CN116137596A - AI information transmission method and device - Google Patents

AI information transmission method and device Download PDF

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CN116137596A
CN116137596A CN202111358543.7A CN202111358543A CN116137596A CN 116137596 A CN116137596 A CN 116137596A CN 202111358543 A CN202111358543 A CN 202111358543A CN 116137596 A CN116137596 A CN 116137596A
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information
model representation
communication device
model
mode
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任千尧
孙鹏
纪子超
吴晓波
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Vivo Mobile Communication Co Ltd
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Vivo Mobile Communication Co Ltd
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Priority to PCT/CN2022/132085 priority patent/WO2023088268A1/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

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Abstract

The embodiment of the application discloses a transmission method and equipment of AI information, belonging to the technical field of communication. The AI information transmission method of the embodiment of the application comprises the following steps: the first communication device generates AI information in a first AI model representation mode; the AI information support under the first AI model representation mode is converted into AI information under the second AI model representation mode; the first communication device sends AI information in the first AI model representation mode to the second communication device; wherein the second communication device uses the second AI model representation.

Description

AI information transmission method and device
Technical Field
The application belongs to the technical field of communication, and particularly relates to a transmission method and equipment of artificial intelligence (Artificial Intelligence, AI) information, wherein the equipment can comprise communication equipment such as an AI information transmission device, a terminal or network side equipment and the like.
Background
The application of AI technology to a communication system can significantly improve communication system performance. However, since the common AI model representation (such as AI framework) is more, the emphasis points of different AI model representations and even the supported development languages are different, and the description and the implementation of the functions of the AI model are different, so that AI information cannot be transferred between two or more communication devices using different AI model representations, and the performance of the communication system is affected.
Disclosure of Invention
The embodiment of the application provides an AI information transmission method and equipment, which can solve the problem that the performance of a communication system is affected because AI information cannot be transmitted between communication equipment using different AI expression methods.
In a first aspect, there is provided a transmission method of AI information, including: the first communication device generates AI information in a first AI model representation mode; the AI information support under the first AI model representation mode is converted into AI information under the second AI model representation mode; the first communication device sends AI information in the first AI model representation mode to the second communication device; wherein the second communication device uses the second AI model representation.
In a second aspect, there is provided a transmission apparatus of AI information, including: the generation module is used for generating the AI information in the first AI model representation mode; the AI information support under the first AI model representation mode is converted into AI information under the second AI model representation mode; a sending module, configured to send AI information in the first AI model representation mode to a second communication device; wherein the second communication device uses the second AI model representation.
In a third aspect, there is provided a communication device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which program or instruction when executed by the processor implements the method according to the first aspect.
In a fourth aspect, a communication device is provided, including a processor and a communication interface, where the processor is configured to generate AI information in a first AI model representation; the communication interface is used for sending the AI information in the first AI model representation mode to the second communication equipment; wherein the second communication device uses the second AI model representation.
In a fifth aspect, there is provided a readable storage medium having stored thereon a program or instructions which when executed by a processor implement the method according to the first aspect.
In a sixth aspect, there is provided a chip comprising a processor and a communication interface coupled to the processor for running a program or instructions to implement the method of the first aspect.
In a seventh aspect, a computer program/program product is provided, the computer program/program product being stored in a non-transitory storage medium, the computer program/program product being executed by at least one processor to implement the method according to the first aspect.
In the embodiment of the application, the first communication device generates the AI information in the first AI model representation and sends the AI information to the second communication device, and because the AI information in the first AI model representation is supported and converted into the AI information in the second AI model representation, the second communication device using the second AI model representation receives the AI information in the first AI model representation and loads the AI information in the second AI model representation to obtain the AI information in the second AI model representation. According to the embodiment of the application, the communication equipment using different AI model expression modes can keep consistent understanding of the AI information, so that the communication equipment using different AI model expression modes can transmit the AI information, and the communication system performance is improved.
Drawings
Fig. 1 is a schematic diagram of a wireless communication system according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a transmission method of AI information according to an embodiment of the application;
Fig. 3 is a schematic structural diagram of a transmission apparatus of AI information according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a communication device according to an embodiment of the present application;
fig. 5 is a schematic structural view of a terminal according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a network-side device according to an embodiment of the present application.
Detailed Description
Technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the terms "first" and "second" are generally intended to be used in a generic sense and not to limit the number of objects, for example, the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/" generally means a relationship in which the associated object is an "or" before and after.
It is noted that the techniques described in embodiments of the present application are not limited to long term evolution (Long Term Evolution, LTE)/LTE evolution (LTE-Advanced, LTE-a) systems, but may also be used in other wireless communication systems, such as code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), single carrier frequency division multiple access (Single carrier-Frequency Division Multiple Access, SC-FDMA), and other systems. The terms "system" and "network" in embodiments of the present application are often used interchangeably, and the techniques described may be used for both the above-mentioned systems and radio technologies, as well as other systems and radio technologies. The following description describes a New air interface (New radius for purposes of illustrationo, NR) systems, and in most of the following description the NR terminology is used, these techniques are also applicable to applications other than NR system applications, such as generation 6 (6) th Generation, 6G) communication system.
Fig. 1 shows a schematic diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 may also be called a terminal Device or a User Equipment (UE), and the terminal 11 may be a terminal-side Device such as a mobile phone, a tablet (Tablet Personal Computer), a Laptop (Laptop Computer) or a notebook (Personal Digital Assistant, PDA), a palm Computer, a netbook, an ultra-mobile personal Computer (ultra-mobile personal Computer, UMPC), a mobile internet Device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/Virtual Reality (VR) Device, a robot, a Wearable Device (VUE), a pedestrian terminal (PUE), a smart home (home Device with a wireless communication function, such as a refrigerator, a television, a washing machine, or furniture, etc.), and the Wearable Device includes: intelligent watches, intelligent bracelets, intelligent headphones, intelligent glasses, intelligent jewelry (intelligent bracelets, intelligent rings, intelligent necklaces, intelligent bracelets, intelligent footchains, etc.), intelligent bracelets, intelligent clothing, game machines, etc. Note that, the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may be a base station or a core network, wherein the base station may be called a node B, an evolved node B, an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service set (Basic Service Set, BSS), an extended service set (Extended Service Set, ESS), a node B, an evolved node B (eNB), a next generation node B (gNB), a home node B, a home evolved node B, a WLAN access point, a WiFi node, a transmission and reception point (Transmitting Receiving Point, TRP), or some other suitable terminology in the field, and the base station is not limited to a specific technical vocabulary, and it should be noted that, in the embodiment of the present application, only the base station in the NR system is taken as an example, but the specific type of the base station is not limited.
The following describes in detail, with reference to the attached drawings, the AI information transmission method and apparatus provided by the embodiments of the present application through some embodiments and application scenarios thereof.
As shown in fig. 2, the embodiment of the present application provides a method 200 for transmitting AI information, which may be performed by a first communication device, in other words, by software or hardware installed in the first communication device, where the first communication device may be a network side device or a terminal, and the method includes the following steps.
S202: the first communication device generates AI information in a first AI model representation mode; wherein the AI information support in the first AI model representation is converted to AI information support in the second AI model representation.
S204: the first communication device sends AI information in the first AI model representation mode to the second communication device; wherein the second communication device uses the second AI model representation.
The AI model representation modes mentioned in the embodiments of the present application, such as the first AI model representation mode, the second AI model representation mode, the third AI model representation mode, and the like, may specifically be an AI framework, or may be a template or a data structure (such as ONNX, etc.) that independently represents an AI model, where the template or the data structure is only responsible for representing the AI model, and is not responsible for training inference, and the like. The AI frameworks include TensorFlow, pyTorch, keras, MXNet, caffe2, etc., and each AI framework describes the AI model by using its own method to complete the operations of building, training, and deducing the AI model.
The AI information referred to in various embodiments of the present application may include AI models (or AI networks), parameters, structures, etc., including, for example, neural network models, decision tree models, support vector machine models, bayesian classifier models, etc. The following embodiments will be described by taking an AI model as an example, and it should be understood that the AI information is not limited to the AI model.
In one example, the first communication device uses the third AI model representation, and generating AI information in the first AI model representation in S202 includes: the first communication device converts or saves the AI information in the third AI model representation as AI information in the first AI model representation. The "conversion" mentioned in this example, for example, the third AI model representation is a TensorFlow, the first AI model representation is a pyrerch, and the first communication device saves the AI model under the TensorFlow as information in the TensorFlow format and then converts it to information in the pyrerch format. In this example, the "save" is, for example, that the third AI model expression is TensorFlow, the first AI model expression is pyrerch, and the first communication device saves the AI model under TensorFlow as information in pyrerch format.
In this example, the first AI model representation may be referred to as an intermediate AI model representation (e.g., an intermediate AI framework), i.e., the first communication device and the second communication device select one of the intermediate AI model representations to transmit AI information, which supports AI information converted into the second AI model representation. In this way, after receiving the AI information in the first AI model representation, the second communication device using the second AI model representation may load the AI information in the second AI model representation to obtain the AI information in the second AI model representation. Alternatively, the intermediate AI model representation may be an open neural network exchange (Open Neural Network Exchange, ONNX).
In another example, the first communication device uses the first AI model representation. In this example, the first communication device may generate AI information (e.g., AI model) using the first AI model representation (e.g., AI framework). For example, the first communication device may train the AI model using the AI framework, resulting in a trained AI model.
In this example, the second communication device may learn the type of the first AI model representation through a predetermined manner or according to an instruction of the first communication device, for example, the second communication device may learn that the first AI model representation is PyTorch, and the AI information in the first AI model representation is an AI model and a parameter in PyTorch. In this way, the second communication device can load the received AI information into the second AI model representation, and combine the types of the first AI model representation to obtain the AI information in the second AI model representation, for example, the AI model in MXNet is obtained by converting the MXNet module by using the PyTorch developed in MXNet.
According to the AI information transmission method provided by the embodiment of the application, the first communication device generates the AI information in the first AI model representation mode and sends the AI information to the second communication device, and the AI information in the first AI model representation mode is converted into the AI information in the second AI model representation mode, so that the second communication device using the second AI model representation mode receives the AI information in the first AI model representation mode and loads the AI information in the second AI model representation mode to obtain the AI information in the second AI model representation mode. According to the embodiment of the application, the communication equipment using different AI model expression modes can keep consistent understanding of the AI information, so that the communication equipment using different AI model expression modes can transmit the AI information, and the communication system performance is improved.
The embodiment 200 mainly describes the transmission process of AI information, and actually, before the embodiment 200, the AI model representation for transmitting AI information may be configured in advance; the AI model representation used to transmit AI information may also be modified during or after execution of embodiment 200.
Optionally, the method provided in embodiment 200 may further include the steps of: the first communication device sends configuration information, and the second communication device may also receive the configuration information, where the configuration information is used to configure or modify the AI model representation used to transmit the AI information.
This embodiment may configure or modify the AI model representation for transmitting AI information, for example, by higher-layer signaling. Alternatively, the network side device may know the AI model representation used by the terminal side, and configure each terminal with a corresponding AI model representation, where the network side may need to train the AI network under multiple AI model representations at the same time. Optionally, the network side device may also instruct the terminal to use the same framework AI model representation as the network side device. Alternatively, the network-side device may specify the use of an intermediate AI model representation (e.g., an intermediate AI framework) to transmit AI information.
In one example, the AI model representation configured by the configuration information for transmitting AI information is the first AI model representation, and the first communication device uses the first AI model representation, and the method further includes: the first communication device receives AI information from the second communication device and loads the received AI information into the first AI model representation; the second communication device is further configured to convert AI information in the second AI model representation mode into AI information in the first AI model representation mode. In S200, the first communication device transmits AI information, and the first communication device receives AI information.
In this example, for example, the first communication device is a network-side device, the second communication device is a terminal, the AI model is expressed as an AI framework, and the AI framework configured by the network-side device for transmitting AI information is the same as the AI framework used by the network-side device. In this way, the terminal needs to convert the AI model under the AI framework used by itself into the AI information under the AI framework configured according to the AI framework configured by the network side device, and send the AI information to the network side device. After the network side equipment receives the AI information, the received AI information is loaded into the AI framework according to the preset AI framework, and the specific loading mode is realized for the network side equipment.
In another example, the AI model representation configured by the configuration information for transmitting AI information is the second AI model representation, and the first communication device uses a first AI model representation, and the method further includes: the first communication device converts or stores the AI information in the first AI model representation mode into the AI information in the second AI model representation mode; and the first communication device sends the AI information in the second AI model representation mode to the second communication device.
In this example, for example, the first communication device is a network-side device, the second communication device is a terminal, the AI model is expressed in an AI framework, and the AI framework configured by the network-side device for transmitting AI information is the same as the AI framework used by the terminal. In this way, the network side device converts the AI model under the AI framework of the network side device into the AI information under the AI framework of the network side device according to the AI framework of the network side device and sends the AI information to the terminal. After the terminal receives the AI information, the terminal loads the received AI information into the AI frame according to the configured AI frame, and the specific loading mode is realized by the terminal.
Optionally, on the basis of the foregoing embodiments, the generating, by the first communication device, AI information in the first AI model representation includes: and the first communication equipment respectively generates AI information in the first AI model representation mode according to the AI model representation modes used by each second communication equipment.
In this example, for example, the first communication device is a network-side device, the second communication device is a terminal, and the network-side device may know AI model representations used by a plurality of terminals, respectively, where the network-side may need to train the AI network under the plurality of AI model representations at the same time.
Optionally, on the basis of the foregoing embodiments, the method further includes at least one of:
1) The first communication device sends first indication information to the second communication device, wherein the first indication information is used for indicating the second communication device to use the same AI model representation mode as the first communication device. For example, the network side device may instruct the terminal to use the same framework AI model representation as the network side device.
2) The first communication device sends second indication information to the second communication device, wherein the second indication information is used for indicating the type of the first AI model representation mode. The network-side device may specify that the AI information is to be transmitted using an intermediate AI model representation (e.g., an intermediate AI framework), where the first AI model representation is the intermediate AI model representation.
In the foregoing embodiments, the first communication device is a network-side device, and the second communication device is a terminal, which is described by taking as an example, in fact, the first communication device and the second communication device may also be two parallel nodes, for example, both are terminals, so S200 may further include the following steps: the first communication device receives configuration information from the third communication device, wherein the configuration information is used for configuring or modifying an AI model representation mode for transmitting AI information; wherein the third communication device is further configured to send the configuration information to the second communication device.
In this embodiment, the first communication device and the second communication device may be two parallel nodes, e.g. both terminals; the third communication device is a third party node, for example, the third communication device is a network side device, and the network side device configures or modifies an AI model representation for transmitting AI information for the terminal.
Optionally, before the first communication device generates the AI information in the first AI model representation, on the basis of embodiment 200, the method further includes: the first communication device sends third indication information, wherein the third indication information is used for indicating the type of the first AI model representation mode.
In this embodiment, the first communication device may indicate the type of the first AI model representation to the second communication device, so that the second communication device may load the received AI information into the second AI model representation, and combine with the type of the first AI model representation to obtain the AI information in the second AI model representation.
In this embodiment, specifically, for example, the first communication device sends the used AI model representation to the second communication device in advance, and the second communication device loads the content such as the AI model and the parameter in the AI information into its own AI model representation according to the AI model representation corresponding to each AI information.
In this embodiment, in the case where the first communication device is a network-side device and the second communication device is a terminal, the network-side device may configure an AI model representation used by an AI network of a certain function for a period of time when AI information is transferred each time through radio resource control (Radio Resource Control, RRC) signaling (e.g., the third indication information described above), and then the AI information in the AI model representation is transferred at a corresponding time. Alternatively, before each AI information transfer, the network side device instructs the AI model representation to be used by using downlink control information (Downlink Control Information, DCI) or medium access control unit (Media Access Control Control Element, MAC CE), and then transfers the AI information in the AI model representation.
Optionally, before the first communication device generates the AI information in the first AI model representation, on the basis of embodiment 200, the method further includes: the first communication device determines the first AI model representation mode from a plurality of AI model representation modes according to AI information to be sent; the AI information sent by the first communication device includes fourth indication information, where the fourth indication information is used to indicate a type of the first AI model representation mode. Optionally, the data amount of the AI information in the first AI model representation is the smallest data amount in the plurality of AI information in the plurality of AI model representations.
In this embodiment, for example, the AI information sent by the first communication device may use different AI model representations each time, for example, the first communication device may adjust the applicable AI model representations according to the content of the AI information to be sent, ensure that the transmission bits are as low as possible, and add information indicating the AI model representations used to the transmitted information. After the second communication device receives the AI information, the second communication device determines the used representation method according to the information of the AI model representation mode, and then loads the received AI information into the AI model representation mode.
In this embodiment, when the first communication device is a network side device and the second communication device is a terminal, the network side device carries fourth indication information in the transmitted AI information, where the fourth indication information may be represented by a specific location, and the terminal first decodes the fourth indication information and then uses a corresponding method to load the subsequent AI information.
In order to describe the AI information transmission method provided in the embodiments of the present application in detail, several specific embodiments will be described below.
Example 1
In this embodiment, two nodes (e.g., a first communication device and a second communication device) using different AI frameworks select one intermediate AI framework for AI-information transfer.
For example, node a stores information such as AI model and parameters trained under its own AI framework according to the structure of ONNX; the node A transmits the produced ONNX file to the node B; and the node B loads the ONNX file under the own AI frame according to the received ONNX file, and converts the ONNX file to obtain an AI model under the own AI frame and corresponding parameters.
In this embodiment, the node a and the node B may be any two higher-level nodes using AI functions, or may be a base station and a terminal or a terminal and a base station. The intermediate AI framework may employ ONNX or other defined AI structures. The ONNX is a neural network interaction structure and is mainly used for transmitting the neural network among different AI frameworks. ONNX defines a general computational graph into which computational graphs constructed by different neural network frameworks can be converted, thereby enabling the transfer of trained AI models to other AI frameworks.
Example two
This embodiment mainly describes CSI feedback process based on AI model (or AI network).
The coding and decoding combined training is needed for the CSI feedback based on the AI, the coding is usually carried out at the terminal, the decoding is carried out at the base station, the base station can continuously train the coding and decoding AI network of the base station, when the terminal is accessed to the cell, the base station can send the AI network structure and parameters of the coding part to individual users, after the users receive the information, the user builds the AI network of the base station, then the AI network is utilized to carry out coding and reporting of the CSI information, the base station carries out decoding according to the CSI coding information reported by the terminal, and then the channel is recovered and scheduled.
If the AI frames used by the base station and the user are different, such as PyTorch used by the terminal and TensorFlow used by the network side, the network side stores the AI network trained under the TensorFlow into a file with an ONNX file structure, the ONNX file is transmitted to the terminal, and after the terminal receives the file, the ONNX file is loaded into the PyTorch of the terminal to obtain the same AI network of different description methods under the PyTorch.
Optionally, the base station may send the indication information in advance, or directly configure the terminal with the AI interaction model representation method as a TensorFlow when the terminal is accessed, so that the network side directly stores the trained AI network into a file structure of the TensorFlow, transmits the AI network to the user, and loads the file of the TensorFlow under the PyTorch by the user after receiving the AI network structure and parameters.
Or the network side can inform the terminal of using the model representation method of PyTorch, the network side stores the trained AI network into a file structure of the PyTorch, then sends the file to the terminal, and the terminal directly loads the PyTorch file to obtain the AI network.
Optionally, the network side may not interact with the terminal in advance to form an AI model representation, the network side stores the trained network into a file structure of a TensorFlow, sends the information such as the TensorFlow and the corresponding version information and the trained AI network to the terminal, after the terminal receives the information, firstly obtains the AI network representation as the TensorFlow, then obtains the version information of the TensorFlow, and then loads the received AI network into the pyrerch according to the file structure of the TensorFlow.
Specifically, the TensorFlow and the corresponding version information may be directly transmitted in a character string manner, or may be an index (index) of some parameter combinations agreed by the protocol, and the index is directly transmitted.
Example III
This embodiment mainly describes a positioning procedure based on an AI model (or AI network).
There are many methods for AI-based positioning, for example, a user may perform training for positioning the AI network on the terminal side, and analyze a channel by using a received positioning reference signal to obtain position information.
The training of the AI network requires a large number of samples, if the user a is in a certain area for a long time, the user B just enters the area, and at this time, the user B needs a long time to restart the training, so that the training can be continued by using the AI network already trained by the user a, and the training time is reduced.
If the AI frames used by the user a and the user B are different, for example, caffe2 used by the user a and Keras used by the user B, the user a may save the trained AI network as the ONNX file structure, then transmit the file to the user B, and after receiving the file, the user B loads the ONNX file into its Keras structure, and continues training.
Optionally, the user a may save the trained AI network as a caffe2 structure, then send the caffe2 and the corresponding version information together to the user B, after receiving the information, the user B decodes the description method with the network structure of caffe2, and then loads the caffe2 file into its Keras.
Or the user a may send indication information to the user B through a sidelink (sidelink) in advance, notifying the user B that the network used by itself is caffe2.
Optionally, the user B may send an application to the network side, apply that the network side sends the AI network of the user a to the user B, and report the supportable model representation mode of the user B at the same time, where the network side discovers that the supportable model representation mode reported by the user B and the known supportable model representation mode of the user a do not have the same representation mode, and sends the user a model representation mode of the user a to the user B, and notifies the user a to send an AI network file saved based on the Caffe2 structure, and after the user B receives the file, loads the Caffe2 file into the Keras of the user B, or the network side wants the user a to send the Keras of the model representation mode of the user B, and the user a converts the network trained by the user a into the file of the Keras representation method, and sends the file to the user B.
It should be noted that, in the AI information transmission method provided in the embodiment of the present application, the execution body may be an AI information transmission device, or a control module for executing the AI information transmission method in the AI information transmission device. In the embodiment of the present application, a method for transmitting AI information by using a transmitting device for AI information is taken as an example, and the transmitting device for AI information provided in the embodiment of the present application is described.
Fig. 3 is a schematic structural diagram of an AI information transmission apparatus according to an embodiment of the present application, which may correspond to the first communication device in other embodiments. As shown in fig. 3, the apparatus 300 includes the following modules.
A generating module 302, configured to generate AI information in a first AI model representation; wherein the AI information support in the first AI model representation is converted to AI information support in the second AI model representation.
A sending module 304, configured to send, to a second communication device, AI information in the first AI model representation; wherein the second communication device uses the second AI model representation.
In this embodiment of the present application, the AI information transmission apparatus generates AI information in a first AI model representation and sends the AI information to the second communication device, and because the AI information in the first AI model representation is supported and converted into AI information in a second AI model representation, the second communication device using the second AI model representation receives the AI information in the first AI model representation and loads the AI information in the second AI model representation to obtain AI information in the second AI model representation. According to the embodiment of the application, the communication equipment using different AI model expression modes can keep consistent understanding of the AI information, so that the communication equipment using different AI model expression modes can transmit the AI information, and the communication system performance is improved.
Optionally, as an embodiment, the apparatus uses a third AI model representation, and the generating module 302 is configured to convert or store AI information in the third AI model representation into AI information in the first AI model representation; alternatively, the apparatus uses the first AI model representation.
Optionally, as an embodiment, the sending module 304 is further configured to send configuration information, where the configuration information is used to configure or modify the AI model representation for transmitting AI information.
Optionally, as an embodiment, the AI model representation for transmitting AI information is the first AI model representation, and the apparatus uses the first AI model representation, and the apparatus further includes a receiving module configured to receive AI information from the second communication device and load the received AI information into the first AI model representation; the second communication device is further configured to convert AI information in the second AI model representation mode into AI information in the first AI model representation mode.
Optionally, as an embodiment, the AI model representation for transmitting AI information is the second AI model representation, and the apparatus uses a first AI model representation; the generating module 302 is further configured to convert or store AI information in the first AI model representation into AI information in the second AI model representation; the sending module 304 is further configured to send AI information in the second AI model representation to the second communication device.
Optionally, as an embodiment, the generating module 302 is configured to generate the AI information in the first AI model representation according to the AI model representation used by each of the second communication devices.
Optionally, as an embodiment, the sending module 304 is further configured to at least one of: 1) Transmitting first indication information to the second communication device, wherein the first indication information is used for indicating the second communication device to use the same AI model representation mode as the device; 2) And sending second indicating information to the second communication device, wherein the second indicating information is used for indicating the type of the first AI model representation mode.
Optionally, as an embodiment, the apparatus further includes a receiving module, configured to receive configuration information from the third communications device, where the configuration information is used to configure or modify an AI model representation for transmitting AI information; wherein the third communication device is further configured to send the configuration information to the second communication device.
Optionally, as an embodiment, the sending module 304 is further configured to send third indication information, where the third indication information is used to indicate a type of the first AI model representation.
Optionally, as an embodiment, the apparatus further includes a determining module, configured to determine, according to AI information to be sent, the first AI model representation from a plurality of AI model representations; the AI information sent by the sending module 304 includes fourth indication information, where the fourth indication information is used to indicate a type of the first AI model representation.
Optionally, as an embodiment, the data amount of the AI information in the first AI model representation is the smallest data amount in the plurality of AI information in the plurality of AI model representations.
The apparatus 300 according to the embodiment of the present application may refer to the flow of the method 200 corresponding to the embodiment of the present application, and each unit/module in the apparatus 300 and the other operations and/or functions described above are respectively for implementing the corresponding flow in the method 200, and may achieve the same or equivalent technical effects, which are not described herein for brevity.
The AI information transmission device in this embodiment of the present application may be a device, a device with an operating system, or an electronic apparatus, or may be a component, an integrated circuit, or a chip in a terminal. The apparatus or electronic device may be a mobile terminal or a non-mobile terminal. By way of example, mobile terminals may include, but are not limited to, the types of terminals 11 listed above, and non-mobile terminals may be servers, network attached storage (Network Attached Storage, NAS), personal computers (personal computer, PCs), televisions (TVs), teller machines, self-service machines, etc., and embodiments of the present application are not limited in detail.
The AI information transmission device provided in this embodiment of the present application can implement each process implemented by the method embodiment of fig. 2, and achieve the same technical effects, so that repetition is avoided, and no further description is provided herein.
Optionally, as shown in fig. 4, the embodiment of the present application further provides a communication device 400, including a processor 401, a memory 402, and a program or an instruction stored in the memory 402 and capable of running on the processor 401, where, for example, the communication device 400 is a terminal, the program or the instruction is executed by the processor 401 to implement each process of the above-mentioned AI information transmission method embodiment, and achieve the same technical effects. When the communication device 400 is a network side device, the program or the instruction, when executed by the processor 401, implements the processes of the above-mentioned AI information transmission method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
The embodiment of the application also provides a terminal, which comprises a processor and a communication interface, wherein the processor is used for generating the AI information in the first AI model representation mode; the communication interface is used for transmitting the AI information in the first AI model representation mode to the second communication equipment; wherein the second communication device uses the second AI model representation. The terminal embodiment corresponds to the terminal-side method embodiment, and each implementation process and implementation manner of the method embodiment are applicable to the terminal embodiment and can achieve the same technical effects. Specifically, fig. 5 is a schematic hardware structure of a terminal for implementing an embodiment of the present application.
The terminal 500 includes, but is not limited to: at least some of the components of the radio frequency unit 501, the network module 502, the audio output unit 503, the input unit 504, the sensor 505, the display unit 506, the user input unit 507, the interface unit 508, the memory 509, and the processor 510.
Those skilled in the art will appreciate that the terminal 500 may further include a power source (e.g., a battery) for powering the various components, and the power source may be logically coupled to the processor 510 via a power management system so as to perform functions such as managing charging, discharging, and power consumption via the power management system. The terminal structure shown in fig. 5 does not constitute a limitation of the terminal, and the terminal may include more or less components than shown, or may combine certain components, or may be arranged in different components, which will not be described in detail herein.
It should be appreciated that in embodiments of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042, with the graphics processor 5041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 507 includes a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen. Touch panel 5071 may include two parts, a touch detection device and a touch controller. Other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.
In this embodiment, after receiving downlink data from a network side device, the radio frequency unit 501 processes the downlink data with the processor 510; in addition, the uplink data is sent to the network side equipment. Typically, the radio frequency unit 501 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
The memory 509 may be used to store software programs or instructions as well as various data. The memory 509 may mainly include a storage program or instruction area and a storage data area, wherein the storage program or instruction area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. In addition, the Memory 509 may include a high-speed random access Memory, and may further include a non-transitory Memory, wherein the non-transitory Memory may be a Read Only Memory (ROM), a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable EPROM (EEPROM), or a flash Memory. Such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device.
Processor 510 may include one or more processing units; alternatively, the processor 510 may integrate an application processor that primarily processes operating systems, user interfaces, and applications or instructions, etc., with a modem processor that primarily processes wireless communications, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into the processor 510.
The processor 510 may be configured to generate AI information in a first AI model representation; the AI information support under the first AI model representation mode is converted into AI information under the second AI model representation mode; the radio frequency unit 501 may be configured to send AI information in the first AI mode representation to a second communication device; wherein the second communication device uses the second AI model representation.
In the embodiment of the application, the terminal generates the AI information in the first AI model representation mode and sends the AI information to the second communication device, and because the AI information in the first AI model representation mode supports the AI information converted into the AI information in the second AI model representation mode, the second communication device using the second AI model representation mode receives the AI information in the first AI model representation mode and loads the AI information in the second AI model representation mode to obtain the AI information in the second AI model representation mode. According to the embodiment of the application, the communication equipment using different AI model expression modes can keep consistent understanding of the AI information, so that the communication equipment using different AI model expression modes can transmit the AI information, and the communication system performance is improved.
The terminal 500 provided in this embodiment of the present application may further implement each process of the above embodiment of the AI information transmission method, and may achieve the same technical effects, so that repetition is avoided and no further description is given here.
The embodiment of the application also provides network side equipment, which comprises a processor and a communication interface, wherein the processor is used for generating the AI information in the first AI model representation mode; the communication interface is used for transmitting the AI information in the first AI model representation mode to the second communication equipment; wherein the second communication device uses the second AI model representation. The network side device embodiment corresponds to the network side device method embodiment, and each implementation process and implementation manner of the method embodiment can be applied to the network side device embodiment, and the same technical effects can be achieved.
Specifically, the embodiment of the application also provides network side equipment. As shown in fig. 6, the network side device 600 includes: an antenna 61, a radio frequency device 62, a baseband device 63. The antenna 61 is connected to a radio frequency device 62. In the uplink direction, the radio frequency device 62 receives information via the antenna 61, and transmits the received information to the baseband device 63 for processing. In the downlink direction, the baseband device 63 processes information to be transmitted, and transmits the processed information to the radio frequency device 62, and the radio frequency device 62 processes the received information and transmits the processed information through the antenna 61.
The above-described band processing means may be located in the baseband apparatus 63, and the method performed by the network-side device in the above embodiment may be implemented in the baseband apparatus 63, and the baseband apparatus 63 includes the processor 64 and the memory 65.
The baseband apparatus 63 may, for example, include at least one baseband board, on which a plurality of chips are disposed, as shown in fig. 6, where one chip, for example, a processor 64, is connected to the memory 65 to call a program in the memory 65 to perform the network side device operation shown in the above method embodiment.
The baseband apparatus 63 may also include a network interface 66 for interacting with the radio frequency apparatus 62, such as a common public radio interface (Common Public Radio Interface, CPRI).
Specifically, the network side device in the embodiment of the application further includes: instructions or programs stored in the memory 65 and executable on the processor 64, the processor 64 invokes the instructions or programs in the memory 65 to perform the methods performed by the modules shown in fig. 3 and achieve the same technical effects, and are not repeated here.
The embodiment of the present application further provides a readable storage medium, where the readable storage medium may be volatile or non-volatile, and the readable storage medium may be transient or non-transient, and a program or an instruction is stored on the readable storage medium, where the program or the instruction is executed by a processor to implement each process of the above-mentioned AI information transmission method embodiment, and the process may achieve the same technical effect, so that repetition is avoided and no further description is given here.
The processor may be a processor in the terminal described in the above embodiment. The readable storage medium includes a computer readable storage medium such as a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and the like.
The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled with the processor, and the processor is configured to run a program or an instruction, implement each process of the above AI information transmission method embodiment, and achieve the same technical effect, so that repetition is avoided, and no further description is provided herein.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, or the like.
The embodiments of the present application further provide a computer program product stored in a non-transitory storage medium, where the computer program product is executed by at least one processor to implement each process of the above-mentioned AI information transmission method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and details are not repeated here.
The embodiment of the present application further provides a communication device configured to perform each process of the above embodiment of the AI information transmission method, and achieve the same technical effects, which is not repeated herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network side device, etc.) to perform the method described in the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.

Claims (24)

1. A transmission method of artificial intelligence AI information, comprising:
the first communication device generates AI information in a first AI model representation mode; the AI information support under the first AI model representation mode is converted into AI information under the second AI model representation mode;
the first communication device sends AI information in the first AI model representation mode to the second communication device; wherein the second communication device uses the second AI model representation.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the first communication device uses a third AI model representation, and the first communication device generating AI information in the first AI model representation includes: the first communication device converts or stores the AI information in the third AI model representation mode into the AI information in the first AI model representation mode; or,
the first communication device uses the first AI model representation.
3. The method according to claim 1, wherein the method further comprises:
the first communication device sends configuration information for configuring or modifying an AI model representation for transmitting AI information.
4. The method of claim 3, wherein the AI model representation for transmitting AI information is the first AI model representation, the first communication device using the first AI model representation, the method further comprising:
the first communication device receives AI information from the second communication device and loads the received AI information into the first AI model representation;
the second communication device is further configured to convert AI information in the second AI model representation mode into AI information in the first AI model representation mode.
5. The method of claim 3, wherein the AI model representation for transmitting AI information is the second AI model representation, and the first communication device uses a first AI model representation, the method further comprising:
the first communication device converts or stores the AI information in the first AI model representation mode into the AI information in the second AI model representation mode;
and the first communication device sends the AI information in the second AI model representation mode to the second communication device.
6. The method of any of claims 1 to 3, wherein the first communication device generating AI information for the first AI model representation comprises:
And the first communication equipment respectively generates AI information in the first AI model representation mode according to the AI model representation modes used by each second communication equipment.
7. A method according to any one of claims 1 to 3, further comprising at least one of:
the first communication device sends first indication information to the second communication device, wherein the first indication information is used for indicating the second communication device to use the same AI model representation mode as the first communication device;
the first communication device sends second indication information to the second communication device, wherein the second indication information is used for indicating the type of the first AI model representation mode.
8. The method according to claim 1, wherein the method further comprises:
the first communication device receives configuration information from the third communication device, wherein the configuration information is used for configuring or modifying an AI model representation mode for transmitting AI information;
wherein the third communication device is further configured to send the configuration information to the second communication device.
9. The method of claim 1, wherein prior to the first communication device generating AI information in the first AI model representation, the method further comprises:
The first communication device sends third indication information, wherein the third indication information is used for indicating the type of the first AI model representation mode.
10. The method of claim 1, wherein prior to the first communication device generating AI information in the first AI model representation, the method further comprises:
the first communication device determines the first AI model representation mode from a plurality of AI model representation modes according to AI information to be sent;
the AI information sent by the first communication device includes fourth indication information, where the fourth indication information is used to indicate a type of the first AI model representation mode.
11. The method of claim 10, wherein the step of determining the position of the first electrode is performed,
the data amount of the AI information in the first AI model representation is the smallest data amount among the plurality of AI information in the plurality of AI model representations.
12. An AI information transmission apparatus, comprising:
the generation module is used for generating the AI information in the first AI model representation mode; the AI information support under the first AI model representation mode is converted into AI information under the second AI model representation mode;
A sending module, configured to send AI information in the first AI model representation mode to a second communication device; wherein the second communication device uses the second AI model representation.
13. The apparatus of claim 12, wherein the device comprises a plurality of sensors,
the device uses a third AI model representation mode, and the generation module is used for converting or storing the AI information in the third AI model representation mode into the AI information in the first AI model representation mode; or,
the apparatus uses the first AI model representation.
14. The apparatus of claim 12, wherein the means for transmitting is further configured to transmit configuration information for configuring or modifying AI model representations for transmitting AI information.
15. The apparatus of claim 14, wherein the AI model representation for transmitting AI information is the first AI model representation, the apparatus using the first AI model representation, the apparatus further comprising a receiving module for receiving AI information from the second communication device and loading the received AI information into the first AI model representation;
The second communication device is further configured to convert AI information in the second AI model representation mode into AI information in the first AI model representation mode.
16. The apparatus of claim 14, wherein the AI model representation for transmitting AI information is the second AI model representation, and wherein the apparatus uses a first AI model representation;
the generation module is further configured to convert or store AI information in the first AI model representation mode into AI information in the second AI model representation mode;
the sending module is further configured to send AI information in the second AI model representation mode to the second communication device.
17. The apparatus of any one of claims 12 to 14, wherein the generating module is configured to generate the AI information in the first AI model representations, respectively, according to AI model representations used by each of the second communication devices.
18. The apparatus of any of claims 12 to 14, wherein the transmitting module is further configured to at least one of:
transmitting first indication information to the second communication device, wherein the first indication information is used for indicating the second communication device to use the same AI model representation mode as the device;
And sending second indicating information to the second communication device, wherein the second indicating information is used for indicating the type of the first AI model representation mode.
19. The apparatus of claim 12, further comprising a receiving module configured to receive configuration information from a third communication device, the configuration information being used to configure or modify AI model representations used to transmit AI information;
wherein the third communication device is further configured to send the configuration information to the second communication device.
20. The apparatus of claim 12, wherein the means for transmitting is further configured to transmit third indication information, the third indication information being used to indicate a type of the first AI model representation.
21. The apparatus of claim 12, further comprising a determining module configured to determine the first AI model representation from a plurality of AI model representations based on AI information to be sent;
the AI information sent by the sending module includes fourth indication information, where the fourth indication information is used to indicate a type of the first AI model representation mode.
22. The apparatus of claim 21, wherein the device comprises a plurality of sensors,
the data amount of the AI information in the first AI model representation is the smallest data amount among the plurality of AI information in the plurality of AI model representations.
23. A communication device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, implements the AI-information transmission method of any of claims 1-11.
24. A readable storage medium, wherein a program or an instruction is stored on the readable storage medium, which when executed by a processor, implements the AI information transmission method according to any one of claims 1 to 11.
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