CN116738644A

CN116738644A - Model distribution method, device, electronic equipment and readable storage medium

Info

Publication number: CN116738644A
Application number: CN202210197727.8A
Authority: CN
Inventors: 王碧舳; 陈鲁蒙; 董辰; 许晓东; 韩书君
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2022-03-01
Filing date: 2022-03-01
Publication date: 2023-09-12

Abstract

The disclosure relates to the technical field of internet, and in particular relates to a model distribution method, a model distribution device, electronic equipment and a readable storage medium. The specific implementation scheme is as follows: obtaining a target model to be distributed; classifying the target model according to the model classification index, and dividing the target model into corresponding categories; 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 the corresponding one or more user nodes according to the selected model distribution strategy. According to the method and the device, the classification is carried out according to the characteristics of the model to be distributed, the distribution of the model is carried out through the corresponding model distribution strategy, the network resources are reasonably utilized, and the distribution efficiency of the model in the network is improved.

Description

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

Technical Field

The disclosure relates to the technical field of internet, and in particular relates to a model distribution method, a model distribution device, electronic equipment and a readable storage medium.

Background

Metauniverse (Metaverse) is a virtual world linked and created by technological means, mapped and interacted with the real world, and has a digital living space of a novel social system. The metauniverse is essentially a real-world virtualization, digitizing process that requires extensive modification of content production, economic systems, user experience, and physical world content, etc. But the development of the metauniverse is progressive, and is finally formed by continuous fusion and evolution of a plurality of tools and platforms under the support of shared infrastructure, standards and protocols. The method provides immersive experience based on an augmented reality technology, generates a mirror image of the real world based on a digital twin technology, builds an economic system based on a blockchain technology, integrates the virtual world with the real world closely on an economic system, a social system and an identity system, and allows each user to conduct content production and world editing.

Under the meta-universe system, the number of various events is huge and various types are complicated, the data volume of network transmission is rapidly increased, various data in the meta-universe needs to be continuously updated, for example, figure images in the meta-universe, various events which occur and the like need to occupy a large amount of network resources, and if an event model cannot be reasonably utilized, the load of a server is overlarge and the update requirement of the meta-universe cannot be matched. It is therefore particularly important to optimize the distribution and pre-distribution of event models to more reasonably utilize and schedule network resources.

Disclosure of Invention

The disclosure provides a model distribution method, device, equipment and storage medium for a meta-universe, which aim to reasonably distribute various models in the meta-universe to user nodes by adopting different model distribution strategies.

According to an aspect of the present disclosure, there is provided a model distribution method including:

obtaining a target model to be distributed;

classifying the target model according to a preset model classification index, and dividing the target model into corresponding categories;

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 corresponding user nodes according to the selected model distribution strategy.

Optionally, the target model includes a short-term update event model or a long-term update event model in the meta-universe.

Optionally, the short-term update event model includes a metauniverse image or a social event in the metauniverse; the long-term update event model includes a background data processing model.

Optionally, the model classification index includes: whether the target model needs to be distributed to judge the coordinates of the user nodes, whether the target model needs the adjacent information of the user nodes, and/or whether the target model is distributed to be related to the density of the user nodes.

According to another aspect of the present disclosure, there is provided a model distribution apparatus including:

the acquisition module is used for acquiring the target model to be distributed;

the classification model is used for classifying the target model according to a preset model classification index and dividing the target model into corresponding categories;

a strategy selection model, which is used for selecting the model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies;

and the distribution module is used for distributing the target model to one or more corresponding user nodes according to the selected model distribution strategy.

The present disclosure also provides an electronic device, including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the model distribution method of any one of the above-described solutions.

The present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the model distribution method according to any one of the above embodiments.

The present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the model distribution method according to any of the above embodiments.

The invention provides a model distribution method, a device, electronic equipment and a readable storage medium, wherein the model distribution method, the device, the electronic equipment and the readable storage medium divide categories according to the characteristics of a model to be distributed, distribute the model through a corresponding model distribution strategy, reasonably utilize network resources and improve the distribution efficiency of the model in a network.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a step diagram of a model distribution method in an embodiment of the present disclosure;

FIG. 2 is a flow diagram of a model distribution method in an embodiment of the present disclosure;

fig. 3 is a functional block diagram of a model distribution apparatus in an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The intelligent network transmits service information mainly through an artificial intelligent model, and the first service information to be transmitted is compressed into second service information related to the artificial intelligent model through the artificial intelligent model, so that data traffic in the network is greatly reduced, and the compression efficiency is far higher than that of a traditional compression algorithm. The sending end equipment extracts the first service information by utilizing a preconfigured first model and obtains second service information to be transmitted; and the sending end equipment transmits the second service information to the receiving end equipment. The receiving terminal equipment receives the second service information and carries out recovery processing on the second service information by utilizing a second pre-configured model to obtain third service information; the third service information recovered by the second model has a slight quality difference compared with the original first service information, but the third service information and the first service information are consistent in content, and the experience of the user is almost unchanged. Before the sending end device transmits the second service information to the receiving end device, the method further comprises: the updating module judges whether the receiving end equipment needs to update the second model, and transmits a preconfigured third model to the receiving end equipment when judging that the second model needs to be updated, and the receiving end equipment updates the second model by using the third model. The service information is processed through the pre-trained artificial intelligent model, so that the data transmission quantity in the communication service can be obviously reduced, and the information transmission efficiency is greatly improved. These models are relatively stable and have reusability and transmissibility. The propagation and multiplexing of the model will help to enhance network intelligence while reducing overhead and resource waste. The model can be divided into a plurality of model slices according to different dividing rules, the model slices can be transmitted among different network nodes, and the model slices can be assembled into the model. Model slices may be stored scattered across multiple network nodes. When a network node requests to find itself missing or needing to update a model or a slice of a model, it may request from surrounding nodes that may have the slice by way of a request.

The transmission of the service information and the transmission of the model both occur in a communication network, and communication transmission is performed based on a network protocol. The network nodes passing on the path transmitting the traffic information and the model comprise intelligent Jian Lu routers. The functions of the intelligent Jian Lu router include, but are not limited to, business information transmission, model transmission, absorption model self-updating, security protection and the like. The transport function of the intelligent Jian Lu router involves the transport of traffic information or models from a source node to a destination node, where there are multiple paths between the source node and the destination node. The model transmission function of the intelligent Jian Lu router can transmit model slices, and the model slices are multiplexed by reasonably arranging the model slices to travel a plurality of paths, so that the model transmission rate is improved.

Through the intelligent network, the communication efficiency in the network can be greatly improved, and for a system with huge data communication traffic such as the metauniverse, various data updating of the metauniverse can be realized based on the communication method of the intelligent network, and the required data is transmitted by using a model, so that the communication efficiency is improved.

The present disclosure provides a model distribution method, as shown in fig. 1, including:

step S101, obtaining a target model to be distributed;

step S102, classifying the target model according to the model classification index, and dividing the target model into corresponding categories;

step S103, selecting a model distribution strategy corresponding to the category of the target model from a plurality of pre-configured model distribution strategies;

step S104, distributing the target model to the corresponding one or more user nodes according to the selected model distribution strategy.

Specifically, the present embodiment exemplifies a pyrotechnic performance model in the meta-universe. In the meta-universe, a pyrotechnic show does not require active subscription by a user node, and during this event of the pyrotechnic show, if the user node's location is within the viewable range of the pyrotechnic show, a pyrotechnic show model is distributed to the user node. For example, as shown in fig. 2, a pyrotechnic show event is set as an event model a, the event a occurs during the period of m-n, the event a occurs at the location of the coordinates O (x, y), and the viewable range distance radius is R. During this period of time at m-n, if the coordinates of the user node B are within a viewable area with the origin of the coordinates O (x, y) and the radius R, then the event model a is distributed to the user node B.

Further, during the occurrence of the event a, the density of user nodes near the O-point increases, and the viewable area with the coordinate O as the origin and R as the radius is called a hot spot area. The distribution of the event model a is now related to the density of the user nodes, and the system classifies the event model a as an event model using a distribution strategy based on the density of the user nodes according to model classification metrics. After receiving the event model A, the user node B inquires the current neighbor node number of the node B, selects the user node M which is positioned in the hot spot area and has the maximum neighbor node number, forwards the event model A to the user node M, and similarly, the node M can also continuously forward the model A to other nodes; the forwarding of the event model is limited to the user nodes in the hot spot area, and if all the neighbor nodes are not in the hot spot area, the forwarding is not performed.

Through the technical scheme, the problems of huge quantity and various kinds of events in the meta universe are solved, distribution and pre-distribution of event models are optimized, network resources are more reasonably utilized and scheduled, and communication efficiency is improved.

As an alternative embodiment, the target model includes a short-term update event model or a long-term update event model in the meta-universe. Wherein, the short-term update event model comprises meta-universe images or social events in meta-universe, such as updating of user images, firework performance and the like, which belong to the short-term update model, are events which can be actually seen by a user and need to be updated regularly or irregularly; the long-term update event model updates event uncertainty, which includes a background data processing model, such as an algorithmic model that renders a metauniverse.

As an alternative embodiment, model classification metrics include, but are not limited to: whether the distribution target model needs to judge the coordinates of the user nodes, whether the distribution target model needs the adjacent information of the user nodes, and whether the distribution target model is related to one or more of the densities of the user nodes. The model classification index may be preconfigured in the system, and the system classifies the model according to the model classification index.

The present disclosure also provides a model distribution apparatus, as shown in fig. 3, including:

an obtaining module 301, configured to obtain a target model to be distributed;

the classification model 302 is used for classifying the target model according to a preset model classification index, and classifying the target model into corresponding categories;

a policy selection model 303, configured to select a model distribution policy corresponding to a category of the target model from a plurality of model distribution policies configured in advance;

a distribution module 304, configured to distribute the target model to the corresponding one or more user nodes according to the selected model distribution policy.

Specifically, the present embodiment exemplifies a pyrotechnic performance model in the meta-universe. In the meta-universe, a pyrotechnic show does not require active subscription by a user node, and during this event of the pyrotechnic show, if the user node's location is within the viewable range of the pyrotechnic show, a pyrotechnic show model is distributed to the user node. For example, as shown in fig. 2, assuming that a pyrotechnic show event acquired by the acquisition module 301 is set as an event model a, the event a occurs during the period of m-n, and the event a occurs at the coordinates O (x, y) with the range-of-view distance radius R. During this period of time at m-n, if the coordinates of the user node B are within a viewable area with the origin of the coordinates O (x, y) and the radius R, then the event model a is distributed to the user node B.

Further, during the occurrence of the event a, the density of user nodes near the O-point increases, and the viewable area with the coordinate O as the origin and R as the radius is called a hot spot area. At this time, the distribution of the event model a is related to the density of the user nodes, and the classification model 302 classifies the event model a into the event model using the distribution policy based on the density of the user nodes according to the model classification index, and the policy selection model 303 invokes the model distribution policy based on the density of the user nodes to distribute the event model a to the user nodes B in the hotspot region. After receiving the event model A, the user node B inquires the current neighbor node number of the node B, selects the user node M which is positioned in the hot spot area and has the maximum neighbor node number, forwards the event model A to the user node M, and similarly, the node M can also continuously forward the model A to other nodes; the forwarding of the event model is limited to the user nodes in the hot spot area, and if all the neighbor nodes are not in the hot spot area, the forwarding is not performed.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

In particular, electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

The apparatus includes a computing unit that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) or a computer program loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device may also be stored. The computing unit, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.

A plurality of components in a device are connected to an I/O interface, comprising: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, speakers, and the like; a storage unit such as a magnetic disk, an optical disk, or the like; and communication units such as network cards, modems, wireless communication transceivers, and the like. The communication unit allows the device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing units include, but are not limited to, central Processing Units (CPUs), graphics Processing Units (GPUs), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processors, controllers, microcontrollers, and the like. The computing unit performs the respective methods and processes described above, such as the model distribution method in the above-described embodiment. For example, in some embodiments, the model distribution method may be implemented as a computer software program tangibly embodied on 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 onto the device via the ROM and/or the communication unit. One or more steps of the model distribution method described above may be performed when the computer program is loaded into RAM and executed by a computing unit. 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).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out the model distribution methods of the present disclosure can be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A model distribution method, comprising:

obtaining a target model to be distributed;

classifying the target model according to model classification indexes, and dividing the target model into corresponding categories;

2. The model distribution method according to claim 1, wherein the target model includes a short-term update event model or a long-term update event model in a meta-universe.

3. The model distribution method according to claim 2, wherein the short-term update event model includes a metauniverse image or a social event in the metauniverse; the long-term update event model includes a background data processing model.

4. A model distribution method according to any one of claims 1-3, wherein the model classification index comprises: whether the target model needs to be distributed to judge the coordinates of the user nodes, whether the target model needs the adjacent information of the user nodes, and/or whether the target model is distributed to be related to the density of the user nodes.

5. A model distribution apparatus, comprising:

the classification model is used for classifying the target model according to model classification indexes and dividing the target model into corresponding categories;

6. The model distribution apparatus according to claim 5, wherein the target model includes a short-term update event model or a long-term update event model in a meta space.

7. The model distribution apparatus according to claim 6, wherein the short-term update event model includes a metauniverse image or a social event in the metauniverse; the long-term update event model includes a background data processing model.

8. The model distribution apparatus according to any one of claims 5 to 7, wherein the model classification index includes: whether the target model needs to be distributed to judge the coordinates of the user nodes, whether the target model needs the adjacent information of the user nodes, and/or whether the target model is distributed to be related to the density of the user nodes.

9. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the model distribution method of any one of claims 1-4.

10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the model distribution method according to any one of claims 1-4.