Group printing task dynamic scheduling method and system based on cloud-edge collaborative computing

Abstract

The application discloses a group printing task dynamic scheduling method and system based on cloud-edge collaborative calculation, and aims at the problems of single-point bottleneck, rigid scheduling, difficulty in cross-domain collaboration and unintelligent fault recovery of existing schemes.The application adopts a two-layer architecture of "cloud management platform+edge printing service station": the cloud end is responsible for global task receiving, preprocessing, strategy making and monitoring, and the edge node is responsible for local printer management, state acquisition and dynamic scheduling.The core process is that the cloud end issues task metadata and strategies to the main edge node, the main node splits the task and dispatches through a dynamic weighted scheduling algorithm, each node executes in parallel and feeds back the state, and the subtask is automatically rescheduled when it fails.The application realizes cross-regional collaboration, load balancing and rapid fault transfer, has the characteristics of high reliability, intelligent optimization and scalability, and is suitable for large-scale high-reliable printing scenes such as enterprise office and logistics storage.

Description

Technical Field

[0001] This invention belongs to the field of printer technology, and in particular relates to a method and system for dynamic scheduling of group printing tasks based on cloud-edge collaborative computing. Background Technology

[0002] With the deepening of enterprise digital transformation and the popularization of cross-regional business models, the demand for group printing presents the core requirements of large scale, cross-regional and high reliability, and is widely used in scenarios such as enterprise office document distribution, logistics and warehousing document printing, and production and manufacturing process voucher output. Currently, mainstream group printing solutions mainly fall into two categories: one is a centralized management and control solution based on a central print server. This solution receives and schedules all printing tasks through a single central node. Although it achieves basic centralized management, it has significant single-point bottleneck risks. A failure of the central server or a network interruption can paralyze the entire printing cluster. It is also highly dependent on wide area network bandwidth, with high latency and poor scalability for cross-regional task transmission, making it unsuitable for large-scale distributed printing scenarios. The other is a pure peer-to-peer (P2P) distributed printing solution. It abandons the central node and distributes tasks through direct communication between nodes. Although it avoids single-point failure issues, it lacks global optimization and unified policy management capabilities. It cannot accurately perceive the real-time status of all printers in the cluster (such as ink volume, paper balance, queue load, and fault status) and task attributes (such as urgency, color requirements, and number of copies), resulting in rigid scheduling decisions, poor load balancing, low reliability of cross-wide area network collaboration, and insufficient resource utilization.

[0003] Furthermore, both existing solutions suffer from inadequate fault recovery mechanisms: centralized solutions rely on manual intervention to troubleshoot central nodes or terminal printers, resulting in low failover efficiency; distributed solutions lack a unified fault monitoring and rescheduling mechanism, making it difficult to quickly respond and reassign tasks after subtask execution failures, easily leading to lost or duplicate print jobs. Simultaneously, existing solutions do not provide dedicated high-availability printing mechanisms for critical tasks (such as invoices and core vouchers), failing to meet the demands of scenarios with extremely high requirements for print output integrity and timeliness. In summary, existing group printing solutions struggle to balance global optimization, rapid local response, cross-domain collaboration, and intelligent fault recovery, failing to adapt to large-scale, cross-regional, and highly reliable printing needs, thus limiting the overall throughput and service stability of the printing cluster. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic scheduling method for group printing tasks based on cloud-edge collaborative computing, comprising the following steps: S1: System initialization and resource discovery, completing the registration of edge printing service stations and local printers and building a global resource view; S2: Print task submission and cloud preprocessing. After the user submits the print task, the cloud management platform preprocesses the task and generates a task metadata description file. S3: Based on cloud-edge collaboration, dynamic task scheduling is performed. The cloud management platform selects the target edge printing service station cluster and issues task-related information. The main edge printing service station performs scheduling and task splitting and assignment according to the task situation. S4: Distributed task execution and status coordination. Each edge printing service station executes sub-tasks and reports the status. The main edge printing service station summarizes the status and handles sub-task failures. S5: Mirrored printing and high availability for faults. Provides a mirrored printing mode for mission-critical applications to ensure high availability of printing.

[0005] In a further embodiment of the present invention, S1 specifically includes: S101: After each edge printing service station is started, it discovers and registers local physical printers on the local area network via broadcast or multicast, forming a local printer resource pool. S102: The edge service station registers with the cloud management platform and reports its own network location, the group it belongs to, and the static attributes of the local printer resource pool; S103: The cloud management platform maintains the global registry and printer resource directory of the edge printing service station, forming a global view of the system.

[0006] In a further embodiment of the present invention, the static attributes of the local printer resource pool in S102 include printer model, supported colors, and paper type, and the group to which it belongs is a preset function or region group, including the finance department and warehouse A.

[0007] In a further embodiment of the present invention, S2 specifically includes: S201: Users submit tasks by selecting the cloud group printing virtual printer driver through the application, and the task and its attributes are uploaded to the cloud management platform. S202: The cloud management platform performs format standardization, virus scanning, and user permission-based access control preprocessing on tasks, generating a lightweight task metadata description file containing task instructions, pagination information, and scheduling policy identifiers.

[0008] In a further embodiment of the present invention, S3 specifically includes: S301: The cloud management platform selects the target edge printing service station cluster based on task attributes and real-time status heartbeat information synchronized from each edge printing service station; S302: The cloud management platform distributes the task metadata description file and preliminary scheduling strategy to the main edge printing service station; S303: After receiving a task, the main edge printing service station will start the local dynamic scheduling engine if it is a large batch of tasks, and directly deliver the task to the local printer if it does not need to be split. S304: The local dynamic scheduling engine obtains a list of edge printing service stations in the same group, collects real-time performance indicators of each node and its printer, combines cloud policies and real-time indicators, uses a dynamic weighted scheduling algorithm to calculate the optimal paging scheme, splits the printing task into several sub-task packages and assigns them to this station and other edge printing service stations in the same group.

[0009] In a further embodiment of the present invention, the real-time status heartbeat information in S301 includes the length of each printer queue, busy / idle status, ink / paper balance, and online / offline status; The preliminary scheduling strategy in S302 includes load balancing priority and fastest completion priority. The real-time performance metrics in S304 include recent average printing speed and current network latency; The weighting factors of the dynamic weighted scheduling algorithm include the printer's current load, printing speed, resource health, and network overhead with the main edge printing service station. The resource health is set with a corresponding coefficient based on the ink availability, where the ink availability coefficient is 1.0, the ink warning coefficient is 0.5, and the low ink coefficient is 0. The calculation logic is: Weight = (baseline speed / (current queue + 1)) * resource health coefficient. Subtask packages of the corresponding number of pages are allocated based on the weight ratio of each node.

[0010] In a further embodiment of the present invention, step S4 specifically includes: S401: The edge printing service station that receives the subtask package retrieves the corresponding print page bitmap data from the cloud management platform or the main edge printing service station based on the index in the metadata. S402: Each node drives the local printer to execute printing and monitors the printing status in real time; S403: When each subtask package is completed or a fault is encountered, the edge printing service station will send an execution status report containing a success / failure indicator, the range of completed pages, and a description of the fault to the main edge printing service station, and simultaneously copy it to the cloud management platform; S404: The main edge printing service station summarizes the status of all subtasks. If a subtask fails, a dynamic rescheduling process is triggered according to the preset strategy of retrying the nearest node and selecting the second-best node. The failed page number is reassigned and the new scheduling decision is updated to the cloud management platform.

[0011] In a further embodiment of the present invention, step S5 specifically includes: S501: For critical tasks that require multiple copies to be output simultaneously, the user specifies a mirror printing mode; S502: The cloud management platform or the main edge print service station will simultaneously distribute the complete task to multiple designated edge print service stations to perform full printing. S503: The task is considered successful if any mirror node completes printing.

[0012] The cloud-edge collaborative computing-based dynamic scheduling system for group printing tasks, which implements the aforementioned cloud-edge collaborative computing-based dynamic scheduling method for group printing tasks, includes a cloud management platform, multiple edge printing service stations, and a collaborative communication network. The cloud management platform is used to provide global task management, strategy formulation, status monitoring, and resource catalog services. The edge printing service station is deployed in each local area network for local printer resource management, task reception and execution, real-time status reporting, and has local dynamic scheduling capabilities based on policies and real-time information. The collaborative communication network is used to establish secure and reliable data and command transmission channels between the cloud management platform and edge printing service stations, as well as among the edge printing service stations.

[0013] In a further embodiment of the present invention, the cloud management platform is deployed on a public cloud or a private cloud and includes a web management interface, user authentication, task reception and preprocessing, a global policy library, and a real-time monitoring dashboard module. The edge printing service station is a dedicated hardware device or a software service installed on a general PC / server, including a local resource manager, a collaborative communication agent, a dynamic scheduling engine, and a task executor. The local resource manager is used to perform printer discovery and registration operations in S101. The collaborative communication agent is used to maintain a secure long-lived MQTT / WebSocket connection with the cloud management platform to receive instructions and report status, while maintaining a P2P connection with other edge printing service stations in the same group via UDP multicast or TCP direct connection to achieve fast data exchange and status synchronization. The dynamic scheduling engine is used to execute the S304 dynamic weighted scheduling algorithm and related scheduling logic; The task executor is used to perform printer driver and status monitoring operations for the S402.

[0014] The beneficial effects of this invention are: 1. This invention adopts a two-tier collaborative architecture of "cloud management platform + edge printing service station". The edge printing service station has complete local autonomy and can independently complete local printer resource management, task scheduling and execution. Even if the cloud management platform fails or the network is interrupted, the printing tasks that have been sent to the edge printing service station can still be completed collaboratively within the local area network or edge cluster, completely solving the single point bottleneck problem of centralized solutions. At the same time, the collaborative mechanism between the cloud and the edge ensures system elasticity. New printing resources can be seamlessly integrated into the cluster simply by deploying the edge printing service station and registering it with the cloud management platform, without the need to reconstruct the existing architecture, which significantly improves the system's scalability and environmental adaptability.

[0015] 2. The scheduling decision integrates cloud-based global strategies with millisecond-level real-time status of edge printing service stations. The main edge printing service station uses a dynamic weighted scheduling algorithm to comprehensively consider multiple weighting factors such as printer load, printing speed, resource health (ink / paper balance), and network overhead to accurately calculate the optimal pagination scheme and split tasks. Compared to the rigid scheduling logic of existing solutions, this invention enables adaptive distribution of printing tasks, allowing high-load nodes to be reasonably relieved of their load, idle nodes to be fully utilized, and nodes with insufficient resources to be assigned fewer tasks. This maximizes the throughput and resource utilization of the entire printing cluster and effectively reduces task waiting time.

[0016] 3. By constructing a global resource view through a cloud management platform, edge printing service stations and printer resources distributed across different geographical regions and local area networks are uniformly managed, breaking down geographical and network boundary limitations. Users do not need to concern themselves with the specific location of the physical printer; they only need to submit tasks and specify the target group through the "Cloud Group Printing" virtual driver. The cloud management platform can then automatically select the optimal edge cluster to complete task distribution and execution based on task attributes and global resource status. This mechanism enables efficient sharing of printing resources across regions, and is particularly suitable for scenarios such as corporate headquarters and branch offices, and cross-regional logistics and warehousing, significantly reducing the operational complexity and transmission latency of cross-domain printing.

[0017] 4. This invention constructs a full-link real-time status monitoring and intelligent rescheduling mechanism. Edge printing service stations provide real-time feedback on task execution status (success / failure, completed pages, cause of failure). The main edge printing service station can quickly identify subtask execution failures (such as paper jams or ink shortages) and automatically trigger a dynamic rescheduling process, reassigning incomplete pages to available nodes within the cluster, achieving rapid fault transfer and task completion. Simultaneously, a mirrored printing mode designed for critical tasks ensures task success through multi-node full-volume synchronous printing, guaranteeing that completion at any node is considered a successful task, further improving the printing reliability of critical tasks. The entire fault handling process requires no manual intervention, significantly shortening fault recovery time, reducing the risk of task loss, and lowering operation and maintenance costs.

[0018] 5. The cloud management platform integrates security mechanisms such as format standardization, virus scanning, and user access control during the task preprocessing stage, effectively preventing security risks such as malicious file printing and unauthorized access, and ensuring the security of printed content and the system environment. Simultaneously, the system supports multiple scheduling strategies such as "load balancing priority" and "fastest completion priority," adapting to the personalized needs of different scenarios such as enterprise offices, logistics warehousing, and manufacturing. It flexibly switches between mirror printing mode for critical tasks and pagination / split mode for ordinary tasks, meeting both the high availability requirements of core businesses and the resource-saving needs of ordinary businesses, making it widely applicable. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the method steps of the present invention; Figure 2 This is the system architecture diagram of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] In the description of this application, it should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. For ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0022] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, the first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0023] It should be noted that in the description of this application, the directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0024] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0025] This embodiment provides a dynamic scheduling method for group printing tasks based on cloud-edge collaborative computing, aiming to solve the problems of single-point bottlenecks in existing centralized architectures and the lack of global optimization in purely distributed architectures. It achieves cross-regional, highly reliable, and intelligently adaptive collaborative scheduling of printing resources, specifically including the following steps: S1: System initialization and resource discovery, completing the registration of edge printing service stations and local printers and building a global resource view; S2: Print task submission and cloud preprocessing. After the user submits the print task, the cloud management platform preprocesses the task and generates a task metadata description file. S3: Based on cloud-edge collaboration, dynamic task scheduling is performed. The cloud management platform selects the target edge printing service station cluster and issues task-related information. The main edge printing service station performs scheduling and task splitting and assignment according to the task situation. S4: Distributed task execution and status coordination. Each edge printing service station executes sub-tasks and reports the status. The main edge printing service station summarizes the status and handles sub-task failures. S5: Mirrored printing and high availability for faults. Provides a mirrored printing mode for mission-critical applications to ensure high availability of printing.

[0026] Furthermore, S1 specifically includes: S101: After each edge printing service station starts, it immediately initiates a device discovery process within its local area network (LAN). It sends printer detection commands to all devices within the LAN via standard network broadcast protocols (such as UDP broadcast) or multicast protocols (such as IP multicast). The commands include the edge printing service station's identification information, communication port, and resource discovery request. Upon receiving the detection command, physical printers within the LAN automatically return their own device information (such as unique device identifier and network address). After verifying and deduplicating the returned information, the edge printing service station registers qualified physical printers to the local management system, forming a structured local printer resource pool. The resource pool maintains the basic connection status of each printer in real time.

[0027] S102: After completing the construction of the local printer resource pool, the edge printing service station initiates a registration request to the cloud management platform through a preset secure communication channel. The registration information includes three core modules: First, the basic information of the edge printing service station itself, including a unique device number, network location (public IP address, local area network segment), and hardware configuration parameters (CPU model, memory capacity, storage space); second, the group information, which is a preset functional or regional group, including the finance department, warehouse A, production workshop B, etc., used to achieve resource isolation and precise scheduling according to business scenarios or physical locations; third, the static attributes of the local printer resource pool, which includes fixed attribute information such as printer model, supported colors (black and white / color), paper type adaptation range, maximum print resolution, and supported printing modes (single-sided / double-sided).

[0028] S103: After receiving registration requests from each edge printing service station, the cloud management platform verifies the legality of the registration information (including device identity authentication and group permission verification). Upon successful verification, the edge printing service station information is entered into the global edge printing service station registry. Simultaneously, the static attributes of the local printer resource pool are compiled into the global printer resource directory. The global edge printing service station registry is updated in real time with the online status and registration information changes of each edge printing service station. The global printer resource directory is categorized and indexed by group, geographical location, printer type, and other dimensions, ultimately forming a system-wide global view covering all available resources, providing complete resource data support for subsequent task scheduling.

[0029] Furthermore, in addition to printer model, supported colors, and paper type, the static attributes of the local printer resource pool in S102 may also include fixed parameters such as printer manufacturer, serial number, firmware version, interface type, and maximum monthly print volume, ensuring that the cloud management platform and edge printing service stations have a comprehensive understanding of the printer hardware capabilities. The group can be flexibly configured according to the enterprise management needs, supporting division by functional department, division by physical area, and cross-departmental and cross-regional custom groups. Group division is configured and maintained through the web management interface of the cloud management platform, and the configuration information is synchronized to each edge printing service station in real time.

[0030] Furthermore, S2 specifically includes: S201: The user initiates a print request through an application with the "Cloud Group Printing" virtual printer driver installed. In the print settings interface, the user selects the target print group, task priority, number of copies, print quality, color mode, and other task attributes, and submits the print task after confirmation. The application encapsulates the task data into a standard network transmission format through the virtual printer driver and uploads it to the cloud management platform through an encrypted channel. The transmission process uses the TLS encryption protocol to ensure data security.

[0031] S202: After receiving a print job, the cloud management platform initiates a three-level preprocessing process: The first level is format standardization, which converts document formats generated by different applications into a universal printing format to ensure compatibility with printers of all brands and models; the second level is security protection, which uses a built-in virus scanning engine to detect malicious code in the task data, and uses a user access control mechanism to verify whether the submitting user has the printing permissions for the target group and whether the preset printing quota has been exceeded, rejecting illegal or over-quota tasks; the third level is metadata generation, which, after completing format standardization and security verification, extracts the core information of the task to generate a lightweight task metadata description file. This file does not contain complete print data, but only covers key information such as the task's unique identifier, pagination information, scheduling policy identifier, task attribute summary, and data index address, which is used to guide the subsequent scheduling and execution process.

[0032] Furthermore, S3 specifically includes: S301: The cloud management platform, based on the target group attribute in the task metadata and combined with the real-time status heartbeat information synchronized from each edge printing service station, filters out all online edge printing service stations within the target group that have task execution capabilities, forming a target edge printing service station cluster. The real-time status heartbeat information includes dynamic parameters such as the queue length of each printer, busy / idle status, ink / paper balance, online / offline status (whether the printer is properly connected to the network), and fault alarm status (whether there are faults such as paper jams, low ink levels, or overheating).

[0033] S302: The cloud management platform matches a preliminary scheduling strategy from the global policy library based on task attributes (such as priority and print quality requirements). This preliminary scheduling strategy includes preset strategy types such as load balancing priority, fastest completion priority, and resource conservation priority. The load balancing priority strategy is used to balance the task load across edge printing service stations; the fastest completion priority strategy is used for high-priority urgent tasks; and the resource conservation priority strategy prioritizes printers with sufficient ink and low energy consumption. The cloud management platform packages the task metadata description file and the preliminary scheduling strategy and sends it to the primary edge printing service station in the target edge printing service station cluster via a secure long connection. The selection rule for the primary edge printing service station is: prioritize the edge printing service station closest to the user (with the lowest network latency); if multiple stations exist, select the node with the lowest current load as the primary edge printing service station.

[0034] S303: After receiving the task metadata and scheduling policy, the main edge print service station first parses key information such as the total number of pages and print quality requirements to determine whether the task needs to be split. If the total number of pages is ≤50 (the threshold can be adjusted through the cloud management platform) and there is no need for multiple copies to be printed simultaneously, it is determined to be a small batch task. The station directly selects the best local printer (prioritizing printers with idle space and sufficient ink and paper) based on the real-time status of the local printer resource pool to deliver the task for execution. If the total number of pages is >50 or there is a need for multiple copies to be printed simultaneously, it is determined to be a large batch task. The local dynamic scheduling engine is immediately started to execute the distributed split scheduling process.

[0035] S304: After the local dynamic scheduling engine starts, perform the following operations: First, obtain a list of edge printing service stations in the same group. The list is obtained by first selecting target group nodes from the global node list synchronized from the cloud management platform, and simultaneously probing nodes through the local P2P network (UDP multicast) to supplement and discover newly added nodes that have not been synchronized to the cloud, ensuring the completeness of the list; Second, send performance indicator query requests to each edge printing service station in the list to collect real-time performance indicators of each node and its printer. The real-time performance indicators include recent average printing speed (average pages printed per minute in the past 5 minutes), current network latency (round-trip network latency between the main edge printing service station and the target node), CPU / memory utilization (system resource usage of the edge printing service station), task processing response time (average latency from receiving the task to starting printing), etc.; Third, combine the preliminary scheduling strategy issued by the cloud with the collected real-time indicators, and use a dynamic weighted scheduling algorithm to calculate the optimal paging scheme. The weight factors of the dynamic weighted scheduling algorithm include the printer's current load, printing speed, resource health, and network overhead with the main edge printing service station. The calculation logic of each weight factor is as follows: Resource health: Set the corresponding coefficient according to the ink level. The coefficient is 1.0 for sufficient ink (≥50% remaining), 0.5 for ink warning (20%≤remaining <50%), and 0 for low ink (<20% remaining). When the paper level is insufficient, the resource health coefficient for the corresponding paper type is set according to the same rules. Dynamic weight calculation logic: Weight = (Base speed / (Current queue + 1)) * Resource health coefficient * Network quality coefficient, where the base speed is the printer's rated printing speed (pages / minute), the current queue is the total number of pages to be executed by the printer, and the network quality coefficient is set according to the network latency between the main edge printing service station and the target node (latency < 10ms, coefficient is 1.0; 10ms ≤ latency < 50ms, coefficient is 0.8; latency ≥ 50ms, coefficient is 0.5); Fourth step, allocate sub-task packages with corresponding page numbers based on the dynamic weight ratio of each edge printing service station. The specific calculation method is: Number of pages allocated to an edge printing service station = Total number of pages in the task * (Weight of this node / Total weight of all participating nodes). The sub-task package contains information such as the unique identifier of the task, the allocated page number range, the data index address, and the printing parameter configuration; Fifth step, the main edge printing service station distributes the sub-task packages to itself and other edge printing service stations in the same group through P2P connection. During the distribution process, nodes with low network latency and light load are given priority to transmit data to ensure that the sub-tasks are quickly distributed.

[0036] Furthermore, a specific application example of the dynamic weighted scheduling algorithm is as follows: The main edge printing service station receives a 100-page black and white document printing task. The initial scheduling strategy issued by the cloud is "load balancing priority". The dynamic scheduling engine queries three online edge printing service stations (NodeA, NodeB, and NodeC) in the same group, and the real-time status of their associated printers is collected as follows: NodeA: Associated printer queue length 0 pages, base print speed 30 pages / minute, sufficient ink (60% remaining), network latency with main edge print station 8ms; NodeB: Associated printer queue length 5 pages, base print speed 25 pages / minute, sufficient ink (75% remaining), network latency with main edge print station 12ms; NodeC: Associated printer queue length 10 pages, base print speed 40 pages / minute, ink level warning (15% remaining), network latency with main edge print station 15ms; The dynamic weights of each node are calculated based on the algorithm: Setting coefficients: Ink level sufficient coefficient = 1.0, ink level warning coefficient = 0.5; network latency < 10ms coefficient = 1.0, 10ms ≤ latency < 50ms coefficient = 0.8; Calculation of node weights: Weight_A=(30 / (0+1))*1.0*1.0=30.0; Weight_B=(25 / (5+1))*1.0*0.8≈(4.1667)*0.8≈3.3333; Weight_C=(40 / (10+1))*0.5*0.8≈(3.6364)*0.5*0.8≈1.4546; Total weight = 30.0 + 3.3333 + 1.4546 ≈ 34.7879; Page assignment calculation: Pages_A = 100 * (30.0 / 34.7879) ≈ 86 pages; Pages_B = 100 * (3.3333 / 34.7879) ≈ 9 pages; Pages_C = 100 * (1.4546 / 34.7879) ≈ 5 pages; Ultimately, the main edge print service station allocates 86 pages of subtask packages to itself (Node A) for execution, 9 pages to Node B, and 5 pages to Node C, achieving precise load balancing based on real-time status.

[0037] Furthermore, S4 specifically includes: S401: After receiving the sub-task package assigned by the main edge printing service station, each edge printing service station parses the data index address in it and selects the data acquisition method according to the index address: if the index address points to the cloud management platform, the corresponding print page bitmap data is pulled through a secure long connection with the cloud management platform; if the index address points to the main edge printing service station (applicable to local data sharing scenarios for large batch tasks), the data is directly pulled from the main edge printing service station through a P2P high-speed transmission channel. The data transmission process adopts a fragmented transmission and verification mechanism to ensure data integrity.

[0038] S402: After the edge printing service station acquires the printing data, the task executor module converts the data into control commands that the printer can recognize, driving the physical printer to perform the printing operation. At the same time, the task executor monitors various status parameters during the printing process in real time, including printing progress (number of pages completed / total allocated pages), printer working status (paper output, heating, standby), fault information (paper jam, low ink, paper exhausted, communication interruption), etc., with a monitoring frequency of 100ms / time to ensure timely capture of status changes.

[0039] S403: The edge printing service station reports execution status according to the following rules: Upon completion of each sub-task package (all allocated pages printed), a "Task Successful" status report is immediately sent; if a fault is detected during printing (such as paper jam or low ink), printing is immediately paused, and a "Task Failed" status report is sent. The status report includes the following core information: unique task identifier, sub-task package number, success / failure indicator, completed page range (e.g., pages 1-4), explanation of the fault cause (e.g., "Paper jam on page 5", "Ink cartridge remaining less than 5%"), and current printer status parameters. The status report is sent through dual channels: first, to the main edge printing service station for sub-task status aggregation; second, simultaneously copied to the cloud management platform for global monitoring and log recording.

[0040] S404: The main edge printing service station receives status reports from each edge printing service station in real time, establishes a subtask status summary table, and dynamically updates the execution progress of each subtask. When a subtask failure is detected, a dynamic rescheduling process is immediately initiated: First, the status of the faulty node is marked, and the edge printing service station that has failed (such as Node B with a paper jam) is marked as "temporarily downgraded" and temporarily excluded from the scheduling range of the current task; Second, the remaining task volume is reassessed, and the range of unfinished pages is calculated (such as pages 5-12 that Node B has not finished); Third, the latest real-time status of other edge printing service stations in the current cluster is obtained (including the execution progress of assigned tasks, resource availability, and network status); Fourth, according to the preset strategy of "retrying the nearest node and selecting the second-best node", the paging scheme is recalculated through a dynamic weighted scheduling algorithm, and the unfinished pages are assigned to the most suitable node (such as Node A, which has completed part of the task and whose queue has shortened); Fifth, the new scheduling decision is sent to the target edge printing service station, and the scheduling change information is updated to the cloud management platform to ensure global status synchronization. The rescheduling process requires no manual intervention and is completed automatically, with a fault recovery time of ≤3 seconds.

[0041] Furthermore, a specific example of the failover and rescheduling in S404 is as follows: The main edge print service station (NodeA) splits the 100-page task into NodeA (83 pages), NodeB (12 pages), and NodeC (5 pages) for execution. When NodeB experiences a paper jam while printing the 5th page, the following process is triggered: After the NodeB's task executor detects a paper jam, it immediately stops printing and generates a status report: Task unique identifier "Task-20240520-001", subtask package number "Sub-002", failure identifier "True", completed page range "pages 1-4", and fault reason "paper jam on page 5, manual cleaning required". The report is then sent to the main edge printing service station and the cloud management platform. After receiving the report, the main edge printing service station marks the NodeB as "temporarily downgraded", updates the subtask status summary table, and confirms that the incomplete page range is "pages 5-12" (out of 8 pages). The main edge printing service station sends real-time status query requests to NodeA and NodeC to obtain the latest status: NodeA has completed 30 pages, the current queue has 53 pages remaining, ink supply is sufficient, and network latency is 7ms; NodeC has completed 5 pages, the current queue is idle, ink supply warning (18% remaining), and network latency is 14ms. The dynamic scheduling engine recalculates the weights: Weight_A=(30 / (53+1))*1.0*1.0≈30 / 54≈0.5556; Weight_C=(40 / (0+1))*0.5*0.8=40*0.4=16.0; Total weight = 0.5556 + 16.0 ≈ 16.5556; Page allocation: NodeA = 8 * (0.5556 / 16.5556) ≈ 0.27 pages (rounded up to 1 page); NodeC = 8 * (16.0 / 16.5556) ≈ 7.73 pages (rounded down to 7 pages); The main edge printing service station sends the "page 5" subtask to NodeA and the "pages 6-12" subtask to NodeC, and simultaneously synchronizes the scheduling change information to the cloud management platform. After receiving a new task, NodeA and NodeC immediately execute the printing and send a success status report upon completion. The main edge printing service station confirms that all pages have been printed and sends a "task completed" notification to the cloud management platform.

[0042] Furthermore, S5 specifically includes: S501: For critical tasks that require multiple copies to be output simultaneously and have extremely high requirements for printing reliability, when submitting a print job, the user can select "Mirror Printing Mode" through the print settings interface of the application and specify the number of nodes for mirror printing and the target edge printing service station.

[0043] S502: After receiving a task marked with "Mirror Printing Mode," the cloud management platform or the main edge printing service station will no longer perform pagination splitting. Instead, it will simultaneously distribute the complete task metadata description file and all print data to multiple designated edge printing service stations. The selection rules for the mirror node group are as follows: edge printing service stations with different physical locations and different network links are prioritized to avoid mirror printing failure due to regional power outages or network interruptions; if the user does not specify a specific node, the system will select multiple nodes with the lowest current load and most sufficient resources from the target group to form a mirror node group.

[0044] S503: After receiving a complete task, each edge print service station within a mirror node group synchronously initiates the print execution process and independently completes the full print job. As long as any mirror node completes printing all pages without any fault feedback, the main edge print service station determines that the critical task has been successfully printed and sends a "Task successfully completed" notification to the user and the cloud management platform. Print jobs on other mirror nodes can continue until completion or stop according to user settings. If a mirror node fails, it does not affect the overall task determination; the unfinished print job of the failed node can be completed by other mirror nodes, ensuring high availability and output integrity for critical tasks.

[0045] The cloud-edge collaborative computing-based dynamic scheduling system for group printing tasks implements the aforementioned dynamic scheduling method for group printing tasks. It includes a cloud management platform, multiple edge printing service stations, and a collaborative communication network. The functions and interaction logic of each component are as follows: The cloud management platform provides global task management, policy formulation, status monitoring, and resource catalog services. It serves as the core of the entire system's global control, responsible for coordinating all cross-regional and cross-group printing resources and task scheduling. The edge printing service stations, deployed in various local area networks, are the core units for local printing resource management and task execution. They manage local printer resources, receive and execute tasks, report real-time status, and possess local dynamic scheduling capabilities based on policies and real-time information. They can independently execute small batches of tasks and split and coordinate large batches of tasks. The collaborative communication network establishes secure and reliable data and command transmission channels between the cloud management platform and the edge printing service stations, as well as among the edge printing service stations themselves. This includes a secure long-connection network between the cloud and the edge printing service stations, and a P2P collaborative network between the edge printing service stations, ensuring efficient transmission of task data, status information, and scheduling commands.

[0046] Furthermore, the cloud management platform is deployed on a public or private cloud, allowing for flexible selection of deployment modes based on enterprise needs. It comprises five core functional modules: Web Management Interface Module: Provides a visual operation interface, supporting administrators to perform operations such as user management (account creation, permission allocation, print quota settings), group configuration (group creation, node association, policy binding), task monitoring (real-time task progress viewing, historical task query, fault alarm display), and system configuration (scheduling threshold adjustment, algorithm parameter configuration, security rule setting); User authentication module: integrates identity authentication, permission verification, quota management functions, supports multiple methods such as username and password authentication, LDAP integrated authentication, and OAuth2.0 third-party authentication, ensuring that only authorized users can submit print jobs and strictly allocate and use the corresponding group's print resources according to permissions; Task Receiving and Preprocessing Module: Responsible for receiving print tasks submitted by users, performing preprocessing operations such as format standardization conversion, virus scanning, and permission verification, generating task metadata description files, and managing the storage and indexing of task data, providing data access support for edge printing service stations; Global Policy Library Module: Stores and manages various preset scheduling policies (load balancing priority, fastest completion priority, resource conservation priority, etc.), fault handling policies (rescheduling rules, node degradation mechanism), and image printing policies (node ​​selection rules, task judgment criteria). It supports administrators to customize policy parameters to adapt to different business scenario requirements. Real-time monitoring dashboard module: Collects and displays the operating status of the entire system in real time, including the online status of edge printing service stations, printer resource status (ink volume, paper, fault), task execution progress (pending / in execution / completed / failed), system load, etc. It supports fault alarms (SMS, email, platform pop-up) and historical data statistical analysis, providing administrators with a comprehensive system operation and maintenance perspective.

[0047] The edge printing service station is either dedicated hardware or software installed on a general-purpose PC / server, offering flexible deployment capabilities and adaptability to the hardware environments of enterprises of different sizes. It comprises four core functional modules, and the correspondence between each module and the method steps is as follows: Local Resource Manager Module: Specifically designed to perform printer discovery and registration operations for S101, discover physical printers within the local area network via broadcast / multicast protocols, maintain the local printer resource pool, collect static attributes and dynamic status of printers in real time, and provide resource data support for local scheduling; Collaborative Communication Proxy Module: Responsible for maintaining a secure long-term MQTT / WebSocket connection with the cloud management platform to receive instructions and report status, ensuring real-time communication between the cloud and the edge; at the same time, it maintains a P2P connection with other edge printing service stations in the same group via UDP multicast or TCP direct connection for rapid data exchange (such as subtask packet transmission, real-time performance indicator query) and status synchronization, ensuring the efficiency of collaborative scheduling among edge printing service stations; Dynamic scheduling engine module: The core computing module, dedicated to executing the S304 dynamic weighted scheduling algorithm and related scheduling logic, including core operations such as task splitting, node selection, weight calculation, subtask assignment, and fault rescheduling. It is the key unit for realizing distributed dynamic scheduling. Task Executor Module: Specifically designed to execute printer driver and status monitoring operations for the S402, converting received printing data into printer control commands to drive the physical printer to perform printing, while simultaneously monitoring status changes and fault information during the printing process in real time and promptly feeding back to the main edge printing service station and cloud management platform.

[0048] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A dynamic scheduling method for group printing tasks based on cloud-edge collaborative computing, characterized in that, Includes the following steps: S1: System initialization and resource discovery, completing the registration of edge printing service stations and local printers and building a global resource view; S2: Print task submission and cloud preprocessing. After the user submits the print task, the cloud management platform preprocesses the task and generates a task metadata description file. S3: Dynamic task scheduling based on cloud-edge collaboration. The cloud management platform selects the target edge printing service station cluster and issues task-related information. The main edge printing service station performs scheduling and task splitting and assignment according to the task situation. S4: Distributed task execution and status coordination. Each edge printing service station executes sub-tasks and reports the status. The main edge printing service station summarizes the status and handles sub-task failures. S5: Mirrored printing and high availability for faults. Provides a mirrored printing mode for mission-critical applications to ensure high availability of printing.

2. The dynamic scheduling method for group printing tasks based on cloud-edge collaborative computing according to claim 1, characterized in that, S1 specifically includes: S101: After each edge printing service station is started, it discovers and registers local physical printers on the local area network via broadcast or multicast, forming a local printer resource pool. S102: The edge service station registers with the cloud management platform and reports its own network location, the group it belongs to, and the static attributes of the local printer resource pool; S103: The cloud management platform maintains the global registry and printer resource directory of the edge printing service station, forming a global view of the system.

3. The method for dynamic scheduling of group printing tasks based on cloud-edge collaborative computing according to claim 2, characterized in that, The static attributes of the local printer resource pool in S102 include printer model, supported colors, and paper type. The group to which it belongs is a preset function or region group, including the finance department and warehouse A.

4. The dynamic scheduling method for group printing tasks based on cloud-edge collaborative computing according to claim 1, characterized in that, S2 specifically includes: S201: Users submit tasks by selecting the cloud group printing virtual printer driver through the application, and the task and its attributes are uploaded to the cloud management platform. S202: The cloud management platform performs format standardization, virus scanning, and user permission-based access control preprocessing on tasks, generating a lightweight task metadata description file containing task instructions, pagination information, and scheduling policy identifiers.

5. The method for dynamic scheduling of group printing tasks based on cloud-edge collaborative computing according to claim 1, characterized in that, S3 specifically includes: S301: The cloud management platform selects the target edge printing service station cluster based on task attributes and real-time status heartbeat information synchronized from each edge printing service station; S302: The cloud management platform distributes the task metadata description file and preliminary scheduling strategy to the main edge printing service station; S303: After receiving a task, the main edge printing service station will start the local dynamic scheduling engine if it is a large batch of tasks, and directly deliver the task to the local printer if it does not need to be split. S304: The local dynamic scheduling engine obtains a list of edge printing service stations in the same group, collects real-time performance indicators of each node and its printer, combines cloud policies and real-time indicators, uses a dynamic weighted scheduling algorithm to calculate the optimal paging scheme, splits the printing task into several sub-task packages and assigns them to this station and other edge printing service stations in the same group.

6. The method for dynamic scheduling of group printing tasks based on cloud-edge collaborative computing according to claim 5, characterized in that, The real-time status heartbeat information in S301 includes the length of each printer queue, busy / idle status, ink / paper balance, and online / offline status. The preliminary scheduling strategy in S302 includes load balancing priority and fastest completion priority. The real-time performance metrics in S304 include recent average printing speed and current network latency; The weighting factors of the dynamic weighted scheduling algorithm include the printer's current load, printing speed, resource health, and network overhead with the main edge printing service station. The resource health is set with a corresponding coefficient based on the ink availability, where the ink availability coefficient is 1.0, the ink warning coefficient is 0.5, and the low ink coefficient is 0. The calculation logic is: Weight = (baseline speed / (current queue + 1)) * resource health coefficient. Subtask packages of the corresponding number of pages are allocated based on the weight ratio of each node.

7. The method for dynamic scheduling of group printing tasks based on cloud-edge collaborative computing according to claim 1, characterized in that, S4 specifically includes: S401: The edge printing service station that receives the subtask package retrieves the corresponding print page bitmap data from the cloud management platform or the main edge printing service station based on the index in the metadata. S402: Each node drives the local printer to execute printing and monitors the printing status in real time; S403: When each subtask package is completed or a fault is encountered, the edge printing service station will send an execution status report containing a success / failure indicator, the range of completed pages, and a description of the fault to the main edge printing service station, and simultaneously copy it to the cloud management platform; S404: The main edge printing service station summarizes the status of all subtasks. If a subtask fails, a dynamic rescheduling process is triggered according to the preset strategy of retrying the nearest node and selecting the second-best node. The failed page number is reassigned and the new scheduling decision is updated to the cloud management platform.

8. The method for dynamic scheduling of group printing tasks based on cloud-edge collaborative computing according to claim 1, characterized in that, S5 specifically includes: S501: For critical tasks that require multiple copies to be output simultaneously, the user specifies a mirror printing mode; S502: The cloud management platform or the main edge print service station will simultaneously distribute the complete task to multiple designated edge print service stations to perform full printing. S503: The task is considered successful if any mirror node completes printing.

9. A group printing task dynamic scheduling system based on cloud-edge collaborative computing for implementing the group printing task dynamic scheduling method based on cloud-edge collaborative computing as described in any one of claims 1-8, characterized in that, It includes a cloud management platform, multiple edge printing service stations, and a collaborative communication network. The cloud management platform is used to provide global task management, policy formulation, status monitoring, and resource catalog services. The edge printing service station is deployed in each local area network for local printer resource management, task reception and execution, real-time status reporting, and has local dynamic scheduling capabilities based on policies and real-time information. The collaborative communication network is used to establish secure and reliable data and command transmission channels between the cloud management platform and edge printing service stations, as well as among the edge printing service stations.

10. The cloud-edge collaborative group printing system according to claim 9, characterized in that, The cloud management platform is deployed on a public cloud or a private cloud and includes a web management interface, user authentication, task reception and preprocessing, a global policy library, and a real-time monitoring dashboard module. The edge printing service station is a dedicated hardware device or a software service installed on a general PC / server, including a local resource manager, a collaborative communication agent, a dynamic scheduling engine, and a task executor. The local resource manager is used to perform printer discovery and registration operations in S101. The collaborative communication agent is used to maintain a secure long-lived MQTT / WebSocket connection with the cloud management platform to receive instructions and report status, while maintaining a P2P connection with other edge printing service stations in the same group via UDP multicast or TCP direct connection to achieve fast data exchange and status synchronization. The dynamic scheduling engine is used to execute the S304 dynamic weighted scheduling algorithm and related scheduling logic; The task executor is used to perform printer driver and status monitoring operations for the S402.

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