Distributed printing node cooperative operation and maintenance method based on edge computing

Abstract

The present application relates to the technical field of edge computing and distributed computing, and particularly relates to a distributed printing node cooperative operation and maintenance method based on edge computing, comprising: deploying a plurality of edge computing nodes in a distributed printing network, each edge computing node being associated with at least one printing node to form an edge and printing cooperative unit, each edge computing node collecting running state data of the associated printing node in real time, and completing preprocessing and abnormality identification of the state data locally, and the edge computing nodes establishing a cooperative link through point-to-point communication, and based on the local preprocessing result and the cooperative link information, autonomously performing printing task scheduling, printing resource sharing and cooperative replacement of faulty nodes. In the present application, autonomous closed-loop operation at the edge is achieved, system risk resistance, operation efficiency and management and control capability are improved, and operation and maintenance costs are reduced. The problem of low system risk resistance, operation efficiency and management and control capability in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the fields of edge computing and distributed computing technology, and in particular to a collaborative operation and maintenance method for distributed printing nodes based on edge computing. Background Technology

[0002] Print node collaborative operation and maintenance is a cross-entity, full-link collaborative operation and maintenance model for distributed printing devices and terminal nodes. Through the linkage of multiple roles such as IT operation and maintenance, service providers, and users, it can achieve unified monitoring of node status, rapid fault response, intelligent replenishment of consumables, and compliant control of permissions. It solves the problems of inefficiency and delayed response of single-point operation and maintenance, ensures the stability of the printing system, and reduces operation and maintenance costs.

[0003] In existing technologies, a centralized architecture with centralized cloud management is commonly used. This architecture centrally deploys all scheduling decisions, data processing, and operation and maintenance management capabilities in the cloud, only giving edge nodes passive execution capabilities. It does not design an edge autonomous operation and hierarchical collaboration mechanism, which not only results in the disadvantage of strong cloud dependence, but also causes the hidden dangers of insufficient edge collaboration and inability to balance real-time performance and global resource optimization. This leads to problems such as low risk resistance, low operating efficiency, and low management and control capabilities of the entire system. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides the following technical solution: A collaborative operation and maintenance method for distributed printing nodes based on edge computing includes the following steps: S1: Deploy multiple edge computing nodes in a distributed printing network, with each edge computing node associated with at least one printing node, forming an edge-printing collaborative unit; S2: Each edge computing node collects the running status data of the associated printing nodes in real time, and performs preprocessing and anomaly identification of the status data locally; S3: Edge computing nodes establish collaborative links through point-to-point communication. Based on local preprocessing results and collaborative link information, they autonomously execute printing task scheduling, printing resource sharing, and collaborative replacement of faulty nodes. S4: Edge computing nodes synchronize the selected key operation and maintenance data to the cloud management and control platform. The cloud management and control platform only distributes global policies and monitors macro-level operation and maintenance, and does not participate in local real-time collaborative decision-making. S5: When any printing node fails, the corresponding edge computing node triggers a collaborative operation and maintenance mechanism, which distributes the pending tasks of the failed node to normal printing nodes in the same area through a collaborative link, thereby achieving self-healing and continuous service.

[0005] The above technical solution builds a distributed printing collaborative operation and maintenance system based on edge computing architecture. It realizes data processing and autonomous collaboration through the local computing power of edge nodes, while the cloud only performs macro-level management and control. It constructs a two-level operation and maintenance architecture of local decision-making and cloud monitoring to ensure the highly reliable and continuous operation of printing services.

[0006] As an improvement to the above technical solution, S21 includes the remaining consumables at the printing node, device temperature, paper jam status, task queue length, network connectivity, and printing success rate.

[0007] The above technical solution uses a node perception module to collect real-time data on the full-dimensional operating status of printing nodes, covering all indicators of equipment operation, consumables, tasks, and network, and synchronizes this data to the operation and maintenance status monitoring module, providing complete data source support for local anomaly identification.

[0008] As an improvement to the above technical solution, S22: The edge computing node predicts the consumable consumption trend of the printing node based on the historical data of the printing task and triggers the consumable replenishment reminder in advance.

[0009] The above technical solution utilizes local computing power and historical data from printing tasks to predict consumable consumption trends locally. It also links consumable and resource maintenance modules to achieve proactive management and avoid service interruptions caused by consumable depletion.

[0010] As an improvement to the above technical solution, S31: adopts a low-latency local area network communication protocol, and the collaborative decision-making response time does not exceed a preset threshold.

[0011] The above technical solution uses a low-latency local area network communication protocol in the network communication module to support the stable establishment of point-to-point collaborative links between edge nodes, strictly constrain the response time of collaborative decisions, and ensure low latency and high reliability of local collaborative scheduling.

[0012] As an improvement to the above technical solution, S32: Based on the load rate and idle status of each printing node, the newly connected printing tasks are allocated to the optimal idle node to balance the printing load in the area.

[0013] The above technical solution enables the edge computing local collaboration module to obtain the load and idle status of printing nodes in the region in real time through the collaboration link, and complete the optimal allocation of tasks based on the global collaborative scheduling strategy, thereby realizing the dynamic balanced scheduling of regional printing load.

[0014] As an improvement to the above technical solution, S41: The edge computing node performs local encryption processing on the collected status data and synchronizes the encrypted key data to the cloud management platform.

[0015] The above technical solution involves the edge data processing module encrypting and filtering all collected status data locally, and only synchronizing the encrypted core operation and maintenance data to the cloud management module, thus balancing the security of data transmission and storage with the basic data requirements for macro-monitoring in the cloud.

[0016] As an improvement to the above technical solution, S42: Generate a global operation and maintenance report based on the key operation and maintenance data synchronized by each edge computing node, and dynamically update the edge collaborative scheduling strategy.

[0017] The above technical solution involves the operation and maintenance management backend module aggregating key operation and maintenance data synchronized from each edge node, completing the global operation status integration and analysis, generating standardized global operation and maintenance reports, and dynamically optimizing and distributing edge collaborative scheduling strategies.

[0018] As an improvement to the above technical solution, S51: Automatically synchronize the print task parameters and document data of the faulty node to the alternative print node to ensure the consistency of the printed content.

[0019] The above technical solution, when the fault diagnosis and self-healing module triggers the fault collaborative operation and maintenance mechanism, relies on the point-to-point collaborative link of edge nodes to complete the local synchronization of task data, without cloud intervention throughout the process, ensuring seamless task connection and consistency of printed content.

[0020] The beneficial effects of this invention are: 1. It achieves autonomous closed-loop operation at the edge, improving the system's resilience, operational efficiency, and control capabilities, while reducing maintenance costs. This solves the problems of low system resilience, operational efficiency, and control capabilities in existing technologies.

[0021] 2. It achieves hierarchical collaborative scheduling, balancing real-time performance with global resource optimization. This solves the problems of asynchronous hierarchical scheduling and insufficient real-time performance in existing technologies.

[0022] 3. It achieves automated fault self-healing without human intervention, ensuring uninterrupted business operations. This solves the problem of existing technologies where systems cannot self-heal and rely excessively on manual intervention. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the modular system of the present invention; Figure 2 This is a flowchart of the steps of the present invention. Detailed Implementation

[0024] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0025] See appendix Figure 1 , Figure 2 As shown, a collaborative operation and maintenance method for distributed printing nodes based on edge computing includes the following steps: S1: Deploy multiple edge computing nodes in a distributed printing network, with each edge computing node associated with at least one printing node, forming an edge-printing collaborative unit; S2: Each edge computing node collects the running status data of the associated printing nodes in real time, and performs preprocessing and anomaly identification of the status data locally; S3: Edge computing nodes establish collaborative links through point-to-point communication. Based on local preprocessing results and collaborative link information, they autonomously execute printing task scheduling, printing resource sharing, and collaborative replacement of faulty nodes. S4: Edge computing nodes synchronize the selected key operation and maintenance data to the cloud management and control platform. The cloud management and control platform only distributes global policies and monitors macro-level operation and maintenance, and does not participate in local real-time collaborative decision-making. S5: When any printing node fails, the corresponding edge computing node triggers a collaborative operation and maintenance mechanism, which distributes the pending tasks of the failed node to normal printing nodes in the same area through a collaborative link, thereby achieving self-healing and continuous service.

[0026] Fault self-healing task redistribution optimization formula: Core significance: Quantifying the task takeover of fault nodes and the optimization goals and constraints of service self-healing are the ultimate business value realization of the process.

[0027] Symbol definition: The set of fault nodes at any given moment; The set of normally available nodes at any given time; Fault node The pending tasks are assigned to normal nodes. The weights; :node and The task transmission latency between them is optimized with the goal of minimizing the total self-healing latency. Fault node The number of unfinished print jobs; Normal node The remaining available resources are constrained to ensure that the takeover task does not exceed the node's resource limit.

[0028] See Figure 1 , Figure 2 S21: Includes the remaining consumables at the printing node, device temperature, paper jam status, task queue length, network connectivity, and printing success rate.

[0029] See Figure 1 , Figure 2 S22: The edge computing node predicts the consumable consumption trend of the printing node based on the historical data of the printing task and triggers the consumable replenishment reminder in advance.

[0030] Local identification formula for node status: Core significance: The quantitative output of local data collection, printing status, and fault characteristics of edge nodes serves as the basis for judgment on autonomous collaboration.

[0031] Symbol definition: Time Node : Feature vectors collected locally Features include dimensions such as printing temperature, filament balance, network status, task progress, and motor condition. Lightweight local identification models deployed at the edge, such as lightweight neural networks, decision trees, and fault classifiers; :node The status recognition result is 0 for normal, 1 for warning, and 2 for fault.

[0032] See Figure 1 , Figure 2 S31: Adopt a low-latency local area network communication protocol, and the collaborative decision-making response time does not exceed a preset threshold.

[0033] See Figure 1 , Figure 2 S32: Based on the load rate and idle status of each print node, newly connected print tasks are assigned to the optimal idle node to balance the print load within the area.

[0034] See Figure 1 , Figure 2S41: Edge computing nodes encrypt the collected status data locally and synchronize the encrypted key data to the cloud management platform.

[0035] See Figure 1 , Figure 2 S42: Generate a global operation and maintenance report based on the key operation and maintenance data synchronized by each edge computing node, and dynamically update the edge collaborative scheduling strategy.

[0036] See Figure 1 , Figure 2 S51: Automatically synchronizes the print job parameters and document data of the faulty node to the alternative print node to ensure consistency of printed content.

[0037] In summary, this method relies on a supporting distributed printing collaborative operation and maintenance system. The system includes a power supply module that provides power support for the operation of all system nodes, and a node sensing module electrically connected to the power supply module. The node sensing module is connected to the operation and maintenance status monitoring module and the edge computing local collaboration module. The operation and maintenance status monitoring module is connected to the fault diagnosis and self-healing module, the consumables and resource operation and maintenance module, the edge data processing module, and the management and control module in sequence. The edge computing local collaboration module is connected to the network communication module and the global collaborative scheduling module in sequence. The management and control module, the global collaborative scheduling module, and the operation and maintenance status monitoring module are all connected to the operation and maintenance management backend module.

[0038] In one embodiment, the specific implementation process is as follows: First, edge printing collaborative deployment is completed in the distributed printing network by deploying multiple edge computing nodes, each of which is associated with at least one printing node, forming an edge and printing collaborative unit. The power supply module provides stable power to each edge computing node and printing node, thus completing the basic collaborative architecture.

[0039] After deployment, each edge computing node collects full operational status data of its associated printing nodes in real time through the node perception module. Simultaneously, the collected operational status data is transmitted to the operation and maintenance status monitoring module and the edge data processing module. The edge data processing module performs preprocessing operations on the status data locally at the edge and identifies operational anomalies locally based on preset rules, thus achieving a closed loop between edge data collection and local identification.

[0040] After completing local data processing and anomaly identification, each edge computing node establishes a point-to-point collaborative communication link through the network communication module. Relying on the local collaborative module of edge computing, based on the local preprocessing results and the running status information of each node in the collaborative link, it autonomously executes printing task scheduling, printing resource sharing, and collaborative replacement of faulty nodes. At the same time, the global collaborative scheduling module can perform non-real-time global collaborative planning based on the status of all network nodes, without interfering with the real-time collaborative decision-making of the edge end, so as to realize the autonomous collaborative operation of edge nodes.

[0041] During autonomous collaborative operation at the edge, each edge computing node only synchronizes the selected key operation and maintenance data to the cloud management platform through the network communication module. The cloud management platform only distributes global operation and maintenance policies and performs macro-level operation and maintenance monitoring of all network printing nodes through the management module. It does not participate in real-time collaborative decision-making at the edge, thereby reducing cloud computing load and transmission dependence while ensuring global operation and maintenance visibility.

[0042] When the operation and maintenance status monitoring module and the fault diagnosis and self-healing module identify a failure in any printing node, the corresponding associated edge computing node immediately triggers the collaborative operation and maintenance mechanism. Through the established collaborative link, the pending printing tasks of the faulty node are allocated to printing nodes in normal operation within the same area. At the same time, the consumables and resources operation and maintenance module synchronously matches the consumables and operating resources of the corresponding printing node, ultimately realizing the self-healing of printing node failures and continuous printing services. All operation and maintenance data in the entire process are synchronized to the operation and maintenance management backend module, completing the closed-loop management of the entire operation and maintenance process.

[0043] Implementation results of the above embodiments: By completing data preprocessing, anomaly identification, and real-time collaborative decision-making locally at edge nodes, local printing services can still operate normally even in network outage scenarios. Only key operational data after screening is uploaded, significantly reducing cloud computing load and network bandwidth consumption, while greatly shortening the latency of data processing and anomaly response.

[0044] The point-to-point collaborative link of edge nodes can complete local real-time task scheduling, resource sharing and fault replacement. Global scheduling only performs non-real-time overall planning and does not interfere with real-time edge decisions. This ensures the real-time performance and flexibility of printing task execution, and realizes global optimization of printing resources across the entire network, greatly improving resource utilization.

[0045] Once a fault is triggered, the system can automatically complete the task allocation and synchronization matching of consumables and operating resources for normal nodes in the same area. Fault handling can be completed without manual intervention, minimizing downtime and ensuring continuous printing services.

[0046] It establishes a seamless end-to-end system, from node status awareness, data processing, fault diagnosis and self-healing, resource operation and maintenance to backend data synchronization, ensuring traceability of operation and maintenance data throughout the entire process. The cloud enables macro-level operation and maintenance monitoring and global policy distribution, reducing manual operation and maintenance while ensuring global operation and maintenance visibility and control, thereby lowering the overall operation and maintenance cost of the distributed printing network.

[0047] The accompanying power supply module provides stable power support for all nodes, and combined with real-time status awareness across the entire link, it strengthens the hardware foundation for the stable operation of the distributed printing network.

[0048] The above embodiments are merely illustrative of the technical solutions of the present invention and are not intended to limit it. Anyone skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for collaborative operation and maintenance of distributed printing nodes based on edge computing, characterized in that, Includes the following steps: S1: Deploy multiple edge computing nodes in a distributed printing network, with each edge computing node associated with at least one printing node, forming an edge-printing collaborative unit; S2: Each edge computing node collects the running status data of the associated printing nodes in real time, and performs preprocessing and anomaly identification of the status data locally; S3: Edge computing nodes establish collaborative links through point-to-point communication. Based on local preprocessing results and collaborative link information, they autonomously execute printing task scheduling, printing resource sharing, and collaborative replacement of faulty nodes. S4: Edge computing nodes synchronize the selected key operation and maintenance data to the cloud management and control platform. The cloud management and control platform only distributes global policies and monitors macro-level operation and maintenance, and does not participate in local real-time collaborative decision-making. S5: When any printing node fails, the corresponding edge computing node triggers a collaborative operation and maintenance mechanism, which distributes the pending tasks of the failed node to normal printing nodes in the same area through a collaborative link, thereby achieving self-healing and continuous service.

2. The method according to claim 1, characterized in that, The operational status data mentioned in S2 also includes the following steps: S21: Includes the remaining consumables at the printing node, device temperature, paper jam status, task queue length, network connectivity, and printing success rate.

3. The method according to claim 1, characterized in that, S2 further includes the following steps: S22: The edge computing node predicts the consumable consumption trend of the printing node based on the historical data of the printing task and triggers the consumable replenishment reminder in advance.

4. The method according to claim 1, characterized in that, The point-to-point communication between edge computing nodes described in S3 further includes the following steps: S31: Employs a low-latency local area network communication protocol, ensuring that the collaborative decision-making response time does not exceed a preset threshold.

5. The method according to claim 1, characterized in that, The autonomous execution of print task scheduling described in S3 also includes the following steps: S32: Based on the load rate and idle status of each print node, newly connected print tasks are assigned to the optimal idle node to balance the print load within the area.

6. The method according to claim 1, characterized in that, S4 further includes the following steps: S41: Edge computing nodes encrypt the collected status data locally and synchronize the encrypted key data to the cloud management platform.

7. The method according to claim 1, characterized in that, The cloud management platform described in S4 also includes the following steps: S42: Generate global operation and maintenance reports based on the key operation and maintenance data synchronized by each edge computing node, and dynamically update the edge collaborative scheduling strategy.

8. The method according to claim 1, characterized in that, The collaborative replacement of the faulty node described in S5 also includes the following steps: S51: Automatically synchronizes the print job parameters and document data of the faulty node to the alternative print node to ensure consistency of printed content.

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