UV printer intelligent control system and method based on remote task scheduling

Abstract

The invention provides a UV printer intelligent control system and method based on remote task scheduling, and the system comprises a cloud scheduling center module which is used for global task planning and distribution, network state monitoring, equipment state monitoring, and data storage and analysis; the edge gateway node module is used for caching the task data issued by the cloud scheduling center module, serving as a local scheduling center when the network is interrupted, and performing cooperative control and fault diagnosis and early warning on a plurality of devices at the same time; and the UV printer terminal module is used for collecting state data of printing equipment in real time, receiving and executing tasks, locally caching the data and performing man-machine interaction. By combining cloud global optimization, edge local collaboration and terminal offline autonomy, the dependence on a single network is reduced, the overall production efficiency is improved through multi-device collaboration and load balancing, ink waste and invalid loss of devices are reduced based on a prediction model, a printing path is optimized, and energy consumption is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial printing equipment, and in particular to a UV printer intelligent control system and method based on remote task scheduling. BACKGROUND

[0002] A UV printer (Ultraviolet LED Inkjet Printer) is a high-tech plateless full-color digital printer. UV ink is sprayed onto the surface of the printing material through a nozzle, and then a UV lamp is used to irradiate the ink, causing it to dry and solidify instantly, completing the pattern printing. It can be used on various surfaces such as T-shirts, sliding doors, cabinet doors, sliding doors, glass, boards, various signs, crystal, PVC, acrylic, metal, plastic, stone, leather, etc. for color photo-level printing. However, the existing technology still has great limitations in the remote scheduling of UV printers.

[0003] The existing UV printer has strong network dependence, low device coordination efficiency, weak task fault tolerance, and extensive energy consumption and material management in terms of remote scheduling. For example, network interruptions can easily cause task loss, uneven load distribution among multiple devices, and the inability to dynamically adjust tasks based on real-time device status, resulting in resource waste.

[0004] Therefore, there is an urgent need for a UV printer intelligent control system and method that can reduce dependence on a single network, achieve multi-device collaboration, improve task fault tolerance, and precisely manage energy consumption and materials. SUMMARY

[0005] To solve the above problems, the present application provides a UV printer intelligent control system and method based on remote task scheduling, which can reduce network dependence, achieve multi-device dynamic collaboration, have offline fault tolerance, and optimize energy consumption and materials.

[0006] To achieve the above purpose, the technical solution adopted by the present application is: The present application provides a UV printer intelligent control system based on remote task scheduling, which includes a cloud scheduling center module, an edge gateway node module, and a UV printer terminal module connected in communication; The cloud scheduling center module is used for global task planning and distribution, network state monitoring, device state monitoring, and data storage and analysis; The cloud scheduling center module includes a task analysis and priority sorting unit, a device cluster load balancing unit, a network state adaptive scheduling unit, and an energy consumption and material optimization unit; The task analysis and priority sorting unit is used to receive user-uploaded printing tasks, analyze task attributes, and assign priorities to tasks according to pre-set rules, and synchronize the tasks with assigned priorities to the device cluster load balancing unit and the user client. Device cluster load balancing unit: used to acquire real-time status data of printing devices, allocate weights according to the degree of impact on task execution and calculate a comprehensive suitability score, and allocate tasks to the optimal device based on the load balancing algorithm; Network Status Adaptive Scheduling Unit: Used to monitor the network quality of the time center module and edge gateway node module in the cloud scheduling in real time, and dynamically adjust the data transmission strategy according to the divided network status level; Energy consumption and consumables optimization unit: Based on historical data, a prediction model is trained to estimate ink consumption and energy consumption according to task parameters, and compared with the real-time consumables balance data to determine whether the consumables are sufficient. If they are insufficient, a multi-level warning is triggered to replenish the stock in time. The edge gateway node module is used to cache task data issued by the cloud scheduling center module, serve as a local scheduling hub when the network is interrupted, and simultaneously perform coordinated control and fault diagnosis and early warning for multiple devices. The UV printer terminal module is used to collect real-time status data of the printing device, receive and execute tasks, cache data locally, and interact with the human-machine interface.

[0007] The execution process of the task parsing and priority sorting unit includes the following steps: The system automatically generates a unique task ID for print jobs uploaded by users through the client and verifies the validity of the uploaded tasks. By using structured parsing algorithms, key attributes are extracted from the validated task data to form a standardized task attribute table; A priority determination rule base is set up, with each rule corresponding to an independent weight calculation logic. The rule dimensions include time urgency rules, equipment compatibility rules, and production demand rules. The total task weight is obtained by merging the weights of various rules in the calculation. The task priority is then assigned based on the total task weight, and manual adjustment is supported. Add a priority level field to the task attribute table and generate priority tags. Then, synchronize the task data with priority tags to the device cluster load balancing unit and user clients.

[0008] The key attributes include basic attributes, process attributes, time attributes, and resource attributes; the basic attributes include task ID, upload time, and user information; the process attributes include material type, printing precision, and color mode; the time attributes include deadline and expected completion time; and the resource attributes include printing area and estimated ink type.

[0009] The execution process of the device cluster load balancing unit includes the following steps: The edge gateway node module establishes continuous communication with each UV printer terminal, acquires printer state data collected in real time, and the state data includes current task progress data, nozzle health data, idle time data and historical failure rate data; The acquired state data is standardized to eliminate dimensional differences of different dimensions, and each state data is assigned a weight according to the influence degree of the task execution, and a device comprehensive adaptation score model is constructed; The standardized scores of each dimension are multiplied by the corresponding weights and summed to obtain the comprehensive adaptation score of each printer; An improved genetic algorithm is used to increase the device load constraint and the task process matching degree, and the shortest total completion time and the smallest device load difference are used as the target to calculate the optimal task allocation scheme and output; The output optimal allocation scheme is verified for process matching and load upper limit, and after verification, the task instruction is issued to the target printer through the edge gateway node module, and the task-device allocation relationship is recorded in the cloud scheduling center module; During the task execution process, the device state update data synchronized by the edge gateway node module is received in real time, and the load distribution is dynamically adjusted.

[0010] The load balancing algorithm is an improved genetic algorithm, and the calculation of the optimal allocation scheme includes the following steps: The task-device matching relationship is binary coded to generate an initial population; The reciprocal of the total completion time and the reciprocal of the device load standard deviation are used as the core indicators to construct the fitness function to evaluate the pros and cons of the allocation scheme; The roulette selection method is used to select the excellent scheme with a higher value into the next generation according to the proportion of the fitness value, and the selected scheme is subjected to single-point crossover and mutation; Repeat the above selection, crossover and mutation operations until the iteration number reaches the preset value or the fitness value changes less than 0.001 for 10 consecutive iterations, and output the scheme with the highest fitness value as the optimal task allocation scheme.

[0011] The execution process of the network state adaptive scheduling unit includes the following steps: Obtain the network quality parameters of the cloud scheduling center module and the edge gateway node module, including delay, packet loss rate and bandwidth; The collected network quality parameters are standardized and preprocessed, the network state level is determined according to the preset threshold, and it is divided into three levels: good, fluctuation and interruption; According to the determined network state level, the corresponding transmission strategy is called to ensure the data transmission efficiency and reliability; In the process of transmission strategy execution, the effect of strategy execution is monitored in real time, and the strategy is adjusted according to the feedback data.

[0012] The execution process of the energy consumption and consumable optimization unit comprises the following steps: From the historical database of the cloud scheduling center module and the local log of the UV printer terminal, batch collection of historical data related to energy consumption and consumables is performed to construct a basic data set, and the data set is standardized and pretreated; Based on the pretreated historical data set, a gradient boosting regression model is used to train a prediction model to accurately estimate the ink consumption and energy consumption of new tasks, and the model accuracy is continuously iteratively optimized; When the cloud scheduling center module receives a new printing task, the energy consumption and consumable optimization unit extracts the task parameters and inputs them into the trained prediction model, and outputs the ink consumption and energy consumption estimation results; Based on the printing pattern size and printer workbench coordinates of the task, a contour offset algorithm is used in combination with path sorting optimization to plan the optimal nozzle movement path; Real-time acquisition of consumable remaining data of the UV printer terminal is performed, and the estimated ink consumption is compared to determine whether the consumables are sufficient, and if not, a multi-level warning is triggered to replenish in time.

[0013] The edge gateway node module comprises a local task cache and continuation unit, a device cooperative control unit, a fault diagnosis and warning unit; The local task cache and continuation unit is used to cache the task data issued by the cloud scheduling center module, and when the network is interrupted, the local scheduling mode is automatically activated, and the cached tasks are executed according to priority, and after the network is restored, the local execution record is synchronized to the cloud scheduling center module; The device cooperative control unit is used to split large tasks into multiple subtasks to control multiple printers in parallel for local cooperation; The fault diagnosis and warning unit is based on real-time data collected by the printer sensor, and identifies abnormal states through edge computing, and triggers local warnings, and pushes fault codes to the cloud scheduling center module.

[0014] The UV printer terminal module comprises a task execution and state acquisition unit, an offline autonomous unit and a man-machine interaction unit; The task execution and state acquisition unit is used to receive task instructions from the edge gateway node module or the cloud scheduling center module, control the nozzle movement and UV lamp curing, and acquire real-time state data through sensors; The offline autonomous unit has a local storage module built-in, which is used to cache the last 100 task data, and when the edge gateway node module and the UV printer terminal network are interrupted, the preset rules are automatically started; The preset rule is: priority is given to the execution of the cached 1-2 level emergency task; if the task execution is interrupted, the breakpoint coordinates are recorded, and the task is continued from the breakpoint after the network is restored; the task log is locally saved, and the uploading is continued after the network is restored; The man-machine interaction unit is equipped with a touch screen and physical buttons, supports local modification of task parameters, and displays task progress, device state and fault prompt, facilitating on-site operator intervention.

[0015] In another aspect, the application provides a UV printer intelligent control method based on remote task scheduling, comprising the following steps: The user uploaded printing task is acquired, the task is structurally analyzed, the task parameters are extracted, and the task is assigned a priority; Real-time network quality parameters are monitored, network state levels are divided according to threshold values, different levels are matched with corresponding execution strategies, and the network quality parameters include delay, packet loss rate and bandwidth; Real-time state parameters of the printing device are acquired, the comprehensive adaptation score of each device is calculated, and the task is assigned to the optimal device based on a load balancing algorithm; The device executes the task after receiving the task instruction, and feeds back the consumable remaining amount data of the UV printer terminal in real time, compares the consumable remaining amount data with the estimated ink consumption output by the prediction model, judges whether replenishment is needed, and gives an early warning.

[0016] The application has the following advantages: The application provides a UV printer intelligent control system and method based on remote task scheduling, the system comprises a cloud scheduling center module for global task planning and distribution, network state monitoring, device state monitoring, and data storage and analysis; an edge gateway node module for buffering task data issued by the cloud scheduling center module, serving as a local scheduling center during network interruption, and simultaneously performing collaborative control, fault diagnosis and early warning on multiple devices; and a UV printer terminal module for real-time acquisition of state data of the printing device, receiving and executing tasks, local data buffering and man-machine interaction. A three-level hybrid scheduling mode is adopted, combining cloud global optimization, edge local collaboration and terminal offline autonomy, reducing dependence on a single network, allowing the task to continue locally during network interruption, and seamlessly synchronizing after network recovery; an improved genetic algorithm is used to achieve optimal matching of devices and tasks, large task splitting and parallel execution efficiency is improved by more than 40%; multi-device collaboration and load balancing improve overall production efficiency; a prediction model is used to reduce ink waste and device invalid loss, optimize printing path and reduce energy consumption; a CNN model is deployed on the edge node, the fault recognition accuracy is greater than or equal to 92%, the response time is less than 1 second, and the downtime is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1It is the UV printer intelligent control system framework based on remote task scheduling of the present application.

[0018] Figure 2 It is the UV printer intelligent control method flow chart based on remote task scheduling of the present application. DETAILED DESCRIPTION

[0019] Please refer to Figure 1 The present application provides a UV printer intelligent control system based on remote task scheduling, which comprises a cloud scheduling center module, an edge gateway node module and a UV printer terminal module connected in communication; The cloud scheduling center module is used for global task planning and distribution, network state monitoring, device state monitoring and data storage and analysis; The edge gateway node module is used for buffering the task data issued by the cloud scheduling center module as a local scheduling center in case of network interruption, and simultaneously for collaborative control and fault diagnosis and early warning of multiple devices; The UV printer terminal module is used for real-time acquisition of state data of the printing device, receiving and executing tasks, local data buffering and human-computer interaction.

[0020] The cloud scheduling center module comprises a task analysis and priority sorting unit, a device cluster load balancing unit, a network state adaptive scheduling unit and an energy consumption and consumable optimization unit; The task analysis and priority sorting unit is used for receiving the printing task uploaded by the user, analyzing the task attributes and assigning priority to the task according to the preset rules, and synchronizing the task with assigned priority to the device cluster load balancing unit and the user client; The execution process of the task analysis and priority sorting unit comprises the following steps: Receiving the printing task uploaded by the user through the client to automatically generate a unique task ID, and performing legality verification on the uploaded task; The user uploads the printing task (such as pattern file, material parameter, precision requirement, deadline) through the client (PC / mobile terminal management system), the system automatically generates a unique task ID (such as "UV-Task-20250520-001"), records the upload time and user information such as operator account and department, performs legality verification on the uploaded task file, and if the verification fails, generates an error prompt (such as "missing material parameter, please supplement and upload again") and feeds back to the user client, and simultaneously records the error type and time in the system log, and after the user corrects it, triggers the receiving process again, and the verification specifically includes three types: File integrity: verify whether the pattern file (such as CAD, PDF, PNG format) is complete, undamaged or missing (compare by MD5 value to confirm that the file transmission is error-free); Parameter completeness: Check whether the required task parameters are included, including material parameters (such as metal, glass, plastic), accuracy requirements, deadline, and print size; Format compatibility: Determines whether the pattern file format matches the system's supported format library. If the format is incompatible, it automatically triggers format conversion, using the FFmpeg tool to convert the non-standard format to the standard PNG format.

[0021] By using structured parsing algorithms, key attributes are extracted from the validated task data to form a standardized task attribute table; The key attributes include basic attributes, process attributes, time attributes, and resource attributes; the basic attributes include task ID, upload time, and user information; the process attributes include material type, printing precision, and color mode; the time attributes include deadline and expected completion time; and the resource attributes include printing area and estimated ink type.

[0022] A priority determination rule base is set up, with each rule corresponding to an independent weight calculation logic. The rule dimensions include time urgency rules, equipment compatibility rules, and production demand rules. Time urgency weight (40 points): Calculate the ratio of the remaining available time for the task (remaining available time = deadline - current time) to the estimated execution time. The smaller the ratio, the higher the weight. Specifically, the calculated ratio is divided into different intervals, and each interval corresponds to a weighted score. A ratio between (0, 0.3) has a weight of 40 points, corresponding to an extremely urgent task; a ratio between (0.3, 0.5) has a weight of 32-38 points, corresponding to a highly urgent task; a ratio between (0.5, 0.7) has a weight of 25-31 points, corresponding to a medium-urgent task; a ratio between (0.7, 1.0) has a weight of 20-24 points, corresponding to a low-urgent task; and a ratio greater than 1.0 has a weight of 15 points or less, corresponding to a relaxed task. For example, if there are 2 hours of remaining available time and an estimated execution time of 3 hours, the ratio = 2 / 3 ≈ 0.67, corresponding to a weight of 30 points; if there are 1 hour of remaining available time and an estimated execution time of 3 hours, the ratio ≈ 0.33, corresponding to a weight of 40 points.

[0023] Device compatibility weight (30 points): Based on the matching of task process attributes (material, precision) with the device capability library (material types, maximum precision, ink types supported by each printer). The more matching items, the higher the weight. For example, if the task requires "metal material + 600 DPI + white ink", device A supports all 3 items and gets 30 points; device B only supports "metal material + 600 DPI" and gets 20 points.

[0024] Production demand weight (30 points): According to the production type of the task, 30 points are given for urgent orders (such as customer urgent repair), 20 points for regular orders, and 10 points for trial production orders (such as new product sampling). If the task is associated with an after-sales work order (such as product quality reprinting), an additional 5 points are added (the upper limit of the weight is still 30 points).

[0025] The total weight of the task is obtained by fusing the weights of the rules, and the priority of the task is determined based on the total weight, and manual adjustment is supported; According to the total weight of the task, it is divided into 5 priority levels, and the specific correspondence is as follows: 1st level (urgent): total weight ≥ 85 points, corresponding to "very short remaining time, completely matched equipment, after-sales / urgent order"; 2nd level (high priority): 70 points ≤ total weight < 85 points, corresponding to "tight remaining time, basically matched equipment, regular urgent order"; 3rd level (regular): 50 points ≤ total weight < 70 points, corresponding to "sufficient remaining time, equipment adaptation, regular order"; 4th level (low priority): 30 points ≤ total weight < 50 points, corresponding to "ample time, partial equipment adaptation, non-core order"; 5th level (to be arranged): total weight < 30 points, corresponding to "trial production order, standby task with no specific deadline".

[0026] Support administrators (such as production supervisors) to manually adjust the priority in special scenarios, for example, if the number of 1st level tasks is too large (more than 50% of the current idle equipment), some "non-after-sales 1st level tasks" can be downgraded to 2nd level; VIP customer orders inserted temporarily can be directly marked as 1st level, and the adjustment record is synchronized to the system log to ensure traceability.

[0027] Add a priority level field in the task attribute table, and generate a priority label, and synchronize the task data with priority labels to the device cluster load balancing unit and user client.

[0028] Priority labels (such as red labels for 1st level and blue labels for 3rd level) are used for visual identification during subsequent scheduling. Synchronize the task data with priority labels to the device cluster load balancing unit as the core basis for task allocation to specific printers; synchronize to the user client to display the current priority and expected start execution time of the task (such as "1st level task, expected to be allocated to equipment in 10 minutes"), facilitating real-time tracking by users.

[0029] Device cluster load balancing unit: used to obtain real-time state data of printing equipment, assign weights and calculate comprehensive adaptation score based on the influence degree of task execution, and allocate tasks to the optimal equipment based on load balancing algorithm; The execution process of the device cluster load balancing unit comprises the following steps: Through the edge gateway node module, continuous communication is established with each UV printer terminal to obtain real-time collected printer state data, including current task progress data, nozzle health data, idle time data and historical failure rate data; The communication protocol adopts the MQTT lightweight protocol, and the sampling frequency is 1 time / 3 seconds, ensuring the real-time nature of the data.

[0030] The current task progress data is obtained from the task execution and state collection unit of the printer terminal, including the completed printing area / total printing area (such as 0.5 m2 / total 2 m2, the progress is 25%), the current task remaining estimated length (based on the printed speed), if the printing device has no current task, it is marked as idle, and the progress value is set to 0%.

[0031] The nozzle health data is collected by combining the temperature sensor, camera and historical records of the printer terminal, including the current temperature of the nozzle (normal range 40-60℃, if it exceeds, the health degree will decrease), the number of abnormal ink droplet ejection in the last 1 hour (such as the number of ink breakage caused by blockage, 1 abnormal health degree deduction 5 points), the cumulative working time of the nozzle (more than 1000 hours without maintenance, the upper limit of the health degree is reduced to 80 points), and finally the nozzle health degree is output in percentage.

[0032] The idle time data is calculated through two scenarios, if the printing device is currently idle, the idle time is the difference between the current time and the last task completion time; if the printing device is executing a task, the idle time is the difference between the estimated completion time of the current task and the current time.

[0033] The historical failure rate data is extracted from the historical database of the cloud scheduling center module, and the number of faults (such as paper jam, UV lamp failure, nozzle blockage, etc.) and fault repair time of the printing device in the last 30 days are counted, the fault frequency (fault frequency / 30 days, such as 0.5 times / day) and the average fault repair time (such as 2 hours / time) are calculated, the higher the fault frequency and the longer the repair time, the greater the negative impact of this indicator on device matching.

[0034] The obtained state data is standardized to eliminate the dimensional differences of different dimension data, and according to the influence degree of each state data on task execution, the weight of each data dimension is allocated to construct a device comprehensive adaptation degree score model; The Min-Max standardization is used to convert various types of data into standardized scores of 0-100, avoiding weight deviation caused by different data units. According to the influence of each state data on task execution, different weights are assigned. Specifically, the printhead health directly affects the printing quality (such as blockage leading to incomplete patterns), so it is the core influencing factor, and the printhead health is assigned 35%; the idle time determines the task start speed, and the shorter the idle time, the less the task waiting time, which is beneficial to improve the overall production efficiency, so the idle time is assigned 30%; the historical failure rate of the device can reduce the risk of task interruption during execution and reduce the reprint cost, so the historical failure rate is assigned 20%; the current task progress data only affects whether to preferentially select the device close to completing the current task, if the current task progress of the device is 90%, the short task can be preferentially assigned, so the current task progress is assigned 15%.

[0035] The dimension standardized score is multiplied by the corresponding weight and summed to obtain the comprehensive fitness score of each printer; An improved genetic algorithm is used to increase the device load constraint and the task process matching degree as two optimization items, with the shortest total completion time of all tasks and the smallest device load difference as the target, to calculate the optimal task allocation scheme and output; The load balancing algorithm is an improved genetic algorithm, and the calculation of the optimal allocation scheme includes the following steps: The task-device matching relationship is binary coded to generate an initial population; Suppose there are 5 tasks to be allocated (T1-T5) and 3 printers (D1-D3), 100 indicates that the task is allocated to D1, 010 indicates that it is allocated to D2, and 001 indicates that it is allocated to D3; 50 groups of codes are randomly generated as the initial population, each code corresponds to one task allocation scheme, and the initial scheme covers different device allocation combinations.

[0036] The total completion time and the standard deviation of the device load are used as the core indicators to build the fitness function to evaluate the pros and cons of the allocation scheme, and the formula is: Fitness value = (1 / total completion time) x 0.6 + (1 / standard deviation of device load) x 0.4; Where, total completion time = the sum of the execution time of each device allocated task (calculated according to the idle time of the device + the estimated execution time of the task); Device load standard deviation = standard deviation of the total duration of each device allocated task (the smaller the standard deviation, the more balanced the device load).

[0037] For example, the total completion time of scheme A is 120 minutes, and the standard deviation of equipment load is 10; the total completion time of scheme B is 150 minutes, and the standard deviation of equipment load is 5, then the fitness value of scheme A = (1 / 120) x 0.6 + (1 / 10) x 0.4 ≈ 0.005 + 0.04 = 0.045, the fitness value of scheme B = (1 / 150) x 0.6 + (1 / 5) x 0.4 ≈ 0.004 + 0.08 = 0.084, scheme B is better.

[0038] The roulette selection method is used, the excellent schemes with high values are selected according to the proportion of the fitness value, and the selected schemes are subjected to single-point crossover and mutation. The higher the fitness value is, the greater the probability of being selected is, and in the embodiment, 20 groups of schemes with high fitness values are selected into the next generation. The selected schemes are subjected to single-point crossover, for example, two groups of scheme codes “T1:D1, T2:D2, T3:D3” and “T1:D2, T2:D1, T3:D3” are taken, and the new scheme “T1:D1, T2:D1, T3:D3” is obtained by crossing at the T2 position. The crossed scheme is subjected to mutation with a probability of 1% (for example, “T1:D1” is mutated to “T1:D2”), so as to avoid the algorithm from falling into a local optimal solution.

[0039] The selection, crossover and mutation operations are repeated until the number of iterations reaches a preset value or the fitness value changes by less than 0.001 in 10 consecutive iterations, and the scheme with the highest fitness value is output as the optimal task allocation scheme.

[0040] The output optimal allocation scheme is subjected to process matching verification and load upper limit verification, after the verification is passed, the task instructions are issued to the target printer through the edge gateway node module, and the task-equipment allocation relationship is recorded in the cloud scheduling center module; Process matching verification: The process attributes in the task analysis stage are matched with the equipment capability library of the printer terminal, for example, the task requires “glass material + 600 DPI + UV soft ink”, if the allocated printer only supports metal material, it is determined that the process is not matched, and the algorithm is triggered to recalculate (remove the equipment, and generate the optimal scheme from the remaining equipment). Load upper limit verification: whether the total task duration of each equipment exceeds the maximum load threshold (a preset value, such as 8 hours / day) is checked, if the total task duration of a certain equipment is 9 hours which exceeds the threshold, one long duration task of the equipment is reduced (for example, a task with an estimated execution time of 2 hours is adjusted to other idle equipment).

[0041] After verification, task instructions (including pattern compression files, process parameters, and execution priority) are sent to the target printer through the edge gateway node module, and the task-device allocation relationship (such as "task T1 is allocated to D2, and the delivery time is 2025-05-2010:00") is recorded in the cloud scheduling center module, which facilitates subsequent state tracking.

[0042] During task execution, real-time device state update data synchronized by the edge gateway node module is received to dynamically adjust load distribution.

[0043] Device failure triggers adjustment: If a device suddenly fails during task execution (such as UV lamp failure), the edge gateway node module feeds back the fault code, and the device cluster load balancing unit immediately marks the unfinished task of the device as "to be reallocated", calls the load balancing algorithm to reallocate it to the currently highest comprehensive adaptation degree idle device, and pushes the task transfer notification (such as "task T1 is transferred from D2 to D3 due to D2 UV lamp failure") to the administrator terminal. Task priority change triggers adjustment: If the cloud scheduling center module receives high-priority task insertion (such as a 1st level emergency task), the device cluster load balancing unit recalculates the comprehensive adaptation degree of all devices, and preferentially allocates high-priority tasks to devices with the shortest idle time and the highest nozzle health degree. If a device needs to be preempted (such as D3 is executing a 5th level task), the low-priority task of D3 is paused (the breakpoint is saved), the high-priority task is sent, and after the high-priority task is completed, the low-priority task breakpoint is resumed for execution.

[0044] Network state adaptive scheduling unit: used for real-time monitoring of the network quality of the cloud scheduling center module and the edge gateway node module, and dynamically adjusting the data transmission strategy according to the divided network state level; The execution process of the network state adaptive scheduling unit includes the following steps: Obtain the network quality parameters of the cloud scheduling center module and the edge gateway node module, including delay, packet loss rate, and bandwidth; Delay collection: using the ICMP echo request mechanism, the cloud scheduling center module sends a fixed-size test data packet to the edge gateway node module, records the round-trip time (RTT) from sending to receiving the edge gateway node module response, and takes the average value of 10 consecutive collections as the current delay value; if there is no response (timeout >1000ms) for a certain collection, it is marked as abnormal delay and counted in the subsequent packet loss rate statistics.

[0045] Packet loss rate collection: Based on the packet transmission results in the above delay collection process, the proportion of "the number of test packets not received response / total number of sent packets" is calculated; at the same time, the transmission confirmation frame (ACK) of the actual task data between the cloud scheduling center module and the edge gateway node module is monitored, if no ACK is received for 3 times in a row, it is determined that the data is lost, and the packet loss rate statistics are supplemented to ensure data accuracy.

[0046] Bandwidth collection: using the sliding window method, a temporary data transmission channel is established between the cloud scheduling center module and the edge gateway node module, a fixed size test file is transmitted for 10 seconds, the actual transmission completion time is recorded, and the real-time bandwidth is calculated by the formula "actual bandwidth=transmission file size x 8 / transmission time" (such as 100MB file transmission for 10 seconds, actual bandwidth=100x8 / 10=80Mbps); at the same time, the bandwidth proportion occupied by the task data being transmitted is monitored to avoid interference of test bandwidth on actual business transmission.

[0047] Standardize the collected network quality parameters, and determine the network state level according to the preset threshold, which is divided into three levels: good, fluctuation and interruption; Based on the preprocessed parameters, the network state is divided into three levels: good, fluctuation and interruption, as follows: Good level: delay < 50ms, packet loss rate < 1%, actual bandwidth ≥ 120% of task demand bandwidth, all three parameters meet the threshold requirements, and the network is determined to be good; Fluctuation level: 50ms≤delay≤200ms, 1%≤packet loss rate≤5%, 80%≤actual bandwidth of task demand bandwidth < 120%, any one parameter exceeds the threshold of good level, but does not reach the standard of interruption level, and the network is determined to be fluctuant; Interruption level: delay > 200ms or timeout without response, packet loss rate > 5%, actual bandwidth < 80% of task demand bandwidth or no data transmission, any one parameter reaches the interruption threshold, which is determined as network interruption.

[0048] According to the determined network state level, the corresponding transmission strategy is called to ensure the efficiency and reliability of data transmission; Network good strategy (delay < 50ms, packet loss rate < 1%): the data transmission mode adopts real-time synchronous transmission, the cloud scheduling center module directly issues the task complete data (including uncompressed high-definition pattern file, full-amount process parameters) to the edge gateway node module, and simultaneously receives the printer state data feedback from the edge gateway node module in real time, the transmission frequency is consistent with the data collection frequency (1 second / time). CRC check (cyclic redundancy check) is added only when key parameters are transmitted to ensure that the core instructions are error-free, and no additional redundancy is added for non-key data to improve transmission efficiency.

[0049] Network fluctuation strategy (50ms≤delay≤200ms, 1%≤packet loss rate≤5%): Call the LZ77 compression algorithm to compress the task pattern file, compress the file size to 40% of the original size, reduce the data transmission amount; At the same time, only transmit the task key parameters, omit unnecessary data. The transmission method of "segmented transmission + retransmission mechanism" is adopted, the compressed task data is segmented into 10MB / segment data packets, and each transmission is waited for ACK confirmation of the edge gateway node module. If no ACK is received, the segment is retransmitted (at most 3 times), avoiding the loss of overall task data due to packet loss; The state data transmission frequency is reduced to 5 seconds / time, reducing the network load.

[0050] Network interruption strategy (delay>200ms or packet loss rate>5%): The cloud scheduling center module immediately triggers the "edge gateway takeover" instruction, records the current task data to be issued and the state of the issued task, and synchronously marks it as "interrupted to continue", stops actively transmitting data to the edge gateway node module; After receiving the takeover instruction, the edge gateway node module automatically activates the local scheduling mode, calls the locally cached task data (including complete pattern file, parameter configuration), and executes the task according to the priority. The cloud scheduling center module records the task allocation state before interruption (such as "task T1 has been issued to the edge gateway node module, no execution feedback received"), network interruption time and reason (such as "2025-05-2011:30, delay 280ms, packet loss rate 7%"), After the network is restored, only transmit the task data and state update instructions added during the interruption period to the edge gateway node module (incremental synchronization), avoiding repeated transmission.

[0051] During the execution of the transmission strategy, the effect of the strategy execution is monitored in real time, and the strategy is adjusted according to the feedback data.

[0052] The edge gateway node module feeds back the key indicators after the execution of the strategy to the cloud scheduling center module, including the task data transmission completion time (such as the transmission time of an 80MB compressed file under the fluctuation strategy), the data retransmission times (such as the retransmission of a certain data packet 2 times under the fluctuation strategy), and whether the local task execution is normal (such as whether the edge gateway node module successfully starts the local task under the interruption strategy). The cloud scheduling center module performs statistics on the feedback data, and if the data retransmission times under a certain strategy are >5 times / hour, or the task transmission completion time exceeds the expected value (such as the transmission time of a 200MB file under the good strategy >30 seconds), it is marked as "strategy adaptation exception". For the strategy adaptation exception scenario, the strategy parameters are fine-tuned to adapt to the actual network environment, and all optimization adjustments are recorded in the strategy log, including adjustment time, adjustment parameter, adjustment reason, for subsequent tracing and strategy iteration.

[0053] Energy consumption and consumable optimization unit: based on historical data to train prediction model, according to task parameters to estimate ink consumption and energy consumption, and compare with real-time acquisition of consumable data to judge whether the consumables are sufficient, if not enough, trigger multi-level warning and timely replenishment; The execution process of the energy consumption and consumable optimization unit includes the following steps: From the historical database of the cloud scheduling center module and the local log of the UV printer terminal, the historical data related to energy consumption and consumables are obtained to construct a basic data set, and the data set is standardized and pretreated; Task parameter data: collect the process parameters of all printing tasks in the past 12 months, including printing area, color saturation, printing accuracy, material type, and UV lamp power level; Consumable consumption data: collect the actual ink consumption (statistically according to CMYK four colors and white ink, such as cyan ink 15ml, black ink 20ml), nozzle cleaning frequency and cleaning ink consumption corresponding to the task; Energy consumption data: collect the total power consumption corresponding to the task, and further divide the UV lamp energy consumption (60%-70% of the total energy consumption), nozzle drive energy consumption (15%-20%), and motor operation energy consumption (10%-15%).

[0054] According to the task dimension, each task corresponds to a complete data record, ensuring that the data matches the task one by one. The collected data is subjected to outlier rejection and standardized processing, and the data set is divided into metal, glass, plastic and other sub-data sets according to the material type. Each sub-data set is further subdivided according to the printing accuracy to ensure the pertinence of subsequent model training.

[0055] Based on the preprocessed historical data set, a gradient boosting regression model (GBR) is used to train a prediction model to accurately estimate the ink consumption and energy consumption of new tasks, and the model accuracy is continuously optimized through iteration; "Printing area, color saturation, printing accuracy, material type, and UV lamp power level" are used as model input features (independent variables), and the material type is converted into numerical features through one-hot encoding (e.g. metal = 100, glass = 010, plastic = 001); "CMYK four-color ink consumption, white ink consumption, and total power consumption" are used as model output targets (dependent variables); 70% of the historical data set is used as the training set and 30% as the test set. The gradient boosting algorithm minimizes the mean square error (MSE) between the predicted value and the actual value, iteratively adjusts the model hyperparameters (such as learning rate and decision tree number), until the prediction error on the test set is <5% (i.e. the deviation between the estimated ink consumption and the actual consumption is ≤5%).

[0056] When the cloud dispatch center module receives a new printing task, the energy consumption and consumable optimization unit extracts the task parameters and inputs them into the trained prediction model, outputting ink consumption and energy consumption estimates. The analysis task obtains task parameters, which are input into the trained prediction model to obtain energy consumption and consumable estimates. The estimated data is synchronized to the cloud dispatch center module and edge gateway node module as the basis for printing path optimization and consumable replenishment judgment.

[0057] Consumable estimation: ink consumption by color (e.g., cyan 22ml, magenta 20ml, yellow 18ml, black 25ml, white ink 30ml), estimated cleaning frequency and cleaning ink consumption (e.g., 1 cleaning before printing, ink consumption 3ml), and total ink consumption (e.g., 22+20+18+25+30+3=118ml). Energy consumption estimation: subdivide UV lamp energy consumption (e.g., 3㎡ metal printing, UV lamp 80% power running 2 hours, energy consumption = 0.8kW x 2h = 1.6kWh), printhead drive energy consumption (0.3kWh), and motor operation energy consumption (0.2kWh), and total energy consumption (1.6+0.3+0.2=2.1kWh). Based on the printing pattern size of the task and the printer workbench coordinates, the contour offset algorithm is used in combination with path sorting optimization to plan the optimal printhead movement path. Image recognition is performed on the pattern file of the task to extract the outer contour coordinates of the pattern (e.g., the four vertex coordinates of a rectangular pattern), and the blank area inside the pattern (the area that does not need to be printed) is marked to avoid printhead movement in the blank area. The contour offset algorithm is used to generate a contour offset path, which generates a printing path from the outer contour of the pattern to the inside by a printhead width, ensuring that the path covers all the printing area and the distance between adjacent paths is equal to the printhead width, avoiding repeated printing or missing printing. The generated printing path is sorted by the greedy algorithm to prioritize the printing order of adjacent areas, reducing the long-distance movement of the printhead in non-printing areas. For example, if the pattern contains three independent printing areas A, B, and C, and A is adjacent to B and B is adjacent to C, the path is planned as "A→B→C" instead of "A→C→B", reducing the long-distance movement of A to C. The optimized path coordinates (including printing path and necessary empty movement path) are converted into G code that the printer can recognize and sent to the task execution unit of the UV printer terminal to control the printhead to move according to the optimized path, reducing the empty movement distance and reducing invalid energy consumption and printhead wear.

[0058] Real-time acquisition of consumable remaining amount data of the UV printer terminal, comparison with estimated ink consumption, judgment of whether the consumables are sufficient, if not, trigger multi-level warning and timely replenishment.

[0059] The current remaining amount of each color ink is collected in real time through the ink tank weight sensor (or liquid level sensor) of the UV printer terminal, and is synchronized to the energy consumption and consumable optimization unit through the edge gateway node module. Compare the current remaining amount of each color ink with the estimated ink consumption of the new task, and calculate: sufficient rate = (current remaining amount - estimated ink consumption) / estimated ink consumption x 100%; if the sufficient rate is greater than 20%, it is determined that the consumables are sufficient and no warning is needed; if the sufficient rate is between 0% and 20%, it is determined that the consumables are critically insufficient and attention is needed; if the sufficient rate is less than 0%, it is determined that the consumables are absolutely insufficient and cannot meet the current task requirements, and immediate restocking is needed.

[0060] When it is determined that the consumable remaining amount is insufficient, multi-level warnings are triggered, and all warning information (including trigger time, warning level, and processing result) is recorded in the cloud log for subsequent tracing of consumable use period and restocking efficiency: First-level warning (sufficient rate < 0%): immediately push a red warning message to the administrator terminal (PC / mobile APP), including "task ID, missing ink color, current remaining amount, and estimated ink replenishment amount (such as 20ml of white ink)", and pause the current task distribution until the consumables are replenished; Second-level warning (0% ≤ sufficient rate ≤ 20%): push a yellow warning message to the administrator terminal, including "consumables will be insufficient, please replenish within 24 hours", and mark the ink tank as "to be restocked" in the consumable management interface of the cloud dispatch center.

[0061] The edge gateway node module includes a local task cache and continuation unit, a device cooperative control unit, a fault diagnosis and warning unit; Local task cache and continuation unit: used to cache task data distributed by the cloud dispatch center module, automatically activate local dispatch mode when the network is interrupted, and execute cached tasks according to priority, and synchronize local execution records to the cloud dispatch center module after network recovery; Device cooperative control unit: used to split large tasks into multiple sub-tasks and control multiple printers in parallel for local cooperation; For example, a 3m x 6m glass printing task is split into 3 2m x 3m sub-tasks and assigned to 3 adjacent printers for synchronous printing, and the edge gateway real-time calibrates the printing coordinates of each device (error ≤ 0.1mm) to ensure the splicing accuracy.

[0062] Fault diagnosis and warning unit: based on real-time data collected by printer sensors, identify abnormal states through edge computing, and trigger local warnings, and push fault codes to the cloud dispatch center module.

[0063] The core data related to the equipment running state is continuously collected through the hardware sensors of the UV printer terminal, including the nozzle temperature (collected by a temperature sensor, range 0-100℃, accuracy ±0.5℃), UV lamp power (collected by a power sensor, range 0-100% rated power, accuracy ±1%), motor current (collected by a current sensor, range 0-5A, accuracy ±0.01A); at the same time, auxiliary data such as ink droplet ejection frequency and printing speed are associated. The collected data is standardized and preprocessed to eliminate noise interference.

[0064] Two types of key features are extracted, one is "real-time absolute value feature" (such as current nozzle temperature 55℃, UV lamp power 80%), and the other is "trend change feature" (such as nozzle temperature rise rate in 5 seconds, motor current fluctuation amplitude in 10 seconds); among them, the frequency fluctuation value of ink droplet ejection frequency is calculated: frequency fluctuation value = (current frequency - historical average frequency) / historical average frequency × 100%", which is used to identify the precursor of nozzle blockage (fluctuation > 5% is marked as a high-risk feature).

[0065] The edge gateway node module deploys a lightweight CNN model (based on MobileNet architecture compression, parameter size <5MB, inference time <0.1s) to infer the preprocessed feature data and determine whether the equipment is in an abnormal state. Specifically, the extracted "real-time absolute value feature + trend change feature" is combined into a 128-dimensional feature vector and input into the CNN model; the model output is "normal", "mild abnormality", "severe abnormality" 3 state labels, and the corresponding fault type probability (such as "nozzle blockage: 92%, UV lamp aging: 5%"). If the model outputs the "normal" label and all fault type probabilities are <10%, the equipment is determined to be running normally; if it outputs "mild abnormality" (such as ink droplet ejection frequency fluctuation 4.5%, nozzle blockage probability 35%), it is marked as "to be observed" state and the data change is continuously tracked; if it outputs "severe abnormality" (such as nozzle temperature 75℃, which exceeds the normal range 40-60℃, fault probability >80%), or the key parameters trigger the preset threshold (such as UV lamp power <30% rated power), it is directly determined as a fault state. According to the abnormality recognition result, the corresponding local warning and cloud synchronization operation is triggered to ensure that the fault is handled in time. After the warning is triggered, the fault related information is automatically recorded and the subsequent traceability is supported.

[0066] The UV printer terminal module includes a task execution and state collection unit, an offline autonomous unit, and a human-computer interaction unit. Task execution and state collection unit: used to receive task instructions from the edge gateway node module or the cloud scheduling center module, control nozzle movement, UV lamp curing, and collect real-time state data through sensors; Off-line autonomous unit: built-in local storage module for caching the last 100 task data, when the edge gateway node module is disconnected from the UV printer terminal network, automatically start the preset rules; The preset rules are: priority execution of cached 1-2 level emergency tasks; if the task is interrupted during execution, record the breakpoint coordinates and continue printing from the breakpoint after the network is restored; save the task log (including printing time, consumable consumption, fault record) locally, and continue uploading after the network is restored; Human-computer interaction unit: equipped with touch screen and physical buttons, supporting local modification of task parameters, and displaying task progress, device status and fault prompt, facilitating on-site operator intervention.

[0067] System workflow: users upload printing tasks through the client (PC / mobile phone), the cloud scheduling center module parses the tasks and assigns priorities; the cloud scheduling center module assigns tasks to target printers according to device load and network status, and sends task data (including compressed pattern files and parameters) through the edge gateway node module; the edge gateway node module and the printer terminal cache task data respectively to ensure local execution in case of network interruption; the printer receives the task instruction and starts printing according to the task instruction, while collecting state data in real time and feeding back to the edge gateway node module, which synchronizes the data to the cloud scheduling center module.

[0068] And in the process of data transmission, if the network is interrupted, the edge gateway node module takes over the scheduling, the printer executes the local cached task and records the breakpoint; if the device fails, the edge gateway node module triggers local warning and the cloud scheduling center reassigns tasks to standby devices. After the task is completed, the printer feeds back the task result (including finished product image and consumable consumption), the cloud scheduling center generates a production report, and the edge gateway node module clears the completed task cache.

[0069] On the other hand, referring to Figure 2 The present application provides a UV printer intelligent control method based on remote task scheduling, comprising the following steps: Obtain the user uploaded printing task, structure the task, extract the task parameters and assign priorities to the task; Real-time monitoring of network quality parameters, network state level is divided according to threshold value, different levels are matched with corresponding execution strategies, the network quality parameters include delay, packet loss rate and bandwidth; Get the real-time state parameters of the printing equipment, calculate the comprehensive adaptation score of each device, and assign tasks to the optimal device based on the load balancing algorithm; The device receives the task instruction, executes the task, and feeds back the consumable remaining amount data of the UV printer terminal in real time, compares the consumable remaining amount data with the estimated ink consumption output by the prediction model, judges whether replenishment is needed, and gives a warning.

[0070] The above embodiments only describe the preferred embodiments of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by ordinary engineering technicians in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A UV printer intelligent control system based on remote task scheduling, characterized in that, The cloud scheduling center module, the edge gateway node module and the UV printer terminal module are connected by communication connection; The cloud scheduling center module is used for global task planning and distribution, network state monitoring, equipment state monitoring and data storage and analysis; The cloud scheduling center module comprises a task analysis and priority sorting unit, an equipment cluster load balancing unit, a network state adaptive scheduling unit and an energy consumption and consumable optimization unit; The task analysis and priority sorting unit is used for receiving a user uploaded printing task, analyzing task attributes and assigning priorities to the task according to preset rules, and synchronizing the task with assigned priorities to the equipment cluster load balancing unit and a user client; The equipment cluster load balancing unit is used for acquiring state data of a printing device in real time, assigning weights and calculating a comprehensive adaptation score according to an influence degree on task execution, and distributing the task to an optimal device based on a load balancing algorithm; The network state adaptive scheduling unit is used for monitoring network quality of the cloud scheduling center module and the edge gateway node module in real time, and dynamically adjusting a data transmission strategy according to a divided network state level; The energy consumption and consumable optimization unit is used for training a prediction model based on historical data, estimating ink consumption and energy consumption according to task parameters, comparing the ink consumption and energy consumption with real-time acquired consumable remaining data, judging whether the consumables are sufficient, and triggering multi-level early warning and timely replenishment if the consumables are insufficient; The edge gateway node module is used for buffering task data distributed by the cloud scheduling center module, serving as a local scheduling hub in case of network interruption, and cooperatively controlling, diagnosing and warning a plurality of devices; The UV printer terminal module is used for acquiring state data of a printing device in real time, receiving a task and executing the task, locally buffering data and interacting with a human.

2. The remote task scheduling based intelligent control system for UV printer according to claim 1, wherein, The execution process of the task analysis and priority sorting unit comprises the following steps: Receiving a printing task uploaded by a user through a client to automatically generate a unique task ID, and performing legality verification on the uploaded task; Extracting key attributes from the verified task data by a structured analysis algorithm to form a standardized task attribute table; Setting a priority judgment rule library, each rule corresponding to independent weight calculation logic, wherein the rule dimensions include time urgency rules, device adaptation rules and production demand rules; Fusing the rule weights to obtain a task total weight, dividing the task priority based on the task total weight, and supporting manual adjustment; Adding a priority level field in the task attribute table, generating a priority label, and synchronizing the task data with the priority label to the equipment cluster load balancing unit and the user client.

3. The remote task scheduling based intelligent control system for UV printer according to claim 2, wherein, The key attributes include basic attributes, process attributes, time attributes and resource attributes; the basic attributes include a task ID, an upload time and user information; the process attributes include material types, printing precision and color modes; the time attributes include a deadline and an expected completion time; and the resource attributes include a printing area and an estimated ink type.

4. The remote task scheduling based intelligent control system for UV printer according to claim 1, wherein, The execution process of the equipment cluster load balancing unit comprises the following steps: The edge gateway node module establishes continuous communication with each UV printer terminal, acquires real-time collected printer state data, and the state data includes current task progress data, nozzle health data, idle time data and historical failure rate data; The acquired state data is standardized to eliminate the dimensional differences of different dimension data, and weights are assigned to each data dimension according to the influence of each state data on task execution, and a device comprehensive adaptation score model is constructed; The standardized scores of each dimension are multiplied by the corresponding weights and summed to obtain the comprehensive adaptation score of each printer; An improved genetic algorithm is adopted, two optimization items of device load constraint and task process matching degree are added, the shortest total completion time of all tasks and the smallest load difference of the device are taken as the target, the optimal task allocation scheme is calculated and output; After the verification, the task instruction is sent to the target printer through the edge gateway node module, and the task-device allocation relationship is recorded in the cloud scheduling center module; During the task execution process, real-time device state update data synchronized by the edge gateway node module is received, and the load distribution is dynamically adjusted.

5. The remote task scheduling based intelligent control system for UV printer according to claim 4, wherein, The load balancing algorithm is an improved genetic algorithm, and the calculation of the optimal allocation scheme includes the following steps: The task-device matching relationship is binary coded to generate an initial population; A fitness function is constructed to evaluate the pros and cons of the allocation scheme based on the core indicators of the reciprocal of the total completion time and the reciprocal of the standard deviation of the device load; The selected schemes are single-point crossed and mutated; The above selection, crossing and mutation operations are repeated until the iteration number reaches the preset value or the fitness value changes less than 0.001 for 10 consecutive iterations, and the scheme with the highest fitness value is output as the optimal task allocation scheme.

6. The remote task scheduling based intelligent control system for UV printer of claim 1, wherein, The execution process of the network state adaptive scheduling unit includes the following steps: Obtain the network quality parameters of the cloud scheduling center module and the edge gateway node module, including delay, packet loss rate and bandwidth; The collected network quality parameters are standardized and preprocessed, and the network state level is determined according to the preset threshold, which is divided into three levels: good, fluctuation and interruption; According to the determined network state level, the corresponding transmission strategy is called to ensure the data transmission efficiency and reliability; During the execution of the transmission strategy, the effect of the strategy is monitored in real time, and the strategy is adjusted according to the feedback data.

7. The remote task scheduling based intelligent control system for UV printer of claim 1, wherein, The execution process of the energy consumption and consumable optimization unit includes the following steps: Batch collect historical data related to energy consumption and consumables from the historical database of the cloud scheduling center module and the local log of the UV printer terminal to construct a basic data set, and the data set is standardized and preprocessed; Based on the preprocessed historical data set, a gradient boosting regression model is used to train a prediction model to accurately estimate the ink consumption and energy consumption of new tasks, and the model accuracy is continuously iteratively optimized; When the cloud scheduling center module receives a new printing task, the energy consumption and consumable optimization unit extracts the task parameters and inputs them into the trained prediction model, outputting ink consumption and energy consumption estimates; Based on the task-based printing pattern size and printer workbench coordinates, the contour offset algorithm is used in combination with path sorting optimization to plan the optimal nozzle movement path; Real-time acquisition of consumable remaining data from the UV printer terminal is compared with the estimated ink consumption to determine whether the consumables are sufficient, and if not, a multi-level warning is triggered to replenish in a timely manner.

8. The remote task scheduling based intelligent control system for UV printer of claim 1, wherein, The edge gateway node module includes a local task cache and continuation unit, a device cooperative control unit, and a fault diagnosis and warning unit; The local task cache and continuation unit is used to cache task data issued by the cloud scheduling center module, automatically activate the local scheduling mode when the network is interrupted, and execute the cached tasks according to priority, and synchronize the local execution record to the cloud scheduling center module after the network is restored; The device cooperative control unit is used to split large tasks into multiple sub-tasks and control multiple printers in parallel for local cooperation; The fault diagnosis and warning unit identifies abnormal states through edge computing based on real-time data collected by the printer sensors and triggers local warnings, while pushing fault codes to the cloud scheduling center module.

9. The remote task scheduling based intelligent control system for UV printer of claim 1, wherein, The UV printer terminal module includes a task execution and state acquisition unit, an offline autonomous unit, and a human-machine interaction unit; The task execution and state acquisition unit is used to receive task instructions from the edge gateway node module or the cloud scheduling center module, control nozzle movement and UV lamp curing, and collect real-time state data through sensors; The offline autonomous unit has a local storage module built-in to cache the last 100 task data, and automatically start the preset rules when the edge gateway node module and the UV printer terminal network is interrupted; The preset rules are: prioritize the execution of 1-2 level emergency tasks that have been cached; if the task execution is interrupted, record the breakpoint coordinates and continue printing from the breakpoint after the network is restored; Save the task log locally and continue uploading after the network is restored; The human-machine interaction unit is equipped with a touch screen and physical buttons, supports local modification of task parameters, and displays task progress, device status and fault prompts, making it easy for on-site operators to intervene.

10. The intelligent control method of UV printer based on remote task scheduling, applied to the intelligent control system of UV printer based on remote task scheduling according to any one of claims 1-9, characterized in that, The method comprises the following steps: Obtain the printing task uploaded by the user, structure the task, extract the task parameters, and assign a priority to the task; Real-time monitoring of network quality parameters, judgment and classification of network state level according to threshold, matching of corresponding execution strategies based on different levels, the network quality parameters include delay, packet loss rate and bandwidth; Obtain the real-time state parameters of the printing equipment, calculate the comprehensive adaptation score of each device, and distribute the task to the optimal device based on the load balancing algorithm; The device receives the task instruction and executes the task, and real-time feedback of the consumable remaining data of the UV printer terminal is compared with the estimated ink consumption output by the prediction model to determine whether replenishment is needed and to warn.

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