Method and system for processing downlink bandwidth allocation information

Abstract

The embodiment of the invention provides a downlink bandwidth allocation information processing method and system, and relates to the field of FTTR networking terminal equipment, and the method comprises the steps: obtaining equipment data obtained through collection of Internet of Things equipment, obtaining network bandwidth data of an optical fiber network where local equipment is located, and obtaining network flow data of the optical fiber network; performing local preprocessing on the equipment data, the network bandwidth data and the network flow data to obtain first to-be-analyzed data; and obtaining a first decision instruction output by the local analysis model based on the first to-be-analyzed data, the first decision instruction carrying the first downlink bandwidth allocation information, and controlling the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction. By adopting the technical scheme, the problem of low service efficiency caused by the fact that the bandwidth cannot be flexibly adjusted in the bandwidth allocation process in the prior art is solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of terminal devices for FTTR networking. Specifically, it relates to a method and system for processing downlink bandwidth allocation information. Background Art

[0002] With the continuous development of optical networks, bandwidth network services are gradually moving towards the F5.5G era dominated by FTTR (Fiber to The Room) and 50G-PON. Compared with previous generations of fixed access technologies, F5.5G has a series of excellent characteristics such as enhanced fixed bandwidth, all-optical connection, and real-time resilient connection. As Figure 1 shown, the F5.5G home private network extends the optical fiber to the room on the basis of fiber to the home, realizes all-optical networking within the home, and combines 10G-PON, 50G-PON, Wi-Fi 6, and Wi-Fi 7 technologies to achieve gigabit coverage throughout the house, solve problems such as insufficient Wi-Fi signal coverage and substandard rates in the home, and achieve secure and reliable gigabit coverage throughout the house. Among them, the signal coverage distance of home networking can be 50 meters or 100 meters. The signal coverage distance of commercial micro networking can be 200m, and the signal coverage distance of Wi-Fi access can be 10m.

[0003] FTTR includes three parts: a master device, a slave device, and an indoor optical fiber distributed network. Based on the optical fiber P2MP (Point to Multiple Point) physical topology, the master device is deployed at the access point position of a home or small and micro enterprise and serves as the center to build a full-optical network for the home or small and micro enterprise. The master device connects to the port of the OLTPON (Optical Line Terminal Passive Optical Network) towards the central office, and multiple slave devices are connected indoors. The slave devices can extend to the areas required by users according to the layout of the home or small and micro enterprise, providing network coverage for each area and achieving high-quality networks.

[0004] FTTR currently uses the ITU-T G.fin and G.Xfin series of standards internationally, including standards such as G.fin SA, G.fin DLL / G.Xfin DLL, and G.fin PHY / G.Xfin PHY. Domestically, it uses standards such as the fiber-to-room data link layer and the fiber-to-room physical layer. Among them, the fiber-to-room data link layer requires DBA (Dynamic Bandwidth Allocation), including three methods: SR-DBA, TM-DBA, and CO-DBA. In the SR-DBA method, bandwidth allocation is based on status reports. The MFU allocates bandwidth according to the explicit cache occupancy status report. The status report is requested by the MFU (Main FTTR Unit, the main device of FTTR) from the SFU (Sub FTTR Unit, the slave device of FTTR), and the SFU submits a response. In the TM-DBA method, bandwidth allocation is based on traffic monitoring. The MFU observes idle FEM / XFEM frames and compares them with the corresponding bandwidth mapping to allocate bandwidth. In the CO-DBA method, collaborative bandwidth allocation is performed. The MFU allocates bandwidth based on the management function external to the DLL layer or the information provided by the application for the MFU.

[0005] For the CO-DBA bandwidth allocation method, the current FTTR standard does not clearly define the method by which the management function or application external to the DLL layer provides information to the MFU. Its relatively fixed bandwidth allocation method has the deficiency of being unable to flexibly adjust the bandwidth. Moreover, when there are many devices connected under the MFU or traffic congestion occurs, it will cause high-priority service packet loss or increased latency, reducing service efficiency and affecting the user experience. It can be seen that in the related technology, there is a problem of low service efficiency due to the inability to flexibly adjust the bandwidth during the bandwidth allocation process.

[0006] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the Invention

[0007] The embodiments of the present application provide a method and system for processing downlink bandwidth allocation information to at least solve the problem of low service efficiency caused by the inability to flexibly adjust the bandwidth during the bandwidth allocation process.

[0008] According to one aspect of the embodiments of the present application, a method for processing downlink bandwidth allocation information is provided, including: obtaining device data collected from the Internet of Things device, obtaining network bandwidth data of the optical fiber network where the local device is located, and obtaining network traffic data of the optical fiber network; performing local preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain first data to be analyzed; obtaining a first decision instruction output by a local analysis model based on the first data to be analyzed, where the first decision instruction carries first downlink bandwidth allocation information, and controlling the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction.

[0009] In an exemplary embodiment, obtaining device data collected from the Internet of Things device includes: collecting device data from the Internet of Things device through the master device; or collecting device data from the Internet of Things device through the master device and at least two slave devices.

[0010] In an exemplary embodiment, obtaining network bandwidth data of the optical fiber network where the local device is located includes: collecting bandwidth configuration data of the optical fiber network through the master device; and / or collecting bandwidth jitter data of the optical fiber network through at least two slave devices.

[0011] In an exemplary embodiment, performing local preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain first data to be analyzed includes: performing local preprocessing on the device data, the network bandwidth data, and the network traffic data according to preprocessing steps, and determining first data to be analyzed according to the processing result of the local preprocessing; the preprocessing steps at least include: performing data cleaning on the device data, the network bandwidth data, and the network traffic data to obtain cleaned data; performing data format conversion on the cleaned data to obtain converted data; performing data standardization processing on the converted data to obtain standard data; performing data feature extraction on the standard data to obtain a data feature vector, and determining the processing result according to the data feature vector; and / or performing data anomaly detection on the standard data to obtain anomaly data and normal data, and storing the normal data.

[0012] In an exemplary embodiment, controlling the slave device to perform data transmission within the allocated bandwidth indicated by the first downlink bandwidth allocation information according to the first decision instruction includes: sending the first decision instruction to the slave device through the master device, so that the slave device parses the first downlink bandwidth allocation information to obtain an allocation indicator corresponding to the device number of the slave device, where the allocation indicator corresponds to a transmission time slot, and uplink physical data is sent within the time slot window of the transmission time slot.

[0013] In an exemplary embodiment, a dynamic link library management channel for data transmission is established between the master device and the slave device. The dynamic link library management channel includes sub-management channels of different channel types, which are used to encapsulate data according to the data format corresponding to the predetermined channel type, and transmit the encapsulated data through the sub-management channels of the predetermined channel type.

[0014] In an exemplary embodiment, the sub-management channels of different channel types include a wavelength division multiplexing management communication channel (WMCC) management channel, a fiber management communication channel (FMCC) management channel, a forward path linear organization access method (F-PLOAM) management channel, and an operation, administration, and maintenance (OAM) management channel for embedding. Before encapsulating data according to the data format corresponding to the predetermined channel type and transmitting the encapsulated data through the sub-management channels of the predetermined channel type, the method further includes: determining the WMCC management channel, the FMCC management channel, and the F-PLOAM management channel as the sub-management channels of the predetermined channel type.

[0015] The present application also proposes a method for processing downlink bandwidth allocation information, which is applied to a master device. The master device is connected to at least two slave devices, and both the master device and the slave devices belong to local devices. The method includes: determining bandwidth allocation data according to device data collected from Internet of Things devices, network bandwidth data of the optical fiber network where the local device is located, and network traffic data of the optical fiber network. The Internet of Things devices are connected to the slave devices; obtaining first data to be analyzed obtained after locally preprocessing the bandwidth allocation data, and obtaining a first decision instruction output by a local analysis model based on the first data to be analyzed. The first decision instruction carries first downlink bandwidth allocation information; and sending the bandwidth allocation data to a cloud platform, and obtaining a second decision instruction generated by the cloud platform based on the bandwidth allocation data; controlling the slave devices to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction and the second decision instruction.

[0016] In an exemplary embodiment, before sending the bandwidth allocation data to the cloud platform and obtaining the second decision instruction generated by the cloud platform based on the bandwidth allocation data, the method further includes: establishing a data transmission channel for transmitting data between the master device of the local device and the cloud platform; sending the bandwidth allocation data to the cloud platform through the data transmission channel, and obtaining the second decision instruction generated by the cloud platform based on the bandwidth allocation data through the data transmission channel.

[0017] This application also proposes a method for processing downlink bandwidth allocation information, which is applied to a cloud platform and includes: obtaining bandwidth allocation data from a local device, where the bandwidth allocation data at least includes device data collected by the local device for Internet of Things devices, obtaining network bandwidth data of the optical fiber network where the local device is located, and obtaining network traffic data of the optical fiber network; performing cloud preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain second data to be analyzed; obtaining a second decision instruction output by a cloud analysis model based on the second data to be analyzed, where the second decision instruction carries second downlink bandwidth allocation information, and sending the second decision instruction to the local device, where the local device is used to control slave devices in the local device to perform data transmission in a transmission time slot indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction.

[0018] In an exemplary embodiment, obtaining bandwidth allocation data from a local device includes: receiving the bandwidth allocation data through a data transmission channel, where the data transmission channel represents a channel for transmitting data between the master device of the local device and the cloud platform.

[0019] In an exemplary embodiment, performing cloud preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain second data to be analyzed includes: performing cloud preprocessing on the device data, the network bandwidth data, and the network traffic data according to preprocessing steps, and determining second data to be analyzed according to a processing result of the cloud preprocessing; the preprocessing steps at least include: performing data cleaning on the device data, the network bandwidth data, and the network traffic data to obtain cleaned data; performing data format conversion on the cleaned data to obtain converted data; performing data standardization processing on the converted data to obtain standard data; performing data feature extraction on the standard data to obtain a data feature vector, and determining the processing result according to the data feature vector; and / or, performing data anomaly detection on the standard data to obtain anomaly data and normal data, and storing the normal data.

[0020] According to another aspect of the embodiments of the present application, a processing system for downlink bandwidth allocation information is further provided, including a local device; the local device includes a master device and a slave device; the master device and the slave device are used to jointly obtain the device data collected by the slave device from the Internet of Things device, obtain the network bandwidth data of the optical fiber network where the local device is located, and obtain the network traffic data of the optical fiber network; the master device and the slave device are further used to jointly perform local preprocessing on the device data, the network bandwidth data and the network traffic data to obtain first data to be analyzed; the master device is further used to obtain a first decision instruction output by the local analysis model based on the first data to be analyzed, and the first decision instruction carries first downlink bandwidth allocation information; the slave device is further used to control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction.

[0021] According to another aspect of the embodiments of the present application, a processing system for downlink bandwidth allocation information is further provided, including: a data acquisition module disposed in the local device, the data acquisition module is connected to the monitoring feedback module, and is used to collect the Internet of Things device to obtain device data, obtain the network bandwidth data of the optical fiber network where the local device is located, and obtain the network traffic data of the optical fiber network from the monitoring feedback module; a data preprocessing module, connected to the data acquisition module, and is used to perform local preprocessing on the device data, the network bandwidth data and the network traffic data from the data acquisition module to obtain first data to be analyzed; a model training and inference module located in the local device, a local analysis model is preset in the model training and inference module, and the model training and inference module is connected to the data preprocessing module, and is used to receive a first decision instruction output by the local analysis model based on the first data to be analyzed, and the first decision instruction carries first downlink bandwidth allocation information; a bandwidth allocation module, connected to the model training and inference module, and is used to control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction from the model training and inference module.

[0022] According to another aspect of the embodiments of the present application, a processing system for downlink bandwidth allocation information is further provided, including: a cloud platform and a local device; the cloud platform is configured to obtain bandwidth allocation data from the local device; perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain second data to be analyzed; obtain a second decision instruction output by the cloud analysis model based on the second data to be analyzed, where the second decision instruction carries second downlink bandwidth allocation information, and send the second decision instruction to the local device; the local device is configured to collect data from the Internet of Things device to obtain device data, obtain the network bandwidth data of the optical fiber network where the local device is located and the network traffic data of the optical fiber network, determine the bandwidth allocation data according to the device data, the network bandwidth data, and the network traffic data, obtain first data to be analyzed obtained after local preprocessing of the bandwidth allocation data, and obtain a first decision instruction output by the local analysis model based on the first data to be analyzed, where the first decision instruction carries first downlink bandwidth allocation information, and control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction.

[0023] According to another aspect of the embodiments of the present application, there is also provided a processing system for downlink bandwidth allocation information, including: a data acquisition module disposed in a local device, the data acquisition module being connected to a monitoring feedback module, configured to collect Internet of Things devices to obtain device data, acquire network bandwidth data of the optical fiber network where the local device is located, and acquire network traffic data of the optical fiber network from the monitoring feedback module; a data preprocessing module, including a local data preprocessing unit located in the local device and a cloud platform data preprocessing unit located in the cloud platform, the local data preprocessing unit being connected to the data acquisition module, configured to perform local preprocessing on the device data, the network bandwidth data, and the network traffic data from the data acquisition module to obtain first data to be analyzed; the cloud platform data preprocessing unit being connected to the data acquisition module, configured to perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data from the data acquisition module to obtain second data to be analyzed; a model training and inference module, including a local analysis model located in the local device and a cloud analysis model located in the cloud platform, the local analysis model being connected to the data preprocessing module, configured to receive the first data to be analyzed and output a first decision instruction based on the first data to be analyzed, the first decision instruction carrying first downlink bandwidth allocation information; the cloud analysis model being connected to the data preprocessing module, configured to receive the second data to be analyzed and output a second decision instruction based on the second data to be analyzed, the second decision instruction carrying second downlink bandwidth allocation information; a bandwidth allocation module, connected to the model training and inference module, configured to control the slave device to perform data transmission in the transmission time slots indicated by the first downlink bandwidth allocation information and the second downlink bandwidth allocation information according to the first decision instruction and the second decision instruction from the model training and inference module.

[0024] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, and wherein the computer program is configured to execute the above-mentioned processing method for downlink bandwidth allocation information when running.

[0025] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and wherein the above-mentioned processor executes the above-mentioned processing method for downlink bandwidth allocation information through the computer program.

[0026] According to another aspect of the present application, there is also provided a computer program product, including a computer program, and wherein the steps in any of the above method embodiments are implemented when the computer program is executed by a processor.

[0027] The solution provided by this application realizes the efficient acquisition and processing of data of Internet of Things devices through local devices. At the same time, according to the real-time bandwidth and traffic data of the fiber optic network, the downlink bandwidth allocation is dynamically adjusted, reducing the burden of data transmission. Combining with the local analysis model to output decision instructions, it improves the accuracy and real-time performance of decisions, solves the problem of low business efficiency caused by the inability to flexibly adjust the bandwidth during the bandwidth allocation process, improves the efficiency and stability of data transmission, and provides a strong guarantee for the efficient operation of Internet of Things devices. Description of the Drawings

[0028] The drawings described herein are used to provide a further understanding of this application, and constitute a part of this application. The exemplary embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0029] Figure 1 is a schematic diagram of the FTTR networking architecture in the related art;

[0030] Figure 2 is a hardware structure block diagram of a computer terminal for the method of processing downlink bandwidth allocation information according to an embodiment of this application;

[0031] Figure 3 is a flowchart (one) of the method for processing downlink bandwidth allocation information according to an embodiment of this application;

[0032] Figure 4 is a flowchart (one) of the FTTR networking AI DBA issuing decision according to an embodiment of this application;

[0033] Figure 5 is a flowchart (two) of the FTTR networking AI DBA issuing decision according to an embodiment of this application;

[0034] Figure 6 is a schematic diagram (one) of the FTTR networking AI DBA data preprocessing according to an embodiment of this application;

[0035] Figure 7 is a schematic diagram of the FTTR networking AI DBA data transmission according to an embodiment of this application;

[0036] Figure 8 is a schematic diagram (two) of the FTTR networking AI DBA data preprocessing according to an embodiment of this application;

[0037] Figure 9 is a schematic diagram of the FTTR networking AI DBA bandwidth dynamic adjustment according to an embodiment of this application;

[0038] Figure 10 is a flowchart (two) of the method for processing downlink bandwidth allocation information according to an embodiment of this application;

[0039] Figure 11 is the flowchart (III) of the method for processing downlink bandwidth allocation information according to an embodiment of the present application;

[0040] Figure 12 is the schematic diagram of the principle of collaborative processing of the FTTR networking AI DBA model according to an embodiment of the present application;

[0041] Figure 13 is the schematic diagram of the FTTR networking AI DBA monitoring feedback according to an embodiment of the present application;

[0042] Figure 14 is the block diagram (I) of the processing system for downlink bandwidth allocation information according to an embodiment of the present application;

[0043] Figure 15 is the block diagram (II) of the processing system for downlink bandwidth allocation information according to an embodiment of the present application;

[0044] Figure 16 is the block diagram (III) of the processing system for downlink bandwidth allocation information according to an embodiment of the present application;

[0045] Figure 17 is the block diagram (IV) of the processing system for downlink bandwidth allocation information according to an embodiment of the present application. Detailed implementation manners

[0046] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0047] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily need to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0048] The method embodiments provided in the embodiments of the present application can be executed on a computer terminal or a similar computing device. Taking the operation on a computer terminal as an example, Figure 2 is a hardware structure block diagram of a computer terminal for the method of processing downlink bandwidth allocation information in the embodiments of the present application. As Figure 2 shown, the computer terminal may include one or more ( Figure 2 only one is shown in the figure) processors 202 (the processor 202 may include, but is not limited to, a microprocessor (abbreviated as MPU) or a programmable logic device (abbreviated as PLD)) and a memory 204 for storing data. In an exemplary embodiment, the above computer terminal may further include a transmission device 206 for communication functions and an input / output device 208. Those of ordinary skill in the art can understand that Figure 2 the structure shown is only illustrative and does not limit the structure of the above computer terminal. For example, the computer terminal may further include more or fewer components than Figure 2 shown in the figure, or have an equivalent function to Figure 2 shown in the figure or different configurations with more functions than Figure 2 shown in the figure.

[0049] The memory 204 can be used to store computer programs. For example, software programs and modules of application software, such as the computer program corresponding to the method of processing downlink bandwidth allocation information in the embodiments of the present application. The processor 202 executes various functional applications and data processing by running the computer program stored in the memory 204, that is, implements the above method. The memory 204 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 204 may further include a memory remotely provided relative to the processor 202, and these remote memories can be connected to the computer terminal through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0050] The transmission device 206 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the computer terminal. In one instance, the transmission device 206 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 206 may be a radio frequency (abbreviated as RF) module, which is used to communicate with the Internet wirelessly.

[0051] Figure 3 is a flowchart (one) of a method for processing downlink bandwidth allocation information according to an embodiment of the present application. The execution entity may be a local device, such as Figure 3 shown, and the steps of the method include:

[0052] Step S302, obtain device data collected from the Internet of Things device, obtain network bandwidth data of the optical fiber network where the local device is located, and obtain network traffic data of the optical fiber network.

[0053] Step S304, perform local preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain first data to be analyzed.

[0054] Step S306, obtain a first decision instruction output by a local analysis model based on the first data to be analyzed. The first decision instruction carries first downlink bandwidth allocation information, and control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction.

[0055] In the embodiment of the present application, by obtaining device data collected from the Internet of Things device, obtaining network bandwidth data of the optical fiber network where the local device is located, and obtaining network traffic data of the optical fiber network; performing local preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain first data to be analyzed; obtaining a first decision instruction output by a local analysis model based on the first data to be analyzed. The first decision instruction carries first downlink bandwidth allocation information, and control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction, the efficient collection and processing of Internet of Things device data are realized based on the local device. At the same time, according to the real-time bandwidth and traffic data of the optical fiber network, the downlink bandwidth allocation is dynamically adjusted, reducing the burden of data transmission. Combining with the decision instruction output by the local analysis model, the accuracy and real-time performance of the decision are improved, the problem of low service efficiency caused by the inability to flexibly adjust the bandwidth during the bandwidth allocation process is solved, the efficiency and stability of data transmission are improved, and a strong guarantee is provided for the efficient operation of Internet of Things devices.

[0056] Optionally, the Internet of Things device includes, for example, but is not limited to, various sensors, actuators, or other intelligent devices. The Internet of Things device accesses the optical fiber network through a slave device and is used to collect information such as environmental data and device status.

[0057] Among them, the network bandwidth data of the optical fiber network reflects the actual available bandwidth of the network, while the network traffic data represents the data transmission volume in the current network.

[0058] It should be noted that the local analysis model can predict the optimal downlink bandwidth allocation strategy based on the preprocessed data. The training process of the above local analysis model is as follows:

[0059] Obtain training data: Use the data collection module of MFU to collect device operation status information, environmental data, and obtain data such as device exception reports and user behavior data from the monitoring feedback module.

[0060] Perform data preprocessing, formatting, feature extraction, and data storage in sequence. Among them, data preprocessing includes local cleaning: removing invalid or incorrect data to ensure the quality of model training. Formatting includes unifying the data format for easy model reading. Feature extraction includes selecting key features to reduce computational complexity. For example, when using MobileNetV2 to process image data, only edge information or color distribution features can be retained. Data storage includes storing the processed data in a local database for convenient invocation.

[0061] Then use a simple model such as MobileNetV2 for training. Due to resource limitations, a small-batch data iterative training method is usually adopted. Finally, test performance such as accuracy and response time on an independent dataset, and select the model that meets the performance requirements as the above local analysis model.

[0062] The method of this embodiment corresponds to the scenario where the cloud platform is disconnected or does not include the cloud platform in FTTR networking AI DBA (Artificial Intelligence Dynamic Bandwidth Allocation), as Figure 4 shown in steps 4.1 to 4.9, taking the case where 2 SFUs are hung under MFU ( Figure 4 the master device). Taking MFU and SFU1 ( Figure 4 slave device 1) and SFU2 ( Figure 4 slave device 2) as examples, DLL layer channels are established between MFU and SFU1 and SFU2 respectively. SFU1 and SFU2 periodically report bandwidth allocation-related data through their respective established DLL channels. At the same time, MFU also collects local bandwidth allocation-related data and network performance monitoring data. MFU and SFU perform local preprocessing on the bandwidth allocation-related data and monitoring data, and then send them to the local analysis model for analysis, reasoning, and decision-making and distribution. MFU adjusts the BWmap field according to the decision and distributes it to SFU1 and SFU2. After receiving the BWmap field, SFU1 and SFU2 will respond with bandwidth for data transmission to ensure the bandwidth and delay of high-priority services in FTTR networking, and improve bandwidth utilization and user experience.

[0063] Among them, steps 4.1 to 4.9 are as follows:

[0064] 4.1 Complete the establishment of the DLL layer channel between the MFU and the SFU1.

[0065] 4.2 Complete the establishment of the DLL layer channel between the MFU and the SFU2.

[0066] 4.3 Report the data cycle related to the SFU1 bandwidth allocation.

[0067] 4.4 Report the data cycle related to the SFU2 bandwidth allocation.

[0068] 4.5 The MFU and the SFU collect data, perform local preprocessing on the monitoring data, send it to the local model for analysis, inference, and decision-making and distribution.

[0069] 4.6 The MFU adjusts the BWmap field according to the decision and distributes it.

[0070] 4.7 The SFU1 transmits data to the MFU.

[0071] 4.8 The MFU adjusts the BWmap field according to the decision and distributes it.

[0072] 4.9 The SFU2 transmits data to the MFU.

[0073] Correspondingly, the embodiments with the cloud platform and the local device as the execution entities in the following text correspond to the scenario where the cloud platform is included in the FTTR networking AI DBA. As Figure 5 shown in steps 5.1 to 5.14, taking the MFU ( Figure 5 main device) with 2 SFUs attached, and the MFU connected to the cloud platform as an example, the MFU and the SFU1 ( Figure 5 slave device 1) and ( Figure 5For the slave devices 1), SFU2 respectively establishes DLL layer channels. SFU1 and SFU2 periodically report data related to bandwidth allocation through the DLL channels they establish respectively. At the same time, MFU also collects data related to local bandwidth allocation and network performance monitoring data. The data related to bandwidth allocation and monitoring data of MFU and SFU will be preprocessed locally and sent to the local analysis model for analysis and reasoning. MFU and the cloud platform also establish a cloud channel. MFU will periodically report the data related to bandwidth allocation of MFU and SFU to the cloud platform. The cloud platform collects user experience monitoring data, preprocesses the data related to bandwidth allocation and monitoring data of MFU and SFU in the cloud and sends them to the cloud analysis model for analysis and reasoning. The cloud platform will send the analysis and reasoning results to the local. MFU will make a comprehensive decision and send it down according to the analysis and reasoning results of the cloud analysis model and the local analysis model. At the same time, it will adjust the BWmap field according to the decision and send it to SFU1 and SFU2. After receiving the BWmap field, SFU1 and SFU2 will respond with bandwidth respectively for data transmission to ensure the bandwidth and delay of high-priority services in the FTTR network, and improve the bandwidth utilization rate and user experience.

[0074] Among them, steps 5.1 to 5.14 are as follows:

[0075] 5.1 Complete the establishment of the cloud channel between the cloud platform and MFU.

[0076] 5.2 Complete the establishment of the DLL layer channel between MFU and SFU1.

[0077] 5.3 Complete the establishment of the DLL layer channel between MFU and SFU2.

[0078] 5.4 Periodically report data related to SFU1 bandwidth allocation.

[0079] 5.5 Periodically report data related to SFU2 bandwidth allocation.

[0080] 5.6 Periodically report data related to the bandwidth allocation of MFU and SFU.

[0081] 5.7 Locally preprocess the collected data and monitoring data of MFU and SFU, and send them to the local model for analysis and reasoning.

[0082] 5.8 Preprocess the collected data and monitoring data of MFU and SFU in the cloud, and send them to the cloud model for analysis and reasoning.

[0083] 5.9 The cloud platform sends the analysis and reasoning results to MFU.

[0084] 5.10 MFU makes a comprehensive decision and sends it down according to the analysis and reasoning results of the cloud model and the local model.

[0085] 5.11 The MFU adjusts the BWmap domain according to the decision and distributes it to the SFU1.

[0086] 5.12 The SFU1 transmits data to the MFU.

[0087] 5.13 The MFU adjusts the BWmap domain according to the decision and distributes it to the SFU2.

[0088] 5.14 The SFU2 transmits data to the MFU.

[0089] In an exemplary embodiment, obtaining device data collected from the Internet of Things devices may include: collecting device data through the master device for the Internet of Things devices; or collecting device data through the master device and at least two slave devices for the Internet of Things devices.

[0090] Among them, the device data may include, but is not limited to, various physical or environmental parameters such as temperature, humidity, light intensity, and device health status.

[0091] In this embodiment, collecting data through the master device alone or through the cooperation of the master device and multiple slave devices can ensure the comprehensiveness and accuracy of the data, and at the same time improve the redundancy of the system. Even if a certain slave device fails, other devices can still continue the data collection work, ensuring the continuous operation of the system. This flexible collection method can adapt to Internet of Things application scenarios of different scales and complexities, improving the reliability and efficiency of data collection.

[0092] In an exemplary embodiment, obtaining network bandwidth data of the optical fiber network where the local device is located includes: collecting bandwidth configuration data of the optical fiber network through the master device; and / or collecting bandwidth jitter data of the optical fiber network through at least two slave devices.

[0093] Among them, the bandwidth configuration data, such as DBRu, idle frame ratio, bandwidth configuration, etc., represents the basic capacity information of the network, while the bandwidth jitter data reflects the fluctuation of the network bandwidth. These data are crucial for evaluating the bearing capacity and stability of the network. The joint collection by the master device and the slave devices can more accurately reflect the real situation of the network, contribute to formulating a reasonable bandwidth allocation strategy, avoid data transmission failures or delays caused by network fluctuations, and improve the continuity and efficiency of data transmission.

[0094] Such as Figure 6As shown, both the master device and the slave device include data acquisition modules, which collect data from the attached IOT (Internet of Things) devices or bandwidth-related data. The range of IOT devices is relatively wide, such as mobile terminals, smart door locks, mobile phones, and cameras. The data generated by IOT devices mainly includes service data and device data. Service data, such as data generated by games, voice, IPTV (Internet Protocol Television), OTT (Over The Top), etc., and device data, such as data generated by fingerprint authentication, device actions, etc. These data can be collected by the MFU and SFU data acquisition modules, or can be uniformly collected by the MFU data acquisition module. In addition to IOT device data, the SFU data acquisition module also needs to collect bandwidth-related data such as DBRu and bandwidth jitter. The MFU data acquisition module also needs to collect related data such as DBRu, idle frame ratio, bandwidth configuration, and network traffic. After the data is collected, it will be transmitted to the data preprocessing module for unified processing.

[0095] Optionally, the master device and the slave device can communicate through, for example, the IFDN (Integrated Frame Data Network) protocol.

[0096] Among them, DBRu (Database Request Unit) can be understood as the smallest unit of data read and write operations in the database. For example, DBRu is used to measure the read and write capabilities of a disk, and one DBRu can correspond to a certain number of data read and write operations.

[0097] In network communication, data is transmitted in the form of frames at the physical layer. The idle frame, also known as the idle frame, refers to the idle frame sent by network devices to keep the link active when there is no data transmission in the network. The idle frame ratio refers to the proportion of idle frames in all the frames sent within a certain period of time. A high idle frame ratio may indicate low network utilization and low data transmission efficiency.

[0098] In data communication, bandwidth jitter refers to the instability of the bandwidth of a network connection over time. Usually, the bandwidth should be constant, but in actual use, due to network congestion, device performance changes, signal interference, etc., the actual bandwidth may fluctuate within a certain range. Excessive bandwidth jitter may affect the quality of data transmission, such as causing video stream stuttering.

[0099] Bandwidth configuration: Refers to the setting of data transmission speed in network devices or services. In wired or wireless networks, bandwidth is usually measured in Mbps (megabits per second) or Gbps (gigabits per second). Bandwidth configuration determines the maximum transmission speed of the network and how to allocate network resources among different services or applications.

[0100] In an exemplary embodiment, a dynamic link library management channel for data transmission is established between the master device and the slave device. The dynamic link library management channel includes sub-management channels of different channel types, which are used to encapsulate data according to the data format corresponding to the predetermined channel type and transmit the encapsulated data through the sub-management channels of the predetermined channel type. The establishment of the dynamic link library management channel allows for efficient and flexible exchange of data and control instructions between the master device and the slave device. By using sub-management channels of different channel types, the most suitable transmission method can be selected according to the data type and transmission requirements. For example, high-priority data can be transmitted through high-speed channels, while low-frequency data can be transmitted through low-speed but stable channels. This embodiment not only improves the efficiency of data transmission but also enhances the stability and security of data transmission, and is applicable to various complex network environments and data transmission requirements.

[0101] Among them, the dynamic link library management channel corresponds to Figure 7 the DDL management channel.

[0102] In an exemplary embodiment, the sub-management channels of different channel types include a wavelength division multiplexing management communication channel WMCC management channel, an optical fiber management communication channel FMCC management channel, a forward path linear organization access method F-PLOAM management channel, and an embedded operation, administration, and maintenance OAM management channel. Before encapsulating data according to the data format corresponding to the predetermined channel type and transmitting the encapsulated data through the sub-management channels of the predetermined channel type, the method further includes: determining the WMCC management channel, the FMCC management channel, and the F-PLOAM management channel as the sub-management channels of the predetermined channel type.

[0103] Among them, the WMCC management channel utilizes wavelength division multiplexing technology, allowing optical signals of different wavelengths to be transmitted on the same optical fiber, improving the utilization rate of the optical fiber; the FMCC management channel is specifically used for management communication in optical fiber networks, ensuring the rapid transmission of control instructions; the F-PLOAM management channel is a management channel for forward path linear organization access and is applicable to chain network structures. Such as Figure 7As shown in FIG. 1 , the DLL management channel between the MFU and the SFU includes four channels: the WMCC (Wavelength Division Multiplexed Management Channel), the FMCC (Fiber Management Channel), the F-PLOAM (Forward Path Linearly Organized Access Method) and the embedded OAM (Operations, Administration and Maintenance). The data collected by the SFU data acquisition module can be transmitted through any of the WMCC, FMCC and F-PLOAM management channels, and encapsulated in the corresponding message format, such as WMCI messages, FMCI messages or F-PLOAM messages. After being transmitted to the MFU, it can be transmitted locally or further to the cloud for processing. By predetermining the use of these sub-management channels, the most appropriate channel can be selected for data transmission according to different data types and transmission requirements, thereby improving the efficiency of data transmission and the utilization of network resources.

[0104] In an exemplary embodiment, the device data, the network bandwidth data and the network traffic data are locally preprocessed to obtain first data to be analyzed, including: locally preprocessing the device data, the network bandwidth data and the network traffic data according to a preprocessing step, and determining the first data to be analyzed according to the processing result of the local preprocessing; the preprocessing step at least includes: data cleaning the device data, the network bandwidth data and the network traffic data to obtain cleaned data; data format conversion of the cleaned data to obtain converted data; data standardization of the converted data to obtain standard data; data feature extraction of the standard data to obtain a data feature vector, and determining the processing result according to the data feature vector; and / or data anomaly detection of the standard data to obtain abnormal data and normal data, and data storage of the normal data.

[0105] Among them, data cleaning can remove invalid or erroneous data, data format conversion ensures data consistency, data standardization allows data from different sources to be compared at the same scale, and data feature extraction extracts key factors that affect bandwidth allocation. Through preprocessing, this embodiment can significantly improve the accuracy and efficiency of subsequent data analysis, quickly respond to network changes, and reduce decision-making deviations caused by data quality issues.

[0106] likeFigure 8 As shown, the data preprocessing module includes steps such as data cleaning, format conversion, normalization, feature extraction, anomaly detection processing, and data storage. Data cleaning mainly processes the collected data by removing noise data (outliers, incorrect data), filling missing values, and deduplication. Format conversion mainly performs unified data format and timestamp processing. Normalization mainly unifies the standards, such as normalizing key indicators such as bandwidth and latency, so that all feature scales are consistent. Feature extraction extracts key features (such as bandwidth and latency), and at the same time generates new features (peak bandwidth feature, user behavior feature). Anomaly detection processing mainly detects and marks and eliminates abnormal data. Data storage mainly stores the preprocessed data in the database for local and cloud analysis models to call and analyze and infer. Through preprocessing, the data quality input to the local analysis model can be ensured, thereby improving the prediction accuracy of the model.

[0107] In an exemplary embodiment, controlling the slave device to perform data transmission within the allocated bandwidth indicated by the first downlink bandwidth allocation information according to the first decision instruction specifically includes: sending the first decision instruction to the slave device through the master device, so that the slave device parses the first downlink bandwidth allocation information to obtain an allocation indicator corresponding to the device number of the slave device, where the allocation indicator corresponds to a transmission time slot, and uplink physical data is sent within the time slot window of the transmission time slot.

[0108] Among them, the first decision instruction contains bandwidth resource information that each slave device should use within a specific time window. By sending these instructions from the master device to the slave device, it can ensure that the slave device follows the optimized bandwidth allocation strategy for data transmission. The setting of the transmission time slot can avoid multiple devices competing for limited bandwidth resources at the same time, thereby reducing data collisions and retransmissions, and improving the network utilization rate and data transmission efficiency.

[0109] Such as Figure 9As shown, after the MFU receives the AI DBA decision distribution instruction, the bandwidth allocation module performs dynamic bandwidth adjustment, mainly adjusting the G.fin or G.Xfin TAmap. Among them, the G.fin TAmap mainly consists of Alloc-ID (Allocation Identifier), StartTime (start time), and StopTime (end time). Alloc-ID binds to a specific TCONT, and the bandwidth allocation time slot is determined by StartTime and StopTime. The G.Xfin TAmap mainly consists of Alloc-ID, StartTime, and GrantSize. Alloc-ID binds to a specific TCONT, and the bandwidth allocation time slot is determined by StartTime and GrantSize. After receiving the TAmap, the SFU will perform parsing, and at the same time, judge the transmission time slot according to the Alloc-ID and send the uplink PHY burst on the corresponding transmission time slot.

[0110] Next, taking the local device and the cloud platform as the execution entities respectively, the processing process of the downlink bandwidth allocation information will be described.

[0111] In this embodiment, the master device is connected to at least two slave devices, such as Figure 10 As shown, the processing process of the downlink bandwidth allocation information with the local device as the execution entity includes:

[0112] S1002, determining the bandwidth allocation data according to the device data collected from the Internet of Things devices, the network bandwidth data of the optical fiber network where the local device is located, and the network traffic data of the optical fiber network. The Internet of Things devices are connected to the slave devices;

[0113] S1004, obtaining the first data to be analyzed obtained after local preprocessing of the bandwidth allocation data, and obtaining the first decision instruction output by the local analysis model based on the first data to be analyzed. The first decision instruction carries the first downlink bandwidth allocation information;

[0114] S1006, and sending the bandwidth allocation data to the cloud platform to obtain the second decision instruction generated by the cloud platform based on the bandwidth allocation data;

[0115] S1008, controlling the slave devices to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction and the second decision instruction.

[0116] The master device determines a preliminary bandwidth allocation plan through comprehensive analysis of the data of IoT devices, network bandwidth data, and network traffic data. Subsequently, the master device sends this data to the cloud platform and utilizes the powerful computing power of the cloud platform for in-depth analysis to generate a more optimized bandwidth allocation strategy, solving the problem of low business efficiency caused by the inability to flexibly adjust the bandwidth during the bandwidth allocation process and improving the efficiency and stability of data transmission.

[0117] In an exemplary embodiment, before sending the bandwidth allocation data to the cloud platform and obtaining the second decision instruction generated by the cloud platform based on the bandwidth allocation data, a data transmission channel for transmitting data may also be established between the master device of the local device and the cloud platform; the bandwidth allocation data is sent to the cloud platform through the data transmission channel, and the second decision instruction generated by the cloud platform based on the bandwidth allocation data is obtained through the data transmission channel. By establishing the data transmission channel, the security and efficiency of data transmission between the master device and the cloud platform are ensured. Through this channel, the master device can transmit the collected device data, network bandwidth data, and network traffic data to the cloud platform without loss for in-depth analysis by the cloud analysis model. At the same time, the cloud platform can also feedback the analysis result, that is, the second decision instruction, to the master device in a timely manner through this channel to guide its bandwidth allocation. This embodiment can effectively shorten the decision-making cycle and improve the timeliness of decision-making.

[0118] It should be noted that in this embodiment, for other solutions implemented by the master device and the slave device, refer to the above-mentioned method for processing the downlink bandwidth allocation information with the local device as the execution entity, and this application will not elaborate here.

[0119] This application also proposes a method for processing downlink bandwidth allocation information applied to a cloud platform, as Figure 11 shown, including:

[0120] S1102, obtain bandwidth allocation data from a local device, where the bandwidth allocation data at least includes device data collected by the local device for IoT devices, obtain network bandwidth data of the optical fiber network where the local device is located, and obtain network traffic data of the optical fiber network;

[0121] S1104, perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain second data to be analyzed;

[0122] S1106. Obtain the second decision instruction output by the cloud analysis model based on the second data to be analyzed. The second decision instruction carries second downlink bandwidth allocation information, and send the second decision instruction to the local device. The local device is used to control slave devices in the local device to perform data transmission in the transmission time slots indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction.

[0123] In this embodiment, after the bandwidth allocation data received by the cloud platform, which includes the status information of IoT devices, the real-time bandwidth and traffic data of the network, etc., is preprocessed by the cloud, it is input into the cloud analysis model to generate an optimized bandwidth allocation strategy. By sending the second decision instruction back to the local device in this embodiment, the cloud platform can assist the local device to perform more efficient bandwidth resource allocation, improving the smoothness of data transmission and the stability of the network.

[0124] Among them, the training process of the above cloud analysis model includes: collecting other relevant data such as device status information through a data collection module. Sequentially performing data preprocessing, formatting, feature extraction, and data storage. Among them, data preprocessing can be a cloud cleaning process. Subsequently, a complex model based on Transformer technology is used for training. In this way, the local analysis model quickly processes real-time data to provide preliminary analysis results and immediate decision-making suggestions. At the same time, the cloud analysis model receives local preprocessed data and feedback for in-depth analysis to provide more accurate decision-making basis. When both are available, the master device (MFU) will comprehensively consider the results of the local and cloud analysis models for comprehensive decision-making to improve the accuracy of decision-making. If the cloud connection is disconnected, the local analysis model will undertake all decision-making tasks. Although the accuracy may decrease, it can ensure that the basic functions of the device are not interrupted. Through the combination of the local analysis model and the cloud analysis model, it not only ensures the real-time performance and robustness of device operation, but also improves the accuracy and flexibility of analysis and decision-making, which is a key technology combination for realizing intelligent IoT applications.

[0125] In an exemplary embodiment, obtaining bandwidth allocation data from a local device includes: receiving the bandwidth allocation data through a data transmission channel, where the data transmission channel represents a channel for transmitting data between the master device of the local device and the cloud platform. By establishing a data transmission channel, it provides a basis for data transmission between the local device and the cloud platform. Through this channel, the cloud platform can receive in real time the bandwidth allocation data from the local device, including device data, network bandwidth data, and network traffic data, so as to perform in-depth analysis in a timely manner and generate an optimized bandwidth allocation strategy. This embodiment can effectively shorten the decision-making cycle and improve the real-time performance of decision-making.

[0126] Such as Figure 12As shown, the AI model includes a local analysis model and a cloud analysis model. The local analysis model is a simple model, such as simple models like MobileNetV2, YoLoV5, and ResNet50. The cloud analysis model is a complex model such as DeepSeek, Qwen, and OpenAI. Local data includes data collected by the MFU and SFU data acquisition modules and data from the monitoring feedback module. After collection, local preprocessing will be performed, including local cleaning, formatting, feature extraction, and data storage, etc. The locally preprocessed data will be transmitted to the local analysis model for analysis and inference. Cloud data includes data collected by the MFU and SFU data acquisition modules or data after local preprocessing, as well as data feedback from the monitoring module. After collection, cloud preprocessing will be performed, including cloud cleaning, formatting, feature extraction, and data storage, etc. The cloud-preprocessed data will be transmitted to the cloud analysis model for analysis and inference, and at the same time, the analysis and inference results will be sent down to the local side. The MFU will make a comprehensive decision and send it down based on the inference results of the local analysis model and referring to the inference results of the cloud analysis model. When the cloud connection is disconnected, the local analysis model will perform analysis, inference, and decision-making and send them down. Among them, as Figure 13 shown, the monitoring feedback module monitors key performances such as the bandwidth utilization rate, packet loss rate, and alarms of local devices, as well as collects user experience data from the cloud.

[0127] In addition, by establishing a dynamic link library to manage the communication channels and data transmission channels, the data interaction between the master device, slave device, and cloud platform is optimized, further enhancing the flexibility and reliability of the system.

[0128] In an exemplary embodiment, cloud preprocessing of the device data, the network bandwidth data, and the network traffic data to obtain second data to be analyzed may include: performing cloud preprocessing on the device data, the network bandwidth data, and the network traffic data according to preprocessing steps, and determining the second data to be analyzed based on the processing results of the cloud preprocessing; the preprocessing steps at least include: performing data cleaning on the device data, the network bandwidth data, and the network traffic data to obtain cleaned data; performing data format conversion on the cleaned data to obtain converted data; performing data standardization processing on the converted data to obtain standard data; performing data feature extraction on the standard data to obtain a data feature vector, and determining the processing results based on the data feature vector; and / or, performing data anomaly detection on the standard data to obtain anomaly data and normal data, and storing the normal data. The cloud preprocessing steps are similar to the local preprocessing, but the cloud platform usually has more powerful computing resources and can handle more complex data preprocessing tasks. Data cleaning, format conversion, standardization, and feature extraction can ensure the data quality of the data input into the cloud analysis model, thereby improving the prediction accuracy of the model. Data anomaly detection can help the cloud platform identify abnormal situations in the network, such as device failures or network attacks, and then take corresponding measures to protect the security of the network. Through cloud preprocessing, the cloud platform can more effectively analyze and process large-scale data, make more accurate decisions, and is applicable to various scenarios that require advanced data analysis capabilities, including but not limited to big data analysis, artificial intelligence applications, and network security monitoring, ensuring the high performance and high reliability of the system.

[0129] In addition, the above embodiments of the present application are not only applicable to the FTTR system, but also have a certain applicability to the FTTH system, such as 10G-PON, 50G-PON, and VHSP systems. In the future, it may be combined with WIFI8 technology to provide a better user experience.

[0130] Next, the processing method of the downlink bandwidth allocation information will be further described in conjunction with the following embodiments.

[0131] Based on the above embodiments, the processing process of AI DBA in the FTTR networking is further provided:

[0132] 1. The data acquisition module performs data acquisition.

[0133] Both the MFU and the SFU include a data acquisition module, which acquires data of the IOT devices or bandwidth-related data connected below. The IOT device data can be acquired by the data acquisition modules of the MFU and the SFU, or the data acquisition module of the MFU can acquire the IOT device data uniformly. In addition to the IOT device data, the SFU data acquisition module also needs to acquire bandwidth-related data such as DBRu and bandwidth jitter, and the MFU data acquisition module also needs to acquire data such as DBRu, idle frame ratio, and bandwidth configuration. After acquisition, the data will be transmitted to the data preprocessing module for unified processing.

[0134] The range of IOT devices is relatively wide, such as PCs, smart door locks, mobile phones, and cameras. The IOT device data mainly includes service data and device data. The service data includes data generated by Game, IPTV, Voice, OTT, etc., and the device data includes data generated by fingerprint authentication, device actions, etc.

[0135] 2. Use DLL to manage the channel for transmitting the acquired data.

[0136] Such as Figure 7 As shown, the DLL (Dynamic Link Library) management channels established by the MFU and the SFU respectively include 4 channels: WMCC (WDM Management Communication Channel), FMCC management channel (Fiber Management Communication Channel), F-PLOAM (Forward Path Linearly Organized Access Method) management channel, and Embedded OAM management channel (Embedded Operations, Administration and Maintenance).

[0137] The information acquired by the SFU data acquisition module can be transmitted through any one of the WMCC management channel, FMCC management channel, and F-PLOAM management channel, and the corresponding message format is encapsulated at the same time, such as being encapsulated into WMCI message, FMCI message, or F-PLOAM message, etc., and then transmitted to the MFU for processing locally or continuing to be transmitted to the cloud.

[0138] 3. Make decision and issue.

[0139] The AI models within the model training and inference module include a local analysis model and a cloud analysis model. The local analysis model represents simple models such as MobileNetV2, YoLoV5, and ResNet50 based on DNN, CNN, and RNN technologies, while the cloud analysis model represents complex models such as DeepSeek, Qwen, and OpenAI based on Transformer technology.

[0140] Local data includes the data collected by the MFU and SFU data acquisition modules and the data fed back by the monitoring module. After collection, local preprocessing will be carried out, including local cleaning, formatting, feature extraction, and data storage, etc. The locally preprocessed data will be transmitted to the local analysis model for analysis and inference.

[0141] Cloud data includes the data collected by the MFU and SFU data acquisition modules or the locally preprocessed data and the data fed back by the monitoring module. After collection, cloud preprocessing will be carried out, and cloud preprocessing includes cloud cleaning, formatting, feature extraction, and data storage, etc.

[0142] The cloud preprocessed data will be transmitted to the cloud analysis model for analysis and inference, and at the same time, the analysis and inference results will be sent down to the local side. The MFU will make a comprehensive decision and send it down based on the inference results of the local analysis model and with reference to the inference results of the cloud analysis model. When the cloud is disconnected, the local analysis model will perform analysis, inference, and decision-making and sending down.

[0143] 4. Execute the decision-making and sending-down instruction to perform dynamic bandwidth adjustment.

[0144] After receiving the decision-making instruction, the MFU performs dynamic bandwidth adjustment through the bandwidth allocation module, mainly adjusting G.fin or G.XfinTAmap. Among them, "G.Fin" refers to the frame boundary indication signal of GPON (Gigabit-Capable Passive Optical Network), which is used to synchronize the frame boundaries of the OLT (Optical Line Terminal) and ONU to ensure the correct transmission of data. "G.Xfin" is a control signal in GPON, which is used for the OLT to send specific control information such as activating the ONU, deactivating the ONU, or sending system parameter updates to the ONU (Optical Network Unit).

[0145] TAmap (Transmission Allocation map) is a mechanism in GPON for allocating downstream bandwidth. TAmap is sent by the OLT and contains the downstream bandwidth allocation information for each ONU, including the allocated time slots, bandwidth sizes, priorities, etc.

[0146] As Figure 9 shown, G.fin TAmap mainly consists of Alloc-ID (Allocation Identifier, the allocation identifier, such as Figure 9 the internal allocation identifier X), StartTime (start time), and StopTime (end time). Alloc-ID is bound to a specific TCONT (Transmission Container), and the bandwidth allocation time slot is determined by StartTime and StopTime. G.Xfin TAmap mainly consists of Alloc-ID (such as Figure 9 the internal allocation identifier Y), StartTime, and GrantSize. Alloc-ID is bound to a specific TCONT, and the bandwidth allocation time slot is determined by StartTime and GrantSize (grant size).

[0147] After receiving the TAmap, the slave device will parse it, and at the same time, judge the transmission time slot according to Alloc-ID, and send the PHY burst (corresponding to Figure 9 the corresponding data stream transmission) in the corresponding transmission time slot. Among them, PHY burst is a concept related to data transmission in a wireless communication system, especially in a cellular network. "Uplink" transmission refers to sending data from a user device (such as a mobile phone, tablet, Internet of Things device, etc.) to a base station. "PHY" refers to the physical layer, which is the lowest layer of the network protocol stack and processes the hardware transmission and reception of signals; "burst" means the concentrated transmission of a large amount of data in a short period of time. The uplink-sent PHY burst can include data on multiple physical channels, such as control information (PUCCH, Physical Uplink Control Channel), user data (PUSCH, Physical Uplink Shared Channel), etc. The device will package this data and send it within a short time window to improve transmission efficiency and reduce interference.

[0148] 5. The monitoring and feedback module collects user experience information.

[0149] As Figure 12 shown, the monitoring and feedback module locally has the functions of network performance monitoring, user experience collection, and feedback optimization. Among them, network performance monitoring is implemented by the master device of the local device, mainly monitoring and collecting data on key performance indicators such as bandwidth utilization rate, packet loss rate, and alarms. User experience collection is implemented by the cloud (i.e., the cloud platform), mainly collecting user experience data, including user research and mobile APP feedback, etc. The data of network performance monitoring and the data of user experience collection will be regularly fed back to the local and the cloud for preprocessing and model analysis and inference.

[0150] Through the above steps, the data acquisition module collects data from the MFU and SFU, the DLL management channel transmits the collected data, the local and cloud preprocessing modules and the model training and inference modules perform data analysis, inference, and decision-making, the bandwidth allocation module executes the decision-making instructions to dynamically adjust the bandwidth, and the monitoring and feedback module collects and feeds back the user experience information after the bandwidth change for optimization, ensuring the bandwidth and latency of high-priority services and improving the bandwidth utilization rate and user experience. The method of this application is not only applicable to the FTTR system, but also has a certain applicability to the FTTH system, such as 10G-PON, 50G-PON, and VHSP systems.

[0151] In this embodiment, a processing system for downlink bandwidth allocation information is also provided. This system is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" may be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0152] Figure 14 is a structural block diagram of a processing system for downlink bandwidth allocation information according to an embodiment of the present application. As Figure 14 shown, the processing system for downlink bandwidth allocation information includes a local device 1402;

[0153] The local device includes a master device and a slave device;

[0154] The master device and the slave device are used to jointly obtain the device data collected by the slave device from the Internet of Things device, obtain the network bandwidth data of the optical fiber network where the local device is located, and obtain the network traffic data of the optical fiber network;

[0155] The master device and the slave device are also used to jointly perform local preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain first data to be analyzed;

[0156] The master device is further used to obtain a first decision-making instruction output by a local analysis model based on the first data to be analyzed, and the first decision-making instruction carries first downlink bandwidth allocation information;

[0157] The slave device is further used to control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision-making instruction.

[0158] In an embodiment of the present application, device data obtained by collecting the Internet of Things (IoT) device is acquired, network bandwidth data of the optical fiber network where the local device is located is acquired, and network traffic data of the optical fiber network is acquired; the device data, the network bandwidth data, and the network traffic data are locally preprocessed to obtain first data to be analyzed; a first decision instruction output by a local analysis model based on the first data to be analyzed is acquired, the first decision instruction carries first downlink bandwidth allocation information, and according to the first decision instruction, the slave device is controlled to perform data transmission in a transmission time slot indicated by the first downlink bandwidth allocation information. Based on the local device, efficient acquisition and processing of IoT device data are achieved. At the same time, according to the real-time bandwidth and traffic data of the optical fiber network, the downlink bandwidth allocation is dynamically adjusted, reducing the burden of data transmission. Combining the decision instruction output by the local analysis model improves the accuracy and real-time performance of the decision, solves the problem of low business efficiency caused by the inability to flexibly adjust the bandwidth during the bandwidth allocation process, improves the efficiency and stability of data transmission, and provides a strong guarantee for the efficient operation of IoT devices.

[0159] Through the collaborative work of the master device and the slave device, this system architecture achieves efficient acquisition and preprocessing of IoT device data. At the same time, according to the real-time bandwidth and traffic data of the optical fiber network, the downlink bandwidth allocation is dynamically adjusted, improving the efficiency and stability of data transmission. Local preprocessing and analysis can quickly respond to network changes and reduce data transmission delays, while the slave device performs data transmission according to the decision instruction, ensuring the reasonable utilization of network resources. This embodiment is particularly applicable to scenarios where IoT devices are densely deployed, and can effectively solve bottleneck problems in the data acquisition and transmission process, including but not limited to fields such as smart home, industrial automation, and smart city, ensuring the efficient operation of the system and the reasonable allocation of resources.

[0160] In an exemplary embodiment, the system is further configured to collect device data of the IoT device through the master device; or, collect device data of the IoT device through the master device and at least two of the slave devices.

[0161] In an exemplary embodiment, the system is further configured to collect bandwidth configuration data of the optical fiber network through the master device; and / or, collect bandwidth jitter data of the optical fiber network through at least two of the slave devices.

[0162] In an exemplary embodiment, the local device is further configured to perform local preprocessing on the device data, the network bandwidth data, and the network traffic data according to a preprocessing step, and determine first data to be analyzed based on the processing result of the local preprocessing; the preprocessing step at least includes: performing data cleaning on the device data, the network bandwidth data, and the network traffic data to obtain cleaned data; performing data format conversion on the cleaned data to obtain converted data; performing data standardization processing on the converted data to obtain standard data; performing data feature extraction on the standard data to obtain a data feature vector, and determining the processing result according to the data feature vector; and / or, performing data anomaly detection on the standard data to obtain anomaly data and normal data, and storing the normal data.

[0163] In an exemplary embodiment, the system is further configured to send the first decision instruction to the slave device through the master device, so that the slave device parses the first downlink bandwidth allocation information to obtain an allocation indicator corresponding to the device number of the slave device, where the allocation indicator corresponds to a transmission time slot, and uplink physical data is sent within the time slot window of the transmission time slot.

[0164] In an exemplary embodiment, a dynamic link library management channel for data transmission is established between the master device and the slave device, and the dynamic link library management channel includes sub-management channels of different channel types, which are used to encapsulate data according to the data format corresponding to the predetermined channel type, and transmit the encapsulated data through the sub-management channels of the predetermined channel type.

[0165] In an exemplary embodiment, the sub-management channels of different channel types include a wavelength division multiplexing management communication channel WMCC management channel, an optical fiber management communication channel FMCC management channel, a forward path linear organization access method F-PLOAM management channel, and an embedded operation, administration, and maintenance OAM management channel. Before encapsulating data according to the data format corresponding to the predetermined channel type and transmitting the encapsulated data through the sub-management channels of the predetermined channel type, the system is further configured to determine the WMCC management channel, the FMCC management channel, and the F-PLOAM management channel as the sub-management channels of the predetermined channel type.

[0166] Figure 15 is a structural block diagram of a downlink bandwidth allocation information processing system according to an embodiment of the present application. As Figure 15 shown, the downlink bandwidth allocation information processing system includes:

[0167] A data acquisition module 1501 disposed within a local device, the data acquisition module being connected to a monitoring and feedback module 1505, for acquiring an Internet of Things device to obtain device data, acquiring network bandwidth data of the optical fiber network where the local device is located, and acquiring network traffic data of the optical fiber network from the monitoring and feedback module;

[0168] A data preprocessing module 1502, connected to the data acquisition module, for locally preprocessing the device data, the network bandwidth data, and the network traffic data from the data acquisition module to obtain first data to be analyzed;

[0169] A model training and inference module 1503 located in the local device, a local analysis model being pre - set in the model training and inference module, the model training and inference module being connected to the data preprocessing module, for receiving a first decision instruction output by the local analysis model based on the first data to be analyzed, the first decision instruction carrying first downlink bandwidth allocation information;

[0170] A bandwidth allocation module 1504, connected to the model training and inference module, for controlling the slave device to perform data transmission in a transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction from the model training and inference module.

[0171] Through the modular architecture of the above - mentioned system, the separation of data acquisition, preprocessing, analysis, and decision - making is realized, improving the scalability and maintainability of the system. Among them, the data acquisition module is responsible for collecting data of Internet of Things devices, the monitoring and feedback module monitors the real - time state of the network, the data preprocessing module cleans, transforms, and standardizes the collected data, the model training and inference module analyzes the pre - processed data to generate a bandwidth allocation strategy, and finally, the bandwidth allocation module controls the data transmission of the slave device according to the strategy. This embodiment can effectively solve the problem of low business efficiency caused by the inability to flexibly adjust the bandwidth during the bandwidth allocation process. Through intelligent bandwidth allocation, the efficiency and stability of data transmission are improved. In addition, this modular design includes but is not limited to using a microservices architecture, and can flexibly add or subtract modules according to actual needs, improving the adaptability and flexibility of the system.

[0172] Figure 16 is a structural block diagram of a processing system for downlink bandwidth allocation information according to an embodiment of the present application. As Figure 16 shown, the processing system for downlink bandwidth allocation information includes:

[0173] A cloud platform 1602 and a local device 1604;

[0174] The cloud platform is used to obtain bandwidth allocation data from the local device; perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain second data to be analyzed; obtain a second decision instruction output by the cloud analysis model based on the second data to be analyzed, where the second decision instruction carries second downlink bandwidth allocation information, and send the second decision instruction to the local device;

[0175] The local device is used to collect data from Internet of Things devices to obtain device data, obtain the network bandwidth data of the optical fiber network where the local device is located and the network traffic data of the optical fiber network, determine the bandwidth allocation data according to the device data, the network bandwidth data, and the network traffic data, obtain first data to be analyzed obtained after local preprocessing of the bandwidth allocation data, and obtain a first decision instruction output by the local analysis model based on the first data to be analyzed, where the first decision instruction carries first downlink bandwidth allocation information, and control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction.

[0176] Through the above embodiments, through the collaborative work of the local device and the cloud platform, efficient data processing and intelligent decision-making are achieved. The local device is responsible for the preliminary collection and preprocessing of data, and the cloud platform conducts in-depth analysis to generate an optimized bandwidth allocation strategy. By combining the decision instructions of the local and cloud sides, a more accurate bandwidth allocation plan is formulated, solving the problem of low business efficiency caused by the inability to flexibly adjust the bandwidth during the bandwidth allocation process, and ensuring the high efficiency and stability of data transmission. In addition, this collaborative work mode includes but is not limited to using a hybrid cloud architecture, which can flexibly allocate computing resources between the local and cloud sides according to actual needs, improving the overall performance and reliability of the system.

[0177] The master device is connected to at least two slave devices. The system is further used to determine bandwidth allocation data according to the device data collected from the Internet of Things devices, the network bandwidth data of the optical fiber network where the local device is located, and the network traffic data of the optical fiber network. The Internet of Things devices are connected to the slave devices; obtain first data to be analyzed obtained after local preprocessing of the bandwidth allocation data, and obtain a first decision instruction output by the local analysis model based on the first data to be analyzed, where the first decision instruction carries first downlink bandwidth allocation information; and send the bandwidth allocation data to the cloud platform, obtain a second decision instruction generated by the cloud platform based on the bandwidth allocation data; control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction and the second decision instruction.

[0178] In an exemplary embodiment, before sending the bandwidth allocation data to the cloud platform and obtaining the second decision instruction generated by the cloud platform based on the bandwidth allocation data, the system is further configured to establish a data transmission channel for transmitting data between the master device of the local device and the cloud platform; send the bandwidth allocation data to the cloud platform through the data transmission channel, and obtain the second decision instruction generated by the cloud platform based on the bandwidth allocation data through the data transmission channel.

[0179] In an exemplary embodiment, the cloud platform is further configured to obtain bandwidth allocation data from the local device, where the bandwidth allocation data at least includes device data collected by the local device from Internet of Things devices, obtain network bandwidth data of the optical fiber network where the local device is located, and obtain network traffic data of the optical fiber network; perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain second data to be analyzed; obtain a second decision instruction output by the cloud analysis model based on the second data to be analyzed, where the second decision instruction carries second downlink bandwidth allocation information, and send the second decision instruction to the local device, and the local device is configured to control slave devices in the local device to perform data transmission in a transmission time slot indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction.

[0180] In an exemplary embodiment, the cloud platform is further configured to receive the bandwidth allocation data through the data transmission channel, where the data transmission channel represents a channel for transmitting data between the master device of the local device and the cloud platform.

[0181] In an exemplary embodiment, the cloud platform is further configured to perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data according to a preprocessing step, and determine second data to be analyzed according to a processing result of the cloud preprocessing; the preprocessing step at least includes: performing data cleaning on the device data, the network bandwidth data, and the network traffic data to obtain cleaned data; performing data format conversion on the cleaned data to obtain converted data; performing data standardization processing on the converted data to obtain standard data; performing data feature extraction on the standard data to obtain a data feature vector, and determining the processing result according to the data feature vector; and / or, performing data anomaly detection on the standard data to obtain anomaly data and normal data, and storing the normal data.

[0182] Figure 17 is a structural block diagram of a downlink bandwidth allocation information processing system according to an embodiment of the present application. As Figure 17 shown, the downlink bandwidth allocation information processing system includes:

[0183] A data acquisition module 1701 provided in the local device, the data acquisition module is connected to a monitoring and feedback module 1705, and is used to collect Internet of Things devices to obtain device data, acquire network bandwidth data of the optical fiber network where the local device is located, and acquire network traffic data of the optical fiber network from the monitoring and feedback module;

[0184] A data preprocessing module 1702 includes a local data preprocessing unit located in the local device and a cloud platform data preprocessing unit located in the cloud platform. The local data preprocessing unit is connected to the data acquisition module and is used to perform local preprocessing on the device data, the network bandwidth data, and the network traffic data from the data acquisition module to obtain first data to be analyzed; the cloud platform data preprocessing unit is connected to the data acquisition module and is used to perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data from the data acquisition module to obtain the second data to be analyzed;

[0185] A model training and inference module 1703 includes a local analysis model located in the local device and a cloud analysis model located in the cloud platform. The local analysis model is connected to the data preprocessing module and is used to receive the first data to be analyzed and output a first decision instruction based on the first data to be analyzed. The first decision instruction carries first downlink bandwidth allocation information; the cloud analysis model is connected to the data preprocessing module and is used to receive the second data to be analyzed and output a second decision instruction based on the second data to be analyzed. The second decision instruction carries second downlink bandwidth allocation information;

[0186] A bandwidth allocation module 1704 is connected to the model training and inference module and is used to control the slave device to perform data transmission in the transmission time slots indicated by the first decision instruction and the second decision instruction from the model training and inference module.

[0187] Through this embodiment, the master device determines a preliminary bandwidth allocation scheme through comprehensive analysis of Internet of Things device data, network bandwidth data, and network traffic data. Subsequently, the master device sends this data to the cloud platform, uses the powerful computing power of the cloud platform for in-depth analysis, generates a more optimized bandwidth allocation strategy, and assists the local device to perform more efficient bandwidth resource allocation. By combining the local and cloud analysis results, a more accurate decision instruction for bandwidth allocation is formulated, solving the problem in the related art that the bandwidth cannot be flexibly adjusted during the bandwidth allocation process, resulting in low service efficiency, and improving the efficiency and stability of data transmission.

[0188] Such as Figure 17As shown, the data acquisition module (both MFU and SFU can be used) performs data acquisition. The data preprocessing module and the model training and inference module include local and cloud parts, and can make collaborative decisions through the local analysis model and the cloud analysis model. The bandwidth allocation module specifically allocates bandwidth through the MFU, and the SFU performs bandwidth response. The monitoring and feedback module mainly collects the network performance and user experience information after the bandwidth change and feeds it back to the data preprocessing module for processing.

[0189] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present application.

[0190] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: USB flash drive, read-only memory (abbreviated as ROM), random access memory (abbreviated as RAM), mobile hard disk, magnetic disk, or optical disc and other various media that can store computer programs.

[0191] The specific examples in this embodiment can refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated here.

[0192] The embodiment of the present application also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0193] Optionally, in this embodiment, the above processor can be configured to execute the following steps through the computer program:

[0194] S1, obtain the device data collected for the Internet of Things device, obtain the network bandwidth data of the optical fiber network where the local device is located, and obtain the network traffic data of the optical fiber network;

[0195] S2, perform local preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain the first data to be analyzed;

[0196] S3. Obtain the first decision instruction output by the local analysis model based on the first data to be analyzed. The first decision instruction carries first downlink bandwidth allocation information, and control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction.

[0197] Alternatively, the above processor can also be set to execute the following steps through a computer program:

[0198] S1. Determine bandwidth allocation data according to the device data collected from the Internet of Things devices, the network bandwidth data of the optical fiber network where the local device is located, and the network traffic data of the optical fiber network. The Internet of Things devices are connected to the slave device.

[0199] S2. Obtain the first data to be analyzed obtained after local preprocessing of the bandwidth allocation data, and obtain the first decision instruction output by the local analysis model based on the first data to be analyzed. The first decision instruction carries first downlink bandwidth allocation information.

[0200] S3. And send the bandwidth allocation data to the cloud platform, and obtain the second decision instruction generated by the cloud platform based on the bandwidth allocation data; control the slave device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction and the second decision instruction.

[0201] Alternatively, the above processor can also be set to execute the following steps through a computer program:

[0202] S1. Obtain the bandwidth allocation data from the local device. The bandwidth allocation data at least includes the device data collected by the local device from the Internet of Things devices, obtain the network bandwidth data of the optical fiber network where the local device is located, and obtain the network traffic data of the optical fiber network.

[0203] S2. Perform cloud preprocessing on the device data, the network bandwidth data, and the network traffic data to obtain the second data to be analyzed.

[0204] S3. Obtain the second decision instruction output by the cloud analysis model based on the second data to be analyzed. The second decision instruction carries second downlink bandwidth allocation information, and send the second decision instruction to the local device. The local device is used to control the slave device in the local device to perform data transmission in the transmission time slot indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction.

[0205] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0206] Optionally, in this embodiment, the above electronic device may also be configured to execute the above steps S1, S2, and S3 through a computer program.

[0207] An embodiment of the present application also provides a computer program product. The above computer program product includes a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0208] An embodiment of the present application also provides another computer program product, including a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0209] An embodiment of the present application also provides a computer program. The computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in any one of the above method embodiments.

[0210] Specific examples in this embodiment may refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated here.

[0211] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the present application is not limited to any specific combination of hardware and software.

[0212] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for processing downlink bandwidth allocation information, characterized in that: Applied to a local device, the local device includes at least a master device and a slave device, and the slave device is mounted with an Internet of Things device, including: Acquire device data collected from the IoT device, acquire network bandwidth data of the optical fiber network where the local device is located, and acquire network traffic data of the optical fiber network; Locally preprocessing the device data, the network bandwidth data, and the network traffic data to obtain first data to be analyzed; Obtain a first decision instruction output by the local analysis model based on the first data to be analyzed, the first decision instruction carrying first downlink bandwidth allocation information, and control the slave device to transmit data in a sending time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction.

2. The method for processing downlink bandwidth allocation information according to claim 1, characterized in that: Obtain device data collected from IoT devices, including: The main device collects data from the IoT device to obtain device data; Alternatively, the Internet of Things device is collected through the master device and at least two of the slave devices to obtain device data.

3. The method for processing downlink bandwidth allocation information according to claim 1, characterized in that: Obtaining network bandwidth data of the optical fiber network where the local device is located, including: Collecting bandwidth configuration data of the optical fiber network through the main device; And / or, bandwidth jitter data of the optical fiber network is collected by at least two of the slave devices.

4. The method for processing downlink bandwidth allocation information according to claim 1, characterized in that: The device data, the network bandwidth data and the network traffic data are locally preprocessed to obtain first data to be analyzed, including: Performing local preprocessing on the device data, the network bandwidth data and the network traffic data according to the preprocessing step, and determining first data to be analyzed according to the processing result of the local preprocessing; The pre-processing step at least comprises: Cleaning the device data, the network bandwidth data and the network traffic data to obtain cleaned data; Performing data format conversion on the cleaned data to obtain converted data; Performing data standardization processing on the conversion data to obtain standard data; Extracting data features from the standard data to obtain a data feature vector, and determining the processing result according to the data feature vector; And / or, performing data anomaly detection on the standard data to obtain abnormal data and normal data, and data storing the normal data.

5. The method for processing downlink bandwidth allocation information according to claim 1, characterized in that: Controlling the slave device to perform data transmission within the allocated bandwidth indicated by the first downlink bandwidth allocation information according to the first decision instruction includes: The first decision instruction is sent to the slave device through the master device, so that the slave device parses the first downlink bandwidth allocation information to obtain an allocation indicator corresponding to the device number of the slave device, wherein the allocation indicator corresponds to a sending time slot, and uplink physical data is sent within the time slot window of the sending time slot.

6. The method for processing downlink bandwidth allocation information according to claim 5, characterized in that: A dynamic link library management channel for transmitting data is established between the master device and the slave device. The dynamic link library management channel includes sub-management channels of different channel types, which are used to encapsulate data according to a data format corresponding to a predetermined channel type, and transmit the encapsulated data through the sub-management channel of the predetermined channel type.

7. The method for processing downlink bandwidth allocation information according to claim 6, characterized in that: The sub-management channels of different channel types include a wavelength division multiplexing management communication channel WMCC management channel, a fiber management communication channel FMCC management channel, a forward path linear organization access method F-PLOAM management channel and an embedded operation, management and maintenance OAM management channel. Before encapsulating data according to a data format corresponding to a predetermined channel type and transmitting the encapsulated data through the sub-management channel of the predetermined channel type, the method further includes: The WMCC management channel, the FMCC management channel and the F-PLOAM management channel are determined as sub-management channels of the predetermined channel type.

8. A method for processing downlink bandwidth allocation information, characterized in that: Applied to a master device, the master device is connected to at least two slave devices, and the master device and the slave devices are both local devices, including: Determine bandwidth allocation data based on device data collected from the IoT device, network bandwidth data of the optical fiber network where the local device is located, and network traffic data of the optical fiber network, wherein the IoT device is connected to the slave device; Acquire first data to be analyzed obtained after local preprocessing of the bandwidth allocation data, and acquire a first decision instruction output by a local analysis model based on the first data to be analyzed, wherein the first decision instruction carries first downlink bandwidth allocation information; and sending the bandwidth allocation data to a cloud platform to obtain a second decision instruction generated by the cloud platform based on the bandwidth allocation data; According to the first decision instruction and the second decision instruction, the slave device is controlled to perform data transmission in the sending time slot indicated by the first downlink bandwidth allocation information.

9. The method according to claim 8, characterized in that Before sending the bandwidth allocation data to the cloud platform and obtaining a second decision instruction generated by the cloud platform based on the bandwidth allocation data, the method further includes: Establishing a data transmission channel for transmitting data between the master device of the local device and the cloud platform; The bandwidth allocation data is sent to the cloud platform through the data transmission channel, and a second decision instruction generated by the cloud platform based on the bandwidth allocation data is obtained through the data transmission channel.

10. A method for processing downlink bandwidth allocation information, characterized in that: Applied to cloud platforms, including: Obtain bandwidth allocation data from a local device, the bandwidth allocation data at least including device data collected by the local device from an IoT device, network bandwidth data of an optical fiber network where the local device is located, and network traffic data of the optical fiber network; Performing cloud preprocessing on the device data, the network bandwidth data and the network traffic data to obtain second data to be analyzed; Obtain a second decision instruction output by the cloud analysis model based on the second data to be analyzed, the second decision instruction carries second downlink bandwidth allocation information, and send the second decision instruction to the local device. The local device is used to control the slave device in the local device to transmit data in the sending time slot indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction.

11. The method according to claim 10, characterized in that Get bandwidth allocation data from local devices, including: The bandwidth allocation data is received via a data transmission channel, where the data transmission channel represents a channel for transmitting data between a master device of the local device and the cloud platform.

12. The method according to claim 10, characterized in that The device data, the network bandwidth data and the network traffic data are pre-processed in the cloud to obtain second data to be analyzed, including: Performing cloud preprocessing on the device data, the network bandwidth data and the network traffic data according to the preprocessing step, and determining the second data to be analyzed according to the processing result of the cloud preprocessing; The pre-processing step at least comprises: Cleaning the device data, the network bandwidth data and the network traffic data to obtain cleaned data; Performing data format conversion on the cleaned data to obtain converted data; Performing data standardization processing on the conversion data to obtain standard data; Extracting data features from the standard data to obtain a data feature vector, and determining the processing result according to the data feature vector; And / or, performing data anomaly detection on the standard data to obtain abnormal data and normal data, and data storing the normal data.

13. A system for processing downlink bandwidth allocation information, characterized in that: Including local devices; The local device includes a master device and a slave device; The master device and the slave device are used to jointly obtain device data collected by the slave device on the IoT device, obtain network bandwidth data of the optical fiber network where the local device is located, and obtain network traffic data of the optical fiber network; The master device and the slave device are also used to jointly perform local preprocessing on the device data, the network bandwidth data and the network traffic data to obtain first data to be analyzed; The master device is further configured to obtain a first decision instruction output by a local analysis model based on the first data to be analyzed, where the first decision instruction carries first downlink bandwidth allocation information; The slave device is further configured to control the slave device to perform data transmission in a transmission time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction.

14. A system for processing downlink bandwidth allocation information, characterized in that: include: A data acquisition module is provided in the local device, the data acquisition module is connected to the monitoring feedback module, and is used to collect data from the IoT device to obtain device data, obtain network bandwidth data of the optical fiber network where the local device is located, and obtain network traffic data of the optical fiber network from the monitoring feedback module; A data preprocessing module, connected to the data acquisition module, and used to perform local preprocessing on the device data, the network bandwidth data and the network flow data from the data acquisition module to obtain first data to be analyzed; A model training and reasoning module located in the local device, wherein a local analysis model is pre-installed in the model training and reasoning module, and the model training and reasoning module is connected to the data preprocessing module, and is used to receive a first decision instruction output by the local analysis model based on the first data to be analyzed, wherein the first decision instruction carries first downlink bandwidth allocation information; A bandwidth allocation module is connected to the model training reasoning module and is used to control the slave device in the local device to transmit data in the sending time slot indicated by the first downlink bandwidth allocation information according to the first decision instruction from the model training reasoning module.

15. A system for processing downlink bandwidth allocation information, characterized in that: include: Cloud platforms and local devices; The local device is used to collect IoT devices, obtain device data, obtain network bandwidth data of the optical fiber network where the local device is located and network traffic data of the optical fiber network, determine the bandwidth allocation data according to the device data, the network bandwidth data and the network traffic data, obtain first data to be analyzed obtained after local preprocessing of the bandwidth allocation data, and obtain a first decision instruction output by a local analysis model based on the first data to be analyzed, the first decision instruction carries first downlink bandwidth allocation information, and control the slave device to transmit data in a sending time slot indicated by the first downlink bandwidth allocation information according to the second decision instruction and the first decision instruction; The cloud platform is used to obtain bandwidth allocation data from the local device; The device data, the network bandwidth data and the network traffic data are pre-processed in the cloud to obtain second data to be analyzed; a second decision instruction output by a cloud analysis model based on the second data to be analyzed is obtained, the second decision instruction carries second downlink bandwidth allocation information, and the second decision instruction is sent to the local device.

16. A system for processing downlink bandwidth allocation information, characterized in that: include: A data acquisition module is provided in the local device, the data acquisition module is connected to the monitoring feedback module, and is used to collect data from the IoT device to obtain device data, obtain network bandwidth data of the optical fiber network where the local device is located, and obtain network traffic data of the optical fiber network from the monitoring feedback module; A data preprocessing module, comprising a local data preprocessing unit located on the local device and a cloud platform data preprocessing unit located on the cloud platform, wherein the local data preprocessing unit is connected to the data acquisition module and is used to perform local preprocessing on the device data, the network bandwidth data and the network traffic data from the data acquisition module to obtain first data to be analyzed; The cloud platform data preprocessing unit is connected to the data acquisition module and is used to perform cloud-based preprocessing on the device data, the network bandwidth data and the network traffic data from the data acquisition module to obtain second data to be analyzed; a model training inference module, comprising a local analysis model located on the local device and a cloud analysis model located on the cloud platform, wherein the local analysis model is connected to the data preprocessing module and is used to receive the first data to be analyzed and output a first decision instruction based on the first data to be analyzed, wherein the first decision instruction carries first downlink bandwidth allocation information; The cloud analysis model is connected to the data preprocessing module, and is used to receive the second data to be analyzed, and output a second decision instruction based on the second data to be analyzed, wherein the second decision instruction carries second downlink bandwidth allocation information; A bandwidth allocation module is connected to the model training and reasoning module, and is used to control the slave device in the local device to transmit data in the sending time slot indicated by the first downlink bandwidth allocation information and the second downlink bandwidth allocation information according to the first decision instruction and the second decision instruction from the model training and reasoning module.

17. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 12 when executed.

18. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the method according to any one of claims 1 to 12 through the computer program.

19. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 12 when being executed by a processor.

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