Intelligent packaging control method and system for bag type packaging machine

Abstract

The invention relates to the technical field of packaging, in particular to an intelligent packaging control method and system of a bag type packaging machine. Comprising the following steps that S1, the packaging product type of the bag type packaging machine is obtained, and packaging parameters of the bag type packaging machine are extracted based on the packaging product type; the packaging product type comprises a product physical state and a switching node of the product physical state; s2, extracting a process trigger signal of the bag type packaging machine based on the packaging parameters; based on the process trigger signal of the bag type packaging machine, packaging parameter replacement nodes are extracted; the packaging parameter replacement node comprises a parameter adjustment node triggered by equipment state change in the same packaging product type. According to the method, drift is inhibited by dynamically correcting the parameters, the nodes are accurately judged and adjusted through time sequence analysis, the parameters are intelligently classified and reused, the process trend is pre-judged, polymorphic self-adaptive packaging is achieved, manual intervention is reduced, efficiency is improved, and quality is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of packaging, and particularly to an intelligent packaging control method and system for a bag-type packaging machine. Background Art

[0002] Existing bag-type packaging machines have significant limitations in packaging multiple categories of products: Firstly, when facing products with different physical forms such as liquids, powders, and solids, it is necessary to rely on manual experience to preset fixed parameters. Due to the poor adaptability of parameters such as sealing pressure and filling speed caused by morphological differences, the switching efficiency is low, and packaging defects such as poor sealing and overfilling are likely to occur due to parameter mismatches; Secondly, during the long-term operation of the equipment, there is a lack of a real-time dynamic correction mechanism for parameter drift caused by factors such as mechanical wear and changes in environmental temperature and humidity. Frequent shutdowns for manual calibration are required, resulting in production line interruptions; Thirdly, the parameter reset logic is single when switching products, and the common parameters (such as bag-making length) that can be reused across categories and the specific product-specific parameters (such as liquid sealing temperature) are not distinguished, resulting in repeated configuration and redundant operations; Traditional methods usually use fixed thresholds (such as temperature deviation ±5°C) to judge parameter adjustment nodes, and cannot distinguish the gradual parameter shift (such as the sealing pressure decreasing by 0.1 MPa per month) caused by equipment wear within the same product type from the sudden parameter reset requirements (such as resetting the filling speed when switching from solids to liquids) during cross-product switching. Moreover, the excessive weight of outdated historical data is likely to cause misjudgment; In addition, the existing technology lacks effective exploration of the migration characteristics of packaging parameters, making it difficult to achieve cross-category parameter reuse, further restricting the improvement of packaging efficiency. Summary of the Invention

[0003] In order to overcome the shortcomings of the contradiction between dynamic anomaly recognition and real-time regulation, the present invention provides an intelligent packaging control method and system for a bag-type packaging machine.

[0004] The technical implementation solution of the present invention is: an intelligent packaging control method for a bag-type packaging machine, including the following steps: S1: Obtain the packaging product type of the bag-type packaging machine, and based on the packaging product type, extract the packaging parameters of the bag-type packaging machine; the packaging product type includes the product physical state and the switching node of the product physical state; S2: Based on the packaging parameters, extract the process trigger signals of the bag-type packaging machine; based on the process trigger signals of the bag-type packaging machine, extract the packaging parameter replacement nodes; the packaging parameter replacement nodes include: parameter adjustment nodes triggered by equipment state changes within the same packaging product type; parameter reset nodes triggered when switching between different packaging product types; S3: Replace nodes based on the packaging parameters, classify the nodes replaced according to the same packaging parameters and those replaced according to different packaging parameters, and obtain the packaging offset parameters; the packaging offset parameters include the offset of the packaging parameters of the bag packaging machine itself during the packaging process for the same type of packaged product, and the offset of the packaging parameters generated by the bag packaging machine during the process of replacing the type of packaged product for different types of packaged products. S4: Obtain packaging similarity parameters based on the packaging offset parameters; control the bag packaging machine to perform intelligent packaging based on the packaging similarity parameters; the packaging similarity parameters include migration parameters and independent parameters; the migration parameters refer to the general process parameters that can be reused across product types; the independent parameters refer to the dedicated parameters that are only applicable to specific types of packaged products.

[0005] Preferably, obtain the type of packaged product of the bag packaging machine, and extract the packaging parameters of the bag packaging machine based on the type of packaged product; the type of packaged product includes the physical state of the product and the switching nodes of the physical state of the product, including: Based on the physical state of the product, obtain the initial parameter set for bag making, filling, and sealing of the bag packaging machine and the corresponding time period of the initial parameter set; Based on the switching nodes of the physical state of the product, obtain the adjustment parameter set for bag making, filling, and sealing of the bag packaging machine and the corresponding time period of the adjustment parameter set; Based on the initial parameter set and the corresponding time period of the initial parameter set, use the adjustment parameter verification formula to obtain the corrected adjustment parameter set; Based on the initial parameter set and the corrected adjustment parameter set, obtain the packaging parameters of the bag packaging machine.

[0006] Preferably, the step of using the adjustment parameter verification formula to obtain the corrected adjustment parameter set based on the initial parameter set and the corresponding time period of the initial parameter set includes: obtaining the parameter deviation amount of each stage through the adjustment parameter verification formula, and obtaining the adjustment parameter correction amount through the adjustment parameter correction formula; the adjustment parameter verification formula is as follows,

[0007] where, is the parameter deviation amount of the stage, is the weight coefficient of the physical state of the product in the stage, is the switching node of the physical state of the product, is the stage parameter adjustment time, is the initial parameter setting time, is the time decay factor, is the minimum value, = 1, 2, 3 correspond to the bag-making, filling, and sealing stages respectively; the adjustment parameter correction formula is as follows,

[0008] Among them, is the corrected parameter value in the stage, is the original parameter value in the stage, = 1, 2, 3 correspond to the bag-making, filling, and sealing stages respectively.

[0009] Preferably, extracting the process trigger signal of the bag-type packaging machine based on the packaging parameters includes: Extracting the adjustment parameter set according to the packaging parameters to generate the first process trigger signal; Extracting the corrected adjustment parameter set according to the packaging parameters to generate the second process trigger signal; Based on the first process trigger signal and the second process trigger signal, obtaining the process signal trigger trends of the same packaging product type and different packaging product types in the three stages of bag-making, filling, and sealing; Based on the process signal trigger trends, obtaining the process trigger signal of the bag-type packaging machine in advance or postponed.

[0010] Preferably, extracting the packaging parameter replacement node based on the process trigger signal of the bag-type packaging machine includes: By calculating the Pearson correlation coefficient between the process trigger signal and the reference timing data corresponding to the historical parameter adjustment node, if the Pearson correlation coefficient is greater than the preset Pearson correlation coefficient threshold, it is determined as the trend consistency parameter adjustment node and used as the parameter adjustment node of the same packaging product type; By calculating the Pearson correlation coefficient between the process trigger signal and the reference timing data corresponding to the historical parameter reset node, if the Pearson correlation coefficient is less than the preset Pearson correlation coefficient threshold, it is determined as the trend reverse parameter adjustment node and used as the parameter reset node of different packaging product types.

[0011] Preferably, the calculating the Pearson correlation coefficient between the process trigger signal and the reference timing data corresponding to the historical parameter adjustment node includes: The Pearson correlation coefficient formula is as follows,

[0012] Among them, is the Pearson correlation coefficient, is the timing data of the process trigger signal, is the reference timing data, is the standard deviation of the timing data of the process trigger signal, is the standard deviation of the reference timing data.

[0013] Preferably, obtaining the process trigger signal of the bag packaging machine in advance or postponed based on the process signal trigger trend includes: If the process signal trigger trend is greater than the preset process signal trigger trend threshold, obtain the process trigger signal of the bag packaging machine in advance; If the process signal trigger trend is less than the preset process signal trigger trend threshold, obtain the process trigger signal of the bag packaging machine postponed.

[0014] Preferably, classifying the replacement nodes based on the packaging parameters into the same packaging parameter replacement nodes and different packaging parameter replacement nodes, and obtaining the packaging offset parameter includes: Extract the parameter adjustment nodes triggered by the change of the equipment state within the same packaging product type, and mark them as the same packaging parameter replacement nodes; Extract the parameter reset nodes triggered by the switching between different packaging product types, and mark them as different packaging parameter replacement nodes; Based on the same packaging parameter replacement nodes, calculate the parameter offset within the same product type, and record it as the first packaging offset parameter; Based on different packaging parameter replacement nodes, calculate the parameter offset when switching product types, and record it as the second packaging offset parameter; Take the weighted sum of the first packaging offset parameter and the second packaging offset parameter as the final packaging offset parameter.

[0015] Preferably, obtaining the packaging similarity parameter based on the packaging offset parameter; controlling the bag packaging machine for intelligent packaging based on the packaging similarity parameter includes: Taking bag making, filling, and sealing as the reference timing, analyze the common characteristics in the packaging offset parameter, extract the parameter set that can be reused across product types, and mark it as the migration parameter; Analyze the differential characteristics in the packaging offset parameter, extract the parameter set that is only applicable to a specific product type, and mark it as the independent parameter; Take the migration parameter as the global control reference and apply it to the packaging processes of all product types; Dynamically adjust the packaging parameters of a specific product type according to the independent parameter, and combine the migration parameter to achieve adaptive control.

[0016] Preferably, the intelligent packaging control system of a bag packaging machine includes: Parameter Classification and Offset Calculation Module: Extract parameter adjustment nodes for the same type of packaged products and parameter reset nodes for different product types, respectively count the replacement times of the two types of nodes and sum them up to generate packaging offset parameters; monitor the changes in device status and product switching signals in real time, record the parameter adjustment time points, and quantify the degree of parameter offset through frequency statistics; Similar Parameter Extraction and Control Module: Based on the packaging offset parameters, analyze the common and different characteristics in the bag-making, filling, and sealing stages; extract migratable parameters that can be reused across products as global benchmarks, and dynamically adjust them in combination with the independent parameters of specific product types; determine the reusability of parameters through the correlation of time-series data, and optimize the parameter values using the deviation correction formula to achieve adaptive packaging control; Process Trigger Signal Optimization Module: Through trend analysis triggered by process signals, trigger parameter adjustment instructions in advance or delay, and calculate the correlation between the signal and historical nodes according to the Pearson correlation coefficient, and set thresholds to determine the type of adjustment nodes.

[0017] Beneficial Effects: The present invention suppresses parameter drift caused by equipment wear and environmental fluctuations through a dynamic parameter correction mechanism, accurately discriminates the types of parameter adjustment nodes by using time-series correlation analysis, and reduces misjudgment of product switching; extracts common parameters and dedicated parameters based on the parameter migration characteristics, realizes cross-category parameter reuse and scenario adaptation, and reduces the cost of repeated configuration; the trend prediction mechanism of the process trigger signal optimizes the mechanical action coordination and improves the stability of the packaging rhythm. Compared with traditional methods, the present invention can adapt to the packaging requirements of multi-form products, significantly reduce the intensity of manual intervention. In addition, the parameter adjustment accuracy and the overall production line efficiency are both significantly improved, and the packaging quality in specific scenarios is guaranteed. Brief Description of the Drawings

[0018] Figure 1 is a flow chart of the intelligent packaging control method for the bag packaging machine of the present invention; Figure 2 is a structural schematic diagram of the intelligent packaging control system for the bag packaging machine of the present invention. Detailed Embodiments

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment 1: An intelligent packaging control method for a bag packaging machine, as Figure 1 shown, includes the following steps: S1: Obtain the packaging product type of the bag packaging machine. Based on the packaging product type, extract the packaging parameters of the bag packaging machine. The packaging product type includes the product physical state and the switching node of the product physical state. S2: Based on the packaging parameters, extract the process trigger signal of the bag packaging machine. Based on the process trigger signal of the bag packaging machine, extract the packaging parameter replacement node. The packaging parameter replacement node includes: the parameter adjustment node triggered by the change of equipment state within the same packaging product type; the parameter reset node triggered when switching between different packaging product types. S3: Based on the packaging parameter replacement node, classify according to the same packaging parameter replacement node and different packaging parameter replacement nodes to obtain the packaging offset parameter. The packaging offset parameter includes the packaging parameter offset of the bag packaging machine itself during the packaging process of the same packaging product type, and the packaging parameter offset generated by the bag packaging machine during the process of changing the product type between different packaging product types. S4: Based on the packaging offset parameter, obtain the packaging similarity parameter. Based on the packaging similarity parameter, control the bag packaging machine to perform intelligent packaging. The packaging similarity parameter includes the migration parameter and the independent parameter. The migration parameter refers to the general process parameter that can be reused across product types. The independent parameter refers to the special parameter that is only applicable to a specific packaging product type.

[0021] Obtain the packaging product type of the bag packaging machine. Based on the packaging product type, extract the packaging parameters of the bag packaging machine. The packaging product type includes the product physical state and the switching node of the product physical state, including: Based on the product physical state, obtain the initial parameter set for bag making, filling, and sealing of the bag packaging machine and the corresponding time period of the initial parameter set. Based on the switching node of the product physical state, obtain the adjustment parameter set for bag making, filling, and sealing of the bag packaging machine and the corresponding time period of the adjustment parameter set. Based on the initial parameter set and the corresponding time period of the initial parameter set, use the adjustment parameter verification formula to obtain the corrected adjustment parameter set. Based on the initial parameter set and the corrected adjustment parameter set, obtain the packaging parameters of the bag packaging machine.

[0022] For further explanation, product physical state: Divide the initial parameters according to the product physical state (such as liquid, powder, solid). For example, liquid packaging requires higher sealing pressure. Switching node of product physical state: Capture the key time point of product form change (such as switching from solid to liquid).

[0023] Technical effect: Automatically match parameters for different forms, reducing manual intervention. The adjustment parameter correction formula suppresses parameter drift caused by equipment wear or environmental fluctuations.

[0024] Example, liquid packaging: The initial sealing temperature is set at 180°C. If it is detected that the environmental humidity increases and causes poor sealing, the temperature is adjusted to 185°C through the parameter correction formula; Solid packaging: The initial filling speed is 50 packs per minute. Due to the decrease in equipment efficiency, it is corrected to 48 packs per minute.

[0025] Supplementary note, (the switching node of the physical state of the product) needs to be detected in real time through sensors (such as an optoelectronic switch triggering a form switching signal). The initial parameter set time period refers to the time interval from parameter setting to the first detection of the adjustment requirement ( ), which is used to verify the stability of the initial parameters; The adjusted parameter set time period refers to the time interval from the first adjustment to the subsequent switching node (such as - ), which is used to dynamically correct the parameters.

[0026] Based on the initial parameter set and the corresponding time period of the initial parameter set, the corrected adjusted parameter set is obtained using the adjusted parameter verification formula, including: obtaining the parameter deviation amount of each stage through the adjusted parameter verification formula, and obtaining the adjusted parameter correction amount through the adjusted parameter correction formula; The adjusted parameter verification formula is as follows,

[0027] Where, is the parameter deviation amount of the stage, is the weight coefficient of the physical state of the product in the stage, is the switching node of the physical state of the product, is the parameter adjustment time of the stage, is the initial parameter setting time, is the time decay factor, is the minimum value, = 1, 2, 3 correspond to the bag-making, filling, and sealing stages respectively; The adjusted parameter correction formula is as follows,

[0028] Where, is the corrected parameter value of the stage, is the original parameter value of the stage, = 1, 2, 3 correspond to the bag-making, filling, and sealing stages respectively.

[0029] For further illustration, the parameter deviation is dynamically quantified by adjusting the parameter verification formula. The parameter adjustment correction formula normalizes the total deviation amount and distributes the parameter adjustment amplitude proportionally to avoid excessive correction of a single parameter. is the minimum value, which is used to avoid numerical instability caused by a zero denominator. is used to sum up the parameter deviation amounts for all stages. The weight coefficient has the same physical dimension as the parameter to be adjusted, to ensure the dimensional unity of the parameter deviation amount . For example, when the parameter to be adjusted is the sealing temperature, has the dimension of temperature ( ); when the parameter to be adjusted is the filling speed, has the dimension of speed (LT −1 ). The time decay factor has the dimension of the reciprocal of time (T −1 ), which is used to control the decay rate of the historical parameter weight.

[0030] Technical effects: Capturing parameter offsets in real time and adapting to environmental changes (such as temperature fluctuations); suppressing noise data interference through and .

[0031] Example: If the sealing temperature drifts after the initial setting due to equipment aging, the parameter verification formula will calculate the deviation amount of the sealing temperature and correct the temperature value based on the total deviation ratio to avoid poor sealing. The value of the time decay factor is related to the rate of change of the equipment state. For example, in a high-speed production line, is set to 0.5 to quickly decay the influence of historical parameters; in scenarios with high stability requirements (such as precision liquid packaging), is set to 0.2 to retain more historical data. The weight coefficient adjusts the importance of different stages according to the physical state of the product (such as = 0.8 for the liquid sealing stage, = 0.5 for the solid filling stage).

[0032] Supplementary note: needs to be preset according to actual production experience. For example, the sealing stage of liquid packaging should be higher than that of solid packaging; the time decay factor needs to be dynamically adjusted according to the equipment response speed: in a high-speed production line, the value increases to accelerate the decay of the historical parameter weight and ensure that real-time data dominates parameter correction. The parameter adjustment time for the stage Obtained in the following ways: The sensor records in real time: Deploy high-precision sensors (such as photoelectric sensors, pressure sensors) at each stage of bag making, filling, and sealing. When it detects that the parameter deviation exceeds the preset sensor threshold, record the current time as ; Historical data optimization: According to the historical production data, statistically calculate the adjustment time intervals at each stage, and dynamically update in combination with the degree of equipment wear ; Dynamic algorithm prediction: By analyzing the trend of process signals in real time (such as the temperature change rate), use the regression model to predict the optimal adjustment time point, and update it to .

[0033] The process trigger signals of the bag packaging machine are extracted based on the packaging parameters, including: Extract the adjustment parameter set according to the packaging parameters to generate the first process trigger signal; Extract the corrected adjustment parameter set according to the packaging parameters to generate the second process trigger signal; Based on the first process trigger signal and the second process trigger signal, obtain the process signal trigger trends at the bag making, filling, and sealing stages for the same packaging product type and different packaging product types; Based on the process signal trigger trends, obtain the process trigger signals of the bag packaging machine in advance or postponed.

[0034] Further explanation: The first process trigger signal reflects the dynamic change trend of the preset parameters, and the second process trigger signal reflects the actual demand trend of the corrected parameters; by comparing the temporal correlation of the two types of signals, analyze the advance or postponement of the trigger trend; The first process trigger signal: generated based on the original adjustment parameter set, reflecting the change of the preset parameters; The second process trigger signal: generated based on the corrected parameters, reflecting the change of the actual demand parameters. By comparing the temporal correlation of the two types of signals ( ), judge the type of parameter adjustment node: Trend consistency node ( > preset Pearson correlation coefficient threshold): Fine-tuning of parameters within the same product type (such as filling speed optimization); Trend reversibility node ( < preset Pearson correlation coefficient threshold): Parameter reset when switching product types (such as powder → liquid packaging).

[0035] Technical effects: Distinguish normal adjustments from abnormal fluctuations, reduce misjudgments; Advance / postpone trigger instructions to avoid mechanical action conflicts.

[0036] Example: When it detects that the sealing temperature signal is reverse to the historical trend ( <0.3), it is determined that the product has been switched, and the parameter reset is triggered.

[0037] Supplementary note: Preset Pearson correlation coefficient threshold setting: It needs to be determined through training with historical data. For example, when switching liquid packaging The preset Pearson correlation coefficient threshold is set to 0.2 for liquids and 0.4 for solids; Noise filtering: For Perform moving average filtering to avoid interference from instantaneous fluctuations in correlation calculations.

[0038] Based on the process trigger signal of the bag packaging machine, extract the packaging parameter replacement nodes, including: By calculating the Pearson correlation coefficient between the process trigger signal and the reference timing data corresponding to the historical parameter adjustment node, if the Pearson correlation coefficient is greater than the preset Pearson correlation coefficient threshold, it is determined as a trend consistency parameter adjustment node and used as the parameter adjustment node for the same packaging product type; By calculating the Pearson correlation coefficient between the process trigger signal and the reference timing data corresponding to the historical parameter reset node, if the Pearson correlation coefficient is less than the preset Pearson correlation coefficient threshold, it is determined as a trend reversibility parameter adjustment node and used as the parameter reset node for different packaging product types.

[0039] Further explanation: Trend consistency node: Through the Pearson correlation coefficient ( ) to judge the similarity between the current signal and the historical adjustment trend ( >0.7); Trend reversibility node: When the signal is significantly different from the reference timing data of the historical parameter reset node ( <0.3), it is determined as a product switch. Consistency check: If > the preset Pearson correlation coefficient threshold, it indicates that the current process trigger signal is highly correlated with the historical parameter adjustment event in terms of time distribution (such as the signal rising trend being synchronized with the adjustment event), and it is determined as the parameter adjustment node for the same product type. Reversibility check: If < the preset Pearson correlation coefficient threshold, it indicates that the signal has no significant association with the historical adjustment event and may be the parameter reset node triggered by product switching.

[0040] Technical effect: Distinguish normal parameter fluctuations (such as temperature fine-tuning) from abnormal switches (such as liquid → solid); Dynamic thresholds adapt to different production line rhythms (lower thresholds for high-speed lines).

[0041] Example: Trend consistency node: The sealing pressure is gradually adjusted from 2.0 MPa to 2.2 MPa ( =0.85), determined as the optimization of the same product; Trend reversibility node: The filling speed drops suddenly from 50 packs / minute to 30 packs / minute ( =0.15), determined as a product switch.

[0042] Supplementary note, Noise processing: For Perform sliding window smoothing (such as 5-point mean filtering) to avoid interference from instantaneous fluctuations in correlation calculation; preset dynamic adjustment of the Pearson correlation coefficient threshold: automatically update the threshold according to the production frequency (e.g., in a high-speed production line preset the Pearson correlation coefficient threshold to be reduced by 10%).

[0043] By calculating the Pearson correlation coefficient between the process trigger signal and the reference timing data corresponding to the historical parameter adjustment node, including: The Pearson correlation coefficient formula is as follows,

[0044] where, is the Pearson correlation coefficient, is the timing data of the process trigger signal, is the reference timing data, is the standard deviation of the timing data of the process trigger signal, is the standard deviation of the reference timing data.

[0045] For further illustration, for example, the reference timing data of the historical parameter adjustment node ( ) can be generated by recording the time points of past parameter adjustment events: on the time axis, the time points when adjustment events occur are marked as 1, and the rest of the time points are 0. The process trigger signal ( ) is the continuously monitored timing data of the sealing temperature. By calculating the Pearson correlation coefficient between the two, it is judged whether the current signal is consistent with the historical adjustment trend.

[0046] Based on the process signal trigger trend, obtain the process trigger signal of the bag-type packaging machine in advance or delay, including: If the process signal trigger trend is greater than the preset process signal trigger trend threshold, obtain the process trigger signal of the bag-type packaging machine in advance; If the process signal trigger trend is less than the preset process signal trigger trend threshold, obtain the process trigger signal of the bag-type packaging machine by delaying.

[0047] For further illustration, early trigger: when the signal trend slope is greater than the preset process signal trigger trend threshold (such as the rising slope > 0.5), it is predicted that the parameters need to be adjusted in advance to avoid action delay; delayed trigger: when the trend slope is less than the preset process signal trigger trend threshold (such as the falling slope < 0.2), the adjustment is postponed to wait for the equipment to stabilize.

[0048] Technical effects: Ensure that parameter adjustment is synchronized with the mechanical action cycle (such as sealing must be triggered within 0.3 s after bag making is completed); reduce the idling time caused by waiting.

[0049] Example, early trigger: When it is detected that the filling speed trend is rising, the supply of the next package of materials is started 0.2 s in advance; delayed trigger: When the sealing temperature fluctuation is greater than the preset sealing temperature threshold, it is delayed for 0.1 s and then sealed after the temperature stabilizes.

[0050] Supplementary note, slope calculation: The least squares method is used to fit the trend line to avoid interference from local fluctuations; safety mechanism: When the delayed trigger times out (e.g., >1 s), it is forced to start to prevent the production line from stagnating.

[0051] Based on the packaging parameter replacement nodes, they are classified according to the replacement nodes with the same packaging parameters and the replacement nodes with different packaging parameters to obtain the packaging offset parameters, including: Extract the parameter adjustment nodes triggered by the change of the equipment state within the same packaging product type and mark them as the replacement nodes with the same packaging parameters; Extract the parameter reset nodes triggered by the switching of different packaging product types and mark them as the replacement nodes with different packaging parameters; Based on the replacement nodes with the same packaging parameters, calculate the parameter offset within the same product type and record it as the first packaging offset parameter; Based on the replacement nodes with different packaging parameters, calculate the parameter offset when switching product types and record it as the second packaging offset parameter; Take the weighted sum of the first packaging offset parameter and the second packaging offset parameter as the final packaging offset parameter.

[0052] Further explanation: Packaging offset parameters: Count the adjustment frequencies of the same nodes (equipment state changes) and different nodes (product switching) to quantify the degree of parameter fluctuation; Migration parameters: Common parameters across product types (such as bag making length); Independent parameters: Specific product-specific parameters (such as the sealing pressure for liquid packaging).

[0053] Technical effects: Parameter reuse, migration parameters reduce the repeated configuration time; Dynamic adaptation, independent parameters ensure the accuracy of specific scenarios.

[0054] Example: Migration parameters (bag making length) are applied to all products, and independent parameters (sealing temperature) are dynamically adjusted according to liquid / solid.

[0055] Based on the packaging offset parameters, obtain the packaging similarity parameters; Based on the packaging similarity parameters, control the bag packaging machine for intelligent packaging, including: Taking bag making, filling, and sealing as the reference time sequence, analyze the common characteristics in the packaging offset parameters, extract the parameter set that can be reused across product types, and mark it as the migration parameter; Analyze the differential characteristics in the packaging offset parameters, extract the parameter set that is only applicable to a specific product type, and mark it as the independent parameter; Take the migration parameters as the global control benchmark and apply them to the packaging processes of all product types; Dynamically adjust the packaging parameters of specific product types according to independent parameters, and achieve adaptive control in combination with the migration parameters.

[0056] For further illustration, in terms of technical effects, the migration parameters reduce the repeated configuration time; the independent parameters ensure the quality of specific scenarios (such as liquid tightness).

[0057] Example: Migration parameters: Based on the bag-making timing sequence, extract a bag-making length of 120 mm as a common parameter across products; Independent parameters: Based on the sealing timing sequence, set the sealing pressure for liquid packaging to 2.5 MPa.

[0058] Supplementary note, conflict handling: When there is a conflict between the migration and independent parameters (such as filling speed and sealing temperature), prioritize ensuring the independent parameters; Dynamic update: Re-cluster the migration parameters within a fixed period to adapt to the addition of new product types.

[0059] Example 2: Based on Example 1, an intelligent packaging control system for a bag-type packaging machine, as Figure 2 shown, includes: A parameter classification and offset calculation module, which extracts the parameter adjustment nodes of the same packaging product type and the parameter reset nodes of different product types, respectively counts the replacement times of the two types of nodes and sums them up to generate packaging offset parameters; monitors the changes in the device status and product switching signals in real time, records the parameter adjustment time points, and quantifies the parameter offset degree through frequency statistics; A similar parameter extraction and control module, based on the packaging offset parameters, analyzes the commonalities and differences in the bag-making, filling, and sealing stages; extracts reusable migration parameters across products as the global benchmark, and dynamically adjusts in combination with the independent parameters of specific product types; determines the parameter reusability through the correlation of time-series data, and optimizes the parameter values using the deviation correction formula to achieve adaptive packaging control; A process trigger signal optimization module, which conducts trend analysis through process signals to trigger parameter adjustment instructions earlier or later, and calculates the correlation between the signal and historical nodes according to the Pearson correlation coefficient, and sets a threshold to determine the type of adjustment node.

[0060] The above has introduced this application in detail. Specific examples are used in this article to elaborate on the principle and implementation method of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, based on the idea of this application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A smart packaging control method for a bag packaging machine, characterized in that: The following steps are involved: S1: Obtain the packaging product type of the bag packaging machine, and based on the packaging product type, extract the packaging parameters of the bag packaging machine; the packaging product type includes the physical state of the product and the switching node of the physical state of the product; S2: extracting a process trigger signal of a bag packaging machine based on the packaging parameters; Extracting a packaging parameter change node based on a process trigger signal of the bag packaging machine; The packaging parameter change node includes: a parameter adjustment node triggered by a change in device status within the same packaging product type; a parameter reset node triggered when switching between different packaging product types; S3: Based on the packaging parameter change nodes, classify the same packaging parameter change nodes and different packaging parameter change nodes to obtain packaging offset parameters; the packaging offset parameters include the packaging parameter offset of the bag packaging machine itself during the packaging process of the same packaging product type and the packaging parameter offset generated by the bag packaging machine during the process of changing product types of different packaging products; S4: Based on the packaging offset parameters, packaging similarity parameters are obtained; based on the packaging similarity parameters, the bag packaging machine is controlled to perform intelligent packaging; the packaging similarity parameters include migration parameters and independent parameters; the migration parameters refer to universal process parameters that are reusable across product types; the independent parameters refer to special parameters that are only applicable to specific packaging product types.

2. According to the intelligent packaging control method of a bag packaging machine according to claim 1, it is characterized in that: The obtaining of the packaging product type of the bag packaging machine, and extracting the packaging parameters of the bag packaging machine based on the packaging product type; the packaging product type includes a product physical state and a switching node of the product physical state, including: Based on the physical state of the product, an initial parameter set of bagging, filling, and sealing of the bag packaging mechanism and a time period corresponding to the initial parameter set are obtained; Based on the switching node of the physical state of the product, a set of adjustment parameters for bagging, filling and sealing of the bag packaging mechanism and a time period corresponding to the set of adjustment parameters are obtained; Based on the initial parameter set and the time period corresponding to the initial parameter set, obtaining a revised adjustment parameter set by using an adjustment parameter verification formula; Based on the initial parameter set and the corrected adjustment parameter set, packaging parameters of the bag packaging machine are obtained.

3. The intelligent packaging control method of a bag packaging machine according to claim 2 is characterized in that: The method of obtaining a corrected adjustment parameter set based on the initial parameter set and the time period corresponding to the initial parameter set by using an adjustment parameter verification formula includes: obtaining a parameter deviation amount in each stage by using an adjustment parameter verification formula, and obtaining an adjustment parameter correction amount by using an adjustment parameter correction formula; the adjustment parameter verification formula is as follows: ； in, For the The parameter deviation of the stage, For the The weight coefficient of the physical state of the stage product, is the switching node of the physical state of the product, For the Stage parameter adjustment time, Set time for initial parameters, is the time decay factor, is the minimum value, =1, 2, 3 correspond to bag making, filling and sealing stages respectively; the adjustment parameter correction formula is as follows: ； in, For the The parameter value after stage correction, For the The original parameter value of the stage, =1, 2, 3 correspond to the bag making, filling and sealing stages respectively.

4. The intelligent packaging control method of a bag packaging machine according to claim 1 is characterized in that: The step of extracting a process trigger signal of a bag packaging machine based on the packaging parameters comprises: Extracting and adjusting a parameter set according to the packaging parameters to generate a first process trigger signal; Extracting a modified adjustment parameter set according to the packaging parameters to generate a second process trigger signal; Based on the first process trigger signal and the second process trigger signal, the process signal trigger trends in the three stages of bag making, filling and sealing for the same packaging product type and different packaging product types are obtained; Based on the process signal triggering trend, the process triggering signal of the bag packaging machine is obtained in advance or in a delayed manner.

5. The intelligent packaging control method of a bag packaging machine according to claim 1 is characterized in that: The step of extracting a packaging parameter change node based on a process trigger signal of the bag packaging machine includes: By calculating the Pearson correlation coefficient of the benchmark time series data corresponding to the process trigger signal and the historical parameter adjustment node, if the Pearson correlation coefficient is greater than the preset Pearson correlation coefficient threshold, it is determined to be a trend consistency parameter adjustment node and used as a parameter adjustment node for the same packaging product type; By calculating the Pearson correlation coefficient of the benchmark time series data corresponding to the process trigger signal and the historical parameter reset node, if the Pearson correlation coefficient is less than the preset Pearson correlation coefficient threshold, it is determined to be a trend-reverse parameter adjustment node, which is used as the parameter reset node for different packaging product types.

6. The intelligent packaging control method of a bag packaging machine according to claim 5, characterized in that: The calculation of the Pearson correlation coefficient of the reference timing data corresponding to the process trigger signal and the historical parameter adjustment node includes: the Pearson correlation coefficient formula is as follows: ； in, is the Pearson correlation coefficient, is the timing data of the process trigger signal, is the benchmark time series data, is the standard deviation of the timing data of the process trigger signal, is the standard deviation of the benchmark time series data.

7. The intelligent packaging control method of a bag packaging machine according to claim 4 is characterized in that: The method of obtaining the process trigger signal of the bag packaging machine in advance or in delay based on the process signal trigger trend includes: If the process signal trigger trend is greater than a preset process signal trigger trend threshold, a process trigger signal of the bag packaging machine is obtained in advance; If the process signal trigger trend is less than a preset process signal trigger trend threshold, obtaining the process trigger signal of the bag packaging machine is delayed.

8. The intelligent packaging control method of a bag packaging machine according to claim 1, characterized in that: The step of classifying the packaging parameter replacement nodes according to the same packaging parameter replacement nodes and the different packaging parameter replacement nodes based on the packaging parameter replacement nodes to obtain the packaging offset parameters includes: Extract parameter adjustment nodes triggered by device status changes within the same packaging product type and mark them as same packaging parameter change nodes; Extract the parameter reset nodes triggered when switching between different packaging product types and mark them as different packaging parameter replacement nodes; Based on the node replacement of the same packaging parameter, the parameter offset within the same product type is calculated and recorded as the first packaging offset parameter; Based on different packaging parameter replacement nodes, the parameter offset when the product type is switched is calculated and recorded as the second packaging offset parameter; The weighted sum of the first packing offset parameter and the second packing offset parameter is taken as the final packing offset parameter.

9. The intelligent packaging control method of a bag packaging machine according to claim 1, characterized in that: Based on the packaging offset parameter, obtaining a packaging similarity parameter; Based on the packaging similarity parameters, the bag packaging machine is controlled to perform intelligent packaging, including: Taking bag making, filling and sealing as the benchmark sequence, analyze the common features in the packaging offset parameters, extract the parameter set that can be reused across product types, and mark it as migration parameters; Analyze the differential features in the packaging offset parameters, extract the parameter set that is only applicable to a specific product type, and mark it as independent parameters; Apply migration parameters as global control benchmarks to the packaging process for all product types; Dynamically adjust packaging parameters for specific product types based on independent parameters, combined with migration parameters to achieve adaptive control.

10. An intelligent packaging control system for a bag packaging machine, used to implement the intelligent packaging control method for a bag packaging machine as claimed in any one of claims 1 to 9, characterized in that: include: The parameter classification and offset calculation module extracts the parameter adjustment nodes of the same packaging product type and the parameter reset nodes of different product types, respectively counts the number of replacements of the two types of nodes and sums them up to generate packaging offset parameters; monitors equipment status changes and product switching signals in real time, records parameter adjustment time points, and quantifies the degree of parameter offset through frequency statistics; The similar parameter extraction and control module analyzes the commonalities and differences in bag making, filling, and sealing stages based on packaging offset parameters; extracts reusable migration parameters across products as global benchmarks, and dynamically adjusts them in combination with independent parameters of specific product types; determines parameter reusability through time series data correlation, and uses deviation correction formulas to optimize parameter values ​​to achieve adaptive packaging control; The process trigger signal optimization module triggers trend analysis of process signals, advances or delays the trigger parameter adjustment instructions, calculates the correlation between the signal and the historical nodes based on the Pearson correlation coefficient, and sets the threshold to determine the adjustment node type.

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