Network resource scheduling method and device based on PON and ONU, equipment and medium

Abstract

The invention relates to the technical field of computer networks, in particular to a PON and ONU-based network resource scheduling method, device and equipment and a medium. In addition, the network resource scheduling method based on the PON and the ONU can also be applied to fiber to room (FTTR), enterprise-level fiber to room (FTTR-B) and broadband fusion terminal products, and the network resource scheduling method based on the PON and the ONU can also be applied to the FTTR. The method comprises the following steps: acquiring multi-dimensional original network information according to a configured multi-dimensional information acquisition strategy; performing information extraction on the multi-dimensional original network information according to an information extraction strategy from the cloud server to obtain multi-dimensional decision reference information; according to an adjusting and optimizing strategy from the cloud server, an adjusting and optimizing decision is made according to the multi-dimensional decision reference information, and a network resource scheduling scheme is obtained; according to the invention, the target optical network unit corresponding to the network resource scheduling scheme is determined, and the network resource scheduling scheme is distributed to the target optical network unit for execution, compared with the prior art, network resource scheduling can be flexibly carried out, and the resource utilization rate is improved.

Description

Technical Field

[0001] This invention relates to the field of computer network technology, and in particular to a network resource scheduling method, network resource scheduling device, computer equipment, and computer-readable storage medium based on PON and ONU. Background Technology

[0002] Passive Optical Network (PON) is a telecommunications network that transmits data over optical fiber lines. It is a key technology supporting network access technologies such as Fiber to the Room (FTTR) and Fiber to the Room-Business (FTTR-B). FTTR / FTTR-B extends optical fiber to each room, installing an optical network unit (ONU) in each room to convert optical signals into electrical signals, providing network access services to terminal devices. In related technologies, to ensure a good user experience for terminal devices, a static QoS policy configuration scheme is typically used, pre-allocating network resources according to fixed service types. While this method can meet basic network requirements, it suffers from low resource utilization. Summary of the Invention

[0003] This invention provides a network resource scheduling method, network resource scheduling device, computer equipment, and computer-readable storage medium based on PON and ONU, which can flexibly schedule network resources and improve resource utilization.

[0004] On the one hand, the network resource scheduling method based on PON and ONU provided by the present invention includes: Collect multi-dimensional raw network information according to the configured multi-dimensional information collection strategy; Based on the information extraction strategy from the cloud server, information is extracted from the multidimensional raw network information to obtain multidimensional decision reference information; Based on the optimization strategy from the cloud server and multi-dimensional decision reference information, an optimization decision is made to obtain a network resource scheduling scheme. Identify the target optical network unit corresponding to the network resource scheduling scheme, and distribute the network resource scheduling scheme to the target optical network unit for execution.

[0005] Optionally, in one embodiment, an optimization decision is made based on multi-dimensional decision reference information according to the optimization strategy from the cloud server to obtain a network resource scheduling scheme, including: Send a tuning strategy enable confirmation request to the cloud server. The tuning strategy enable confirmation request is used to request the cloud server to indicate the tuning strategy that needs to be enabled. Receive the optimization strategy instruction information returned by the cloud server, make optimization decisions according to the target optimization strategy indicated by the optimization strategy instruction information and based on multi-dimensional decision reference information, and obtain a network resource scheduling scheme.

[0006] Optionally, in one embodiment, before extracting information from the multidimensional raw network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information, the method further includes: Determine the current computing load and identify whether the computing load has reached the load threshold; If it is detected that the computing load has not reached the load threshold, information is extracted from the multidimensional original network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information.

[0007] Optionally, in one embodiment, after identifying whether the computing load has reached the load threshold, the method further includes: If the computing load is detected to have reached the load threshold, the multidimensional raw network information is sent to the external computing device. The external computing device uses the multidimensional raw network information to extract information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information. Then, according to the optimization strategy from the cloud server, it makes optimization decisions based on the multidimensional reference information to obtain a network resource scheduling scheme. The steps include receiving the network resource scheduling scheme returned by the external computing power device, determining the target optical network unit corresponding to the network resource scheduling scheme, and distributing the network resource scheduling scheme to the target optical network unit for execution.

[0008] Optionally, in one embodiment, after receiving the network resource scheduling scheme returned by the external computing power device, the method further includes: The network resource scheduling scheme is verified to confirm whether it meets the scheme constraints. If the verification passes, the process moves to determining the target optical network unit corresponding to the network resource scheduling scheme and distributing the network resource scheduling scheme to the target optical network unit for execution.

[0009] Optionally, in one embodiment, the network resource scheduling method based on PON and ONU provided by the present invention further includes: Send a tuning strategy update request to the cloud server. The tuning strategy update request is used to request the cloud server to update the current tuning strategy. Receive tuning strategy update information returned by the cloud server, and update the tuning strategy stored locally according to the tuning strategy update information.

[0010] Optionally, in one embodiment, the multidimensional raw network information includes the device status information of the optical network unit, the network environment information of the network it is in, the terminal information and service information of the access terminal devices, and the multidimensional decision reference information includes the network environment health, the optical network unit health, the service health, and the optical network unit performance indicators and the service performance indicators.

[0011] Secondly, the network resource scheduling device based on PON and ONU provided by the present invention includes: The data acquisition module is used to collect multidimensional raw network information according to the configured multidimensional information acquisition strategy. The analysis module is used to extract information from the multidimensional raw network information according to the information extraction strategy from the cloud server, and obtain multidimensional decision reference information. The control module is used to make optimization decisions based on the optimization strategy from the cloud server and multi-dimensional decision reference information to obtain a network resource scheduling scheme. The execution module is used to determine the target optical network unit corresponding to the network resource scheduling scheme and distribute the network resource scheduling scheme to the target optical network unit for execution.

[0012] Thirdly, the computer device provided by the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the network resource scheduling method based on PON and ONU provided by the present invention.

[0013] Fourthly, the computer-readable storage medium provided by the present invention stores a computer program, which, when executed by a processor, implements the network resource scheduling method based on PON and ONU provided by the present invention.

[0014] This invention provides a receiver sensitivity adjustment scheme based on a PON network. It collects multi-dimensional raw network information according to a configured multi-dimensional information acquisition strategy; extracts information from the raw network information according to an information extraction strategy from a cloud server to obtain multi-dimensional decision reference information; performs optimization decisions based on the multi-dimensional decision reference information according to an optimization strategy from the cloud server to obtain a network resource scheduling scheme; determines the target optical network unit corresponding to the network resource scheduling scheme, and distributes the network resource scheduling scheme to the target optical network unit for execution. Thus, by combining the information extraction strategy and optimization strategy from the cloud server, a closed-loop optimization mechanism is formed through multi-dimensional information acquisition and intelligent analysis, achieving dynamic optimization scheduling of network resources, ensuring the flexibility and accuracy of resource allocation, and improving the overall resource utilization rate of the network. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the application environment of the network resource scheduling method based on PON and ONU provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the network resource scheduling method based on PON and ONU provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the mesh network involved in the embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of a network resource scheduling device based on PON and ONU provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0019] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0021] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0023] Please refer to Figure 1 , Figure 1 This is a schematic diagram illustrating an application environment for the network resource scheduling method based on PON and ONU provided in this application. As one implementation method, this network resource scheduling method can be applied to optical network units (ONUs) with wireless network access capabilities in FTTR / FFTR-B technology. These ONUs can be either primary or secondary optical network units. The method involves: collecting multi-dimensional raw network information according to a configured multi-dimensional information acquisition strategy; extracting information from the multi-dimensional raw network information according to an information extraction strategy from a cloud server to obtain multi-dimensional decision reference information; making optimization decisions based on the multi-dimensional decision reference information according to an optimization strategy from the cloud server to obtain a network resource scheduling scheme; determining the target optical network unit corresponding to the network resource scheduling scheme; and distributing the network resource scheduling scheme to the target optical network unit for execution.

[0024] Please refer to Figure 2 This is a flowchart illustrating a network resource scheduling method based on PON and ONU disclosed in an embodiment of the present invention. This method can be applied to optical network units with wireless network access capabilities in FTTR / FFTR-B technology, such as... Figure 1 As shown, the process of this network resource scheduling method based on PON and ONU can be described as follows: In S110, multi-dimensional raw network information is collected according to the configured multi-dimensional information collection strategy.

[0025] An optical network unit (ONU) is a fiber optic terminal device deployed on the user side in a PON architecture. It is used to perform functions such as photoelectric conversion, data forwarding, and network access. It also has wireless access capabilities. For example, an ONU can use Wi-Fi technology to provide wireless network access services for user devices (such as mobile phones, computers, etc.).

[0026] like Figure 3 As shown, the master optical network unit (BONU) refers to the optical network unit directly connected to the optical line terminal (OLT). Other optical network units can be called slave optical network units (SNRs). The master and slave optical network units form a mesh network. In a distributed fiber-to-the-room (FTTH) networking scenario, the master optical network unit is usually deployed in a central location (such as the living room), while the slave optical network units are usually deployed in each room. The master and slave optical network units form a whole-house coverage through mesh networking.

[0027] It should be noted that the network resource scheduling method based on PON and ONU provided by this invention is applicable not only to the primary optical network unit but also to the secondary optical network unit. For example, a scheduling coordination node can be elected by all optical network units within the mesh network. This scheduling coordination node is responsible for collecting network resource scheduling data for the entire mesh network. For instance, the optical network unit with the highest available computing power can be elected as the scheduling coordination node based on the available computing power of each network unit, or the primary optical network unit can be elected by default.

[0028] In this embodiment of the invention, the optical network unit actively collects multi-dimensional raw network information in the current network environment according to the configured multi-dimensional information collection strategy. The specific content of the multi-dimensional information collection strategy is not limited here. For example, in this embodiment, the collected multi-dimensional raw network information includes the optical network unit's device status information, the network environment information of the network it is in, the terminal information of the accessed terminal devices, and service information. Specifically, the device status information includes information such as processor utilization, memory usage, number of forwarding table entries, and forwarding queue length of all optical network units within the mesh network; the network environment information includes information such as channel congestion level, distribution of surrounding access points, and the number and distribution of accessed terminal devices; the terminal information includes information such as the MAC address and access protocol messages of the terminal devices accessing the mesh network; and the service information includes the service data packets of the terminal devices.

[0029] In S120, information is extracted from the multidimensional raw network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information.

[0030] In this embodiment of the invention, the information extraction strategy is pre-generated by a cloud server and distributed to the optical network unit. The specific generation method of the information extraction strategy is not limited here; for example, the cloud server can generate the information extraction strategy using a combination of laboratory learning and live network learning. Furthermore, this embodiment of the invention does not specifically limit the content of the information extraction strategy. For example, the information extraction strategy includes, but is not limited to, data preprocessing, feature extraction, and feature analysis, to extract multidimensional decision reference information suitable for optimization decisions from multidimensional raw network information.

[0031] Data preprocessing includes cleaning, normalizing, and denoising the multidimensional raw network information. Feature extraction includes extracting key feature parameters from the preprocessed multidimensional raw network information. Feature analysis involves statistical analysis, pattern recognition, and correlation analysis of the extracted key feature parameters to form multidimensional decision reference information that can be used for optimization decisions, thereby providing accurate basis for subsequent optimization decisions.

[0032] For example, taking feature extraction as an example, cloud servers can use artificial intelligence models combined with manual annotation to learn as follows: 1. Training and feature learning for unknown device types; 2. Training for unknown application types, feature learning; 3. Device interaction protocol training and feature learning; 4. Apply KPI feature learning under different experiences; 5. Apply stuttering conditions and feature learning; 6. Extraction of deterministic business experience features.

[0033] The cloud server establishes a feature experience base through the above learning process, and distributes the feature experience base and information extraction strategy to the optical network unit. This enables the optical network unit to quickly and accurately identify and extract key feature parameters from the multi-dimensional raw network information during the information extraction stage. The feature experience base may include extraction paradigms for key feature parameters such as device type, device capabilities, service connection port characteristics, service connection message characteristics, and service lag characteristics.

[0034] Based on the aforementioned information extraction strategy, the optical network unit (ONU) can efficiently extract and analyze features from multidimensional raw network information, thereby generating multidimensional decision reference information with optimization value, providing real-time and accurate data support for dynamic network optimization. For example, the multidimensional decision reference information may include network environment health, ONU health, service health, and ONU performance indicators and service performance indicators. Network environment health describes the stability and reliability of the overall network operation; ONU health reflects the working status and performance of specific devices; service health reflects the smoothness and integrity of various services during transmission; and ONU performance indicators and service performance indicators quantitatively evaluate network service quality and efficiency from the device and service levels, respectively.

[0035] In S130, the network resource scheduling scheme is obtained by making optimization decisions based on the optimization strategy from the cloud server and multi-dimensional decision reference information.

[0036] The optimization strategy is pre-generated by the cloud server and distributed to the optical network unit. The specific generation method of the optimization strategy is not limited here; for example, the cloud server can generate the optimization strategy using a combination of laboratory learning and live network learning. Furthermore, this embodiment of the invention does not specifically limit the content of the optimization strategy. For example, the optimization strategy may include video conferencing assurance strategies, live streaming assurance strategies, online gaming assurance strategies, network video assurance strategies, multi-user recording service assurance strategies, low-density deployment environment service assurance strategies, high-density deployment environment service assurance strategies, and service strategies under energy-saving requirements, etc.

[0037] In this embodiment of the invention, after the optical network unit extracts the multidimensional decision reference information, it makes an optimization decision based on the current network state represented by the multidimensional decision reference information and the optimization strategy issued by the cloud server, thereby obtaining a network resource scheduling scheme and realizing dynamic optimization configuration of network resources.

[0038] For example, according to optimization strategies, optical network units (ONUs) can identify whether the network environment can support service operations and whether manual intervention is required. For instance, the presence of illegal access points or prolonged high-volume use of wireless channels necessitates manual intervention. They can also identify whether ONUs need capacity expansion or reduction, such as long-term overload of the network's forwarding queues, indicating a need for expansion, and vice versa. Furthermore, they can identify whether user experience is degraded, such as bottlenecks or anomalies in service transmission, such as frequent delays or packet loss during peak periods, requiring targeted optimization of bandwidth allocation or priority adjustments. They can also identify whether network resource allocation is reasonable, such as excessive bandwidth usage by specific services during certain periods, affecting the normal operation of other services, requiring dynamic bandwidth adjustments. Finally, they can identify whether collaboration between ONUs is efficient, whether there is redundant scheduling or resource waste, thereby optimizing resource scheduling paths. In addition, they can identify whether user experience issues are caused by this segment (i.e., the network access segment where the mesh network is located). If the physical layer links, air interface resources, and forwarding resources of this segment are sufficient, and the ONU performance indicators and service performance indicators are excellent, then the problem in this segment can be ruled out, and the site can be preserved to prove innocence and assist other network elements in locating the problem.

[0039] Network resource scheduling schemes can include specific scheduling strategies such as air interface resource scheduling schemes, terminal device access point scheduling schemes, terminal device priority scheduling schemes, service priority scheduling schemes, and interactive protocol adaptation schemes. Specifically, the air interface resource scheduling scheme optimizes the allocation and utilization efficiency of wireless spectrum resources, improving network throughput and connection stability; the terminal device access point scheduling scheme guides terminal devices to the optimal access node (i.e., optical network unit) to avoid network congestion; the terminal device priority scheduling scheme provides differentiated access management for devices based on device type or service requirements; the service priority scheduling scheme dynamically adjusts service bandwidth and transmission priority based on the importance and real-time requirements of the service; and the interactive protocol adaptation scheme matches the communication protocols supported by different devices and services, improving compatibility and communication efficiency.

[0040] In S140, the target optical network unit corresponding to the network resource scheduling scheme is determined, and the network resource scheduling scheme is distributed to the target optical network unit for execution.

[0041] In this embodiment of the invention, after determining a network resource scheduling scheme, the optical network unit (ONU) identifies the ONUs involved in the scheme based on the service coverage and equipment distribution involved, and these ONUs are denoted as target ONUs. It is understood that, depending on the actual network conditions, the number of target ONUs can be one or more, and may include both ONUs and slave ONUs, or only slave ONUs or ONUs.

[0042] After identifying the target optical network unit (ONU) corresponding to the network resource scheduling scheme, the ONU distributes the scheme to the target ONU via a control channel or management interface, ensuring efficient execution of the scheme within the target ONU. Conversely, upon receiving the network resource scheduling scheme, the target ONU can adaptively adjust the scheme or execute it directly based on its local operating status and resource availability, thereby optimizing overall network performance and improving quality of service. Furthermore, the target ONU can transmit execution results and feedback information back to the ONU, enabling real-time monitoring of scheduling effectiveness and dynamic iterative optimization of the strategy.

[0043] Optionally, in one embodiment, an optimization decision is made based on multi-dimensional decision reference information according to the optimization strategy from the cloud server to obtain a network resource scheduling scheme, including: Send a tuning strategy enable confirmation request to the cloud server. The tuning strategy enable confirmation request is used to request the cloud server to indicate the tuning strategy that needs to be enabled. Receive the optimization strategy instruction information returned by the cloud server, make optimization decisions according to the target optimization strategy indicated by the optimization strategy instruction information and based on multi-dimensional decision reference information, and obtain a network resource scheduling scheme.

[0044] Understandably, in practical applications, the optimization strategies issued by the cloud server to the optical network unit may include different optimization objectives, and these objectives may conflict. For example, ensuring low latency for gaming services may conflict with the high bandwidth requirements of video services. Therefore, the optical network unit needs to select the appropriate optimization strategy from the various options issued by the cloud server for its optimization decisions.

[0045] The optical network unit (ONU) can send a tuning policy activation confirmation request to the cloud server to request confirmation of the tuning policy that needs to be enabled. This confirmation request can carry information such as the current operating status of the mesh network in which the ONU is located, network load, and service demand characteristics to assist the cloud server in selecting the optimal policy. Upon receiving the confirmation request, the cloud server, based on the overall network operating status and policy priority configuration, determines the tuning policy that needs to be enabled and sends the corresponding tuning policy instruction information back to the ONU.

[0046] Correspondingly, after receiving the optimization strategy instruction information, the optical network unit makes optimization decisions based on the target optimization strategy indicated by the optimization strategy instruction information and multi-dimensional decision reference information, and generates a network resource scheduling scheme that is adapted to the current network environment.

[0047] Optionally, in one embodiment, before extracting information from the multidimensional raw network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information, the method further includes: Determine the current computing load and identify whether the computing load has reached the load threshold; If it is detected that the computing load has not reached the load threshold, information is extracted from the multidimensional original network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information.

[0048] Understandably, in actual operation, the computing resources of an optical network unit (ONU) are limited. When faced with large-scale, multi-dimensional raw network information processing, the computing load may become too high, resulting in insufficient computing power to support its primary network access service tasks. Therefore, before performing information extraction operations, the ONU needs to assess its current computing load to determine whether it has sufficient computing resources to complete the information extraction task.

[0049] In this embodiment of the invention, after acquiring multidimensional raw network information, the optical network unit first evaluates the current computing load, determines the current computing load, and identifies whether the current computing load has reached a preset load threshold. If it is identified that the computing load has not reached the load threshold, it continues to extract information from the multidimensional raw network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information.

[0050] By introducing a computing load assessment mechanism, the stability and reliability of optical network units during network resource scheduling can be effectively guaranteed.

[0051] Optionally, in one embodiment, after identifying whether the computing load has reached the load threshold, the method further includes: If the computing load is detected to have reached the load threshold, the multidimensional raw network information is sent to the external computing device. The external computing device uses the multidimensional raw network information to extract information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information. Then, according to the optimization strategy from the cloud server, it makes optimization decisions based on the multidimensional reference information to obtain a network resource scheduling scheme. The steps include receiving the network resource scheduling scheme returned by the external computing power device, determining the target optical network unit corresponding to the network resource scheduling scheme, and distributing the network resource scheduling scheme to the target optical network unit for execution.

[0052] In this embodiment of the invention, when the optical network unit detects that its own computing power load has reached the load threshold, it no longer extracts information locally, but uploads the multi-dimensional raw network information to an external computing power device with stronger processing capabilities. In this way, the external computing power device completes the information extraction and optimization decision-making tasks, and then generates a network resource scheduling scheme.

[0053] External computing devices can be edge computing nodes deployed specifically for processing computing power. These nodes possess higher computing capabilities and more abundant resources, enabling them to efficiently complete information extraction and optimization decision-making tasks from large-scale data. Alternatively, external computing devices can be other optical network units connected to the mesh network. When under low load, they can share the information extraction and optimization decision-making tasks, thereby achieving flexible scheduling and efficient utilization of computing resources. External computing devices can also be terminal devices connected to the mesh network, possessing certain computing capabilities and assisting in information extraction and optimization decision-making tasks when the computing load is low.

[0054] For example, when the external computing device is an edge computing node, the optical network unit (ONU) can upload multi-dimensional raw network information to the edge computing node via a high-speed transmission channel. The edge computing node then extracts multi-dimensional decision reference information based on the information extraction strategy of the cloud server, performs decision analysis in conjunction with optimization strategies, generates a network resource scheduling scheme, and returns it to the requesting ONU. Upon receiving the network resource scheduling scheme, the ONU determines the target ONU corresponding to the scheme and distributes the scheme to the target ONU for execution.

[0055] When the external computing power device is another optical network unit (ONU), the ONU can select another ONU under low load as a cooperative node based on a load balancing strategy. The multi-dimensional raw network information is transmitted to this cooperative node, which then performs the information extraction and optimization decision-making tasks. After processing, the cooperative node returns the generated network resource scheduling scheme to the original ONU, which then distributes it to the target ONU for execution. In this way, idle computing resources in the network can be fully utilized without additional hardware investment.

[0056] When the external computing power device is a terminal device, the optical network unit can select a suitable terminal device based on its computing power status and communication quality, and transmit the multi-dimensional raw network information to the cooperating node, which will then perform the information extraction and optimization decision-making tasks on its behalf. After completing the processing, the terminal device will also feed back the generated network resource scheduling scheme to the optical network unit for subsequent distribution and execution.

[0057] By introducing a collaborative mechanism with external computing power devices, the information extraction and optimization decision-making tasks that consume a lot of computing power are offloaded to external computing power devices. This not only alleviates the local computing power pressure on the optical network unit, but also improves the overall system's processing efficiency and response speed, and further enhances the intelligence level and dynamic adaptability of network resource scheduling.

[0058] Optionally, in one embodiment, after receiving the network resource scheduling scheme returned by the external computing power device, the method further includes: The network resource scheduling scheme is verified to confirm whether it meets the scheme constraints. If the verification passes, the process moves to determining the target optical network unit corresponding to the network resource scheduling scheme and distributing the network resource scheduling scheme to the target optical network unit for execution.

[0059] In this embodiment of the invention, after receiving a network resource scheduling scheme returned by an external computing device, the optical network unit first performs a verification operation to determine whether the scheme meets the preset scheme constraints. If the verification passes, subsequent operations are performed to further determine the target optical network unit corresponding to the network resource scheduling scheme, and the network resource scheduling scheme is distributed to the target optical network unit for execution.

[0060] The constraints of the scheme can be set by those skilled in the art according to actual needs. For example, the constraints may include the rationality of network resource allocation, the feasibility of resource scheduling scheme, load balancing requirements, and communication quality assurance conditions. If the verification fails, the optical network unit may refuse to execute the scheme and report the reason for the verification failure to the external computing power device, prompting it to re-optimize and generate a new network resource scheduling scheme and return it.

[0061] By introducing a verification mechanism, the reliability and security of the network resource scheduling scheme are improved, and the stability and efficiency of network operation are further guaranteed.

[0062] Optionally, in one embodiment, the network resource scheduling method provided by the present invention further includes: Send a tuning strategy update request to the cloud server. The tuning strategy update request is used to request the cloud server to update the current tuning strategy. Receive tuning strategy update information returned by the cloud server, and update the tuning strategy stored locally according to the tuning strategy update information.

[0063] In this embodiment of the invention, the optical network unit periodically or based on actual operational status assessments sends optimization strategy update requests to the cloud server to obtain the latest scheduling optimization strategy. Upon receiving the optimization strategy update information from the cloud server, the optical network unit dynamically updates the locally stored optimization strategy, thereby ensuring that the basis for generating the network resource scheduling scheme remains optimal at all times.

[0064] The above approach involves periodically or on-demand sending optimization strategy update requests to the cloud server to obtain the latest optimization strategy information, thereby ensuring consistency between local optimization strategies and global cloud strategies. By introducing optimization guidance from a global cloud perspective, network resource scheduling becomes more precise and efficient, further improving network service quality and resource utilization efficiency. Simultaneously, the dynamic update mechanism of the optimization strategy provides a reliable guarantee for continuous network operation optimization and performance improvement.

[0065] As can be seen from the above, the network resource scheduling scheme based on PON and ONU provided by this invention collects multi-dimensional raw network information according to a configured multi-dimensional information collection strategy; extracts information from the multi-dimensional raw network information according to an information extraction strategy from the cloud server to obtain multi-dimensional decision reference information; makes optimization decisions based on the multi-dimensional decision reference information according to an optimization strategy from the cloud server to obtain a network resource scheduling scheme; determines the target optical network unit corresponding to the network resource scheduling scheme, and distributes the network resource scheduling scheme to the target optical network unit for execution. Thus, by combining the information extraction strategy and optimization strategy from the cloud server, a closed-loop optimization mechanism is formed through multi-dimensional information collection and intelligent analysis, realizing dynamic optimization scheduling of network resources, ensuring the flexibility and accuracy of resource allocation, and improving the overall resource utilization rate of the network.

[0066] To facilitate better implementation of the above-described network resource scheduling method based on PON and ONU, this embodiment of the invention also provides a corresponding network resource scheduling device based on PON and ONU. The meanings of the terms used are the same as in the above-described network resource scheduling method based on PON and ONU; for specific implementation details, please refer to the descriptions in the above method embodiments.

[0067] Please refer to Figure 4 The network resource scheduling device based on PON and ONU may include a data acquisition module 210, an analysis module 220, a control module 230, and an execution module 240. Detailed descriptions of each functional module are as follows: The acquisition module 210 is used to acquire multidimensional raw network information according to the configured multidimensional information acquisition strategy; Analysis module 220 is used to extract information from multidimensional raw network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information; The control module 230 is used to make optimization decisions based on the optimization strategy from the cloud server and multi-dimensional decision reference information to obtain a network resource scheduling scheme. The execution module 240 is used to determine the target optical network unit corresponding to the network resource scheduling scheme and distribute the network resource scheduling scheme to the target optical network unit for execution.

[0068] Optionally, in one embodiment, the control module 230 is used to send an optimization strategy activation confirmation request to the cloud server. The optimization strategy activation confirmation request is used to request the cloud server to indicate the optimization strategy that needs to be activated at present. The control module 230 receives optimization strategy indication information returned by the cloud server, and makes optimization decisions based on multi-dimensional decision reference information according to the target optimization strategy indicated by the optimization strategy indication information to obtain a network resource scheduling scheme.

[0069] Optionally, in one embodiment, the network resource scheduling device based on PON and ONU provided by the present invention further includes a computing power evaluation module, used to determine the current computing power load and identify whether the computing power load has reached the load threshold. The analysis module 220 is used to extract information from the multidimensional raw network information according to the information extraction strategy from the cloud server when the computing load does not reach the load threshold, so as to obtain multidimensional decision reference information.

[0070] Optionally, in one embodiment, the network resource scheduling device based on PON and ONU provided by the present invention further includes a coordination module, which is used to send multi-dimensional raw network information to an external computing device when the computing load reaches the load threshold. The multi-dimensional raw network information is used by the external computing device to extract information according to the information extraction strategy from the cloud server to obtain multi-dimensional decision reference information, and then to make optimization decisions based on the multi-dimensional reference information according to the optimization strategy from the cloud server to obtain a network resource scheduling scheme. The execution module 240 is also used to receive the network resource scheduling scheme returned by the external computing power device, determine the target optical network unit corresponding to the network resource scheduling scheme, and distribute the network resource scheduling scheme to the target optical network unit for execution.

[0071] Optionally, in one embodiment, the execution module 240 is used to verify the network resource scheduling scheme to confirm whether it meets the scheme constraints. If the verification passes, the target optical network unit corresponding to the network resource scheduling scheme is determined, and the network resource scheduling scheme is distributed to the target optical network unit for execution.

[0072] Optionally, in one embodiment, the network resource scheduling device for PON and ONU provided by the present invention further includes an update module, which is used to send a tuning strategy update request to a cloud server, wherein the tuning strategy update request is used to request the cloud server to update the current tuning strategy; receive tuning strategy update information returned by the cloud server, and update the tuning strategy stored locally according to the tuning strategy update information.

[0073] Optionally, in one embodiment, the multidimensional raw network information includes the device status information of the optical network unit, the network environment information of the network it is in, the terminal information and service information of the access terminal devices, and the multidimensional decision reference information includes the network environment health, the optical network unit health, the service health, and the optical network unit performance indicators and the service performance indicators.

[0074] Specific limitations regarding PON and ONU-based network resource scheduling devices can be found in the limitations of PON and ONU-based network resource scheduling methods described above, and will not be repeated here. Each module in the aforementioned PON and ONU-based network resource scheduling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0075] In one embodiment, a computer device is provided, which may be an optical network unit, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The network interface connects to external wireless clients, providing wireless network access services to the connected clients. When the computer program is executed by the processor, it implements the PON and ONU-based network resource scheduling method provided by this invention.

[0076] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the network resource scheduling method based on PON and ONU described in the above embodiment.

[0077] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the network resource scheduling method based on PON and ONU described in the above embodiment.

[0078] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0080] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A network resource scheduling method based on PON and ONU, applicable to optical network units, characterized in that, include: Collect multi-dimensional raw network information according to the configured multi-dimensional information collection strategy; According to the information extraction strategy from the cloud server, information is extracted from the multidimensional original network information to obtain multidimensional decision reference information; Based on the optimization strategy from the cloud server and the multi-dimensional decision reference information, an optimization decision is made to obtain a network resource scheduling scheme. The target optical network unit corresponding to the network resource scheduling scheme is determined, and the network resource scheduling scheme is distributed to the target optical network unit for execution.

2. The network resource scheduling method according to claim 1, characterized in that, The step of making optimization decisions based on the optimization strategy from the cloud server and the multi-dimensional decision reference information to obtain a network resource scheduling scheme includes: Send a tuning strategy activation confirmation request to the cloud server. The tuning strategy activation confirmation request is used to request the cloud server to indicate the tuning strategy that needs to be activated. The system receives optimization strategy instruction information returned by the cloud server, makes optimization decisions based on the target optimization strategy indicated by the optimization strategy instruction information and the multi-dimensional decision reference information, and obtains a network resource scheduling scheme.

3. The network resource scheduling method according to claim 1, characterized in that, Before extracting information from the multidimensional raw network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information, the process further includes: Determine the current computing load and identify whether the computing load has reached the load threshold; If it is detected that the computing power load has not reached the load threshold, then information is extracted from the multidimensional original network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information.

4. The network resource scheduling method according to claim 3, characterized in that, After identifying whether the computing power load has reached the load threshold, the method further includes: If the computing load is detected to have reached the load threshold, the multidimensional raw network information is sent to the external computing device. The multidimensional raw network information is used by the external computing device to extract information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information, and then to make optimization decisions based on the multidimensional reference information according to the optimization strategy from the cloud server to obtain a network resource scheduling scheme. The steps include receiving the network resource scheduling scheme returned by the external computing power device, determining the target optical network unit corresponding to the network resource scheduling scheme, and distributing the network resource scheduling scheme to the target optical network unit for execution.

5. The network resource scheduling method according to claim 4, characterized in that, After receiving the network resource scheduling scheme returned by the external computing power device, the method further includes: The network resource scheduling scheme is verified to confirm whether it meets the scheme constraints. If the verification passes, the process proceeds to the step of determining the target optical network unit corresponding to the network resource scheduling scheme and distributing the network resource scheduling scheme to the target optical network unit for execution.

6. The network resource scheduling method according to claim 1, characterized in that, Also includes: Send a tuning strategy update request to the cloud server, the tuning strategy update request being used to request the cloud server to update the current tuning strategy; Receive the tuning strategy update information returned by the cloud server, and update the tuning strategy stored locally according to the tuning strategy update information.

7. The network resource scheduling method according to claim 1, characterized in that, The multidimensional raw network information includes the device status information of the optical network unit, the network environment information of the network it is in, the terminal information and service information of the access terminal devices, and the multidimensional decision reference information includes network environment health, optical network unit health, service health, and optical network unit performance indicators and service performance indicators.

8. A network resource scheduling device based on PON and ONU, characterized in that, include: The data acquisition module is used to collect multidimensional raw network information according to the configured multidimensional information acquisition strategy; The analysis module is used to extract information from the multidimensional raw network information according to the information extraction strategy from the cloud server to obtain multidimensional decision reference information; The control module is used to make optimization decisions based on the multi-dimensional decision reference information according to the optimization strategy from the cloud server, and to obtain a network resource scheduling scheme. The execution module is used to determine the target optical network unit corresponding to the network resource scheduling scheme and distribute the network resource scheduling scheme to the target optical network unit for execution.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer device is configured as an optical network unit, the processor executes the computer program to implement the network resource scheduling method based on PON and ONU as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the network resource scheduling method based on PON and ONU as described in any one of claims 1 to 7.

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