ARD printing task processing method and system based on cloud scheduling and storage medium

Abstract

The invention provides an ARD printing task processing method and system based on cloud scheduling and a storage medium, and the method comprises the steps: firstly, generating printing task information through analysis of a production order, and dynamically generating an optimal printer sequence through a task distribution network model in combination with an ARD printer cluster state collected in real time; then, the task is converted into a device-level instruction through an edge gateway, a selected printer is driven to execute printing, and a device feedback signal is monitored in real time; in addition, when abnormal conditions such as continuous label taking failure or task timeout are detected, model rescheduling is automatically triggered, a printer sequence is dynamically switched, and tasks are distributed again; according to the invention, the idle time of equipment is reduced through cloud intelligent distribution, the utilization rate of a printer cluster and the response speed of an emergency order are improved, and through a three-layer architecture of cloud decision, edge execution and abnormal closed loop, the pain points of response delay and slow fault recovery of a traditional printing system are solved, and industrial-grade high-reliability printing task scheduling is realized.

Description

Technical Field

[0001] This invention relates to the field of ARD printers, and more specifically, to a cloud-based ARD printing task processing method, system, and storage medium. Background Technology

[0002] In the field of industrial automation, roll label printing equipment such as ARD printers are widely used in SMT production lines, logistics sorting, and other scenarios. Traditional print job scheduling relies on manual or local PLC control, which suffers from slow response, poor fault tolerance, and low resource utilization. Traditional production orders require manual allocation of equipment, and urgent tasks cannot be automatically prioritized; when equipment malfunctions such as label picking failure, print jobs cannot be switched in a timely manner; in addition, printer clusters lack global scheduling, resulting in high equipment idle rates.

[0003] Currently, existing technologies attempt to alleviate single-point control problems through centralized scheduling via local industrial control computers, but these technologies suffer from limitations in computing power. For example, they cannot integrate multi-dimensional parameters such as label process complexity, equipment cumulative lifespan, and equipment operating environment in real time, making it difficult to dynamically respond to anomalies and easily leading to task backlog.

[0004] Therefore, a cloud-based intelligent scheduling architecture is urgently needed to build a highly reliable industrial printing system. Summary of the Invention

[0005] In view of the above problems, the purpose of this invention is to provide an ARD printing task processing method, system, and storage medium based on cloud scheduling. Through a three-layer architecture of cloud decision-making, edge execution, and anomaly closed-loop, it solves the pain points of slow response and slow fault recovery in traditional printing systems, achieving industrial-grade high-reliability printing task scheduling. First, printing task information is generated by parsing production orders. Combined with real-time data collection of the ARD printer cluster status, an optimal printer sequence is dynamically generated using a task allocation network model. Then, the task is converted into device-level instructions through an edge gateway, driving the selected printer to execute printing, and device feedback signals are monitored in real time. Furthermore, when anomalies such as continuous tagging failures or task timeouts are detected, model rescheduling is automatically triggered, dynamically switching the printer sequence and reassigning the task. This invention reduces device idle time through intelligent cloud allocation, improving printer cluster utilization and emergency order response speed.

[0006] The first aspect of this invention provides a cloud-based scheduling method for ARD printing tasks, the method comprising: Receive and parse the production order instructions transmitted from the preset production management system to obtain print task information; Obtain the working status information of the ARD printer cluster, combine it with the printing task information, and obtain the printer sequence based on a preset task allocation network model; Based on the printer sequence, determine the edge gateway and select the ARD printer; The print task information is converted into print instructions through the edge gateway, and the print operation is executed according to the print instructions. The printing operation status of the ARD printer is collected through the edge gateway to obtain printing feedback information; Determine whether there is a printing error based on the printed feedback information; If so, the printer sequence is dynamically adjusted based on the task allocation network model. Based on the adjusted printer sequence, the edge gateway is redefined and the ARD printer is selected to perform the printing operation.

[0007] In this scheme, the process of obtaining the printer sequence based on a preset task allocation network model is as follows: The process complexity index is determined based on the label size, printing content, and priority of the printing task information. Based on the process complexity index, a first ARD printer that meets the printing requirements is selected, and the working status information of the first ARD printer is collected. By integrating the process complexity index, the working status information and priority of the first ARD printer, the printing completion time is predicted; Based on preset sorting rules, the corresponding first ARD printers are sorted according to the printing completion time to obtain a printer sequence.

[0008] This solution also includes a hierarchical queue based on the print task information, specifically including: Create at least two print task queues and store the print task information in a hierarchical manner according to the priority of the print task information. The print task queues include an emergency queue and a normal queue. Before a task is dispatched to the edge gateway, the emergency queue is traversed first. When the emergency queue is empty and the first ready signal is received, the task is retrieved from the normal queue.

[0009] In this solution, dynamically adjusting the printer sequence based on the task allocation network model specifically includes: When the ARD printer completes initialization, a first ready signal is transmitted through the edge gateway; In response to the first ready signal, the corresponding ARD printer is added to the screening sequence; In response to the first fetching exception signal, the print task is marked as an exception and reinserted at the head of the task queue; Based on the task allocation network model, the printer sequence is adjusted according to the printing task.

[0010] In this solution, the step of converting the print task information into print instructions via an edge gateway specifically includes: The physical signals of the 12-pin interface of the ARD printer are converted into data fields through the IOT module; Based on the preset AES-256 encryption channel, encrypted data is obtained according to the data field and transmitted through the edge gateway; Based on the print task information, the preset RS232 protocol instructions are converted into print instructions.

[0011] In this solution, determining whether a printing error exists based on the printing feedback information specifically involves: If three consecutive first-stage sampling anomaly signals are detected, it is determined to be a printing anomaly; If the execution time of a task exceeds a preset percentage of the predicted value, it is considered a printing error. If the duration of the first ready signal is not detected to exceed the preset duration threshold, it is determined to be a printing abnormality.

[0012] A second aspect of the present invention provides an ARD printing task processing system based on cloud scheduling, including an ARD printing task processing method program based on cloud scheduling, wherein the ARD printing task processing method program based on cloud scheduling, when executed by the processor, performs the following steps: Receive and parse the production order instructions transmitted from the preset production management system to obtain print task information; Obtain the working status information of the ARD printer cluster, combine it with the printing task information, and obtain the printer sequence based on a preset task allocation network model; Based on the printer sequence, determine the edge gateway and select the ARD printer; The print task information is converted into print instructions through the edge gateway, and the print operation is executed according to the print instructions. The printing operation status of the ARD printer is collected through the edge gateway to obtain printing feedback information; Determine whether there is a printing error based on the printed feedback information; If so, the printer sequence is dynamically adjusted based on the task allocation network model. Based on the adjusted printer sequence, the edge gateway is redefined and the ARD printer is selected to perform the printing operation.

[0013] In this scheme, the process of obtaining the printer sequence based on a preset task allocation network model is as follows: The process complexity index is determined based on the label size, printing content, and priority of the printing task information. Based on the process complexity index, a first ARD printer that meets the printing requirements is selected, and the working status information of the first ARD printer is collected. By integrating the process complexity index, the working status information and priority of the first ARD printer, the printing completion time is predicted; Based on preset sorting rules, the corresponding first ARD printers are sorted according to the printing completion time to obtain a printer sequence.

[0014] This solution also includes a hierarchical queue based on the print task information, specifically including: Create at least two print task queues and store the print task information in a hierarchical manner according to the priority of the print task information. The print task queues include an emergency queue and a normal queue. Before a task is dispatched to the edge gateway, the emergency queue is traversed first. When the emergency queue is empty and the first ready signal is received, the task is retrieved from the normal queue.

[0015] A third aspect of the present invention provides a computer-readable storage medium comprising a cloud-based ARD printing task processing method program, wherein when the cloud-based ARD printing task processing method program is executed by a processor, it implements the steps of the cloud-based ARD printing task processing method as described in any of the preceding claims.

[0016] This invention provides a cloud-based ARD printing task processing method, system, and storage medium. First, printing task information is generated by parsing production orders. Combined with real-time data collection on the ARD printer cluster status, an optimal printer sequence is dynamically generated using a task allocation network model. Then, the task is converted into device-level instructions via an edge gateway, driving the selected printer to execute printing, and device feedback signals are monitored in real time. Furthermore, when anomalies such as continuous tagging failures or task timeouts are detected, model rescheduling is automatically triggered, dynamically switching the printer sequence and reassigning the task. This invention reduces device idle time through cloud-based intelligent allocation, improves printer cluster utilization and emergency order response speed, and solves the pain points of slow response and slow fault recovery in traditional printing systems through a three-layer architecture of cloud decision-making, edge execution, and anomaly closed-loop, achieving industrial-grade high-reliability printing task scheduling. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope.

[0018] Figure 1 A flowchart of an ARD printing task processing method based on cloud scheduling according to the present invention is shown; Figure 2 The flowchart of a task allocation network model provided by an embodiment of the present invention is shown; Figure 3 The flowchart of a hierarchical queuing mechanism for printing tasks provided by an embodiment of the present invention is shown; Figure 4 A block diagram of an ARD printing task processing system based on cloud scheduling according to the present invention is shown. Detailed Implementation

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Unless otherwise defined, all terms (including technical and scientific terms) used in embodiments of this invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in a common dictionary shall be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and not as being interpreted in an idealized or highly formalized sense, unless expressly defined in this embodiment of the invention.

[0021] The terms "first," "second," and similar words used in the embodiments of this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "an," "a," or "the" do not indicate a quantity limitation, but rather indicate the presence of at least one. Similarly, terms such as "including" or "comprising" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The steps preceding or following the steps in the method of the embodiments of this invention are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0022] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0023] Figure 1 A flowchart of an ARD printing task processing method based on cloud scheduling according to the present invention is shown.

[0024] like Figure 1 As shown, the first aspect of this invention discloses a cloud-based scheduling method for processing ARD printing tasks, the method comprising: Receive and parse the production order instructions transmitted from the preset production management system to obtain print task information; Obtain the working status information of the ARD printer cluster, combine it with the printing task information, and obtain the printer sequence based on a preset task allocation network model; Based on the printer sequence, determine the edge gateway and select the ARD printer; The print task information is converted into print instructions through the edge gateway, and the print operation is executed according to the print instructions. The printing operation status of the ARD printer is collected through the edge gateway to obtain printing feedback information; Determine whether there is a printing error based on the printed feedback information; If so, the printer sequence is dynamically adjusted based on the task allocation network model. Based on the adjusted printer sequence, the edge gateway is redefined and the ARD printer is selected to perform the printing operation.

[0025] It should be noted that the print task information includes at least label size, print content, print quantity, and priority; the working status information includes at least idle / busy status, print tasks, and cumulative working time. In this embodiment, the cloud receives production order instructions transmitted from production management systems such as MES or ERP, and parses them based on preset production order instruction rules to obtain print task information such as label size, print content, print quantity, and priority. The edge gateway collects working status information such as idle / busy status, print tasks, and cumulative working time of the ARD printer cluster. The print task information and working status information are input into a pre-trained task allocation network model. An AI engine analyzes the printing process complexity, equipment status, and priority, and then outputs a printer sequence to determine the ARD printer matching the print task. AI scheduling reduces ARD printer idle time and improves utilization; furthermore, load balancing based on cumulative working time extends the lifespan of the ARD printer. The edge gateway corresponding to the ARD printer receives the print task, converts the print task information into print instructions, and transmits the print instructions to the printer through the ARD printer's 12-pin aviation interface to execute the corresponding printing operation. During the printing operation of the ARD printer, the operational status of the ARD printer is collected and uploaded in real time through the edge gateway. Based on the printing feedback information recorded in the ARD printer operation status, the cloud determines whether printing anomalies such as labeling failure or printing timeout have occurred. When a printing anomaly is determined, the printer sequence is readjusted through the task allocation network model. Based on the adjusted printer sequence, a backup ARD printer is selected to execute the printing task corresponding to the abnormal printer, thereby realizing real-time transfer of abnormal tasks and avoiding production line shutdown.

[0026] Figure 2 The diagram illustrates the operation flowchart of a task allocation network model provided by an embodiment of the present invention.

[0027] According to embodiments of the present invention, such as Figure 2 As shown, the printer sequence is obtained based on a preset task allocation network model, specifically as follows: The process complexity index is determined based on the label size, printing content, and priority of the printing task information. Based on the process complexity index, a first ARD printer that meets the printing requirements is selected, and the working status information of the first ARD printer is collected. By integrating the process complexity index, the working status information and priority of the first ARD printer, the printing completion time is predicted; Based on preset sorting rules, the corresponding first ARD printers are sorted according to the printing completion time to obtain a printer sequence.

[0028] It should be noted that this embodiment provides the operational logic of the task allocation network model. In this embodiment, firstly, a complexity score is generated based on the length, width, and thickness of the printed label, the printed content, and the priority. The printed content and size determine the printing accuracy. Secondly, based on the process complexity score, devices that do not meet the requirements for printing size and accuracy are excluded, and the first ARD printer that meets the printing task requirements is selected as the candidate device. Then, the idle / busy status and cumulative working time of the candidate devices are collected. Combining the process complexity index and the printing task priority, a pre-trained time prediction model is used to output the time required for each candidate device to complete printing, which is recorded as the printing completion time. Finally, the printing completion times are sorted in ascending order for the corresponding candidate devices, thus obtaining the printer sequence.

[0029] Figure 3 The diagram illustrates the operation flowchart of a hierarchical queuing mechanism for printing tasks provided by an embodiment of the present invention.

[0030] According to embodiments of the present invention, such as Figure 3 As shown, it also includes a hierarchical queue based on the print task information, specifically including: Create at least two print task queues and store the print task information in a hierarchical manner according to the priority of the print task information. The print task queues include an emergency queue and a normal queue. Before a task is dispatched to the edge gateway, the emergency queue is traversed first. When the emergency queue is empty and the first ready signal is received, the task is retrieved from the normal queue.

[0031] It should be noted that this implementation provides a hierarchical queuing mechanism for print jobs. In this embodiment, an emergency queue and a regular queue are established in the cloud. The emergency queue stores high-priority print job orders, while the regular queue stores regular print job orders. The emergency queue is traversed first, and tasks are dispatched to the edge gateway in queue order to be issued to ready devices. Only when the emergency queue is empty and the first ready signal of the ARD printer is detected is a print job retrieved from the regular queue. This embodiment reduces the production waiting time for high-priority orders through hierarchical task queuing.

[0032] According to an embodiment of the present invention, dynamically adjusting the printer sequence based on the task allocation network model specifically includes: When the ARD printer completes initialization, a first ready signal is transmitted through the edge gateway; In response to the first ready signal, the corresponding ARD printer is added to the screening sequence; In response to the first fetching exception signal, the print task is marked as an exception and reinserted at the head of the task queue; Based on the task allocation network model, the printer sequence is adjusted according to the printing task.

[0033] It should be noted that this embodiment provides an anomaly handling process for dynamically adjusting the sequence. In this embodiment, when the ARD printer completes initialization, it sends a first ready signal to the cloud via the edge gateway. When the cloud receives the first ready signal from the ARD printer, it adds the corresponding ARD printer to the filtering sequence, waiting for the print job. If the ARD printer currently executing a print job sends a first tagging anomaly signal, indicating an anomaly in the print job execution, the print job is marked and inserted at the head of the print queue, waiting to be rescheduled for execution by the ARD printer. This embodiment promptly adds idle devices to the task pool based on the first ready signal and promptly schedules new ARD printers to execute print jobs with printing anomalies.

[0034] According to an embodiment of the present invention, the step of converting the print task information into print instructions through an edge gateway specifically includes: The physical signals of the 12-pin interface of the ARD printer are converted into data fields through the IOT module; Based on the preset AES-256 encryption channel, encrypted data is obtained according to the data field and transmitted through the edge gateway; Based on the print task information, the preset RS232 protocol instructions are converted into print instructions.

[0035] It should be noted that this embodiment provides a cloud-based transformation solution for ARD printers. Data transmission between the printer and the cloud is achieved through an IoT module connected to the ARD printer's 12-pin interface. The IoT module converts the printer's 12-pin interface signal into data fields, which are then encrypted using AES-256 and transmitted through an edge gateway, thus enabling local data uploading. The edge gateway decrypts and parses the instructions sent from the cloud, generates print commands according to a preset RS232 protocol, and transmits them to the corresponding ARD printer, thus enabling cloud data download.

[0036] According to an embodiment of the present invention, determining whether a printing abnormality exists based on the printing feedback information specifically involves: If three consecutive first-stage sampling anomaly signals are detected, it is determined to be a printing anomaly; If the execution time of a task exceeds a preset percentage of the predicted value, it is considered a printing error. If the duration of the first ready signal is not detected to exceed the preset duration threshold, it is determined to be a printing abnormality.

[0037] It should be noted that this embodiment provides a logic for determining printing anomalies. During the printing operation, the status of the ARD printer is monitored in real time. If three consecutive abnormal signals are detected, the printing task duration exceeds 150% of the predicted printing duration, or no ready signal from the ARD printer is detected for more than 3 minutes, a printing anomaly is determined, and the cloud-based anomaly handling process is executed. This embodiment uses multi-dimensional judgment to reduce the false alarm rate.

[0038] It is worth mentioning that, before the print command is executed, the following steps are also included: Based on the label size in the printing task information, a three-dimensional calibration instruction set is generated; The three-dimensional calibration instruction set includes calibration instructions for the X-axis, Y-axis, and Z-axis directions; When executing the X-axis calibration command, the X-axis direction is adjusted based on the preset mechanical limiter; When executing the Y-axis calibration command, the Y-axis direction is adjusted based on preset compensation parameters; When executing the Z-axis calibration command, the Z-axis is lifted based on a preset screw mechanism.

[0039] It should be noted that this implementation provides a self-calibration command before printing. Based on the label size, a calibration command is generated in the cloud to adjust the print head along the X, Y, and Z axes. For the X-axis calibration, the printer's limit adjuster for the X-axis is used. For the Y-axis calibration, compensation parameters pre-stored in the cloud are used. For the Z-axis calibration, the screw mechanism controlling the Y-axis is used for lifting and resetting. The calibration command is generated based on the printing task, replacing manual operation with automatic calibration.

[0040] It is worth mentioning that it also includes an anti-adhesion mechanism, specifically: Real-time acquisition of temperature and humidity information from the ARD printer; The thickness information of the label paper is obtained through a preset laser thickness sensor; Based on the pre-trained release force model, combined with the temperature information, humidity information and thickness information, the adhesion coefficient is calculated; If the adhesion coefficient exceeds the preset adhesion threshold, a Z-axis height adjustment command or a vacuum adsorption force enhancement command will be automatically generated.

[0041] It should be noted that this embodiment provides an anti-adhesion mechanism, which calculates the adhesion coefficient of the label paper based on a pre-trained release force model. In this embodiment, the operating temperature and humidity values ​​of the ARD printer are monitored in real time using a temperature and humidity sensor; and the thickness of the label paper is detected using a laser thickness sensor. Then, based on the pre-trained release force model, the adhesion coefficient of the label paper is calculated; when the adhesion coefficient exceeds a preset adhesion threshold, a Z-axis height adjustment command or a vacuum adsorption force enhancement command is automatically generated, thereby reducing the probability of label paper adhesion.

[0042] Figure 4 A block diagram of an ARD printing task processing system based on cloud scheduling according to the present invention is shown.

[0043] like Figure 4 As shown, a second aspect of the present invention discloses an ARD printing task processing system 4 based on cloud scheduling, including a memory 41 and a processor 42. The memory includes a cloud-based ARD printing task processing method program, which, when executed by the processor, performs the following steps: Receive and parse the production order instructions transmitted from the preset production management system to obtain print task information; Obtain the working status information of the ARD printer cluster, combine it with the printing task information, and obtain the printer sequence based on a preset task allocation network model; Based on the printer sequence, determine the edge gateway and select the ARD printer; The print task information is converted into print instructions through the edge gateway, and the print operation is executed according to the print instructions. The printing operation status of the ARD printer is collected through the edge gateway to obtain printing feedback information; Determine whether there is a printing error based on the printed feedback information; If so, the printer sequence is dynamically adjusted based on the task allocation network model. Based on the adjusted printer sequence, the edge gateway is redefined and the ARD printer is selected to perform the printing operation.

[0044] It should be noted that the print task information includes at least label size, print content, print quantity, and priority; the working status information includes at least idle / busy status, print tasks, and cumulative working time. In this embodiment, the cloud receives production order instructions transmitted from production management systems such as MES or ERP, and parses them based on preset production order instruction rules to obtain print task information such as label size, print content, print quantity, and priority. The edge gateway collects working status information such as idle / busy status, print tasks, and cumulative working time of the ARD printer cluster. The print task information and working status information are input into a pre-trained task allocation network model. An AI engine analyzes the printing process complexity, equipment status, and priority, and then outputs a printer sequence to determine the ARD printer matching the print task. AI scheduling reduces ARD printer idle time and improves utilization; furthermore, load balancing based on cumulative working time extends the lifespan of the ARD printer. The edge gateway corresponding to the ARD printer receives the print task, converts the print task information into print instructions, and transmits the print instructions to the printer through the ARD printer's 12-pin aviation interface to execute the corresponding printing operation. During the printing operation of the ARD printer, the operational status of the ARD printer is collected and uploaded in real time through the edge gateway. Based on the printing feedback information recorded in the ARD printer operation status, the cloud determines whether printing anomalies such as labeling failure or printing timeout have occurred. When a printing anomaly is determined, the printer sequence is readjusted through the task allocation network model. Based on the adjusted printer sequence, a backup ARD printer is selected to execute the printing task corresponding to the abnormal printer, thereby realizing real-time transfer of abnormal tasks and avoiding production line shutdown.

[0045] According to an embodiment of the present invention, obtaining the printer sequence based on a preset task allocation network model specifically involves: The process complexity index is determined based on the label size, printing content, and priority of the printing task information. Based on the process complexity index, a first ARD printer that meets the printing requirements is selected, and the working status information of the first ARD printer is collected. By integrating the process complexity index, the working status information and priority of the first ARD printer, the printing completion time is predicted; Based on preset sorting rules, the corresponding first ARD printers are sorted according to the printing completion time to obtain a printer sequence.

[0046] It should be noted that this embodiment provides the operational logic of the task allocation network model. In this embodiment, firstly, a complexity score is generated based on the length, width, and thickness of the printed label, the printed content, and the priority. The printed content and size determine the printing accuracy. Secondly, based on the process complexity score, devices that do not meet the requirements for printing size and accuracy are excluded, and the first ARD printer that meets the printing task requirements is selected as the candidate device. Then, the idle / busy status and cumulative working time of the candidate devices are collected. Combining the process complexity index and the printing task priority, a pre-trained time prediction model is used to output the time required for each candidate device to complete printing, which is recorded as the printing completion time. Finally, the printing completion times are sorted in ascending order for the corresponding candidate devices, thus obtaining the printer sequence.

[0047] According to an embodiment of the present invention, the system further includes a hierarchical queue based on the print task information, specifically comprising: Create at least two print task queues and store the print task information in a hierarchical manner according to the priority of the print task information. The print task queues include an emergency queue and a normal queue. Before a task is dispatched to the edge gateway, the emergency queue is traversed first. When the emergency queue is empty and the first ready signal is received, the task is retrieved from the normal queue.

[0048] It should be noted that this implementation provides a hierarchical queuing mechanism for print jobs. In this embodiment, an emergency queue and a regular queue are established in the cloud. The emergency queue stores high-priority print job orders, while the regular queue stores regular print job orders. The emergency queue is traversed first, and tasks are dispatched to the edge gateway in queue order to be issued to ready devices. Only when the emergency queue is empty and the first ready signal of the ARD printer is detected is a print job retrieved from the regular queue. This embodiment reduces the production waiting time for high-priority orders through hierarchical task queuing.

[0049] According to an embodiment of the present invention, dynamically adjusting the printer sequence based on the task allocation network model specifically includes: When the ARD printer completes initialization, a first ready signal is transmitted through the edge gateway; In response to the first ready signal, the corresponding ARD printer is added to the screening sequence; In response to the first fetching exception signal, the print task is marked as an exception and reinserted at the head of the task queue; Based on the task allocation network model, the printer sequence is adjusted according to the printing task.

[0050] It should be noted that this embodiment provides an anomaly handling process for dynamically adjusting the sequence. In this embodiment, when the ARD printer completes initialization, it sends a first ready signal to the cloud via the edge gateway. When the cloud receives the first ready signal from the ARD printer, it adds the corresponding ARD printer to the filtering sequence, waiting for the print job. If the ARD printer currently executing a print job sends a first tagging anomaly signal, indicating an anomaly in the print job execution, the print job is marked and inserted at the head of the print queue, waiting to be rescheduled for execution by the ARD printer. This embodiment promptly adds idle devices to the task pool based on the first ready signal and promptly schedules new ARD printers to execute print jobs with printing anomalies.

[0051] According to an embodiment of the present invention, the step of converting the print task information into print instructions through an edge gateway specifically includes: The physical signals of the 12-pin interface of the ARD printer are converted into data fields through the IOT module; Based on the preset AES-256 encryption channel, encrypted data is obtained according to the data field and transmitted through the edge gateway; Based on the print task information, the preset RS232 protocol instructions are converted into print instructions.

[0052] It should be noted that this embodiment provides a cloud-based transformation solution for ARD printers. Data transmission between the printer and the cloud is achieved through an IoT module connected to the ARD printer's 12-pin interface. The IoT module converts the printer's 12-pin interface signal into data fields, which are then encrypted using AES-256 and transmitted through an edge gateway, thus enabling local data uploading. The edge gateway decrypts and parses the instructions sent from the cloud, generates print commands according to a preset RS232 protocol, and transmits them to the corresponding ARD printer, thus enabling cloud data download.

[0053] According to an embodiment of the present invention, determining whether a printing abnormality exists based on the printing feedback information specifically involves: If three consecutive first-stage sampling anomaly signals are detected, it is determined to be a printing anomaly; If the execution time of a task exceeds a preset percentage of the predicted value, it is considered a printing error. If the duration of the first ready signal is not detected to exceed the preset duration threshold, it is determined to be a printing abnormality.

[0054] It should be noted that this embodiment provides a logic for determining printing anomalies. During the printing operation, the status of the ARD printer is monitored in real time. If three consecutive abnormal signals are detected, the printing task duration exceeds 150% of the predicted printing duration, or no ready signal from the ARD printer is detected for more than 3 minutes, a printing anomaly is determined, and the cloud-based anomaly handling process is executed. This embodiment uses multi-dimensional judgment to reduce the false alarm rate.

[0055] It is worth mentioning that, before the print command is executed, the following steps are also included: Based on the label size in the printing task information, a three-dimensional calibration instruction set is generated; The three-dimensional calibration instruction set includes calibration instructions for the X-axis, Y-axis, and Z-axis directions; When executing the X-axis calibration command, the X-axis direction is adjusted based on the preset mechanical limiter; When executing the Y-axis calibration command, the Y-axis direction is adjusted based on preset compensation parameters; When executing the Z-axis calibration command, the Z-axis is lifted based on a preset screw mechanism.

[0056] It should be noted that this implementation provides a self-calibration command before printing. Based on the label size, a calibration command is generated in the cloud to adjust the print head along the X, Y, and Z axes. For the X-axis calibration, the printer's limit adjuster for the X-axis is used. For the Y-axis calibration, compensation parameters pre-stored in the cloud are used. For the Z-axis calibration, the screw mechanism controlling the Y-axis is used for lifting and resetting. The calibration command is generated based on the printing task, replacing manual operation with automatic calibration.

[0057] It is worth mentioning that it also includes an anti-adhesion mechanism, specifically: Real-time acquisition of temperature and humidity information from the ARD printer; The thickness information of the label paper is obtained through a preset laser thickness sensor; Based on the pre-trained release force model, combined with the temperature information, humidity information and thickness information, the adhesion coefficient is calculated; If the adhesion coefficient exceeds the preset adhesion threshold, a Z-axis height adjustment command or a vacuum adsorption force enhancement command will be automatically generated.

[0058] It should be noted that this embodiment provides an anti-adhesion mechanism, which calculates the adhesion coefficient of the label paper based on a pre-trained release force model. In this embodiment, the operating temperature and humidity values ​​of the ARD printer are monitored in real time using a temperature and humidity sensor; and the thickness of the label paper is detected using a laser thickness sensor. Then, based on the pre-trained release force model, the adhesion coefficient of the label paper is calculated; when the adhesion coefficient exceeds a preset adhesion threshold, a Z-axis height adjustment command or a vacuum adsorption force enhancement command is automatically generated, thereby reducing the probability of label paper adhesion.

[0059] A third aspect of the present invention provides a computer-readable storage medium comprising a cloud-based ARD printing task processing method program, wherein when the cloud-based ARD printing task processing method program is executed by a processor, it implements the steps of the cloud-based ARD printing task processing method as described in any of the preceding claims.

[0060] In summary, this invention provides a cloud-based ARD printing task processing method, system, and storage medium. First, it generates printing task information by parsing production orders and, combined with real-time data collection on the ARD printer cluster status, dynamically generates the optimal printer sequence using a task allocation network model. Then, it converts the tasks into device-level instructions via an edge gateway, driving the selected printer to execute printing, and monitors device feedback signals in real time. Furthermore, when anomalies such as continuous tagging failures or task timeouts are detected, it automatically triggers model rescheduling, dynamically switching the printer sequence and reassigning the task. This invention reduces device idle time through cloud-based intelligent allocation, improves printer cluster utilization and emergency order response speed, and solves the pain points of slow response and slow fault recovery in traditional printing systems through a three-layer architecture of cloud decision-making, edge execution, and anomaly closed-loop, achieving industrial-grade high-reliability printing task scheduling.

[0061] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cloud-based scheduling method for processing ARD printing tasks, characterized in that, The method includes: Receive and parse the production order instructions transmitted from the preset production management system to obtain print task information; Obtain the working status information of the ARD printer cluster, combine it with the printing task information, and obtain the printer sequence based on a preset task allocation network model; Based on the printer sequence, determine the edge gateway and select the ARD printer; The print task information is converted into print instructions through the edge gateway, and the print operation is executed according to the print instructions. The printing operation status of the ARD printer is collected through the edge gateway to obtain printing feedback information; Determine whether there is a printing error based on the printed feedback information; If so, the printer sequence is dynamically adjusted based on the task allocation network model. Based on the adjusted printer sequence, the edge gateway is redefined and the ARD printer is selected to perform the printing operation.

2. The ARD printing task processing method based on cloud scheduling according to claim 1, characterized in that, The printer sequence is obtained based on the preset task allocation network model, specifically as follows: The process complexity index is determined based on the label size, printing content, and priority of the printing task information. Based on the process complexity index, a first ARD printer that meets the printing requirements is selected, and the working status information of the first ARD printer is collected. By integrating the process complexity index, the working status information and priority of the first ARD printer, the printing completion time is predicted; Based on preset sorting rules, the corresponding first ARD printers are sorted according to the printing completion time to obtain a printer sequence.

3. The ARD printing task processing method based on cloud scheduling according to claim 1, characterized in that, It also includes a hierarchical queue based on the print task information, specifically including: Create at least two print task queues and store the print task information in a hierarchical manner according to the priority of the print task information. The print task queues include an emergency queue and a normal queue. Before a task is dispatched to the edge gateway, the emergency queue is traversed first. When the emergency queue is empty and the first ready signal is received, the task is retrieved from the normal queue.

4. The ARD printing task processing method based on cloud scheduling according to claim 1, characterized in that, The dynamic adjustment of the printer sequence based on the task allocation network model specifically includes: When the ARD printer completes initialization, a first ready signal is transmitted through the edge gateway; In response to the first ready signal, the corresponding ARD printer is added to the screening sequence; In response to the first fetching exception signal, the print task is marked as an exception and reinserted at the head of the task queue; Based on the task allocation network model, the printer sequence is adjusted according to the printing task.

5. The ARD printing task processing method based on cloud scheduling according to claim 1, characterized in that, The step of converting the print task information into print instructions via the edge gateway specifically includes: The physical signals of the 12-pin interface of the ARD printer are converted into data fields through the IOT module; Based on the preset AES-256 encryption channel, encrypted data is obtained according to the data field and transmitted through the edge gateway; Based on the print task information, the preset RS232 protocol instructions are converted into print instructions.

6. The ARD printing task processing method based on cloud scheduling according to claim 1, characterized in that, The step of determining whether there is a printing error based on the printing feedback information specifically involves: If three consecutive first-stage sampling anomaly signals are detected, it is determined to be a printing anomaly; If the execution time of a task exceeds a preset percentage of the predicted value, it is considered a printing error. If the duration of the first ready signal is not detected to exceed the preset duration threshold, it is determined to be a printing abnormality.

7. An ARD printing task processing system based on cloud scheduling, characterized in that, The system includes a memory and a processor. The memory includes a cloud-based scheduling method program for ARD printing tasks. When the processor executes the cloud-based scheduling method program for ARD printing tasks, it performs the following steps: Receive and parse the production order instructions transmitted from the preset production management system to obtain print task information; Obtain the working status information of the ARD printer cluster, combine it with the printing task information, and obtain the printer sequence based on a preset task allocation network model; Based on the printer sequence, determine the edge gateway and select the ARD printer; The print task information is converted into print instructions through the edge gateway, and the print operation is executed according to the print instructions. The printing operation status of the ARD printer is collected through the edge gateway to obtain printing feedback information; Determine whether there is a printing error based on the printed feedback information; If so, the printer sequence is dynamically adjusted based on the task allocation network model. Based on the adjusted printer sequence, the edge gateway is redefined and the ARD printer is selected to perform the printing operation.

8. The ARD printing task processing system based on cloud scheduling according to claim 7, characterized in that, The printer sequence is obtained based on the preset task allocation network model, specifically as follows: The process complexity index is determined based on the label size, printing content, and priority of the printing task information. Based on the process complexity index, a first ARD printer that meets the printing requirements is selected, and the working status information of the first ARD printer is collected. By integrating the process complexity index, the working status information and priority of the first ARD printer, the printing completion time is predicted; Based on preset sorting rules, the corresponding first ARD printers are sorted according to the printing completion time to obtain a printer sequence.

9. The ARD printing task processing system based on cloud scheduling according to claim 7, characterized in that, It also includes a hierarchical queue based on the print task information, specifically including: Create at least two print task queues and store the print task information in a hierarchical manner according to the priority of the print task information. The print task queues include an emergency queue and a normal queue. Before a task is dispatched to the edge gateway, the emergency queue is traversed first. When the emergency queue is empty and the first ready signal is received, the task is retrieved from the normal queue.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer-readable storage medium includes a cloud-based ARD printing task processing method program, which, when executed by a processor, implements the steps of the cloud-based ARD printing task processing method as described in any one of claims 1 to 6.

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