Model distribution method and device, electronic equipment and readable storage medium

Abstract

The present disclosure relates to the technical field of Internet, and particularly relates to a model distribution method and device, electronic equipment and readable storage medium. The specific implementation scheme is: obtaining a target model to be distributed; classifying the target model according to a model classification index, and dividing the target model into a corresponding category; selecting a model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies; and distributing the target model to one or more user nodes according to the selected model distribution strategy. The present disclosure classifies categories according to the characteristics of the model to be distributed, and distributes the model through the corresponding model distribution strategy, reasonably utilizes the network resources, and improves the distribution efficiency of the model in the network.

Description

Technical Field

[0001] This disclosure relates to the field of Internet technology, and in particular to model distribution methods, apparatus, electronic devices, and readable storage media. Background Technology

[0002] The Metaverse is a virtual world created and linked through technological means, mapping and interacting with the real world, and possessing a new social system within its digital living space. Essentially, the Metaverse is the virtualization and digitization of the real world, requiring significant modifications to content production, economic systems, user experience, and physical world content. However, the development of the Metaverse is gradual, taking shape through the continuous integration and evolution of numerous tools and platforms, supported by shared infrastructure, standards, and protocols. It provides immersive experiences based on extended reality technology, generates mirror images of the real world based on digital twin technology, and builds an economic system based on blockchain technology, closely integrating the virtual and real worlds in terms of economic, social, and identity systems, and allowing each user to produce content and edit the world.

[0003] Within the metaverse framework, the sheer number and complexity of events lead to a dramatic increase in network data transmission. Various data within the metaverse require constant updates, such as character appearances and events, consuming substantial network resources. Failure to utilize event models effectively will inevitably result in excessive server load, hindering the metaverse's update demands. Therefore, optimizing event model distribution and pre-distribution, and more rationally utilizing and scheduling network resources, is crucial. Summary of the Invention

[0004] This disclosure provides a model distribution method, apparatus, device, and storage medium for the metaverse, aiming to rationally employ different model distribution strategies to distribute various models in the metaverse to various user nodes.

[0005] According to one aspect of this disclosure, a model distribution method is provided, comprising:

[0006] Obtain the target model to be distributed;

[0007] The target model is classified according to a preset model classification index, and the target model is assigned to the corresponding category;

[0008] Select the model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies;

[0009] The target model is distributed to one or more corresponding user nodes according to the selected model distribution strategy.

[0010] Optionally, the target model includes a short-term update event model or a long-term update event model in the metaverse.

[0011] Optionally, the short-term update event model includes metaverse images or social events in the metaverse; the long-term update event model includes a background data processing model.

[0012] Optionally, the model classification metrics include: whether the distribution of the target model requires determining the coordinates of user nodes, and / or whether the distribution of the target model requires the adjacent information of the user nodes, and / or whether the distribution of the target model is related to the density of user nodes.

[0013] According to another aspect of this disclosure, a model distribution apparatus is provided, comprising:

[0014] The acquisition module is used to acquire the target model to be distributed;

[0015] A classification model is used to classify the target model according to a preset model classification index, and to assign the target model to the corresponding category;

[0016] A strategy selection model is used to select the model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies;

[0017] The distribution module is used to distribute the target model to one or more corresponding user nodes according to the selected model distribution strategy.

[0018] Optionally, the target model includes a short-term update event model or a long-term update event model in the metaverse.

[0019] Optionally, the short-term update event model includes metaverse images or social events in the metaverse; the long-term update event model includes a background data processing model.

[0020] Optionally, the model classification metrics include: whether the distribution of the target model requires determining the coordinates of user nodes, and / or whether the distribution of the target model requires the adjacent information of the user nodes, and / or whether the distribution of the target model is related to the density of user nodes.

[0021] This disclosure also provides an electronic device, including:

[0022] At least one processor; and

[0023] A memory communicatively connected to the at least one processor; wherein,

[0024] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the model distribution method described in any of the above technical solutions.

[0025] This disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the model distribution method according to any one of the above embodiments.

[0026] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the model distribution method according to any one of the above embodiments.

[0027] This disclosure provides a model distribution method, apparatus, electronic device, and readable storage medium. The model is categorized according to its characteristics and distributed using a corresponding model distribution strategy, thereby making reasonable use of network resources and improving the efficiency of model distribution in the network.

[0028] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0029] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0030] Figure 1 This is a flowchart illustrating the steps of the model distribution method in an embodiment of this disclosure;

[0031] Figure 2 This is a flowchart illustrating the model distribution method in an embodiment of this disclosure;

[0032] Figure 3 This is a schematic block diagram of the model distribution device in an embodiment of this disclosure. Detailed Implementation

[0033] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0034] In the intelligent and simplified network, business information is primarily disseminated through artificial intelligence (AI) models. By using AI models to compress the first business information to be transmitted into second business information related to the AI ​​model, the data communication volume in the network is significantly reduced, with compression efficiency far exceeding traditional compression algorithms. Specifically, the sending device uses a pre-configured first model to extract the first business information and obtain the second business information to be transmitted; the sending device then transmits the second business information to the receiving device. The receiving device receives the second business information and uses a pre-configured second model to recover the second business information to obtain third business information. The third business information recovered by the second model has slight quality differences compared to the original first business information, but the content is identical, providing a virtually indistinguishable user experience. Before the sending device transmits the second business information to the receiving device, an update module determines whether the receiving device needs to update the second model, and if so, transmits a pre-configured third model to the receiving device. The receiving device then uses the third model to update the second model. By processing business information through a pre-trained AI model, the data transmission volume in communication services can be significantly reduced, greatly improving information transmission efficiency. These models are relatively stable and possess reusability and propagation capabilities. Model propagation and reuse will help enhance network intelligence while reducing overhead and resource waste. The model can be divided into several model slices according to different segmentation rules. These model slices can also be transmitted between different network nodes, and the model slices can be assembled into a model. Model slices can be stored distributed across multiple network nodes. When a network node discovers that it is missing or needs to update a certain model or model slice, it can request it from surrounding nodes that may have that slice.

[0035] The transmission of the business information and the model both occur within a communication network, based on network protocols. The network nodes traversed along the paths for transmitting the business information and the model include intelligent routers. The functions of intelligent routers include, but are not limited to, business information transmission, model transmission, absorbing model self-updates, and security protection. The transmission function of intelligent routers involves transmitting business information or models from a source node to a destination node, with multiple paths existing between the source and destination nodes. The model transmission function of intelligent routers can transmit model slices, improving the model transmission rate by rationally arranging model slices to travel along multiple paths and transmitting model slices via multiple paths.

[0036] Through the aforementioned intelligent and simplified network, the communication efficiency in the network can be greatly improved. For a system like the metaverse with a huge amount of data communication, the communication method of the intelligent and simplified network can be used to realize the updating of various data in the metaverse, and the required data can be transmitted using the model, thereby improving communication efficiency.

[0037] This disclosure provides a model distribution method, such as Figure 1 As shown, it includes:

[0038] Step S101: Obtain the target model to be distributed;

[0039] Step S102: Classify the target model according to the model classification index and assign the target model to the corresponding category;

[0040] Step S103: Select the model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies;

[0041] Step S104: Distribute the target model to one or more corresponding user nodes according to the selected model distribution strategy.

[0042] Specifically, this embodiment uses a fireworks display model in the metaverse as an example. In the metaverse, fireworks displays do not require user nodes to actively subscribe. During the fireworks display event, if a user node's location is within the viewing range of the fireworks display, the fireworks display model is distributed to that user node. For example... Figure 2 As shown, a fireworks display event is designated as event model A. Event A occurs within the time interval m-n, at coordinate O(x,y), and its viewable range has a radius of R. During the time interval m-n, if user node B's coordinates are within the viewable area with coordinate O(x,y) as the origin and R as the radius, then event model A is distributed to user node B.

[0043] Furthermore, during the occurrence of event A, the density of user nodes near point O increases. The viewable area with coordinate O as the origin and R as the radius is called the hotspot area. At this point, the distribution of event model A is related to the density of user nodes. The system classifies event model A as an event model using a distribution strategy based on user node density according to model classification indicators. After receiving event model A, user node B queries the number of its current neighbor nodes and selects user node M, which is located within the hotspot area and currently has the largest number of neighbor nodes, to forward event model A to user node M. Similarly, node M can continue to forward model A to other nodes. Forwarding of the event model is limited to user nodes within the hotspot area; if all neighbor nodes are not within the hotspot area, it is not forwarded.

[0044] The above technical solutions address the challenges of dealing with a large number and diverse types of events in the metaverse, optimize the distribution and pre-distribution of event models, more rationally utilize and schedule network resources, and improve communication efficiency.

[0045] As an optional implementation, the target model includes either a short-term update event model or a long-term update event model within the metaverse. The short-term update event model includes metaverse images or social events, such as updates to user avatars or fireworks displays. These are events that users can actually see and require periodic or intermittent updates. The long-term update event model, on the other hand, involves uncertain update events and includes background data processing models, such as algorithmic models for rendering metaverse images.

[0046] As an optional implementation, model classification metrics include, but are not limited to, one or more of the following: whether the target model needs to determine user node coordinates, whether the target model needs adjacent information of user nodes, and whether the target model is related to user node density. Model classification metrics can be pre-configured in the system, and the system classifies models based on these metrics.

[0047] This disclosure also provides a model distribution device, such as Figure 3 As shown, it includes:

[0048] Module 301 is used to acquire the target model to be distributed;

[0049] Classification model 302 is used to classify the target model according to the preset model classification index and assign the target model to the corresponding category;

[0050] Strategy selection model 303 is used to select the model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies;

[0051] The distribution module 304 is used to distribute the target model to one or more corresponding user nodes according to the selected model distribution strategy.

[0052] Specifically, this embodiment uses a fireworks display model in the metaverse as an example. In the metaverse, fireworks displays do not require user nodes to actively subscribe. During the fireworks display event, if a user node's location is within the viewing range of the fireworks display, the fireworks display model is distributed to that user node. For example... Figure 2 As shown, suppose the fireworks display event acquired by the acquisition module 301 is designated as event model A. Event A occurs within the time period m-n, at coordinate O(x,y), and its viewable range has a radius of R. During the time period m-n, if the coordinates of user node B are located within the viewable area with coordinate O(x,y) as the origin and R as the radius, then event model A is distributed to user node B.

[0053] Furthermore, during the occurrence of event A, the density of user nodes near point O increases, and the viewable area with coordinate O as the origin and R as the radius is called the hotspot area. At this point, the distribution of event model A is related to the density of user nodes. Classification model 302 classifies event model A as an event model using a distribution strategy based on user node density according to model classification indicators. Strategy selection model 303 retrieves the model distribution strategy based on user node density and distributes event model A to user node B within the hotspot area. After receiving event model A, user node B queries the number of its current neighbor nodes and selects user node M, which is located within the hotspot area and currently has the largest number of neighbor nodes, to forward event model A to user node M. Similarly, node M can continue to forward model A to other nodes. Forwarding of the event model is limited to user nodes within the hotspot area; if all neighbor nodes are not within the hotspot area, it is not forwarded.

[0054] The above technical solutions address the challenges of dealing with a large number and diverse types of events in the metaverse, optimize the distribution and pre-distribution of event models, more rationally utilize and schedule network resources, and improve communication efficiency.

[0055] As an optional implementation, the target model includes either a short-term update event model or a long-term update event model within the metaverse. The short-term update event model includes metaverse images or social events, such as updates to user avatars or fireworks displays. These are events that users can actually see and require periodic or intermittent updates. The long-term update event model, on the other hand, involves uncertain update events and includes background data processing models, such as algorithmic models for rendering metaverse images.

[0056] As an optional implementation, model classification metrics include, but are not limited to, one or more of the following: whether the target model needs to determine user node coordinates, whether the target model needs adjacent information of user nodes, and whether the target model is related to user node density. Model classification metrics can be pre-configured in the system, and the system classifies models based on these metrics.

[0057] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0058] Specifically, electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0059] The device includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0060] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0061] The computing unit can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing units include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit performs the various methods and processes described above, such as the model distribution method in the above embodiments. For example, in some embodiments, the model distribution method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the computing unit, one or more steps of the model distribution method described above may be performed. Alternatively, in other embodiments, the computing unit may be configured to perform the model distribution method by any other suitable means (e.g., by means of firmware).

[0062] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0063] The program code used to implement the model distribution method of this disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0064] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0065] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0066] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0067] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0068] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0069] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A model distribution method, characterized in that, include: Obtain the target model to be distributed, which includes short-term update event models or long-term update event models in the metaverse; The target model is classified according to the model classification index, and the target model is divided into the corresponding category. The model classification index includes: whether the distribution of the target model needs to determine the coordinates of user nodes, whether the distribution of the target model needs the adjacent information of user nodes, and whether the distribution of the target model is related to the density of user nodes. The category of the target model is selected from multiple pre-configured model distribution strategies. The corresponding model distribution strategy; The target model is distributed to one or more corresponding user nodes according to the selected model distribution strategy.

2. The model distribution method according to claim 1, characterized in that, The short-term update event model includes metaverse images or social events in the metaverse; the long-term update event model includes a background data processing model.

3. A model distribution device, characterized in that, include: The acquisition module is used to acquire the target model to be distributed, which includes short-term update event models or long-term update event models in the metaverse. A classification model is used to classify the target model according to model classification metrics, and to divide the target model into corresponding categories. The model classification metrics include: whether the target model needs to determine the coordinates of user nodes, whether the target model needs the adjacent information of user nodes, and whether the target model is related to the density of user nodes. A strategy selection model is used to select the model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies; The distribution module is used to distribute the target model to one or more corresponding user nodes according to the selected model distribution strategy.

4. The model distribution device according to claim 3, characterized in that, The short-term update event model includes metaverse images or social events in the metaverse; the long-term update event model includes a background data processing model.

5. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the model distribution method according to claim 1 or 2.

6. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the model distribution method according to claim 1 or 2.

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