Intelligent monitoring management method and system for packaging and printing production line

Abstract

The invention belongs to the technical field of printing production line management, and particularly relates to an intelligent monitoring management method and system for a packaging and printing production line, and the system comprises a production quality monitoring unit which comprises an industrial camera, an image processing module and a defect grading alarm module; the batch traceability unit comprises a laser coding machine, a three-dimensional storage location distribution engine and a block chain database; based on dynamic storage location allocation strategy matching of a batch traceability unit, storage locations can be allocated to different batches of printed products according to risk coefficients, visualization of subsequent storage of the printed products is ensured, in addition, traceability codes of the printed products are bound with storage location coordinates, and it is ensured that data cannot be tampered based on a block chain; in the environment regulation and control process, automatic temperature and humidity regulation is driven based on deviation degree calculation, and secondary defects are blocked; and finally, the central control unit gets through a'defect discovery-traceability marking-storage location isolation-environment suppression 'full link, solves the problem of data splitting caused by equipment heterogeneity, and constructs a printing production full life cycle monitoring system.

Description

Technical Field

[0001] The present invention belongs to the technical field of printing production line management, and specifically relates to an intelligent monitoring and management method and system for a packaging printing production line. Background Art

[0002] In the packaging and printing industry, product quality stability, production efficiency, and cost control are core competitive advantages. Currently, the level of intelligent and digital production lines is generally low, especially among my country's vast majority of small and medium-sized packaging and printing companies, leading to several prominent and interrelated technical bottlenecks.

[0003] Currently, the detection and management of quality defects (such as misregistration, uneven ink color, missed prints, foreign matter, die-cutting cracks / burrs, and misalignment) on packaging and printing production lines primarily rely on the following methods, which present significant challenges: The most common existing technology relies on scheduled manual inspections or random spot checks. This method is inefficient, has limited random inspection coverage, struggles to capture transient defects or minor flaws, relies heavily on worker experience and commitment, and has a high rate of missed and false positives.

[0004] Furthermore, some companies may deploy offline inspection stations (such as manual inspection stations) or expensive single-point automated inspection equipment (such as vision systems that detect only printing defects) after key processes. The problem with these devices is that inspection data is often independent of the main production management system or simply recorded on paper forms. Defect information is poorly correlated with specific order batches, production times, and machine parameters, creating information silos.

[0005] When a defective product is discovered (especially one with a batch defect), existing technology makes it difficult to quickly and accurately locate information such as the product's production time, specific machine, raw material batch, and environmental conditions at the time. Manual record-keeping is cumbersome, error-prone, and inefficient, making effective root cause analysis and accurate batch traceability impossible.

[0006] To this end, the present invention provides an intelligent monitoring and management method and system for a packaging and printing production line. Summary of the Invention

[0007] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0008] The technical solution adopted by the present invention to solve the technical problem is: an intelligent monitoring and management system for a packaging and printing production line according to the present invention comprises: Production quality monitoring unit, including industrial camera, image processing module and defect classification alarm module; The industrial camera and image processing module are used to serve the defect grading alarm module, which is used to identify defective printed products; Batch traceability unit, including laser coding machine, 3D storage allocation engine and blockchain database; The laser coding machine is used to generate traceability codes according to coding rules, and the three-dimensional storage location allocation engine allocates storage locations for printed products according to requirements; An environmental control unit, comprising: Warehouse environment subsystem, used to dynamically monitor and adjust environmental status data; Abnormal response engine, used to calculate the deviation of environmental status data and trigger graded alarms; The central control unit communicates with the above units and realizes data synchronization.

[0009] Preferably, the image processing module performs: Acquire RGB images based on industrial cameras; Perform HSV color space conversion and extract the hue H, saturation S and lightness V of the printing area corresponding to each channel; Calculate hue standard deviation , saturation standard deviation and brightness standard deviation ; The defect classification alarm module performs: Compare the standard deviation of each channel with the reference value of each channel, and trigger multi-level alarms based on the number of times the standard is exceeded: Level 1: When or or When the audible and visual alarm is activated; Level 2: When the standard deviation of any channel is greater than the channel reference value for three consecutive times, the machine will be stopped and the defect location will be marked; Level 3: When the standard deviation of any channel in the critical area is greater than 1.5 times the channel reference value, an interception command is sent.

[0010] Preferably, the method for the laser coding machine to generate the traceability code according to the coding rules is: Get the printing time, including year and week; Record the production line to which the printed product belongs, generate the production line number, and generate the batch number based on the printing batch of the printed product; Record the ID of the quality inspector in the quality inspection process of printed products; Generate a disposition status code corresponding to the printed product based on the defect multi-level alarm module; The printing time, production line number, batch number, quality inspector ID and disposal status code are combined to generate a traceability code.

[0011] Preferably, the three-dimensional storage allocation engine calculates the risk factor according to the product weight and the remaining delivery days. , and allocate storage locations for printed products according to the risk factor R, including regular storage locations and buffer storage locations; The risk factor R is calculated according to the following formula: ; in, is the product weight, The maximum load-bearing capacity of the storage location; The remaining days for delivery, The shortest delivery cycle; 、 is the weight coefficient, and ; According to the calculated risk factor, the storage location is allocated to the printed product. The allocation strategy is: , allocated to regular storage locations; , allocated to the buffer storage location; The blockchain database is used to record the storage location coordinates of printed products and bind the traceability code of the printed products to the storage location coordinates.

[0012] Preferably, the storage environment subsystem includes: Temperature and humidity sensor array, bound to storage location coordinates; Light-sensitive sensor module for monitoring ultraviolet radiation intensity; The exception response engine performs: Get the temperature and humidity data output by the temperature and humidity sensor array, including temperature difference , humidity difference and maximum storage temperature , Maximum storage humidity ; Calculate the deviation between temperature and humidity data and temperature and humidity reference values and the deviation threshold Comparison, triggering multi-level responses; The multi-level response includes: Level 1: When Adjust warehouse temperature and humidity; Level 2: When , the terminal displays the alarm information; Level 3: When , the AGV transfers the printed products and activates the sound and light alarm at the same time; The exception response engine also performs: Get UV radiation intensity and compared with the ultraviolet radiation intensity reference value For comparison: when When the light aging warning is displayed, the terminal will display a light aging warning.

[0013] Preferably, it also includes an isolated storage location. When the printed product triggers a second-level or third-level alarm or triggers a third-level response in the buffer storage location or regular storage location, the three-dimensional storage location allocation engine allocates the printed product to the isolated storage location.

[0014] Preferably, the deviation The calculation formula is as follows: ; ; ; ; ; in, is the actual maximum allowable temperature difference, is the actual maximum allowable humidity difference; The actual maximum allowable temperature difference and the actual maximum allowable humidity difference Calculated using the following formula: ; ; in, To preset the maximum allowable temperature difference, The preset maximum allowable moisture difference; is the dynamic adjustment factor; 、 、 、 are weight coefficients, and , ; The calculated deviation , and the deviation threshold Comparisons are made as conditions for triggering multi-level responses.

[0015] An intelligent monitoring and management method for a packaging and printing production line, comprising the following steps: S1: Monitor the production quality of printed products if: Abnormal printed product quality triggers the defect classification alarm module, which then executes the following steps: generating a unique traceability code, allocating an isolated location using the 3D location allocation engine, storing the product separately using the AGV, marking the defect, and generating process optimization instructions. The quality of printed products is normal, and traceability codes are generated according to coding rules and are waiting to be put into storage; S2: Store printed products in unique storage locations according to the allocation strategy and complete the binding between printed products and storage location coordinate numbers; S3: After the printed products are put into storage, the environmental status monitoring of the corresponding storage location coordinates is started simultaneously. If: Abnormal environmental conditions trigger multi-level responses; The environmental status is normal and does not trigger a multi-level response; S4: until the printed products at the storage location coordinates are cleared, the environmental status monitoring of the corresponding storage location coordinates is automatically turned off.

[0016] Preferably, it also includes after-sales methods for sold printed products: S51: Receive a traceability code query request from the client; S52. Retrieve related data from the blockchain database, where the related data includes: a. Production time and batch of printed products; b. Original quality inspection images and quality inspection results of the printing product production process; c. Warehouse location coordinates and storage environment status data of printed products, including temperature, humidity and ultraviolet radiation intensity; S53. Generate a traceability report.

[0017] Preferably, when the central control unit receives a traceability code query request from a client, it automatically extracts the traceability code of the printed product, queries the historical defect data of printed products of the same batch and the same production line in the blockchain database, and calculates the defect rate: If the defect rate of printed products on the same production line is greater than the threshold, the production line equipment is marked as abnormal; If the defect rate of printed products in the same batch is greater than the threshold, the raw material is marked as abnormal; If the defect rate of printed products at the same storage location coordinates is greater than the threshold, the storage location coordinate environment is marked as abnormal.

[0018] The beneficial effects of the present invention are as follows: The intelligent monitoring and management method and system of a packaging and printing production line described in the present invention can allocate storage locations for different batches of printed products according to risk factors based on the dynamic storage location allocation strategy matching of the batch traceability unit, ensuring the visualization of the subsequent storage of the printed products. In addition, the traceability code of the printed product is bound to the storage location coordinates, and the blockchain is used to ensure that the data cannot be tampered with; during the environmental control process, the deviation calculation is used to drive automatic temperature and humidity adjustment to block secondary defects; finally, the central control unit opens up the full link of "defect discovery-traceability marking-storage location isolation-environmental suppression", solves the data fragmentation problem caused by equipment heterogeneity, and builds a full life cycle monitoring system for printing production. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below with reference to the accompanying drawings.

[0020] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0021] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0022] like Figure 1 As shown, an intelligent monitoring and management system for a packaging and printing production line according to an embodiment of the present invention includes: Production quality monitoring unit, including industrial camera, image processing module and defect classification alarm module; The industrial camera and image processing module are used to serve the defect grading alarm module, which is used to identify defective printed products; Batch traceability unit, including laser coding machine, 3D storage allocation engine and blockchain database; The laser coding machine is used to generate traceability codes according to coding rules, and the three-dimensional storage location allocation engine allocates storage locations for printed products according to requirements; An environmental control unit, comprising: Warehouse environment subsystem, used to dynamically monitor and adjust environmental status data; Abnormal response engine, used to calculate the deviation of environmental status data and trigger graded alarms; The central control unit communicates with the above units and realizes data synchronization.

[0023] Small and medium-sized packaging and printing factories generally have problems with mixed equipment types and incompatible communication protocols, which leads to isolated and scattered production quality data. In addition, defective products rely on manual recording and tracing, which is inefficient and prone to errors. Warehouse environment control is lagging behind, and printed products are easily affected by temperature and humidity fluctuations, resulting in secondary defects.

[0024] In one embodiment of the present invention, a production quality monitoring unit captures printed product images using an industrial camera. An image processing module converts the images to the HSV color space and calculates the three-channel standard deviation. A defect grading alarm module triggers a multi-level response based on the exceeding of standards, such as an audible and visual alarm or a shutdown command. The batch traceability unit's laser coder generates a traceability code containing the quality inspection status (e.g., "_B1" identifies a returnable product). A three-dimensional storage location allocation engine calculates a risk factor based on product weight and delivery urgency, automatically allocating regular storage locations and buffer locations. A blockchain database is used to bind traceability codes to storage location coordinates in real time. It should be noted that after a printed product is sold, if a customer reports a quality issue with the printed product, the traceability code generated by the laser coder can be used for traceability, including re-inspection of the quality of unsold printed products from the same batch in storage and re-inspection of the quality of other printed products from the same production line. Based on this, it can be used to improve the production parameters of the production line and avoid quality issues with more printed products caused by an imbalanced storage environment. In addition, the environmental control unit monitors the temperature, humidity and ultraviolet intensity of the storage location through a sensor array. The abnormal response engine calculates the environmental deviation and triggers hierarchical regulation, such as automatically starting the dehumidifier or adjusting the blackout curtains; the central control unit is used to connect the entire process in series, including synchronizing quality alarm signals, driving traceability code generation, coordinating storage location allocation, and linking environmental regulation to achieve cross-unit data connectivity.

[0025] In one embodiment, the central control unit is used to be compatible with multi-protocol equipment, and HSV multi-channel defect detection significantly improves coverage; the dynamic storage location allocation strategy (conventional / buffered / isolated) matching based on the batch traceability unit can allocate storage locations for different batches of printed products according to the risk factor, ensuring the visualization of the subsequent storage of printed products. In addition, the traceability code of the printed product is bound to the storage location coordinates, and the blockchain is used to ensure that the data cannot be tampered with; during the environmental control process, automatic temperature and humidity adjustment is driven based on deviation calculation to block secondary defects; finally, the central control unit opens up the entire link of "defect discovery-traceability marking-storage location isolation-environmental suppression", solves the data fragmentation problem caused by equipment heterogeneity, and builds a full life cycle monitoring system for printing production.

[0026] In one embodiment, the image processing module performs: Acquire RGB images based on industrial cameras; Perform HSV color space conversion and extract the hue H, saturation S and lightness V of the printing area corresponding to each channel; Calculate hue standard deviation , saturation standard deviation and brightness standard deviation ; The defect classification alarm module performs: Compare the standard deviation of each channel with the reference value of each channel, and trigger multi-level alarms based on the number of times the standard is exceeded: Level 1: When or or When the audible and visual alarm is activated; Level 2: When the standard deviation of any channel is greater than the channel reference value for three consecutive times, the machine will be stopped and the defect location will be marked; Level 3: When the standard deviation of any channel in the critical area is greater than 1.5 times the channel reference value, an interception command is sent.

[0027] As mentioned above, quality inspection of printed products is carried out during the production process of printed products. On the one hand, it can supervise the process and equipment of printing production, and on the other hand, it can avoid defective products from being mixed with qualified products, causing disputes after sales. Specifically, taking a small printing product company as an example, during the production process, the RGB image of the printed packaging box is collected in real time based on the industrial camera and transmitted to the image processing module. In the image processing module, the RGB image is converted into HSV format, and the three channels of hue (H), saturation (S) and brightness (V) are separated. Then, the printing area data is extracted. For example, the trademark printing area on the packaging box is located, the H, S, and V values ​​of the area are extracted, and the standard deviation is calculated; the calculated multi-channel standard deviation is compared with the reference value of each channel recorded in the historical data, and the defect grading alarm module is used for execution. If the defect alarm module outputs a first-level alarm, that is, an audible and visual alarm, it means that the standard deviation of any channel is greater than the reference value. Taking the saturation standard deviation as an example, if the saturation is too high, the H, S, and V values ​​of the area are extracted, and the standard deviation is calculated. If the saturation standard deviation is greater than the saturation reference value and a level one alarm is generated, it may be that the ink in the trademark area is uneven, resulting in abnormal saturation. Similarly, when the defect alarm module outputs a level two alarm, it means that two consecutive printed products that have been inspected have the same problem. At this time, it is necessary to stop the machine and mark the defect position, that is, rely on a robotic arm or manual labor to spray a red mark at the same defect position. If the defect alarm module outputs a level three alarm, for example, if the luminance standard deviation is detected to be greater than 1.5 times the reference value in the key area (production date coding), it is necessary to send an interception instruction to remove all defective products currently being produced from the production line via AGV, and adjust and optimize the production line. In one embodiment, the image processing module is used in conjunction with the defect multi-level alarm module to realize quality monitoring of printed products in the production line. By comparing the multi-channel standard deviation with the reference value, the problem of the defective printed product can be learned to avoid defective products from flowing into genuine products and causing after-sales problems. In addition, it is also used to optimize the production line.

[0028] In one embodiment, the method for the laser coding machine to generate the traceability code according to the coding rules is: Get the printing time, including year and week; Record the production line to which the printed product belongs, generate the production line number, and generate the batch number based on the printing batch of the printed product; Record the ID of the quality inspector in the quality inspection process of printed products; Generate a disposition status code corresponding to the printed product based on the defect multi-level alarm module; The printing time, production line number, batch number, quality inspector ID and disposal status code are combined to generate a traceability code.

[0029] In one embodiment of the present invention, printed products output from the production line are all generated with traceability codes by a laser coding machine according to coding rules. After the printed products are put into storage, the traceability codes can be bound to the storage location coordinates, thereby facilitating the in-and-out management of printed products. At the same time, it is more noteworthy that if the client raises quality issues after the printed products are sold, the quality of the same batch of printed products can be re-inspected based on the traceability codes, and the quality of printed products on the same production line can also be re-inspected. Specifically, the traceability codes are as follows: Example: [last two digits of the year] [week number]-[production line number]-[batch number]-[quality inspector ID]-[disposition status code]: 24W23-PR02-0085-ZD135_N

[0030] It is worth noting that in one embodiment, after the first, second and third level alarms are output by the defect grading alarm module, the corresponding printed products need to be marked with a specific disposal status code. As shown above, "_B1" indicates a returnable defective product, that is, during the production process, only the printed product has a channel standard deviation greater than the reference value. It is a defect but can be returned for repair and needs to be determined by the quality inspector. Here, the quality inspector only needs to determine whether the defective product can be returned for repair. Secondly, the printed products marked with "_B2" and "_B3" are non-returnable products. The three-dimensional warehouse allocation engine needs to allocate special warehouse locations for this type of defective products, and they need to be separated from other normal printed products to avoid mixing of qualified products and defective products, which leads to subsequent after-sales problems. In one embodiment, with the five-segment traceability code (time-production line-batch-quality inspector-status) as the core, the entire process of production judgment-warehouse positioning-after-sales tracking is carried out to realize a closed-loop monitoring system.

[0031] In one embodiment, the three-dimensional storage allocation engine calculates the risk factor based on product weight and remaining delivery days. , and allocate storage locations for printed products according to the risk factor R, including regular storage locations and buffer storage locations; The risk factor R is calculated according to the following formula: ; in, is the product weight, The maximum load-bearing capacity of the storage location; The remaining days for delivery, The shortest delivery cycle; 、 is the weight coefficient, and ; According to the calculated risk factor, the storage location is allocated to the printed product. The allocation strategy is: , allocated to regular storage locations; , allocated to the buffer storage location; The blockchain database is used to record the storage location coordinates of printed products and bind the traceability code of the printed products to the storage location coordinates.

[0032] In one embodiment of the present invention, the allocation rules of the three-dimensional storage location allocation engine are described with a specific example as follows: ; ; Weight , Calculate the risk factor: ; Allocation results: because , so the three-dimensional storage location allocation engine allocates the factor product to the buffer storage location; Through the central control unit to coordinate data calls, AGV and 3D storage allocation engine are used to perform isolation operations; to achieve a real-time closed loop from defect detection to prevention, such as optimizing the process immediately after identifying production line anomalies, reducing scrap rates, and strengthening the integrity of the method steps in claim 8 (directly entering the optimization process after S1 defect alarm). In addition, the weight coefficient and Adaptable adjustment, the specific rules are as follows: ; ; It should be noted that the total defect rate refers to the ratio of the number of defective products that appear after sales of all products stored in the warehouse location or the same warehouse location to the total storage volume of the warehouse location, and the delivery time defect ratio can be understood as the number of defective products caused by delivery time among all products after sales. In the above embodiment, the allocation rules of the three-dimensional warehouse location allocation engine are defined and formulated based on weight and delivery time. It can be understood that in conventional warehouse management, the heavier the product, the more attention needs to be paid to its warehouse management. In addition, the more urgent the delivery time of the product, the more attention needs to be paid to its warehouse management compared with other products with relatively long delivery times. In one embodiment of the present invention, conventional warehouse locations are defined for storing printed products with light weight and long remaining delivery time, while printed products with heavy weight and short remaining delivery time are preferentially stored in the buffer area. It should be noted that the buffer area is close to the warehouse exit, and the route that the AGV needs to travel when shipping is easier to plan. The conventional warehouse locations are located inside the warehouse and are used to store printed products with long remaining delivery time.

[0033] In one embodiment, the storage environment subsystem includes: Temperature and humidity sensor array, bound to storage location coordinates; Light-sensitive sensor module for monitoring ultraviolet radiation intensity; The exception response engine performs: Get the temperature and humidity data output by the temperature and humidity sensor array, including temperature difference , humidity difference and maximum storage temperature , Maximum storage humidity ; Calculate the deviation between temperature and humidity data and temperature and humidity reference values and the deviation threshold Comparison, triggering multi-level responses; The multi-level response includes: Level 1: When Adjust warehouse temperature and humidity; Level 2: When , the terminal displays the alarm information; Level 3: When , the AGV transfers the printed products and activates the sound and light alarm at the same time; The exception response engine also performs: Get UV radiation intensity and compared with the ultraviolet radiation intensity reference value For comparison: when When the light aging warning is displayed, the terminal will display a light aging warning.

[0034] In one embodiment of the present invention, since printed products are easily affected by the environment, printed products may still cause defects during the storage process in addition to the quality inspection link on the production line. Specifically, taking a specific scenario as an example, a printing factory warehouse suddenly experiences a high temperature and humidity environment due to the rainy season, and the printed packaging boxes in the warehouse may have the risk of ink smudging. At this time, the real-time humidity of a certain storage location coordinate detected by the temperature and humidity sensor array in the warehouse environment subsystem exceeds the baseline value. The abnormal environment data is then retrieved by the abnormal response engine, and the deviation is calculated to obtain the calculated Value, calculated based on The central control unit automatically generates instructions to control the storage environment subsystem to adjust the temperature and humidity of the warehouse or the AGV to transfer printed products, and generate alarms. In this embodiment, the environmental deviation Value is the cornerstone of regulation. It identifies the sensitivity of defective products through processing status codes and triggers a hierarchical response mechanism. The central control unit connects the storage environment subsystem, the exception response engine, and the AGV scheduling system, implementing an intelligent protection system that combines "real-time determination of environmental anomalies, precise isolation of defective products, and closed-loop control and verification." This reduces the secondary defect rate in the storage process to near zero.

[0035] In one embodiment, an isolation storage location is further included. When the printed product triggers a second-level or third-level alarm or triggers a third-level response in the buffer storage location or regular storage location, the three-dimensional storage location allocation engine allocates the printed product to the isolation storage location.

[0036] In one embodiment, the deviation The calculation formula is as follows: ; ; ; ; ; in, is the actual maximum allowable temperature difference, is the actual maximum allowable humidity difference; The actual maximum allowable temperature difference and the actual maximum allowable humidity difference Calculated using the following formula: ; ; in, To preset the maximum allowable temperature difference, The preset maximum allowable moisture difference; is the dynamic adjustment factor; 、 、 、 are weight coefficients, and , ; The calculated deviation , and the deviation threshold Comparisons are made as conditions for triggering multi-level responses.

[0037] As mentioned above, a specific embodiment is used as an example to illustrate: Assume that the parameters of a product are: , , =5 days, =2 days; Preset =5℃, =10%, =0.6, ; Dynamic calculation: ; ; ; ; result: The actual allowable temperature difference of this product is tightened from ±5℃ to ±2.7℃, and the humidity difference is tightened from ±10% to ±5.4%.

[0038] In one embodiment of the present invention, the maximum allowable temperature difference and the maximum allowable humidity difference of the solid state may affect the deviation response engine. In one embodiment, the abnormal response engine uses dynamic baseline values ​​to calculate EDI, making the environmental response more accurate, reducing false positives for sensitive products, improving warehousing efficiency, and reducing AGV transfer operations so that it is triggered only when necessary.

[0039] An intelligent monitoring and management method for a packaging and printing production line, comprising the following steps: S1: Monitor the production quality of printed products if: Abnormal printed product quality triggers the defect classification alarm module, which then executes the following steps: generating a unique traceability code, allocating an isolated location using the 3D location allocation engine, storing the product separately using the AGV, marking the defect, and generating process optimization instructions. The quality of printed products is normal, and traceability codes are generated according to coding rules and are waiting to be put into storage; S2: Store printed products in unique storage locations according to the allocation strategy and complete the binding between printed products and storage location coordinate numbers; S3: After the printed products are put into storage, the environmental status monitoring of the corresponding storage location coordinates is started simultaneously. If: Abnormal environmental conditions trigger multi-level responses; The environmental status is normal and does not trigger a multi-level response; S4: until the printed products at the storage location coordinates are cleared, the environmental status monitoring of the corresponding storage location coordinates is automatically turned off.

[0040] As described above, in one embodiment of the present invention, the management method of the packaging and printing production line is as follows: First, start the production line to produce printed products. After each production link of the printed product is completed, start the industrial camera to shoot the printed product to obtain the RGB image of the printed product. Then, the RGB image of the printed product is converted to HSV through the image processing module. Specifically, the RGB image is converted to HSV format, and the three channels of hue (H), saturation (S) and brightness (V) are separated. Then, the printing area data is extracted. For example, the trademark printing area on the packaging box is located, the H, S, and V values ​​of the area are extracted, and the standard deviation is calculated; the standard deviation of each channel is compared with the reference value to determine whether there is a quality problem with the printed product. If the quality of the printed product is abnormal, the defect grading alarm module is triggered to output a first, second or third level alarm. At the same time, a displacement traceability code is generated for the printed product, and an isolated storage location is allocated to the defective printed product through the three-dimensional storage location allocation engine. It should be noted here that after the three-dimensional storage location allocation engine allocates the defective printed product to a unique isolated storage location, the traceability code of the defective printed product needs to be bound to the coordinates of the unique isolated storage location for easy management. The AGV then transfers the defective printed products to a unique isolated storage location and marks them with defective labels. Finally, optimization instructions are generated based on the defect type of the defective printed products to provide feedback for optimizing the production line. On the contrary, if the quality of the printed products is normal, the laser coding machine generates a traceability code for the qualified printed products according to the coding rules, and the three-dimensional storage location allocation engine allocates the qualified printed products to a unique storage location. After the AGV transfers the qualified printed products to the displacement storage location, it automatically starts the environmental status monitoring corresponding to the storage location, which is provided by the warehouse environment subsystem. During the warehouse management process, if an environmental anomaly occurs, the corresponding response action is triggered, otherwise the corresponding response action is not triggered. Based on the above, the entire production process of packaging and printing products can be fully controlled, and effective monitoring can be carried out from the production of packaging and printing products to the later warehouse management. When an abnormal situation occurs, a quick response can be responded to, and staff can be notified, and response measures can be automatically initiated, such as shutting down the machine and intercepting defective printed products, thereby effectively improving the monitoring efficiency and management efficiency of the entire process of packaging and printing products.

[0041] In one embodiment, the invention further includes an after-sales method for sold printed products: S51: Receive a traceability code query request from the client; S52. Retrieve related data from the blockchain database, where the related data includes: a. Production time and batch of printed products; b. Original quality inspection images and quality inspection results of the printing product production process; c. Warehouse location coordinates and storage environment status data of printed products, including temperature, humidity and ultraviolet radiation intensity; S53. Generate a traceability report.

[0042] In addition to the above-mentioned full-process management of packaging and printing products from production to warehousing, after the packaging and printing products are sold, if the client raises product quality issues, the production time, batch, quality inspection image and quality inspection results of the printed products can be queried based on the traceability code corresponding to the printed products generated during the production process. In addition, based on the unique storage location generated for the printed products by the three-dimensional storage location allocation engine, the environmental status data of the printed products during the warehousing management process can be searched. When there are still remaining printed products from the same batch, re-inspections can be carried out to ensure that the causes of quality defects of the sold printed products are queried and completed, that is, a traceability report is generated to facilitate the factory's product quality management.

[0043] In one embodiment, when the central control unit receives a traceability code query request from a client, it automatically extracts the traceability code of the printed product, queries the blockchain database for historical defect data of printed products from the same batch and production line, and calculates the defect rate: If the defect rate of printed products on the same production line is greater than the threshold, the production line equipment is marked as abnormal; If the defect rate of printed products in the same batch is greater than the threshold, the raw material is marked as abnormal; If the defect rate of printed products at the same storage location coordinates is greater than the threshold, the storage location coordinate environment is marked as abnormal.

[0044] It is worth noting that in the above description, if the client claims that the printed products sold have quality defects, the factory can accept the client's traceability code query, and based on the traceability code of the defective printed products sold, query whether there are remaining printed products from the same batch in the local warehouse, and re-inspect the printed products from the same batch, and calculate the defect rate. The defect rate is the number of defective printed products / the number of printed products in the entire batch. The obtained defect rate will be compared with the threshold. The threshold mentioned here is pre-set by the manufacturer. Generally speaking, the defect rate shall not be greater than 0.5%. Similarly, if there are no remaining printed products from the same batch, it is necessary to query whether There are products printed on the same production line, and the defect rate is calculated based on the remaining products printed on the same production line. The obtained defect rate is then compared with the threshold value preset by the manufacturer. In summary, if the defect rate of products printed on the same production line is greater than the threshold, the production line equipment is marked as abnormal; if the defect rate of products printed in the same batch is greater than the threshold, the raw material is marked as abnormal. In addition, it is necessary to consider whether the storage environment subsystem of the storage location has failed, resulting in an abnormal environment and failure to adjust in time, causing defects. To this end, the manufacturer also needs to calculate the defect rate of printed products at the same storage location coordinates and compare it with the threshold value preset by the manufacturer to determine whether the storage location has an environmental defect. It is understandable that if there are remaining printed products from the same batch, and the defect rate of the printed products is greater than the threshold, if the defect rate of the printed products from the same production line is also greater than the threshold at this time, it proves that the production line equipment may be abnormal, and the specific reason needs to be screened. For example, the production line may be abnormal, and the quality inspection link has not successfully identified the defect. On the contrary, if the defect rate of printed products from the same production line is less than the threshold, it means that the raw materials may be abnormal. If the defect rates of printed products from the same production line and the same batch are both less than the threshold, and only other batches of printed products at the same warehouse location have abnormalities, it means that the storage environment subsystem corresponding to the warehouse location may be abnormal, resulting in untimely environmental adjustment, leading to product defects. Based on the above, through monitoring and mutual verification of each link, problems in the entire process of printing products can be quickly queried, which facilitates manufacturers to adjust and optimize production processes in a timely manner.

[0045] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring and management system for a packaging and printing production line, characterized by: include: Production quality monitoring unit, including industrial camera, image processing module and defect classification alarm module; The industrial camera and image processing module are used to serve the defect grading alarm module, which is used to identify defective printed products; Batch traceability unit, including laser coding machine, 3D storage allocation engine and blockchain database; The laser coding machine is used to generate traceability codes according to coding rules, and the three-dimensional storage location allocation engine allocates storage locations for printed products according to requirements; An environmental control unit, comprising: Warehouse environment subsystem, used to dynamically monitor and adjust environmental status data; Abnormal response engine, used to calculate the deviation of environmental status data and trigger graded alarms; The central control unit communicates with the above units and realizes data synchronization.

2. The intelligent monitoring and management system for a packaging and printing production line according to claim 1, characterized in that: The image processing module performs: Acquire RGB images based on industrial cameras; Perform HSV color space conversion and extract the hue H, saturation S and lightness V of the printing area corresponding to each channel; Calculate hue standard deviation , saturation standard deviation and brightness standard deviation ; The defect classification alarm module performs: Compare the standard deviation of each channel with the reference value of each channel, and trigger multi-level alarms based on the number of times the standard is exceeded: Level 1: When or or When the audible and visual alarm is activated; Level 2: When the standard deviation of any channel is greater than the channel reference value for three consecutive times, the machine will be stopped and the defect location will be marked; Level 3: When the standard deviation of any channel in the critical area is greater than 1.5 times the channel reference value, an interception command is sent.

3. The intelligent monitoring and management system for a packaging and printing production line according to claim 2, characterized in that: The method for the laser coding machine to generate the traceability code according to the coding rules is as follows: Get the printing time, including year and week; Record the production line to which the printed product belongs, generate the production line number, and generate the batch number based on the printing batch of the printed product; Record the ID of the quality inspector in the quality inspection process of printed products; Generate a disposition status code corresponding to the printed product based on the defect multi-level alarm module; The printing time, production line number, batch number, quality inspector ID and disposal status code are combined to generate a traceability code.

4. The intelligent monitoring and management system for a packaging and printing production line according to claim 3, characterized in that: The three-dimensional storage allocation engine calculates the risk factor based on product weight and remaining delivery days. , and allocate storage locations for printed products according to the risk factor R, including regular storage locations and buffer storage locations; The risk factor R is calculated according to the following formula: ； in, is the product weight, The maximum load-bearing capacity of the storage location; The remaining days for delivery, The shortest delivery cycle; 、 is the weight coefficient, and ; According to the calculated risk factor, the storage location is allocated to the printed product. The allocation strategy is: , allocated to regular storage locations; , allocated to the buffer storage location; The blockchain database is used to record the storage location coordinates of printed products and bind the traceability code of the printed products to the storage location coordinates.

5. The intelligent monitoring and management system for a packaging and printing production line according to claim 4, characterized in that: The storage environment subsystem includes: Temperature and humidity sensor array, bound to storage location coordinates; Light-sensitive sensor module for monitoring ultraviolet radiation intensity; The exception response engine performs: Get the temperature and humidity data output by the temperature and humidity sensor array, including temperature difference , humidity difference and maximum storage temperature , Maximum storage humidity ; Calculate the deviation between temperature and humidity data and temperature and humidity reference values and the deviation threshold Comparison, triggering multi-level responses; The multi-level response includes: Level 1: When Adjust warehouse temperature and humidity; Level 2: When , the terminal displays the alarm information; Level 3: When , the AGV transfers the printed products and activates the sound and light alarm at the same time; The exception response engine also performs: Get UV radiation intensity and compared with the ultraviolet radiation intensity reference value For comparison: when When the light aging warning is displayed, the terminal will display a light aging warning.

6. The intelligent monitoring and management system for a packaging and printing production line according to claim 5, characterized in that: It also includes an isolated storage location. When the printed product triggers a second-level or third-level alarm or triggers a third-level response in the buffer storage location or regular storage location, the three-dimensional storage location allocation engine allocates the printed product to the isolated storage location.

7. The intelligent monitoring and management system for a packaging and printing production line according to claim 6, characterized in that: The deviation The calculation formula is as follows: ； ； ； ； ； in, is the actual maximum allowable temperature difference, is the actual maximum allowable humidity difference; The actual maximum allowable temperature difference and the actual maximum allowable humidity difference Calculated using the following formula: ； ； in, To preset the maximum allowable temperature difference, The preset maximum allowable moisture difference; is the dynamic adjustment factor; 、 、 、 are weight coefficients, and , ; The calculated deviation , and the deviation threshold Comparisons are made as conditions for triggering multi-level responses.

8. An intelligent monitoring and management method for a packaging and printing production line, applicable to the intelligent monitoring and management system according to any one of claims 1 to 7, characterized in that: The monitoring management method comprises the following steps: S1: Monitor the production quality of printed products if: Abnormal printed product quality triggers the defect classification alarm module, which then executes the following steps: generating a unique traceability code, allocating an isolated location using the 3D location allocation engine, storing the product separately using the AGV, marking the defect, and generating process optimization instructions. The quality of printed products is normal, and traceability codes are generated according to coding rules and are waiting to be put into storage; S2: Store printed products in unique storage locations according to the allocation strategy and complete the binding between printed products and storage location coordinate numbers; S3: After the printed products are put into storage, the environmental status monitoring of the corresponding storage location coordinates is started simultaneously. If: Abnormal environmental conditions trigger multi-level responses; The environmental status is normal and does not trigger a multi-level response; S4: until the printed products at the storage location coordinates are cleared, the environmental status monitoring of the corresponding storage location coordinates is automatically turned off.

9. The intelligent monitoring and management method for a packaging and printing production line according to claim 8, characterized in that: Also includes after-sales methods for sold printed products: S51: Receive a traceability code query request from the client; S52. Retrieve related data from the blockchain database, where the related data includes: a. Production time and batch of printed products; b. Original quality inspection images and quality inspection results of the printing product production process; c. Warehouse location coordinates and storage environment status data of printed products, including temperature, humidity and ultraviolet radiation intensity; S53. Generate a traceability report.

10. The intelligent monitoring and management method for a packaging and printing production line according to claim 9, characterized in that: When the central control unit receives a traceability code query request from a client, it automatically extracts the traceability code of the printed product, queries the blockchain database for historical defect data of printed products from the same batch and production line, and calculates the defect rate: If the defect rate of printed products on the same production line is greater than the threshold, the production line equipment is marked as abnormal; If the defect rate of printed products in the same batch is greater than the threshold, the raw material is marked as abnormal; If the defect rate of printed products at the same storage location coordinates is greater than the threshold, the storage location coordinate environment is marked as abnormal.

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