CN117319228A

CN117319228A - Data processing method, device, equipment, storage medium and product

Info

Publication number: CN117319228A
Application number: CN202210716471.7A
Authority: CN
Inventors: 贾宇航; 雷艺学; 张云飞
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2022-06-21
Filing date: 2022-06-21
Publication date: 2023-12-29
Also published as: WO2023246343A1

Abstract

The embodiment of the application discloses a data processing method, a device, equipment, a storage medium and a product. The method comprises the following steps: acquiring a transmission parameter of a target device at a first moment, acquiring a network state parameter of a transmission link of the target device at the first moment, predicting the network state parameter of the transmission link at a second moment according to the transmission parameter at the first moment and the network state parameter at the first moment, determining transmission reference information at the second moment based on the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment, transmitting the transmission reference information at the second moment to the target device, and indicating the target device to adjust the transmission parameter at the second moment by the transmission reference information. It can be seen that by sending the transmission reference information at the second time to the target device, the target device can adjust the transmission parameter at the second time based on the transmission reference information at the second time, so as to adapt to the continuously changing network state.

Description

Data processing method, device, equipment, storage medium and product

Technical Field

The present invention relates to the field of computer technology, and in particular, to a data processing method, a data processing apparatus, a computer device, a storage medium, and a data processing product.

Background

As technological research advances, many traditional businesses are shifted from offline to online. Many online services rely on streaming media, such as online conferences, video calls, intelligent driving, etc. Practice finds that, because the network state is continuously changed under the influence of factors such as geographic position, user number, network fluctuation and the like, the situations of blocking and poor data transmission quality may occur in the on-line service in the process of proceeding, and how to adjust the streaming media transmission strategy to adapt to the continuously changed network state becomes a popular problem in the current research.

Disclosure of Invention

The embodiment of the invention provides a data processing method, a device, equipment, a storage medium and a product, which can adjust a streaming media transmission strategy to adapt to the continuously changing network state.

In one aspect, an embodiment of the present application provides a data processing method, including:

acquiring a transmission parameter of a target device at a first moment, and acquiring a network state parameter of a transmission link of the target device at the first moment;

Predicting the network state parameter of the transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment;

determining transmission reference information at a second moment based on the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment;

and transmitting transmission reference information at the second moment to the target equipment, wherein the transmission reference information is used for indicating the target equipment to adjust the transmission parameters at the second moment, and carrying out data transmission based on the adjusted transmission parameters at the second moment, and the first moment is earlier than the second moment.

In the embodiment of the application, the transmission parameters of the target device at the first moment are acquired, the network state parameters of the transmission link at the second moment are predicted according to the transmission parameters at the first moment and the network state parameters at the first moment, the transmission reference information at the second moment is determined based on the network state parameters at the first moment, the transmission parameters at the first moment and the predicted network state parameters at the second moment, the transmission reference information at the second moment is sent to the target device, the transmission reference information is used for indicating the target device to adjust the transmission parameters at the second moment, and data transmission is performed based on the adjusted transmission parameters at the second moment. It can be seen that the transmission reference information at the second time is generated by predicting the network state parameter at the second time, so that the target device can adjust the transmission parameter at the second time based on the transmission reference information at the second time to adapt to the continuously changing network state.

acquiring a transmission parameter at a first moment, and acquiring a network state parameter of a transmission link at the first moment;

the method comprises the steps of sending a transmission parameter at a first moment and a network state parameter at the first moment to a server, enabling the server to predict the network state parameter of a transmission link at a second moment according to the transmission parameter at the first moment and the network state parameter at the first moment, and determining and returning transmission reference information at the second moment based on the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment;

and adjusting transmission parameters of the second moment based on the transmission reference information of the second moment returned by the server, and carrying out data transmission based on the adjusted transmission parameters of the second moment.

In the embodiment of the application, the transmission parameters of the first moment are acquired, the network state parameters of the transmission link at the first moment are acquired, the transmission parameters of the first moment and the network state parameters of the first moment are sent to the server, so that the server predicts the network state parameters of the transmission link at the second moment according to the transmission parameters of the first moment and the network state parameters of the first moment, determines and returns transmission reference information of the second moment based on the network state parameters of the first moment, the transmission parameters of the first moment and the network state parameters of the second moment obtained by prediction, adjusts the transmission parameters of the second moment based on the transmission reference information of the second moment returned by the server, and performs data transmission based on the adjusted transmission parameters of the second moment. It can be seen that the server returns the transmission reference information at the second time by sending the acquired transmission parameter at the first time and the network state parameter at the first time to the server, and adjusts the transmission parameter at the second time based on the returned transmission reference information at the second time, so as to adapt to the continuously changing network state.

In one aspect, an embodiment of the present application provides a data processing apparatus, including:

the acquisition unit is used for acquiring the transmission parameters of the target equipment at the first moment and acquiring the network state parameters of the transmission link of the target equipment at the first moment;

the processing unit is used for predicting the network state parameter of the transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment; and determining transmission reference information at a second time based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time;

the transmitting unit is used for transmitting transmission reference information of the second moment to the target equipment, the transmission reference information is used for indicating the target equipment to adjust transmission parameters of the second moment, and data transmission is carried out based on the adjusted transmission parameters of the second moment, and the first moment is earlier than the second moment.

the acquisition unit is used for acquiring the transmission parameters at the first moment and acquiring the network state parameters of the transmission link at the first moment;

The transmission unit is used for transmitting the transmission parameter at the first moment and the network state parameter at the first moment to the server so that the server predicts the network state parameter of the transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment, and determines and returns transmission reference information at the second moment based on the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment;

and the processing unit is used for adjusting the transmission parameters of the second moment based on the transmission reference information of the second moment returned by the server and carrying out data transmission based on the adjusted transmission parameters of the second moment.

In one aspect, the present application provides a computer device comprising:

a processor for loading and executing the computer program;

and a memory in which a computer program is stored, which, when executed by the processor, implements the data processing method described above.

In one aspect the present application provides a computer readable storage medium having a computer program stored thereon, the computer program being adapted to be loaded by a processor and to perform the above described data processing method.

In one aspect the present application provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the above-described data processing method.

In the embodiment of the application, a transmission parameter at a first moment is acquired, a network state parameter of a transmission link at the first moment is acquired, and the transmission parameter at the first moment and the network state parameter at the first moment are sent to a server; the server predicts the network state parameter of the target equipment transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment, and determines and returns transmission reference information at the second moment based on the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment; the transmission reference information is used for indicating the target equipment to adjust the transmission parameters at the second moment and transmitting data based on the adjusted transmission parameters at the second moment. It can be seen that the transmission reference information at the second time is generated by predicting the network state parameter at the second time, so that the target device can adjust the transmission parameter at the second time based on the transmission reference information at the second time to adapt to the continuously changing network state.

Drawings

FIG. 1 is a block diagram of a data processing system according to an embodiment of the present application;

FIG. 2 is a flowchart of a data processing method according to an embodiment of the present application;

FIG. 3 is a flowchart of another data processing method according to an embodiment of the present application;

FIG. 4a is a training flowchart of a network state prediction model according to an embodiment of the present application;

FIG. 4b is a schematic diagram of a model structure according to an embodiment of the present disclosure;

FIG. 5 is a flowchart of yet another data processing method according to an embodiment of the present disclosure;

fig. 6a is an application scenario diagram of a data processing method according to an embodiment of the present application;

FIG. 6b is a flowchart of data processing in a smart traffic streaming scenario according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

FIG. 8 is a schematic diagram of another data processing apparatus according to an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

Embodiments of the present application relate to artificial intelligence, and the following briefly describes cloud technology and related terms and concepts of artificial intelligence:

Cloud technology (Cloud technology) refers to a hosting technology for integrating hardware, software, network and other series resources in a wide area network or a local area network to realize calculation, storage, processing and sharing of data.

Artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.

AI technology is a comprehensive discipline, and relates to a wide range of technologies, both hardware and software. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, processing technology for large applications, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Machine learning is a multi-domain interdisciplinary, involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and the like. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the heart of AI, a fundamental approach for making computers intelligent, which is applied throughout various areas of artificial intelligence. Machine learning/deep learning typically includes techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.

Deep learning: the concept of deep learning is derived from the study of artificial neural networks. The multi-layer sensor with multiple hidden layers is a deep learning structure. Deep learning forms more abstract high-level representation attribute categories or features by combining low-level features to discover distributed feature representations of data. The embodiment of the application mainly relates to training an initial model through a training data set to obtain a network state prediction model, and predicting a network state at a second moment based on the network state at the first moment and transmission parameters at the first moment by adopting the network state model.

Based on the above description about the cloud technology and the artificial intelligence related to the embodiments of the present application, the following description briefly describes a data processing scheme provided by the embodiments of the present application based on the cloud technology and the artificial intelligence, so that a target device dynamically adjusts a streaming media transmission policy to adapt to a continuously changing network state. With reference to FIG. 1, FIG. 1 is a block diagram illustrating a data processing system according to an embodiment of the present application. As shown in fig. 1, the data processing system may include: target device 101, server 102. The data processing method provided in the embodiment of the present application may be executed by the server 102. The target device 101 may include, but is not limited to: smart phones (such as Android phones, IOS phones, etc.), tablet computers, portable personal computers, mobile internet devices (MID for short), vehicle terminals, etc., which are not limited in this embodiment of the present application. The server 102 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network ), and basic cloud computing services such as big data and an artificial intelligence platform.

It should be noted that, in fig. 1, the target device 101 and the server 102 may be directly or indirectly connected through a wired communication or a wireless communication, which is not limited herein. The number of target devices and servers is for example only and does not constitute a practical limitation of the present application; for example, server 104 may also be included in a data processing system.

The description about the data processing scheme is as follows:

(1) The target device 101 obtains network status parameters at the first moment, where the network status parameters are used to indicate the network status of the transmission link used by the target device 101, and the network status parameters include at least one of the following: signal to interference ratio (Signal to Interference plus Noise Ratio, SINR), received signal strength indication (Received Signal Strength Indicator, RSSI), reference signal received quality (Reference Signal Receiving Quality, RSRP), reference signal received power (Reference Signal Receiving Power, RSRQ), latency, throughput, physical layer shared channel transport block size, modulation and coding strategy, data transmission rate. After acquiring the network state parameter at the first time, the target device 101 transmits the network state parameter at the first time and the transmission parameter at the first time to the server 102. The transmission parameter at the first time is a transmission parameter configured by the target device 101 when performing data transmission at the first time, and taking streaming media transmission as an example, the transmission parameter includes at least one of the following: the duration of each streaming media fragment, the code rate of each streaming media fragment, the size of the buffer zone and the number of streaming media fragments to be transmitted. The code rate refers to the amount of data transmitted per unit time.

Optionally, the target device 101 may further send alternative information to the server 102 to improve the prediction accuracy of the network state parameter at the second moment, where the alternative information includes at least one of the following: location information, azimuth information, longitude and latitude information of the target device 101. The first time is earlier than the second time, e.g. the first time is the current time and the second time is the next time to the current time.

(2) The server 102 receives the network state parameter at the first time and the transmission parameter at the first time sent by the target device 101, and predicts the network state parameter of the transmission link used by the target device 101 at the second time according to the transmission parameter at the first time and the network state parameter at the first time. In one embodiment, the server 102 may invoke a network state prediction model to predict the transmission parameter at the first time and the network state parameter at the first time, so as to obtain the network state parameter of the transmission link used by the target device 101 at the second time. In another embodiment, the server 102 may obtain historical data, where the historical data includes network state parameters at different times and transmission parameters of the target device 101 at that time, and predict network state parameters at a second time based on the transmission parameters of the target device 101 at the first time and the network state parameters at the first time.

(3) After predicting the network state parameter at the second time, the server 102 determines the transmission reference information at the second time based on the network state parameter at the first time, the transmission parameter of the target device 101 at the first time, and the predicted network state parameter at the second time. The transmission reference information includes transmission quality when data transmission is performed at different code rates. In one embodiment, the server 102 invokes a transmission quality model to perform transmission quality detection on the network state at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time, to obtain data transmission quality information (such as a transmission quality score) corresponding to each code rate, and determines the data transmission quality information corresponding to each code rate as transmission reference information at the second time. After obtaining the transmission reference information at the second time, the server 102 transmits the transmission reference information at the second time to the target device 101.

(4) After receiving the transmission reference information at the second time returned by the server 102, the target device 101 adjusts the transmission parameter at the second time based on the transmission reference information at the second time returned by the server, and performs data transmission based on the adjusted transmission parameter at the second time. For example, if the transmission reference information at the second time indicates that the data transmission quality score of the code rate 1 at the second time is 45, the data transmission quality score of the code rate 2 at the second time is 94, and the data transmission quality score of the code rate 3 at the second time is 75, the target device 101 adjusts the transmission code rate to be the code rate 2, and performs data transmission based on the code rate 2 at the second time.

Based on the above data processing scheme, the embodiments of the present application provide a more detailed data processing method, and the data processing method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Referring to fig. 2, fig. 2 is a flowchart of a data processing method according to an embodiment of the present application. The data processing method may be performed by a computer device, which may be in particular the server 102 shown in fig. 1. As shown in fig. 2, the data processing method may include, but is not limited to, S201-S204:

s201, acquiring a transmission parameter of the target device at a first moment, and acquiring a network state parameter of a transmission link of the target device at the first moment.

The transmission parameters of the target device at the first moment refer to: the transmission parameters configured by the target device when the target device performs data transmission at the first moment take streaming media transmission as an example, and the transmission parameters include at least one of the following: the duration of each streaming media fragment, the code rate of each streaming media fragment, the size of the buffer zone and the number of streaming media fragments to be transmitted. The code rate refers to the amount of data transmitted per unit time. The target device transmission link refers to a transmission link used by the target device, and a network state parameter of the target device transmission link at a first moment is used to indicate a network state of the transmission link, where the network state parameter includes at least one of the following: signal to interference ratio (Signal to Interference plus Noise Ratio, SINR), received signal strength indication (Received Signal Strength Indicator, RSSI), reference signal received quality (Reference Signal Receiving Quality, RSRP), reference signal received power (Reference Signal Receiving Power, RSRQ), latency, throughput, physical layer shared channel transport block size, modulation and coding strategy, data transmission rate.

In the specific implementation process, the transmission parameters of the target device at the first moment and the network state parameters of the transmission link of the target device at the first moment can be acquired by the target device and provided for the computer device; or may be collected by a communications carrier and provided to a computer device.

S202, predicting the network state parameter of the transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment.

The first time is earlier than the second time, which may be the current time, for example, and the second time may be the next time to the first time. The network state parameters at the second time may include throughput and latency at the second time.

In one embodiment, the computer device may invoke a network state prediction model to predict the transmission parameter at the first time and the network state parameter at the first time, to obtain the network state parameter of the transmission link at the second time. The network state prediction model is obtained by training an initial model by using a training data set, the training data set is generated based on historical data, the historical data comprises network state parameters sent by target equipment at different moments, and transmission parameters corresponding to the network state parameters, and the initial model can comprise, but is not limited to, long Short-Term Memory (LSTM), a cyclic neural network model (Recurrent Neural Network, RNN), a gated cyclic unit model (Gated Recurrent Unit, GRU) and the like.

S203, determining transmission reference information of the second moment based on the network state parameter of the first moment, the transmission parameter of the first moment and the predicted network state parameter of the second moment.

The transmission reference information at the second time is used to provide a reference for the target device to determine transmission parameters at the second time. The transmission reference information at the second time may include data transmission quality information at the second time at a different code rate.

In one embodiment, the computer device may invoke the transmission quality model to perform transmission quality detection on the network state of the target device at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time, to obtain data transmission quality information corresponding to each code rate, and determine the data transmission quality information corresponding to each code rate as the transmission reference information at the second time.

S204, transmitting the transmission reference information at the second moment to the target equipment.

The transmission reference information is used for indicating the target equipment to adjust the transmission parameters at the second moment and transmitting data based on the adjusted transmission parameters at the second moment.

The computer equipment sends transmission reference information at the second moment to the target equipment so that the target equipment adjusts transmission parameters at the second moment based on the transmission reference information at the second moment and performs data transmission based on the adjusted transmission parameters at the second moment; for example, if the transmission reference information at the second time indicates that the data transmission quality score of the code rate 1 at the second time is 45, the data transmission quality score of the code rate 2 at the second time is 94, and the data transmission quality score of the code rate 3 at the second time is 75, the target device adjusts the transmission code rate to be the code rate 2 based on the transmission reference information at the second time, and performs data transmission based on the code rate 2 at the second time.

Referring to fig. 3, fig. 3 is a flowchart of another data processing method according to an embodiment of the present application. The data processing method may be performed by a computer device, which may be in particular the server 102 shown in fig. 1. As shown in fig. 3, the data processing method may include, but is not limited to, S301-S306:

S301, acquiring a transmission parameter of the target device at a first moment, and acquiring a network state parameter of a transmission link of the target device at the first moment.

In one embodiment, the data transmitted in the transmission link of the target device includes streaming media data, the streaming media data is divided into at least one streaming media segment during the transmission process, and the transmission parameters of the target device at the first moment include at least one of the following: the duration of each streaming media segment, the code rate of each streaming media segment (including the code rate of the transmitted streaming media segment and the code rate of the streaming media segment to be transmitted), the size of the buffer zone, and the number of streaming media segments to be transmitted.

In one embodiment, the transmission parameters of the target device at the first time and the network state parameters at the first time are transmitted by the target device to the communication device and forwarded by the communication device to the computer device via the core network. Wherein the communication device may include, but is not limited to, one or more of the following: 4G (four-generation) base station, 5G (five-generation) base station, road Side Unit (RSU), wireless fidelity (Wireless Fidelity, wiFi); the core network may be either a core network or a 5G cloudized core network.

It should be noted that, if the acquired network state parameter is the network state parameter that has been subjected to the structuring process, S303 may be directly executed; for example, the target device performs a structuring process on the network state parameter, and provides the server with the network state parameter after the structuring process. If the acquired network state parameter is structured, S302 is continued.

S302, carrying out structuring processing on the network state parameters to obtain the processed network state parameters.

The structuring process is used for extracting key information of network state parameters.

In one embodiment, the computer device may generate a decision tree based on the network state parameters and use the decision tree as the processed network state parameters.

In another embodiment, the computer device may perform feature extraction on the network state parameters; for example, feature extraction is performed on the network state parameters based on semantics to obtain processed network state parameters.

In yet another embodiment, the computer device may screen the network state parameters based on data screening rules to obtain processed network state parameters; for example, network state parameters which do not meet the requirements are screened out according to the configured screening rules, and the processed network state parameters are obtained.

S303, predicting the network state parameter of the transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment.

The first time is earlier than the second time, which may be the current time, for example, and the second time may be the next time to the first time.

In one embodiment, the computer device may invoke a network state prediction model to predict the transmission parameter at the first time and the network state parameter at the first time, to obtain the network state parameter of the transmission link at the second time.

Fig. 4a is a training flowchart of a network state prediction model provided in an embodiment of the present application, and as shown in fig. 4a, a training process of the network state prediction model is as follows: the computer equipment acquires historical data, converts the time series problem into a supervised learning problem, and the historical data comprises network state parameters at different moments and transmission parameters corresponding to the network state parameters. The computer device generating a training data set based on the historical data; for example, if the historical data includes the network state parameters of the target device at time 1-time 10 and the transmission parameters of the target device at time 1-time 10 according to the time sequence, the computer device may use the network state parameters of the target device at time 1-time 9 and the transmission parameters of the target device at time 1-time 9 as the training data set, and use the network state parameters of the target device at time 2-time 10 and the transmission parameters of the target device at time 2-time 10 as the verification data corresponding to the training data set. After the training data set is generated, the computer equipment inputs the training data set into the initial model to obtain prediction data output by the initial model, calculates a loss value between the prediction data and check data corresponding to the training data set, and adjusts parameters in the initial model (such as adjusting the number of layers of the neural network, the number of neurons in each layer of the neural network and the like) based on the loss value to obtain the network state prediction model. The initial model may include, but is not limited to, a Long Short-Term Memory network model (LSTM), a recurrent neural network model (Recurrent Neural Network, RNN), a gated recurrent unit model (Gated Recurrent Unit, GRU), and the like.

In another embodiment, the computer device may simultaneously obtain the transmission parameters of the first time and the network state parameters of the first time sent by the plurality of target devices, and predict the network state parameters of the transmission link at the second time based on the transmission parameters and the network state parameters of the respective target devices at the first time. For example, if the number of network state parameters at a first time indicative of network congestion at the first time exceeds a number threshold, network congestion at a time next to the first time (second time) is predicted.

It should be noted that, the computer device may further mine the plurality of network state parameters and the plurality of transmission parameters at the same time through the network state prediction model, so as to predict the network state parameters at the second time. It can be appreciated that, by taking the coupling between the plurality of target devices into consideration in the above manner, the accuracy of the predicted network state parameter at the second moment can be improved, and the method is more closely combined with the actual application scene.

S304, the predicted network state parameter at the second moment is sent to the target equipment.

And the computer equipment sends the predicted network state parameter at the second moment to the target equipment so that the target equipment adjusts the self operation strategy based on the network state parameter at the second moment.

In one embodiment, the target device may be an on-board terminal mounted in the target vehicle, and the computer device sends the predicted network state parameter at the second time to the target device, so that the target device adjusts the driving strategy of the target vehicle (such as decelerating, ending the remote control and manually taking over, or starting the remote control, etc.) based on the predicted network state parameter at the second time.

In another embodiment, the target device may be a mobile communication device (e.g., a mobile phone), and the computer device sends the predicted network state parameter at the second time to the target device, so that the target device adjusts the communication mode (e.g., switches from a video call to a voice call, switches the resolution of the video call, etc.) based on the predicted network state parameter at the second time.

S305, determining transmission reference information of the second moment based on the network state parameter of the first moment, the transmission parameter of the first moment and the predicted network state parameter of the second moment.

The transmission reference information at the second time is used to provide a reference for the target device to determine transmission parameters at the second time. The transmission reference information at the second time may be directly used to indicate a code rate with the optimal data transmission quality at the second time in the candidate code rates, and the transmission reference information at the second time may also include data transmission quality information at the second time with different code rates.

The transmission quality model may be a reinforcement learning model (DQN), and the transmission quality model includes an environment module, a decision module, and a transmission quality evaluation module, and the computer device invokes the transmission quality model to perform a transmission quality detection process, including:

(1) acquiring configuration information, wherein the configuration information comprises N measurement indexes and weights of each measurement index, and N is a positive integer; the configuration information can be set by the object based on actual requirements or by default by a developer. The N metrics may include, but are not limited to: the code rate is smooth and buffered.

(2) And taking the N measurement indexes and the weight of each measurement index as the evaluation rule of the quality evaluation module. It will be appreciated that if default configuration information is employed, no additional configuration of the transmission quality model (i.e., default evaluation rules) is required.

(3) The context module is configured based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time, and the context module is operable to indicate the predicted network state at the second time.

(4) The decision module is used for selecting the target code rate as the transmission code rate at the second moment, so that the transmission quality evaluation module determines data transmission quality information corresponding to the target code rate according to the transmission code rate at the second moment and the network state at the second moment indicated by the environment module; for example, in the network state at the predicted second moment, the target code rate has an evaluation score of 5 at the index a, an evaluation score of 3 at the index B, an evaluation score of 10 at the index C, a weight of 1 at the index a, a weight of 3 at the index B, and a weight of 2 at the index C, and the data transmission quality score corresponding to the target code rate is: 5+3+10×2=34. The target code rate may be any code rate of the candidate code rates that is not selected.

It can be understood that the data transmission quality information corresponding to each candidate code rate can be obtained by repeatedly executing the above (4), so as to obtain the transmission reference information at the second moment. The network state parameter of the target device at the first time and the transmission parameter at the first time are used for providing reference for determining transmission reference information at the second time, the transmission reference information at the second time is generated based on the data transmission quality scores of different code rates under the network state parameter at the second time, and the network state parameter of the target device at the first time and the transmission parameter at the first time are used as reference basis.

Fig. 4b is a schematic diagram of a model structure according to an embodiment of the present application. As shown in fig. 4b, data is first input into a network state prediction model, where the data includes a transmission parameter of a target device at a first time and a network state parameter at the first time, a network state parameter at a second time output by the network state prediction model is obtained, and the network state parameter at the second time predicted by the network state prediction model, the transmission parameter of the target device at the first time, and the network state parameter at the first time are input into a transmission quality model as environmental information. The transmission quality model updates the network state based on the network state parameter of the second moment predicted by the network state prediction model, the transmission parameter of the target equipment at the first moment and the network state parameter at the first moment, and determines the data transmission quality of different code rates (each time determined by a code rate self-adaptive decision) in the current network state through the neural network, so as to obtain the transmission reference information at the second moment. The transmission quality model may take pensieve as a basic framework, and the state variables are adjusted according to the update of the network state parameters (from the network state parameters at the first time to the network state parameters at the second time) in addition to the transmission parameters of the target device at the first time.

It will be appreciated that the number of layers of the neural network and the number of neurons shown in fig. 4b are only used as examples and do not constitute a practical limitation of the present application, and in practical application, the number of layers of the neural network and the number of neurons may be adjusted based on the training result, so as to achieve a better effect of the transmission quality model.

S306, transmitting the transmission reference information at the second moment to the target equipment.

The specific embodiment of S306 can refer to the embodiment of S204 in fig. 2, and will not be described herein.

The embodiment of the application can better extract the network state characteristics by carrying out the structuring processing on the network state parameters on the basis of the embodiment of fig. 2, thereby providing the accuracy of the predicted network state parameters at the second moment. And sending the predicted network state parameter at the second moment to the target equipment, so that the target equipment can adjust the self operation strategy based on the network state parameter at the second moment to adapt to the continuously changed network state. In addition, the coupling among a plurality of target devices is considered, so that the data transmission effect is improved by being combined with an actual scene more tightly.

Referring to fig. 5, fig. 5 is a flowchart of another data processing method according to an embodiment of the present application. The data processing method may be performed by a computer device, which may be in particular the target device 101 shown in fig. 1. As shown in fig. 5, the data processing method may include, but is not limited to, S501-S503:

S501, acquiring a transmission parameter at a first moment, and acquiring a network state parameter of a transmission link at the first moment.

The transmission parameters at the first time point are: the transmission parameters configured by the computer device when transmitting data at the first moment, taking streaming media transmission as an example, include at least one of the following: the duration of each streaming media fragment, the code rate of each streaming media fragment, the size of the buffer zone and the number of streaming media fragments to be transmitted. The code rate refers to the amount of data transmitted per unit time. The network state parameter of the transmission link at the first moment is used for indicating the network state of the transmission link, and the network state parameter comprises at least one of the following: signal to interference ratio (Signal to Interference plus Noise Ratio, SINR), received signal strength indication (Received Signal Strength Indicator, RSSI), reference signal received quality (Reference Signal Receiving Quality, RSRP), reference signal received power (Reference Signal Receiving Power, RSRQ), latency, throughput, physical layer shared channel transport block size, modulation and coding strategy, data transmission rate.

The manner in which the computer device obtains the transmission parameter at the first time may include: the method comprises the steps of collecting transmission parameters at a first moment and obtaining the transmission parameters at the first moment of object configuration. The manner in which the computer device obtains the network state parameters of the used transmission link at the first moment may include: the computer device collects network state parameters of the used transmission link at the first time, and the computer device obtains the network state parameters of the used transmission link at the first time from the communication carrier or the network carrier.

In one embodiment, the computer device collects the transmission parameters at the first time and the network state parameters at the first time, and uploads the transmission parameters at the first time and the network state parameters at the first time to the communication device through the uplink, and the communication device may include, but is not limited to, one or more of the following: 4G (four-generation) base station, 5G (five-generation) base station, road Side Unit (RSU), wireless fidelity (Wireless Fidelity, wiFi) device. The communication device sends the transmission parameter at the first moment and the network state parameter at the first moment to the server through the core network, and the core network can be any one of the core network or the 5G cloud core network.

S502, transmitting the transmission parameter at the first moment to a server and the network state parameter at the first moment.

The computer equipment sends the transmission parameter at the first moment and the network state parameter at the first moment to the server, so that the server predicts the network state parameter of the transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment, and determines and returns transmission reference information at the second moment based on the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment. The embodiment of the server according to the transmission parameter at the first time and the network state parameter at the first time, predicting the network state parameter of the transmission link at the second time, and based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time may refer to the embodiment in fig. 2 or fig. 3, which is not described herein again.

In one embodiment, the computer device sends the network state parameter at the first time to the server, causing the server to perform a structuring process on the network state parameter at the first time. The embodiment of the server for structuring the network state parameter at the first moment can refer to S302, which is not described herein.

In another embodiment, the computer device performs a structuring process on the network state parameter at the first time, and sends the network state parameter after the structuring process to the server. Specifically, the computer device may generate a decision tree according to the network state parameter, and use the decision tree as the processed network state parameter. The computer equipment can also perform characteristic extraction on the network state parameters; for example, feature extraction is performed on the network state parameters based on semantics to obtain processed network state parameters. The computer equipment can also screen the network state parameters based on the data screening rule to obtain the processed network state parameters; for example, network state parameters which do not meet the requirements are screened out according to the configured screening rules, and the processed network state parameters are obtained.

S503, adjusting transmission parameters of the second moment based on the transmission reference information of the second moment returned by the server, and transmitting data based on the adjusted transmission parameters of the second moment.

The transmission parameters at the second time instant include at least one of: the duration of the streaming media fragments transmitted at the second moment, the code rate of the streaming media fragments transmitted at the second moment and the size of the buffer zone. The transmission reference information at the second time is used to provide a reference for the target device to determine transmission parameters at the second time. The transmission reference information at the second time may include data transmission quality information at the second time at a different code rate.

In one embodiment, the data transmitted in the transmission link comprises streaming media data, which is divided into at least one streaming media segment during transmission. The transmission reference information at the second time includes: the transmission quality of the streaming media fragments with different code rates at the second moment; for example, the transmission reference information at the second time may include a data transmission quality score of the streaming media segment at the second time. The transmission parameters at the second time include: the code rate of the streaming media segment transmitted at the second moment. The computer equipment adjusts the code rate of the streaming media fragments transmitted at the second moment to be a target code rate based on the transmission reference information at the second moment; the target code rate is a code rate, indicated by the transmission reference information at the second moment, of which the transmission quality at the second moment is higher than a transmission quality threshold value; for example, the target code rate may be the code rate with the highest data transmission quality score at the second time point among the candidate code rates.

Fig. 6a is an application scenario diagram of a data processing method according to an embodiment of the present application. As shown in fig. 6a, the application scenario is an intelligent traffic streaming media scenario, which includes a base station, a core network, a remote control server, and traffic services in addition to a vehicle and an application server on which a vehicle-mounted terminal (i.e., a target device) is mounted. Wherein the remote control server may be used to provide remote control services to the vehicle, traffic may include, but is not limited to: unmanned integrated circuit, vehicle-road cooperation, real-time twinning and the like.

Fig. 6b is a flow chart of data processing in the smart traffic streaming media scenario according to an embodiment of the present application. As shown in fig. 6b, the target device or the communication carrier (e.g., the base station) collects the transmission parameters of the target device at the first time, and the network status parameters of the transmission link at the first time. After obtaining the network state parameter at the first moment, the network state parameter can be subjected to structuring processing, and the network state parameter after structuring processing is sent to the application server. The specific embodiment of structuring the network status parameter may refer to the embodiment in S302, which is not described herein. And uploading the network state parameters after the structuring processing and the transmission parameters of the target equipment at the first moment to the communication equipment through a 5G uplink. Wherein the communication device may include, but is not limited to: one or more of 4G/5G base station, RSU and WiFi. The communication equipment forwards the network state parameters after the structuring processing and the transmission parameters of the target equipment at the first moment to the application server through the core network by utilizing the relevant interfaces. The core network may be one of a 5G core network or a 5G cloudized core network. After receiving the network state parameters after the structuring process and the transmission parameters of the target device at the first moment, the application server can preprocess the received data (such as generating a matrix) so that the data accords with the input requirement of the network state prediction model or the transmission quality model. The application server may perform relevant parameter configuration (such as configuration input feature, output feature, prediction feature, parameter of the model, prediction time, proportion of model training and testing, iteration number, model loss function, model optimization function, number of machine learning neurons, etc.) on the network state prediction model and the transmission quality model according to the actual requirement, and input the network state parameter after the structuring process and the transmission parameter of the target device at the first moment into the network state prediction model to obtain the network state parameter at the second moment output by the network state prediction model. In one aspect, the application server may return the network state parameter at the second time to the target device (the vehicle-mounted terminal) to cause the target device to adjust the vehicle driving strategy (e.g., slow down, end remote control and manually take over, or start remote control, etc.) based on the network state parameter at the second time. On the other hand, the application server may input the network state parameter at the second time predicted by the network state prediction model, the network state parameter after the structuring process, and the transmission parameter of the target device at the first time into the transmission quality model, obtain the transmission reference information at the second time output by the transmission quality model, and send the transmission reference information at the second time to the target device, so that the target device adjusts the transmission parameter at the second time based on the transmission reference information at the second time (for example, increases/decreases the bit rate of the camera to adjust the transmission bit rate), and performs data transmission based on the adjusted transmission parameter at the second time.

The foregoing details of the method of embodiments of the present application are set forth in order to provide a better understanding of the foregoing aspects of embodiments of the present application, and accordingly, the following provides a device of embodiments of the present application.

Referring to fig. 7, fig. 7 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application, where the apparatus may be mounted on a computer device, and the computer device may specifically be the server 102 shown in fig. 1. The data processing device shown in fig. 7 may be used to perform some or all of the functions of the method embodiments described above with respect to fig. 2 and 3. Referring to fig. 7, the detailed descriptions of the respective units are as follows:

an obtaining unit 701, configured to obtain a transmission parameter of a target device at a first time, and obtain a network state parameter of a transmission link of the target device at the first time;

a processing unit 702, configured to predict a network state parameter of the transmission link at a second time according to the transmission parameter at the first time and the network state parameter at the first time; and determining transmission reference information at a second time based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time;

the sending unit 703 is further configured to send, to the target device, transmission reference information at a second time, where the transmission reference information is used to instruct the target device to adjust a transmission parameter at the second time, and perform data transmission based on the adjusted transmission parameter at the second time, where the first time is earlier than the second time.

In one embodiment, the processing unit 702 is configured to predict the network state parameter of the transmission link at the second time according to the transmission parameter at the first time and the network state parameter at the first time, specifically configured to:

and calling a network state prediction model, and predicting the transmission parameters at the first moment and the network state parameters at the first moment to obtain the network state parameters of the transmission link at the second moment.

In one embodiment, a training process of a network state prediction model includes:

acquiring historical data, wherein the historical data comprises network state parameters at different moments and transmission parameters corresponding to the network state parameters;

generating a training data set based on the historical data, and inputting the training data set into an initial model to obtain prediction data output by the initial model;

calculating a loss value between the predicted data and the check data corresponding to the training data set;

and adjusting parameters in the initial model based on the loss value to obtain a network state prediction model.

In one embodiment, the processing unit 702 is configured to determine the transmission reference information at the second time based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time, specifically configured to:

Invoking a transmission quality model to detect transmission quality of the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment, and obtaining data transmission quality information corresponding to each code rate;

and determining the data transmission quality information corresponding to each code rate as transmission reference information at the second moment.

In one embodiment, the transmission quality model comprises an environment module, a decision module and a transmission quality evaluation module; the processing unit 702 is configured to invoke a transmission quality model to perform transmission quality detection, specifically configured to:

acquiring configuration information, wherein the configuration information comprises N measurement indexes and weights of each measurement index, and N is a positive integer;

taking N measurement indexes and the weight of each measurement index as the evaluation rule of the quality evaluation module;

configuring an environment module based on the network state parameter at the first moment, the transmission parameter at the first moment and the predicted network state parameter at the second moment;

and selecting the target code rate as the transmission code rate at the second moment through the decision module, so that the transmission quality evaluation module determines the data transmission quality information corresponding to the target code rate according to the transmission code rate at the second moment and the network state at the second moment indicated by the environment module.

In one embodiment, the obtaining unit 701 is configured to obtain a network state parameter of a transmission link of a target device at a first time, specifically:

acquiring network state parameters of a transmission link of target equipment at a first moment;

carrying out structuring treatment on the network state parameters to obtain the treated network state parameters;

wherein the network status parameters include at least one of: signal-to-interference ratio, received signal strength indication, reference signal received quality, reference signal received power, latency, throughput, physical layer shared channel transport block size, modulation and coding strategy, data transmission rate.

In one embodiment, the data transmitted in the transmission link of the target device includes streaming media data, the streaming media data is divided into at least one streaming media segment during the transmission process, and the transmission parameters of the target device at the first moment include at least one of the following: the duration of each streaming media fragment, the code rate of each streaming media fragment, the size of the buffer zone and the number of streaming media fragments to be transmitted.

According to one embodiment of the present application, part of the steps involved in the data processing method shown in fig. 2 and 3 may be performed by respective units in the data processing apparatus shown in fig. 7. For example, S201 shown in fig. 2 may be performed by the acquisition unit 701 shown in fig. 7, S202 and S203 may be performed by the processing unit 702 shown in fig. 7, and S204 may be performed by the transmission unit 703 shown in fig. 7. S301 shown in fig. 3 may be performed by the transceiving unit 701 shown in fig. 7, S302, S303, and S305 may be performed by the processing unit 702 shown in fig. 7, and S304 and S306 may be performed by the transmitting unit 703 shown in fig. 7. The respective units in the data processing apparatus shown in fig. 7 may be individually or all combined into one or several other units, or some (some) of them may be further split into a plurality of units with smaller functions to be formed, which may achieve the same operation without affecting the achievement of the technical effects of the embodiments of the present application. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the present application, the data processing apparatus may also include other units, and in practical applications, these functions may also be implemented with assistance from other units, and may be implemented by cooperation of a plurality of units.

According to another embodiment of the present application, a data processing apparatus as shown in fig. 7 may be constructed by running a computer program (including program code) capable of executing the steps involved in the respective methods as shown in fig. 2 and 3 on a general-purpose computing apparatus such as a computer including a processing element such as a Central Processing Unit (CPU), a random access storage medium (RAM), a read only storage medium (ROM), and the like, and a storage element, and the data processing method of the embodiments of the present application may be implemented. The computer program may be recorded on, for example, a computer-readable recording medium, and loaded into and run in the above-described computing device through the computer-readable recording medium.

Based on the same inventive concept, the principle and beneficial effects of the data processing device provided in the embodiments of the present application are similar to those of the data processing method in the embodiments of the present application, and may refer to the principle and beneficial effects of implementation of the method, which are not described herein for brevity.

Referring to fig. 8, fig. 8 is a schematic structural diagram of another data processing apparatus according to an embodiment of the present application, where the apparatus may be mounted on a computer device, and the computer device may specifically be the target device 101 shown in fig. 1. The data processing device shown in fig. 8 may be used to perform some or all of the functions of the method embodiment described above with respect to fig. 5. Referring to fig. 8, the detailed descriptions of the respective units are as follows:

An obtaining unit 801, configured to obtain a transmission parameter at a first time, and obtain a network state parameter of a transmission link at the first time;

a sending unit 802, configured to send the transmission parameter at the first time and the network state parameter at the first time to a server, so that the server predicts the network state parameter of the transmission link at the second time according to the transmission parameter at the first time and the network state parameter at the first time, and determines and returns transmission reference information at the second time based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time;

the processing unit 803 is configured to adjust a transmission parameter at a second time based on the transmission reference information at the second time returned by the server, and perform data transmission based on the adjusted transmission parameter at the second time.

In one embodiment, the sending unit 802 is configured to send, to the server, a network status parameter at a first time, specifically configured to:

the network state parameters at the first moment are sent to the server, so that the server carries out structuring processing on the network state parameters at the first moment; or,

and carrying out structuring processing on the network state parameters at the first moment, and sending the network state parameters after structuring processing to a server.

In one embodiment, the data transmitted in the transmission link comprises streaming media data, which is divided into at least one streaming media segment during transmission; the transmission reference information at the second time includes: the transmission quality of the streaming media fragments with different code rates at the second moment; the transmission parameters at the second time include: the code rate of the streaming media fragment transmitted at the second moment;

the processing unit 803 is configured to adjust a transmission parameter at a second time based on transmission reference information returned by the server at the second time, specifically configured to:

based on the transmission reference information at the second moment, adjusting the code rate of the streaming media fragment transmitted at the second moment to be a target code rate;

the target code rate is a code rate, indicated by the transmission reference information at the second moment, of which the transmission quality at the second moment is higher than a transmission quality threshold.

According to one embodiment of the present application, part of the steps involved in the data processing method shown in fig. 5 may be performed by respective units in the data processing apparatus shown in fig. 8. For example, S501 shown in fig. 5 may be executed by the acquisition unit 801 shown in fig. 8, S502 may be executed by the transmission unit 802 shown in fig. 8, and S503 may be executed by the processing unit 803 shown in fig. 8. The respective units in the data processing apparatus shown in fig. 8 may be individually or all combined into one or several other units, or some (some) of them may be further split into a plurality of units with smaller functions to be formed, which may achieve the same operation without affecting the achievement of the technical effects of the embodiments of the present application. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the present application, the data processing apparatus may also include other units, and in practical applications, these functions may also be implemented with assistance from other units, and may be implemented by cooperation of a plurality of units.

According to another embodiment of the present application, a data processing apparatus as shown in fig. 8 may be constructed by running a computer program (including program code) capable of executing the steps involved in the respective methods as shown in fig. 5 on a general-purpose computing apparatus such as a computer including a processing element such as a Central Processing Unit (CPU), a random access storage medium (RAM), a read only storage medium (ROM), and the like, and a storage element, and implementing the data processing method of the embodiments of the present application. The computer program may be recorded on, for example, a computer-readable recording medium, and loaded into and run in the above-described computing device through the computer-readable recording medium.

Referring to fig. 9, fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present application, and as shown in fig. 9, the computer device at least includes a processor 901, a communication interface 902 and a memory 903. Wherein the processor 901, the communication interface 902, and the memory 903 may be connected by a bus or other means. Among them, the processor 901 (or central processing unit (Central Processing Unit, CPU)) is a computing core and a control core of a computer device, which can parse various instructions in the computer device and process various data of the computer device, for example: the CPU can be used for analyzing a startup and shutdown instruction sent by a user to the computer equipment and controlling the computer equipment to perform startup and shutdown operation; and the following steps: the CPU may transmit various types of interaction data between internal structures of the computer device, and so on. Communication interface 902 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI, mobile communication interface, etc.), and may be controlled by processor 901 to receive and transmit data; the communication interface 902 may also be used for transmission and interaction of data within a computer device. The Memory 903 (Memory) is a Memory device in a computer device for storing programs and data. It will be appreciated that the memory 903 here may include both built-in memory of the computer device and extended memory supported by the computer device. The memory 903 provides storage space that stores the operating system of the computer device, which may include, but is not limited to: android systems, iOS systems, windows Phone systems, etc., which are not limiting in this application.

The embodiments of the present application also provide a computer-readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer readable storage medium provides storage space that stores a processing system of a computer device. Also stored in this memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor 901. Note that the computer readable storage medium can be either a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory; alternatively, it may be at least one computer-readable storage medium located remotely from the aforementioned processor.

In one embodiment, the computer device may be specifically the server 102 shown in FIG. 1. The processor 901 performs the following operations by executing executable program code in the memory 903:

Acquiring transmission parameters of the target device at a first moment through the communication interface 902, and acquiring network state parameters of a transmission link of the target device at the first moment;

and transmitting transmission reference information of the second moment to the target device through the communication interface 902, wherein the transmission reference information is used for instructing the target device to adjust transmission parameters of the second moment, and transmitting data based on the adjusted transmission parameters of the second moment, and the first moment is earlier than the second moment.

As an alternative embodiment, the specific embodiment of the processor 901 predicting the network state parameter of the transmission link at the second time according to the transmission parameter at the first time and the network state parameter at the first time is:

As an alternative embodiment, the training process of the network state prediction model includes:

As an alternative embodiment, the processor 901 determines, based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time, a specific embodiment of the transmission reference information at the second time as follows:

As an alternative embodiment, the transmission quality model includes an environment module, a decision module, and a transmission quality evaluation module; the specific embodiment of the processor 901 invoking the transmission quality model for transmission quality detection is:

As an alternative embodiment, a specific embodiment of obtaining, through the communication interface 902, a network status parameter of the transmission link of the target device at the first moment is:

As an optional embodiment, the data transmitted in the transmission link of the target device includes streaming media data, the streaming media data is divided into at least one streaming media segment during the transmission process, and the transmission parameter of the target device at the first moment includes at least one of the following: the duration of each streaming media fragment, the code rate of each streaming media fragment, the size of the buffer zone and the number of streaming media fragments to be transmitted.

In another embodiment, the computer device may be specifically the target device 101 shown in FIG. 1. The processor 1001 performs the following operations by executing executable program code in the memory 1003:

the transmission parameters at the first moment and the network state parameters at the first moment are sent to the server through the communication interface 1002, so that the server predicts the network state parameters of the transmission link at the second moment according to the transmission parameters at the first moment and the network state parameters at the first moment, and determines and returns transmission reference information at the second moment based on the network state parameters at the first moment, the transmission parameters at the first moment and the predicted network state parameters at the second moment;

As an alternative embodiment, a specific embodiment of sending the network status parameter at the first moment to the server through the communication interface 1002 is:

the network state parameters at the first moment are sent to the server through the communication interface 1002, so that the server carries out structuring processing on the network state parameters at the first moment; or,

As an alternative embodiment, the data transmitted in the transmission link includes streaming media data, which is divided into at least one streaming media segment during the transmission process; the transmission reference information at the second time includes: the transmission quality of the streaming media fragments with different code rates at the second moment; the transmission parameters at the second time include: the code rate of the streaming media fragment transmitted at the second moment;

based on the transmission reference information returned by the server at the second time, the processor 1001 adjusts the transmission parameters at the second time according to the specific embodiment:

Based on the same inventive concept, the principle and beneficial effects of solving the problem of the computer device provided in the embodiments of the present application are similar to those of solving the problem of the data processing method in the embodiments of the method of the present application, and may refer to the principle and beneficial effects of implementation of the method, which are not described herein for brevity.

The present application also provides a computer readable storage medium having a computer program stored therein, the computer program being adapted to be loaded by a processor and to perform the data processing method of the above method embodiments.

The present application also provides a computer program product comprising a computer program adapted to be loaded by a processor and to perform the data processing method of the above method embodiments.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the data processing method described above.

The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs.

The modules in the device of the embodiment of the application can be combined, divided and deleted according to actual needs.

Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the readable storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.

The foregoing disclosure is only a preferred embodiment of the present application, and it is not intended to limit the scope of the claims, and one of ordinary skill in the art will understand that all or part of the processes for implementing the embodiments described above may be performed with equivalent changes in the claims of the present application and still fall within the scope of the claims.

Claims

1. A method of data processing, the method comprising:

determining transmission reference information of the second moment based on the network state parameter of the first moment, the transmission parameter of the first moment and the predicted network state parameter of the second moment;

and transmitting transmission reference information of the second moment to the target equipment, wherein the transmission reference information is used for indicating the target equipment to adjust transmission parameters of the second moment, and transmitting data based on the adjusted transmission parameters of the second moment, and the first moment is earlier than the second moment.

2. The method of claim 1, wherein predicting the network state parameter of the transmission link at the second time based on the transmission parameter at the first time and the network state parameter at the first time comprises:

and calling a network state prediction model, and performing prediction processing on the transmission parameters at the first moment and the network state parameters at the first moment to obtain the network state parameters of the transmission link at the second moment.

3. The method of claim 2, wherein the training process of the network state prediction model comprises:

4. The method of claim 1, wherein the determining the transmission reference information for the second time based on the network state parameter for the first time, the transmission parameter for the first time, and the predicted network state parameter for the second time comprises:

5. The method of claim 4, wherein the transmission quality model includes an environment module, a decision module, and a transmission quality evaluation module, and the process of invoking the transmission quality model for transmission quality detection includes:

taking the N measurement indexes and the weight of each measurement index as the evaluation rule of the quality evaluation module;

configuring the environment module based on the network state parameter at the first time, the transmission parameter at the first time and the predicted network state parameter at the second time;

and selecting a target code rate as the transmission code rate at the second moment through the decision module, so that the transmission quality evaluation module determines data transmission quality information corresponding to the target code rate according to the transmission code rate at the second moment and the network state at the second moment indicated by the environment module.

6. The method of claim 1, wherein the obtaining network state parameters of the target device transmission link at the first time comprises:

Acquiring network state parameters of the transmission link of the target equipment at the first moment;

carrying out structural processing on the network state parameters to obtain processed network state parameters;

7. The method of claim 1, wherein the data transmitted in the target device transmission link comprises streaming media data, the streaming media data being divided into at least one streaming media segment during transmission;

the transmission parameters of the target device at the first moment comprise at least one of the following: the duration of each streaming media fragment, the code rate of each streaming media fragment, the size of the buffer zone and the number of streaming media fragments to be transmitted.

8. A method of data processing, the method comprising:

the transmission parameters at the first moment and the network state parameters at the first moment are sent to a server, so that the server predicts the network state parameters of the transmission link at the second moment according to the transmission parameters at the first moment and the network state parameters at the first moment, and determines and returns transmission reference information at the second moment based on the network state parameters at the first moment, the transmission parameters at the first moment and the predicted network state parameters at the second moment;

9. The method of claim 8, wherein the sending the network state parameter for the first time to the server comprises:

the network state parameters at the first moment are sent to a server, so that the server carries out structuring processing on the network state parameters at the first moment; or,

10. The method of claim 8, wherein the data transmitted in the transmission link comprises streaming media data, the streaming media data being divided into at least one streaming media segment during transmission; the transmission reference information of the second time includes: the transmission quality of the streaming media fragments with different code rates at the second moment; the transmission parameters of the second time include: the code rate of the streaming media fragment transmitted at the second moment;

the adjusting the transmission parameter at the second time based on the transmission reference information at the second time returned by the server includes:

Based on the transmission reference information of the second moment, adjusting the code rate of the streaming media fragment transmitted at the second moment to be a target code rate;

and the target code rate is a code rate, indicated by the transmission reference information at the second moment, of which the transmission quality is higher than a transmission quality threshold value at the second moment.

11. A data processing apparatus, characterized in that the data processing apparatus comprises:

the processing unit is used for predicting the network state parameter of the transmission link at the second moment according to the transmission parameter at the first moment and the network state parameter at the first moment; and determining transmission reference information of the second time based on the network state parameter of the first time, the transmission parameter of the first time and the predicted network state parameter of the second time;

the transmitting unit is configured to transmit, to the target device, transmission reference information of the second time, where the transmission reference information is used to instruct the target device to adjust a transmission parameter of the second time, and perform data transmission based on the adjusted transmission parameter of the second time, and the first time is earlier than the second time.

12. A data processing apparatus, characterized in that the data processing apparatus comprises:

a sending unit, configured to send the transmission parameter at the first time and the network state parameter at the first time to a server, so that the server predicts the network state parameter of the transmission link at the second time according to the transmission parameter at the first time and the network state parameter at the first time, and determines and returns transmission reference information at the second time based on the network state parameter at the first time, the transmission parameter at the first time, and the predicted network state parameter at the second time;

13. A computer device, comprising: a memory device and a processor;

a memory in which a computer program is stored;

A processor for loading the computer program to implement the data processing method of any of claims 1-7; or for loading said computer program for implementing a data processing method according to any of claims 8-10.

14. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program adapted to be loaded by a processor and to perform the data processing method according to any of claims 1-7; or load and execute a data processing method according to any of claims 8-10.

15. A computer program product, characterized in that the computer program product comprises a computer program adapted to be loaded by a processor and to perform the data processing method according to any of claims 1-7; or load and execute a data processing method according to any of claims 8-10.