virtually roam and experience the demand solutions from different providers within the 3D scene in real time by wearing appropriate devices or using glasses, and promptly understand the computing power and network resources required for similar application scenarios. This allows them to compare the demand solutions from different providers within the virtual-real immersive 3D scene based on the application scenario set, and ultimately select a suitable computing power and network provider.

[0065] Optionally, the service prices (fees) corresponding to the demand solutions in the virtual-real integrated immersive 3D scene can also be pushed to the target users at the same time, so that the target users can compare the demand solutions and corresponding service prices of different suppliers in the virtual-real integrated immersive 3D scene and purchase the appropriate demand solution.

[0066] Optionally, wearable devices may include one or more of the following devices: virtual reality (VR) devices, augmented reality (AR) devices, and mixed reality (MR) devices.

[0067] Of course, wearable devices can also be other devices capable of viewing three-dimensional scenes, and this application does not limit them.

[0068] In one possible implementation, this step can pre-build a 3D visualization model to construct an immersive 3D scene that combines virtual and real elements to match the set of required solutions.

[0069] Here, the 3D visualization model refers to the 3D representation of the computing power network digital twin. Its training process includes: using the demand solution training data as training samples and using the labeled 3D scene corresponding to the demand solution training data as sample labels to train and obtain the 3D visualization model.

[0070] After the 3D visualization model is built, this step can determine the matching virtual and real immersive 3D scene for each requirement solution in the requirement solution set using the built 3D visualization model.

[0071] This embodiment constructs an immersive 3D scene that combines virtual and real elements, allowing target users to enter a virtual computing network scene that presents different suppliers' demand solutions and service differences at different prices. This helps target users choose the appropriate demand solution and supplier after comparing different suppliers' demand solutions, resulting in a better user experience.

[0072] In summary, the demand solution recommendation method for computing power networks provided in this application first obtains the target user's demand information for the computing power network. Then, based on a pre-established digital twin model corresponding to the infrastructure of the computing power network, it determines the set of demand solutions and application scenarios corresponding to the demand information. Finally, it constructs virtual-real immersive 3D scenes that match the set of demand solutions and application scenarios respectively, and recommends these virtual-real immersive 3D scenes to the target user. Considering that the target user cannot directly access the infrastructure of the computing power network, this application constructs a digital twin model that the target user can access, enabling the target user to see and interact with the computing power network based on the digital twin model with naked eyes or through wearable devices. Furthermore, the digital twin model provided in this application can obtain the set of demand solutions and application scenarios corresponding to each supplier based on the target user's demand information. This set of demand solutions and application scenarios can be presented to the target user in a three-dimensional way in a virtual-real immersive 3D scene, allowing the target user to see and interact with immersive demand solutions according to their own needs more intuitively, further deepening the target user's concrete understanding of the computing power network.

[0073] After the aforementioned embodiments recommend the immersive 3D scene combining virtual and reality to the target user, the target user can "see" the set of solutions and application scenarios that the computing power network can provide for their needs through wearable devices or naked eyes. When the target user roams and experiences the immersive 3D scene combining virtual and reality, they may have their own thoughts, resulting in some human information such as actions, expressions, and language to express their thoughts. For example, the target user may not understand the functions of the computing power network software in the 3D scene, resulting in expressions and actions such as frowning or sighing. As another example, the target user may make mistakes such as accidental operation while roaming the immersive 3D scene combining virtual and reality.

[0074] In one optional embodiment, this embodiment can collect the target user's human body information through wearable devices. After receiving the human body information fed back by the target user through the wearable device, the computing power network intelligent advertising system can determine the target user's actual evaluation effect on each demand solution in the demand solution set based on the human body information. Then, based on the actual evaluation effect of each demand solution in the demand solution set, the system determines the target demand solution from the demand solution set and adjusts the three-dimensional scene in real time, recommending the virtual and real combined immersive three-dimensional scene matched with the target demand solution to the target user.

[0075] Optionally, the human body information includes one or more of the following: pose information, eye movement information, gesture information, speech-to-text information, text-to-speech information, facial expression information, and question information.

[0076] It should also be noted that the above-mentioned target requirement solution can be one or more requirement solutions, and this embodiment does not limit this.

[0077] Optionally, in this embodiment, the process of receiving human body information fed back by the target user through a wearable device, determining the target user's actual evaluation effect on each demand solution in the demand solution set based on the human body information, determining the target demand solution from the demand solution set based on the actual evaluation effect of each demand solution in the demand solution set, and recommending the virtual and real combined immersive 3D scene matching the target demand solution to the target user can be achieved through a pre-established advertising and marketing evaluation model. Here, the advertising and marketing evaluation model is trained using a historical demand solution set as training samples, using the labeled demand solutions corresponding to the historical demand solution set as sample labels, and supplemented by corresponding human body information.

[0078] See Figure 2 This is a functional framework diagram of a computing power network intelligent advertising system provided in an embodiment of this application. Consumers (target users) can... Figure 2 The computing power network intelligent advertising system shown allows for a 3D scene roaming of the computing power network. Specifically, when consumers click on the computing power network roaming, they enter a real-time data computing power network digital twin, which presents specific computing power network application scenarios, locations, equipment, and service mechanisms of the East Data West Computing Project. Through wearable devices, first-person or third-person perspectives are collected to present a virtual and real computing power network service scenario. Consumers can also view the utilization rate and energy consumption of different computing power network resources in real time through the computing power network intelligent advertising system.

[0079] Consumers can click on the supplier's solution roaming page to obtain a set of corresponding demand solutions through a digital twin model. This includes a full lifecycle presentation of computing power and network resources involved in the application's purchase, deployment, delivery, service, and release, and also provides a set of application scenarios. Consumers can add new application requirements, and the computing power network intelligent advertising system can reconstruct and present a virtual and real immersive 3D scene that matches the customized computing power network demand solution in real time. Consumers can then experience the solution and compare prices to choose a suitable computing power network provider and service price.

[0080] at the same time, Figure 2 The intelligent advertising system shown can intelligently analyze consumers' human information while roaming in the background, accurately push computing network service providers and target demand solutions that meet their needs, resulting in a better user experience.

[0081] In one implementation of this invention, considering that consumers, suppliers, and operators can all roam the immersive 3D model combining virtual and real elements presented by the wearable device roaming system, in order to better protect the personal information of consumers, suppliers, and operators, such as... Figure 2As shown, an information security and privacy protection mechanism can be set up in the system. When consumers and suppliers register, obtain passwords, and log in, the system needs to perform de-identification processing and privacy data security management. In the collaboration dimension, consumers are associated with each supplier so that both consumers and suppliers can see the three-dimensional scene matching the corresponding demand solution.

[0082] See Figure 3 The diagram shown is an overall flowchart of a demand solution recommendation method provided in an embodiment of this application. First, the three-dimensional models of computing network communication lines, routes, and equipment, as well as the BIM models of computing network data centers and clusters in different geographical locations, are automatically imported into the computing network model. Scenes and views are instantiated and rendered to obtain the physical entity model of the computing network. Then, a digital twin of the computing network is instantiated by combining edge computing server data, real-time monitoring data, and point cloud data.

[0083] After that, target users can interact with the computing power network digital twin in real time through wearable devices such as AR, VR, and MR, input their needs, and view the computing power network resources, East Data West Computing services, energy consumption, and costs of suppliers that meet their needs. After comparison, they can select the appropriate solution and purchase it with one click.

[0084] After purchasing a service plan, users can also experience the service quality and conduct real-time evaluations during the actual service period using wearable devices. This includes viewing any problems or malfunctions discovered and their correction status. If the evaluation results indicate that the currently purchased service plan is no longer suitable for new needs (i.e.,...), the service plan can be used to assess the situation. Figure 3 If the system indicates "yes", users can input new requirements and compare prices again before making a purchase. Otherwise, users can continue to address their needs based on the purchased service plan. After the purchased service plan expires, users can choose whether to renew the service. If yes, they can input new requirements and compare prices again before making a purchase. Otherwise, the process ends.

[0085] In this embodiment, an abstract computing power network is created by combining virtual and real data to form a digital twin of the computing power network, providing a foundation for the target user to experience the computing power network. The demand solutions provided by various suppliers are presented to the target user in a three-dimensional virtual simulation scene, making the abstract computing power network service concrete into a three-dimensional interactive immersive computing power network service experience. On the one hand, this helps the target user understand the service and price they are purchasing; on the other hand, it provides different suppliers' bidding demand solutions, which helps to encourage suppliers and operators to provide better services. Based on the target user's human information, the potential computing power network demand is assessed and suitable demand solutions and suppliers are accurately pushed, so that the target user gets what they buy, resulting in a better user experience.

[0086] This application also provides a demand scheme recommendation device for computing power networks. The demand scheme recommendation device for computing power networks provided in this application embodiment is described below. The demand scheme recommendation device for computing power networks described below can be referred to in correspondence with the demand scheme recommendation method for computing power networks described above.

[0087] Please see Figure 4 This illustration shows a structural schematic diagram of a demand scheme recommendation device for computing power networks provided in an embodiment of this application, such as... Figure 4 As shown, the demand scheme recommendation device for computing power networks may include: a demand acquisition module 401, a demand scheme determination module 402, and a three-dimensional scene construction module 403.

[0088] The demand acquisition module 401 is used to acquire the target user's demand information for the computing power network.

[0089] The demand solution determination module 402 is used to determine the demand solution set and application scenario set corresponding to the demand information based on the digital twin model corresponding to the infrastructure of the pre-established computing power network. The digital twin model refers to a digital three-dimensional immersive virtual entity model constructed based on the computing power of the computing power network and the operation data of each infrastructure in the computing power network. The demand solution set includes demand solutions corresponding to at least one supplier. Each demand solution contains all computing power network resources that the corresponding supplier can provide in the process of solving the demand information.

[0090] The 3D scene construction module 403 is used to construct virtual and real immersive 3D scenes that match the set of demand solutions and the set of application scenarios respectively, and recommend the virtual and real immersive 3D scenes to the target users.

[0091] The demand solution recommendation device for computing power networks provided in this application first obtains the target user's demand information for the computing power network through a demand acquisition module. Then, a demand solution determination module determines the set of demand solutions and application scenarios corresponding to the demand information based on a pre-established digital twin model corresponding to the infrastructure of the computing power network. Finally, a 3D scene construction module constructs virtual-real immersive 3D scenes that match the set of demand solutions and application scenarios respectively, and recommends these virtual-real immersive 3D scenes to the target user. Considering that the target user cannot directly access the infrastructure of the computing power network, this application constructs a digital twin model that the target user can access, allowing the target user to see and interact with the computing power network based on the digital twin model with the naked eye or through wearable devices. Furthermore, the digital twin model provided in this application can obtain the set of demand solutions and application scenarios corresponding to each supplier based on the target user's demand information. This set of demand solutions and application scenarios can be presented to the target user in a three-dimensional way in a virtual-real immersive 3D scene, allowing the target user to see and interact with immersive demand solutions according to their own needs more intuitively, further deepening the target user's concrete understanding of the computing power network.

[0092] Optionally, the above-mentioned requirement determination module 402 can be used specifically for:

[0093] The demand information is parsed to obtain the corresponding feature data.

[0094] Based on the digital twin model, the set of demand solutions and application scenarios corresponding to the feature data are determined.

[0095] Optionally, the aforementioned requirement scheme determination module 402, when parsing the requirement information to obtain the corresponding feature data, can specifically be used for:

[0096] Input the demand information into a pre-built demand model to obtain the feature data output by the demand model.

[0097] Optionally, the 3D scene construction module 403 can be used specifically for constructing a virtual-real hybrid immersive 3D scene that matches the set of required solutions when:

[0098] For each requirement solution in the requirement solution set, a pre-built 3D visualization model is used to determine the matching virtual and real immersive 3D scene for that requirement solution.

[0099] Optionally, the demand scheme recommendation device for computing power networks provided in this application embodiment further includes: a demand scheme evaluation module, which can specifically be used for:

[0100] After recommending an immersive 3D scene that combines virtual and real elements to the target user, the system receives human body information fed back by the target user through wearable devices.

[0101] Determine the actual evaluation effect of each demand solution in the demand solution set for the target user based on human body information;

[0102] Based on the actual evaluation effect of each requirement solution in the requirement solution set, the target requirement solution is determined from the requirement solution set;

[0103] Recommend immersive 3D scenes that combine virtual and real elements to the target users, matching the solutions to their needs.

[0104] Optionally, wearable devices include one or more of the following: virtual reality (VR) devices, augmented reality (AR) devices, and mixed reality (MR) devices.

[0105] Optionally, the human body information includes one or more of the following: pose information, eye movement information, gesture information, speech-to-text information, text-to-speech information, facial expression information, and question information.

[0106] Optionally, the requirements information includes edge computing requirements and basic requirements. Basic requirements refer to the basic requirements for computing power network resources and application scenarios, while edge computing requirements refer to the requirements for providing solutions based on the location, mobility, latency requirements and jitter requirements of the target users.

[0107] This application also provides a demand scheme recommendation device for computing power networks. Optionally, Figure 5 This diagram illustrates the hardware architecture of a recommended device for a computing network, with reference to... Figure 5 The hardware structure of the recommended device for the computing network demand scheme may include: at least one processor 501, at least one communication interface 502, at least one memory 503 and at least one communication bus 504.

[0108] In this embodiment of the application, the number of processor 501, communication interface 502, memory 503 and communication bus 504 is at least one, and processor 501, communication interface 502 and memory 503 communicate with each other through communication bus 504.

[0109] The processor 501 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0110] The memory 503 may include high-speed RAM, or it may also include non-volatile memory, such as at least one disk storage device;

[0111] The memory 503 stores a program, and the processor 501 can call the program stored in the memory 503. The program is used for:

[0112] Obtain information on the target users' needs for computing power networks;

[0113] Based on the digital twin model corresponding to the pre-established computing power network infrastructure, the set of demand solutions and the set of application scenarios corresponding to the demand information are determined. The digital twin model refers to a digital three-dimensional immersive virtual entity model constructed based on the computing power of the computing power network and the operation data of each infrastructure in the computing power network. The set of demand solutions includes demand solutions corresponding to at least one supplier. Each demand solution contains all computing power network resources that the corresponding supplier can provide in the process of solving the demand information.

[0114] Construct immersive 3D scenes that combine virtual and real elements, matching the sets of demand solutions and application scenarios respectively, and recommend these immersive 3D scenes to target users.

[0115] Optionally, the refined and extended functions of the program can be found in the description above.

[0116] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described recommended method for demand schemes in computing power networks.

[0117] Optionally, the refined and extended functions of the program can be found in the description above.

[0118] Finally, it should be noted that in this document, relational terms such as "second" and "etc." are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0120] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for recommending demand solutions for computing power networks, characterized in that, include: Obtain information on the target users' needs for computing power networks; Based on the pre-established digital twin model corresponding to the infrastructure of the computing power network, the set of demand solutions and the set of application scenarios corresponding to the demand information are determined. The digital twin model refers to a digital three-dimensional immersive virtual entity model constructed based on the computing power of the computing power network and the operation data of each infrastructure in the computing power network. The set of demand solutions includes demand solutions corresponding to at least one supplier. Each demand solution includes all computing power network resources that the corresponding supplier can provide in the process of solving the demand information. By overlaying virtual and real data on different services and effects provided by different suppliers, a virtual-real immersive 3D scene is constructed that matches the set of demand solutions and the set of application scenarios, and the virtual-real immersive 3D scene is recommended to the target user.

2. The method for recommending demand schemes for computing power networks according to claim 1, characterized in that, The digital twin model corresponding to the pre-established computing network infrastructure determines the set of demand solutions and application scenarios corresponding to the demand information, including: The requirement information is parsed to obtain the feature data corresponding to the requirement information; Based on the digital twin model, the set of demand solutions and the set of application scenarios corresponding to the feature data are determined.

3. The method for recommending demand schemes for computing power networks according to claim 2, characterized in that, The process of parsing the demand information to obtain the corresponding feature data includes: The requirement information is input into a pre-built requirement model to obtain the feature data output by the requirement model.

4. The method for recommending demand schemes for computing power networks according to claim 1, characterized in that, Constructing an immersive 3D scene that combines virtual and real elements to match the set of required solutions includes: For each requirement solution in the set of requirement solutions, a pre-built 3D visualization model is used to determine the immersive 3D scene that combines virtual and real elements to match the requirement solution.

5. The method for recommending demand schemes for computing power networks according to claim 1, characterized in that, After recommending the immersive 3D scene combining virtual and real elements to the target user, the method further includes: Receive human body information fed back by the target user through the wearable device; Based on the human body information, determine the target user's actual evaluation effect on each demand solution included in the demand solution set; Based on the actual evaluation effect of each requirement solution included in the requirement solution set, the target requirement solution is determined from the requirement solution set; The immersive 3D scene that combines virtual and real elements and matches the target user's needs will be recommended to the target user.

6. The method for recommending demand schemes for computing power networks according to claim 5, characterized in that, The wearable device includes one or more of the following devices: virtual reality (VR) devices, augmented reality (AR) devices, and mixed reality (MR) devices.

7. The method for recommending demand schemes for computing power networks according to claim 5, characterized in that, The human body information includes one or more of the following: posture information, eye movement information, gesture information, speech-to-text information, text-to-speech information, facial expression information, and question information.

8. The method for recommending demand schemes for computing power networks according to claim 1, characterized in that, The demand information includes edge computing demand and basic demand. The basic demand refers to the basic demand for computing power network resources and application scenarios. The edge computing demand refers to the demand for providing demand solutions based on the location, mobility, latency requirements and jitter requirements of the target user.

9. A demand scheme recommendation device for computing power networks, characterized in that, include: The demand acquisition module is used to acquire information about the target users' demand for computing power networks; The demand solution determination module is used to determine the demand solution set and application scenario set corresponding to the demand information based on the pre-established digital twin model corresponding to the infrastructure of the computing power network. The digital twin model refers to a digital three-dimensional virtual entity model constructed based on the computing power of the computing power network and the operation data of each infrastructure in the computing power network. The demand solution set includes demand solutions corresponding to at least one supplier. Each demand solution includes all computing power network resources that the corresponding supplier can provide in the process of solving the demand information. The 3D scene construction module is used to overlay virtual and real data on different services and effects provided by different suppliers to construct a virtual-real immersive 3D scene that matches the set of demand solutions and the set of application scenarios respectively, and recommend the virtual-real immersive 3D scene to the target user.

10. The demand scheme recommendation device for computing power networks according to claim 9, characterized in that, Also includes: The requirement solution evaluation module is used for: After recommending the virtual-real immersive 3D scene to the target user, the system receives human body information fed back by the target user through a wearable device. Based on the human body information, determine the target user's actual evaluation effect on each demand solution included in the demand solution set; Based on the actual evaluation effect of each requirement solution included in the requirement solution set, the target requirement solution is determined from the requirement solution set; The immersive 3D scene that combines virtual and real elements and matches the target user's needs will be recommended to the target user.

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