CN116528375B - Bandwidth prediction method, bandwidth prediction device, and computer-readable storage medium - Google Patents
Bandwidth prediction method, bandwidth prediction device, and computer-readable storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000003860 storage Methods 0.000 title claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 113
- 230000001413 cellular effect Effects 0.000 claims abstract description 15
- 230000006399 behavior Effects 0.000 claims description 67
- 230000001960 triggered effect Effects 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 abstract description 20
- 230000000694 effects Effects 0.000 abstract description 11
- 238000013468 resource allocation Methods 0.000 abstract description 5
- 238000007726 management method Methods 0.000 description 16
- 208000027744 congestion Diseases 0.000 description 15
- 238000010586 diagram Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 5
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0457—Variable allocation of band or rate
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/51—Allocation or scheduling criteria for wireless resources based on terminal or device properties
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/535—Allocation or scheduling criteria for wireless resources based on resource usage policies
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application provides a bandwidth prediction method, a bandwidth prediction apparatus, and a computer-readable storage medium. The bandwidth prediction method comprises the following steps: acquiring terminal parameters of a plurality of monitoring terminals accessed to a cellular network; acquiring the real-time behavior of the monitoring terminal; when the real-time behavior accords with a preset triggering behavior, acquiring a pre-allocation bandwidth based on terminal parameters of all monitoring terminals; and sending the pre-allocated bandwidth to a corresponding monitoring terminal so that the monitoring terminal transmits monitoring data according to the pre-allocated bandwidth. By the method, the bandwidth prediction device pre-judges the terminal behavior in advance based on the terminal service working characteristics, builds a virtual network model at the next moment, performs bandwidth pre-allocation on the virtual model terminal by means of wireless resource intelligent control and an air interface resource allocation algorithm, achieves a bandwidth prediction effect, and helps the terminal to perform code rate self-adaption in advance and improve transmission reliability.
Description
Technical Field
The present application relates to the field of 5G cellular network applications, and in particular, to a bandwidth prediction method, a bandwidth prediction apparatus, and a computer readable storage medium.
Background
For video monitoring service, fluctuation, interference and congestion of a wireless network can greatly influence the service, and different from other services, the monitoring field is sensitive to data loss or video monitoring effect jamming due to the specificity and data importance of the service, so that a perfect code rate self-adaptive system is required to realize the effect of resisting network fluctuation. When the network environment is abnormal, the service rate can be adjusted in time, so that the smoothness of the monitoring effect is ensured by sacrificing certain performance, and the monitoring data is prevented from being lost.
However, the perfect code rate self-adaptive strategy needs reliable network bandwidth situation sensing capability, and for a cellular network, the faster network bandwidth detection sensing brings better self-adaptive effect, but for a terminal side, no matter how fast sensing is lagged, namely, when the bandwidth limitation change is sensed, the bandwidth limitation actually happens actually, and the subsequent actions can only release and optimize the packet loss and the blocking degree of the video stream to the greatest extent.
Disclosure of Invention
The application provides a bandwidth prediction method, a bandwidth prediction device and a computer readable storage medium.
The application provides a bandwidth prediction method, which is applied to a management platform and comprises the following steps:
acquiring terminal parameters of a plurality of monitoring terminals accessed to a cellular network;
acquiring the real-time behavior of the monitoring terminal;
when the real-time behavior accords with a preset triggering behavior, acquiring a pre-allocation bandwidth based on terminal parameters of all monitoring terminals;
and sending the pre-allocated bandwidth to a corresponding monitoring terminal so that the monitoring terminal transmits monitoring data according to the pre-allocated bandwidth.
Wherein, the terminal parameters include: a desired operating code rate, signal to interference plus noise ratio, and/or quality of service.
The preset triggering behavior is active streaming of a user;
and when the real-time behavior accords with a preset triggering behavior, acquiring the pre-allocated bandwidth based on terminal parameters of all monitoring terminals, wherein the method comprises the following steps:
judging whether a user stream pulling instruction is received according to the real-time behavior;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals.
The preset triggering behavior is terminal timing streaming;
and when the real-time behavior accords with a preset triggering behavior, acquiring the pre-allocated bandwidth based on terminal parameters of all monitoring terminals, wherein the method comprises the following steps:
determining a difference value between the behavior time of the monitoring terminal and the starting time of the positioning task according to the real-time behavior;
judging whether the difference value is smaller than a preset protection time or not;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals.
The preset triggering behavior is active plug flow of the terminal;
and when the real-time behavior accords with a preset triggering behavior, acquiring the pre-allocated bandwidth based on terminal parameters of all monitoring terminals, wherein the method comprises the following steps:
acquiring an access instruction of the monitoring terminal according to the real-time behavior;
judging whether the monitoring terminal is a registered terminal or not based on the access instruction;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals.
The bandwidth prediction method further comprises the following steps:
when the monitoring terminal is determined to be a registered terminal, acquiring an access data type of the monitoring terminal based on the access instruction;
judging whether the access data type is video push data or not;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals;
if not, the terminal bandwidth prediction is not triggered.
The preset triggering behavior is that the system bandwidth is recovered from a congestion state to a allowance state;
and when the real-time behavior accords with a preset triggering behavior, acquiring the pre-allocated bandwidth based on terminal parameters of all monitoring terminals, wherein the method comprises the following steps:
according to the real-time behavior, determining that the network at the current moment is in a residual state;
judging whether the network at the historical moment is in a congestion state or not;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals.
The application also provides another bandwidth prediction method which is applied to a bandwidth prediction system, wherein the bandwidth prediction system comprises a monitoring terminal, a base station and a management platform; the bandwidth prediction method comprises the following steps:
the monitoring terminal is accessed to a cellular network of the base station;
the base station sends terminal parameters of the monitoring terminal to the management platform;
the management platform monitors the real-time behavior of the monitoring terminal;
when the real-time behavior accords with a preset triggering behavior, the management platform acquires pre-allocated bandwidth based on terminal parameters of all monitoring terminals;
the management platform sends the pre-allocated bandwidth to a corresponding monitoring terminal through the base station;
and the monitoring terminal adaptively adjusts the monitoring video code rate according to the pre-allocated bandwidth.
The application also provides a bandwidth prediction device, which comprises a processor and a memory, wherein the memory stores program data, and the processor is used for executing the program data to realize the bandwidth prediction method.
The present application also provides a computer readable storage medium for storing program data which, when executed by a processor, is configured to implement the bandwidth prediction method described above.
The beneficial effects of the application are as follows: the bandwidth prediction device acquires terminal parameters of a plurality of monitoring terminals accessed to the cellular network; acquiring the real-time behavior of the monitoring terminal; when the real-time behavior accords with a preset triggering behavior, acquiring a pre-allocation bandwidth based on terminal parameters of all monitoring terminals; and sending the pre-allocated bandwidth to a corresponding monitoring terminal so that the monitoring terminal transmits monitoring data according to the pre-allocated bandwidth. By the method, the bandwidth prediction device pre-judges the terminal behavior in advance based on the terminal service working characteristics, builds a virtual network model at the next moment, performs bandwidth pre-allocation on the virtual model terminal by means of wireless resource intelligent control and an air interface resource allocation algorithm, achieves a bandwidth prediction effect, and helps the terminal to perform code rate self-adaption in advance and improve transmission reliability.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flowchart illustrating an embodiment of a bandwidth prediction method according to the present application;
fig. 2 is an overall flow diagram of a bandwidth prediction method provided by the present application;
FIG. 3 is a schematic diagram illustrating the configuration of an embodiment of a bandwidth prediction system according to the present application;
FIG. 4 is a flowchart illustrating another embodiment of a bandwidth prediction method according to the present application;
fig. 5 is a schematic structural diagram of an embodiment of a bandwidth prediction apparatus according to the present application;
fig. 6 is a schematic structural diagram of an embodiment of a computer readable storage medium according to the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Aiming at the problems in the prior art, the application provides the method and the system for predicting the bandwidth of the terminal network collaborative intelligent network aiming at the congestion scene, which can predict the change of the network environment in advance, predict the bandwidth allocation condition of the terminal in advance from the advance angle, and can carry out self-adaptive strategy before the change of the network environment so as to achieve the effects of no frame loss and no blocking in video monitoring.
Traditional wireless network bandwidth situation sensing is often lagged, namely network environments which are already happened are detected and sensed, then a corresponding strategy is conducted, and the lagged sensing capability is inevitably caused by buffer accumulation or video jamming no matter how small the delay is. If the network environment can be predicted in advance, and the bandwidth of the lower terminal is predicted in advance through the network model of the advanced prediction, the terminal can be early warned in advance before the problem of limited network bandwidth occurs, and the network self-adaption without sense is achieved.
The network prediction mode is usually performed by a terminal network cooperative mode, namely the system performs advanced prediction of the future behavior of the terminal according to the current behavior of the terminal, then converts the future behavior into the future network bandwidth capability and notifies the terminal in advance to cope with the future behavior. The common scenes causing bandwidth limited fluctuation in wireless cellular network environments, such as 4G and 5G environments, are generally channel attenuation scenes and network congestion scenes, are generally used for predicting the channel attenuation scenes, are multipurpose for mobile terminal scenes, and are used for realizing the advanced prediction of the bandwidth through the trend pre-judgment of the signal intensity in the terminal moving process. The method of the application is mainly aimed at the system design of bandwidth prediction in another scene, namely, network congestion scene.
Network congestion scenes are difficult to predict, because congestion is often caused by a large number of accessed terminals, meanwhile, the sudden transmission rate of the terminals is large, so that limited air interface resources are occupied, and air interface resource competition causes that the spectrum resources of cells cannot meet all rate demands, so that congestion occurs, so that congestion is difficult to predict in advance under the conventional condition, and if the access rate condition of the terminals of future cells cannot be predicted, bandwidth can not be predicted in advance naturally.
But for proprietary networks that focus on video surveillance streaming services, some of its transmission characteristics make their networks controllable and predictable. Firstly, different from the conventional wireless terminal, the downlink rate is focused, and the main service of video monitoring focuses on uplink transmission; in addition, the video monitoring service often has controllable service modes, such as a timing distribution control stream pulling service, a low-power consumption wake-up stream pulling service, an alarm active stream pushing service and the like.
According to the application, through combining some service modes of the video monitoring terminal with reporting by the probe and pre-judging in advance by the intelligent wireless controller RIC platform, virtual scheduling is carried out on the number of the terminals, the expected speed, the expected SINR, the signal to interference and noise ratio and other variables by means of the bandwidth resource scheduling algorithm of the base station, the state of the network at the next moment and the bandwidth condition of each terminal are further estimated, and then the platform feeds back the result to each terminal for strategy adjustment in advance.
The RIC provides a PaaS platform for optimizing the 5G RAN system for xApp software of a third party.
Referring to fig. 1 to fig. 3, fig. 1 is a flow chart of an embodiment of a bandwidth prediction method according to the present application, fig. 2 is an overall flow chart of the bandwidth prediction method according to the present application, and fig. 3 is a structural diagram of an embodiment of a bandwidth prediction system according to the present application.
The bandwidth prediction method of the embodiment of the application is applied to a management platform, and can be specifically a RIC platform in the bandwidth prediction system of FIG. 3. It should be noted that the RIC and the platform in fig. 2 and fig. 3 may be integrated into one management platform, or may be divided into two different management platforms according to service logic. When the RIC and platform are integrated into the management platform, the service logic implemented by the platform in fig. 2 can be integrated into the RIC, which is all the service logic that can be implemented by one RIC platform. In consideration of the platform computing power, the RIC and the platform may also be implemented by being divided into two devices, as shown in fig. 2, to implement respective business logic.
Specifically, as shown in fig. 1, the bandwidth prediction method in the embodiment of the present application specifically includes the following steps:
step S11: terminal parameters of a plurality of monitoring terminals accessed to the cellular network are obtained.
In the embodiment of the present application, as shown in fig. 2 and fig. 3, when all video monitoring terminals are first powered on, at the moment when the video monitoring terminals access the cellular network, the base station timely notifies the RIC platform of the IMSI (international mobile equipment identity ) thereof for terminal identity recording and identification.
It should be noted that in other embodiments, other unique identifiers that can be used to distinguish terminals may be used, such as IMSI (international mobile subscriber identity, international Mobile Subscriber Identification Number), ICCID (integrated circuit card identity, integrate circuit card identity), SN (Series numbers, generally the production Number of the product).
The RIC platform discovers that the IMSI is a new registered terminal, and marks the new terminal identity; and the terminal needs to register with a service platform and create a service and keep-alive link, and the link reports the expected working code rate to the platform after the link is created, namely the best and clearest video monitoring effect and the timing task time can be ensured under the code rate condition.
Because the video monitoring terminal of the newly registered network is started, the video monitoring terminal generally does not push a stream immediately, at the moment, the RIC platform only needs to record the expected rate of the video monitoring terminal, and meanwhile, when the terminal sends uplink data, the base station records and reports the signal-to-interference-plus-noise ratio (SINR (Signal-to-interference-plus-noise ratio, signal to Interference plus Noise Ratio) and QoS (Quality of Service ) of the same moment to the RIC server platform.
For the terminal in video transmission, the base station records key factors such as an uplink SINR value, a QoS value and the like in the transmission process of the terminal in real time, which influence bandwidth allocation, and uploads the key factors to the RIC platform for real-time updating, and the key factors are used for triggering the parameter use of bandwidth prediction when the subsequent terminal goes from an idle state to a connected state again to work.
Step S12: and acquiring the real-time behavior of the monitoring terminal.
In the embodiment of the application, the RIC platform directly monitors the real-time behavior of the video monitoring terminal through the base station, and then judges whether the real-time behavior reaches the behavior for triggering bandwidth prediction, if yes, the step S13 is entered; if not, continuing monitoring.
The behavior provided by the application capable of triggering RIC to predict bandwidth mainly comprises, but is not limited to, the following 4 types: APP active streaming, terminal timing streaming task, terminal active streaming, and system bandwidth recovery from congestion state to allowance state.
Step S13: and when the real-time behavior accords with the preset triggering behavior, acquiring the pre-allocated bandwidth based on the terminal parameters of all the monitoring terminals.
In the embodiment of the application, when the real-time behavior of the video monitoring terminal accords with any one of the 4 behaviors, the RIC platform can trigger the bandwidth prediction of the video monitoring terminal.
Specifically, the trigger logic of the behavior in the above 4 is described below:
APP active pulling: after the video monitoring terminal does not transmit service and enters an idle state, the terminal wake-up monitoring preview, namely the active video streaming situation, is expected to be carried out through the platform or the mobile phone side APP. APP streaming needs to be subjected to the processes of cellular radio paging, terminal wake-up outflow, uplink random access and the like, so when active streaming reaches the RIC platform side as a first step, the RIC platform first knows that the terminal is about to perform actions but not performed yet, namely, at the next moment when the terminal is not opened, the streaming monitoring video terminal is about to occupy an air interface for data transmission, and at the moment, the terminal bandwidth prediction is triggered.
Registering terminal timing tasks: the video monitoring often sets a disarming time period, namely, video monitoring streaming service is carried out in some time periods, some time periods are in an idle state, and the terminal synchronizes the disarming time, namely, the timing task time, to the RIC platform when registering, so that the RIC platform can know the timing task starting time of each registered terminal, and the platform can set the timing task starting countdown. When the distance between the terminal and the starting time of the timing task is smaller than the set protection time T, the platform considers that the terminal ready to start the timing task occupies an empty port for data transmission at the next moment, and the terminal bandwidth prediction is triggered at the moment.
The registered terminal actively pushes: the active plug flow means that the video monitoring terminal monitors a certain preset alarm or the PIR (Passive InfraredSensor, human infrared sensor) wakes up and the like to trigger the terminal to actively initiate the plug flow to the platform. The terminal push flow firstly needs to initiate random access to the base station, and at the moment of the terminal accessing the cellular network, the base station timely informs the RIC platform of the IMSI thereof, and the platform is matched with the terminal through the IMSI. If the terminal is found to be already present, the access is known to be for uplink data transmission. But there are two cases that a general terminal actively accesses and transmits data, one is a periodic heartbeat and the other is a video push stream. The periodic heartbeat is periodic, so that the platform can judge that the platform does not process when finding that the periodic heartbeat is at the moment; otherwise, the terminal active push event is considered to occur, and the terminal active push terminal is considered to occupy an air interface for data transmission at the next moment when the terminal active push event is not started, and the terminal bandwidth prediction is triggered at the moment.
The system bandwidth is restored from the congestion state to the headroom state: the congestion state refers to that when the base station judges that the current network air interface physical resource capacity cannot meet the request rates of all terminals, the congestion is considered to occur. The allowance state refers to that the current network air interface physical resource still remains after all terminal transmission rate requests are met, and the allowance exceeds an allowance alarm threshold. When the network enters a congestion state for a period of time, some terminals stop sending data, and at this time, the network may be restored to a residual state. At this time, the platform considers that the terminal which automatically reduces the code rate because of the estimated occurrence of network congestion can try to recover the code rate, so as to improve the transmission performance, and at this time, the bandwidth prediction of the terminal is triggered, so that the terminal is helped to adapt and improve to a more proper code rate.
Step S14: and sending the pre-allocated bandwidth to a corresponding monitoring terminal so that the monitoring terminal can transmit monitoring data according to the pre-allocated bandwidth.
In the embodiment of the application, once the terminal bandwidth prediction is triggered in the above-mentioned several scenarios, the wireless intelligent control server RIC platform will combine with the base station to perform future network model construction and terminal bandwidth prediction.
Specifically, the future network model construction refers to predicting all terminals that will participate in uplink transmission at the next moment through the above-mentioned several triggering modes, and adding the terminal currently performing data transmission is considered as the total uplink terminal number at the next moment.
The terminal bandwidth prediction refers to taking the predicted key data such as the total number of terminals transmitting at the next moment, qoS levels of all terminals, expected request rates of all terminals, uplink SINR values recorded by all terminals last time and the like as parameters, inputting the key data into a resource allocation scheduling algorithm of a base station, and pre-distributing the bandwidths of all terminals to achieve the effect of terminal bandwidth prediction.
The virtual bandwidth allocation is realized by predicting the future network busy condition, virtualizing the network model scene and borrowing the base station algorithm.
After the bandwidth prediction is completed, the RIC platform carries out information interaction on the prediction result through a keep-alive link with the terminal, informs the terminal to carry out code rate self-adaptive operation immediately according to the prediction result, reduces the congestion risk in advance, avoids the conditions of packet loss and blocking, and realizes high-reliability stable data transmission.
In the embodiment of the application, a RIC platform acquires terminal parameters of a plurality of monitoring terminals accessed to a cellular network; acquiring the real-time behavior of the monitoring terminal; when the real-time behavior accords with a preset triggering behavior, acquiring a pre-allocation bandwidth based on terminal parameters of all monitoring terminals; and sending the pre-allocated bandwidth to a corresponding monitoring terminal so that the monitoring terminal transmits monitoring data according to the pre-allocated bandwidth. By the method, the bandwidth prediction device pre-judges the terminal behavior in advance based on the terminal service working characteristics, builds a virtual network model at the next moment, performs bandwidth pre-allocation on the virtual model terminal by means of wireless resource intelligent control and an air interface resource allocation algorithm, achieves a bandwidth prediction effect, and helps the terminal to perform code rate self-adaption in advance and improve transmission reliability.
The application provides a bandwidth prediction method for an uplink transmission anti-congestion scene in the video monitoring field, which is characterized in that the uplink transmission characteristics and a plurality of specific working modes are emphasized in the monitoring field, the terminal action is pre-judged in advance before the terminal works, the future network model construction is carried out, the bandwidth pre-allocation of a virtual network model terminal is carried out through a resource allocation algorithm, the effect of the bandwidth prediction in advance is realized, the scheme is combined with the specific service characteristics, the bandwidth prediction optimization is carried out for the congestion scene, and the terminal is ensured to realize high-reliability stable transmission.
Referring to fig. 4 in conjunction with fig. 2 and fig. 3, fig. 4 is a flowchart illustrating another embodiment of a bandwidth prediction method according to the present application.
The bandwidth prediction method of the embodiment of the application applies a bandwidth prediction system, and the bandwidth prediction system comprises a monitoring terminal, a base station and a management platform. The management platform may be the ric+ platform in fig. 2 and 3, or may be RIC alone, which is not limited herein.
Specifically, as shown in fig. 4, the bandwidth prediction method in the embodiment of the present application specifically includes the following steps:
step S21: the monitoring terminal accesses the cellular network of the base station.
Step S22: and the base station sends the terminal parameters of the monitoring terminal to the management platform.
Step S23: the management platform monitors the real-time behavior of the monitoring terminal.
Step S24: when the real-time behavior accords with the preset triggering behavior, the management platform acquires the pre-allocated bandwidth based on the terminal parameters of all the monitoring terminals.
Step S25: and the management platform sends the pre-allocated bandwidth to the corresponding monitoring terminal through the base station.
Step S26: and the monitoring terminal adaptively adjusts the monitoring video code rate according to the pre-allocated bandwidth.
In the embodiment of the present application, the bandwidth prediction triggering logic of the bandwidth prediction system is specifically described in the bandwidth prediction method described in fig. 1, and is not described herein.
It will be appreciated by those skilled in the art that in the above-described method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.
In order to implement the bandwidth prediction method of the above embodiment, the present application further provides a bandwidth prediction apparatus, and referring to fig. 5 specifically, fig. 5 is a schematic structural diagram of an embodiment of the bandwidth prediction apparatus provided by the present application.
The bandwidth prediction apparatus 300 of the embodiment of the present application includes a memory 31 and a processor 32, wherein the memory 31 and the processor 32 are coupled.
The memory 31 is used for storing program data and the processor 32 is used for executing the program data to implement the bandwidth prediction method described in the above embodiments.
In the present embodiment, the processor 32 may also be referred to as a CPU (Central Processing Unit ). The processor 32 may be an integrated circuit chip having signal processing capabilities. The processor 32 may also be a general purpose processor, a digital signal processor (DSP, digital Signal Process), an application specific integrated circuit (ASIC, application Specific Integrated Circuit), a field programmable gate array (FPGA, field Programmable Gate Array) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The general purpose processor may be a microprocessor or the processor 32 may be any conventional processor or the like.
In order to implement the bandwidth prediction method of the above embodiment, the present application further provides a computer readable storage medium, as shown in fig. 6, where the computer readable storage medium 400 is used to store program data 41, and the program data 41, when executed by a processor, is used to implement the bandwidth prediction method of the above embodiment.
The present application also provides a computer program product, wherein the computer program product comprises a computer program, and the computer program is operable to make a computer execute the bandwidth prediction method according to the embodiment of the present application. The computer program product may be a software installation package.
The bandwidth prediction method according to the above embodiment of the present application may be stored in an apparatus, for example, a computer readable storage medium, when implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the present application.
Claims (8)
1. A bandwidth prediction method, wherein the bandwidth prediction method is applied to a management platform, and the bandwidth prediction method comprises:
acquiring terminal parameters of a plurality of monitoring terminals accessed to a cellular network;
acquiring the real-time behavior of the monitoring terminal;
when the real-time behavior accords with a preset triggering behavior, acquiring a pre-allocation bandwidth based on terminal parameters of all monitoring terminals;
transmitting the pre-allocated bandwidth to a corresponding monitoring terminal so that the monitoring terminal transmits monitoring data according to the pre-allocated bandwidth;
the preset triggering behavior is that a user actively pulls a stream;
and when the real-time behavior accords with a preset triggering behavior, acquiring the pre-allocated bandwidth based on terminal parameters of all monitoring terminals, wherein the method comprises the following steps:
judging whether a user stream pulling instruction is received according to the real-time behavior;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals.
2. The method of bandwidth prediction according to claim 1, wherein,
the terminal parameters include: a desired operating code rate, signal to interference plus noise ratio, and/or quality of service.
3. The method of bandwidth prediction according to claim 1, wherein,
the preset triggering behavior is terminal timing streaming;
and when the real-time behavior accords with a preset triggering behavior, acquiring the pre-allocated bandwidth based on terminal parameters of all monitoring terminals, wherein the method comprises the following steps:
determining a difference value between the behavior time of the monitoring terminal and the starting time of the positioning task according to the real-time behavior;
judging whether the difference value is smaller than a preset protection time or not;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals.
4. The method of bandwidth prediction according to claim 1, wherein,
the preset triggering behavior is active plug flow of the terminal;
and when the real-time behavior accords with a preset triggering behavior, acquiring the pre-allocated bandwidth based on terminal parameters of all monitoring terminals, wherein the method comprises the following steps:
acquiring an access instruction of the monitoring terminal according to the real-time behavior;
judging whether the monitoring terminal is a registered terminal or not based on the access instruction;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals.
5. The method for bandwidth prediction according to claim 4, characterized in that,
the bandwidth prediction method further comprises the following steps:
when the monitoring terminal is determined to be a registered terminal, acquiring an access data type of the monitoring terminal based on the access instruction;
judging whether the access data type is video push data or not;
if yes, triggering terminal bandwidth prediction, and acquiring pre-allocation bandwidth based on terminal parameters of all monitoring terminals;
if not, the terminal bandwidth prediction is not triggered.
6. A bandwidth prediction method, characterized in that the bandwidth prediction method is applied to a bandwidth prediction system, wherein the bandwidth prediction system comprises a monitoring terminal, a base station and a management platform for executing the bandwidth prediction method according to any one of claims 1-5; the bandwidth prediction method comprises the following steps:
the monitoring terminal is accessed to a cellular network of the base station;
the base station sends terminal parameters of the monitoring terminal to the management platform;
the management platform monitors the real-time behavior of the monitoring terminal;
when the real-time behavior accords with a preset triggering behavior, the management platform acquires pre-allocated bandwidth based on terminal parameters of all monitoring terminals;
the management platform sends the pre-allocated bandwidth to a corresponding monitoring terminal through the base station;
and the monitoring terminal adaptively adjusts the monitoring video code rate according to the pre-allocated bandwidth.
7. A bandwidth prediction device, characterized in that it comprises a processor and a memory, said memory having stored therein program data, said processor being adapted to execute said program data to implement the bandwidth prediction method according to any of claims 1-5.
8. A computer readable storage medium for storing program data which, when executed by a processor, is adapted to carry out the bandwidth prediction method of any one of claims 1-5.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110730469A (en) * | 2019-09-03 | 2020-01-24 | 西安工业大学 | Method for predicting bandwidth based on extended Kalman wireless network and congestion control thereof |
| CN112019384A (en) * | 2020-08-24 | 2020-12-01 | 广州市百果园信息技术有限公司 | Bandwidth prediction method, device, equipment and storage medium |
| CN112333556A (en) * | 2020-07-31 | 2021-02-05 | 深圳Tcl新技术有限公司 | Control method for monitoring video transmission bandwidth, terminal equipment and readable storage medium |
| CN112954414A (en) * | 2021-02-09 | 2021-06-11 | 睿云联(厦门)网络通讯技术有限公司 | Streaming media transmission optimization method and system |
| CN113596519A (en) * | 2021-07-26 | 2021-11-02 | 北京金和网络股份有限公司 | Method for regulating and controlling live streaming of monitoring equipment and electronic equipment |
| CN114466226A (en) * | 2022-01-28 | 2022-05-10 | 杭州灵伴科技有限公司 | Method, device, equipment and computer readable medium for determining bandwidth duration ratio |
| CN115942007A (en) * | 2022-12-26 | 2023-04-07 | 上海哔哩哔哩科技有限公司 | Live streaming scheduling method and device |
| CN116260801A (en) * | 2023-03-22 | 2023-06-13 | 广州市迪士普音响科技有限公司 | Stream media audio and video push stream parameter control method and related device |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9756112B2 (en) * | 2015-02-11 | 2017-09-05 | At&T Intellectual Property I, L.P. | Method and system for managing service quality according to network status predictions |
| US10177993B2 (en) * | 2015-11-25 | 2019-01-08 | International Business Machines Corporation | Event-based data transfer scheduling using elastic network optimization criteria |
| KR20190002440A (en) * | 2016-05-06 | 2019-01-08 | 광동 오포 모바일 텔레커뮤니케이션즈 코포레이션 리미티드 | Resource allocation methods, equipment and systems |
| US10122439B2 (en) * | 2016-05-09 | 2018-11-06 | Verizon Patent And Licensing Inc. | Pre-Allocating network resources for communications between a network hub and a remote terminal |
| CN113055308B (en) * | 2019-12-26 | 2024-03-05 | 华为云计算技术有限公司 | Bandwidth scheduling method, traffic transmission method and related products |
| US11553377B2 (en) * | 2021-02-16 | 2023-01-10 | Allot Ltd. | System, device, and method of cellular congestion management without cell awareness |
| US20220374273A1 (en) * | 2021-05-11 | 2022-11-24 | Microsoft Technology Licensing, Llc | Computing resource autoscaling based on predicted metric behavior |
| CN115484223A (en) * | 2021-06-16 | 2022-12-16 | 中兴通讯股份有限公司 | Resource allocation method, terminal, and computer-readable storage medium |
-
2023
- 2023-06-28 CN CN202310776459.XA patent/CN116528375B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110730469A (en) * | 2019-09-03 | 2020-01-24 | 西安工业大学 | Method for predicting bandwidth based on extended Kalman wireless network and congestion control thereof |
| CN112333556A (en) * | 2020-07-31 | 2021-02-05 | 深圳Tcl新技术有限公司 | Control method for monitoring video transmission bandwidth, terminal equipment and readable storage medium |
| CN112019384A (en) * | 2020-08-24 | 2020-12-01 | 广州市百果园信息技术有限公司 | Bandwidth prediction method, device, equipment and storage medium |
| CN112954414A (en) * | 2021-02-09 | 2021-06-11 | 睿云联(厦门)网络通讯技术有限公司 | Streaming media transmission optimization method and system |
| CN113596519A (en) * | 2021-07-26 | 2021-11-02 | 北京金和网络股份有限公司 | Method for regulating and controlling live streaming of monitoring equipment and electronic equipment |
| CN114466226A (en) * | 2022-01-28 | 2022-05-10 | 杭州灵伴科技有限公司 | Method, device, equipment and computer readable medium for determining bandwidth duration ratio |
| CN115942007A (en) * | 2022-12-26 | 2023-04-07 | 上海哔哩哔哩科技有限公司 | Live streaming scheduling method and device |
| CN116260801A (en) * | 2023-03-22 | 2023-06-13 | 广州市迪士普音响科技有限公司 | Stream media audio and video push stream parameter control method and related device |
Non-Patent Citations (2)
| Title |
|---|
| 监控***中实时视频流码率动态调整机制的研究;巴洪涛;陈耀武;周蓉;;计算机科学(04);全文 * |
| 自适应动态网络的流媒体传输策略;洪煌辉;袁清珂;;计算机工程与设计(12);全文 * |
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