Dynamic pressure control system and method of strapping machine

Abstract

The invention relates to the technical field of pressure control, in particular to a dynamic pressure control system and method of a strapping machine. The method comprises the following steps: acquiring information data of an object to be bundled; performing three-dimensional modeling on the to-be-bundled object information data to generate three-dimensional modeling data of the to-be-bundled object; binding mode confirmation is conducted on the binding machine according to the three-dimensional modeling data of the to-be-bound object, binding mode data are obtained, and the binding mode data comprise a single-layer binding mode and a multi-layer binding mode; initial strapping pressure setting is carried out on the information data of the to-be-strapped object based on the single-layer strapping mode, and single-layer strapping initial pressure data is obtained; and carrying out object deformation binding pressure adjustment on the single-layer binding initial pressure data to generate single-layer binding pressure adjustment data. Through three-dimensional modeling, dynamic pressure adjustment and a feedback control mechanism, the intelligence and precision of dynamic pressure control of the strapping machine are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pressure control, and in particular to a dynamic pressure control system and method of a strapping machine. Background Art

[0002] In traditional strapping machines, the tightening effect of strapping often depends on the fixed pressure of the mechanical structure. Once set, it is difficult to dynamically adjust according to actual needs, resulting in uneven strapping or damage due to overtightening. In order to cope with this problem, the application of dynamic pressure control technology in strapping machines has gradually emerged in recent years. The initial dynamic pressure control method was mainly based on a simple pressure sensor, which adjusted the pressure applied to the object through detection and feedback. However, this method is limited by the reaction speed and adjustment accuracy of the control system, and it is difficult to achieve rapid pressure control. Subsequently, the introduction of servo motors and proportional valves significantly improved the control accuracy and response speed, enabling the system to quickly adjust the strapping force according to real-time monitoring data, achieving a more flexible control effect. With the development of the Internet of Things and intelligent control technology, modern strapping machines have gradually adopted intelligent dynamic pressure control systems, combined with big data analysis and adaptive algorithms, to achieve more efficient and intelligent pressure control. This system can automatically adjust the pressure according to different item characteristics and packaging requirements, and conduct real-time monitoring during the working process to ensure the stability and quality of the strapping effect. However, in traditional equipment, the strapping pressure is often pre-set and cannot be adjusted in real time to adapt to the deformation characteristics of the object. At the same time, traditional strapping machines are usually unable to dynamically control the pressure in multi-layer strapping, which can easily lead to excessive or low pressure in certain parts, resulting in low intelligence and accuracy of the strapping machine's dynamic pressure control. Summary of the invention

[0003] Based on this, it is necessary to provide a dynamic pressure control system and method for a strapping machine to solve at least one of the above technical problems.

[0004] To achieve the above object, a dynamic pressure control method of a strapping machine is provided, the method comprising the following steps: Step S1: obtaining information data of the object to be bundled; performing three-dimensional modeling of the object to be bundled based on the information data of the object to be bundled to generate three-dimensional modeling data of the object to be bundled; confirming the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled to obtain bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode; Step S2: setting an initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode to obtain the single-layer bundling initial pressure data; adjusting the object deformation bundling pressure for the single-layer bundling initial pressure data to generate the single-layer bundling pressure adjustment data; setting multi-layer bundling pressure for the information data of the object to be bundled based on the multi-layer bundling mode to obtain the multi-layer bundling initial pressure data; performing bundling pressure strain range analysis on the multi-layer bundling initial pressure data to generate the multi-layer bundling pressure strain range data; Step S3: predicting the time-series stress change of the multi-layer bundling pressure strain range data to generate multi-layer bundling stress prediction data; performing multi-layer bundling dynamic pressure control adjustment on the multi-layer bundling initial pressure data according to the multi-layer bundling stress prediction data to generate multi-layer bundling dynamic pressure control adjustment data; constructing a pressure control strategy for the bundling mode data through the multi-layer bundling dynamic pressure control adjustment data and the single-layer bundling pressure adjustment data to generate a bundling mode pressure control strategy; Step S4: collecting pressure control adjustment feedback data of the strapping mode pressure control strategy to obtain a pressure control adjustment feedback data set; evaluating the strapping efficiency of the pressure control adjustment feedback data set to generate strapping machine strapping efficiency evaluation data; adaptively adjusting the strapping mode pressure control strategy through the strapping machine strapping efficiency evaluation data, thereby generating a dynamic pressure control optimization strategy to perform strapping operations.

[0005] The present invention can fully understand the size, shape and material properties of the object by acquiring the information data of the object to be bundled, and provide basic data for subsequent modeling and bundling. The object to be bundled is three-dimensionally modeled, and the generated three-dimensional modeling data can truly reflect the geometric characteristics of the object and support more accurate bundling operations. Confirming the bundling mode (single layer or multi-layer) according to the three-dimensional modeling data can ensure that the bundling method matches the characteristics of the object, thereby improving the bundling quality and efficiency. The initial pressure setting of the object based on the single-layer bundling mode can achieve the best bundling effect under the premise of ensuring the safety of the object. The bundling pressure is adjusted according to the deformation of the object, so that the bundling process can adapt to the deformation of the object during the bundling process and reduce the risk of damage. The bundling pressure is adjusted according to the deformation of the object, so that the bundling process can adapt to the deformation of the object during the bundling process and reduce the risk of damage. The time-series stress change prediction of the multi-layer bundling pressure strain range data can identify potential bundling problems in advance and ensure that the optimal pressure state is maintained during the bundling process. The initial pressure of the multi-layer bundling is dynamically adjusted according to the stress prediction result, which can more effectively cope with the pressure changes occurring during the bundling process and enhance the bundling stability. By constructing a pressure control strategy for the bundling mode, it is possible to provide systematic management and control for the bundling process, optimize the operating process, and improve efficiency. Pressure control adjustment feedback data collection can monitor the pressure changes in the bundling process in real time, providing a basis for further optimization. Evaluating the bundling efficiency of the bundling machine can quantify the effect of the bundling process, help identify efficiency bottlenecks, and support improvement strategies. Adaptive adjustment of the pressure control strategy based on the efficiency evaluation data of the bundling machine can continuously improve the efficiency and safety of the bundling operation and make the system more intelligent. The combination of the above steps makes the bundling process more flexible and can quickly adapt to different objects and bundling conditions. Through precise pressure control and dynamic adjustment, the damage to objects caused by improper bundling is reduced, and the loss and overall cost are reduced. The entire process optimizes the bundling operation in a data-driven manner, ensuring the improvement of work efficiency and the safety of the operation. Therefore, the present invention improves the intelligence and accuracy of the dynamic pressure control of the bundling machine through three-dimensional modeling, dynamic pressure adjustment and feedback control mechanism.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire information data of the object to be bundled; Step S12: preprocessing the information data of the objects to be bundled to generate standard information data of the objects to be bundled, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: performing object surface feature recognition on the standard information data of the object to be bundled to obtain object surface feature data; performing three-dimensional modeling of the object to be bundled based on the object surface feature data to generate three-dimensional modeling data of the object to be bundled; Step S14: confirming the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled, and obtaining the bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode.

[0007] The present invention uses data preprocessing including data cleaning, denoising, missing value filling and standardization, which can eliminate noise and outliers in the original data, ensure the quality of the information data of the object to be bundled, and thus improve the accuracy of subsequent modeling and bundling mode selection. Object surface feature recognition is based on the preprocessed standard data, which can more accurately identify the detailed features of the object and ensure the authenticity and precision of three-dimensional modeling. This process enables the bundling system to adapt to objects of different shapes, sizes and surface features. The geometric shape and surface feature data of the object are obtained through three-dimensional modeling, and the system can automatically determine the single-layer or multi-layer bundling mode (step S14). This bundling mode confirmation based on object features enables the bundling process to be adaptively adjusted according to the actual situation of the object, thereby improving the stability and safety of the bundling. The method provides automated and intelligent bundling mode selection, avoids the tediousness and uncertainty of manual mode selection, thereby saving time and improving overall work efficiency.

[0008] Preferably, step S14 includes the following steps: Step S141: performing surface smoothness analysis on the three-dimensional modeling data of the object to be bundled to generate surface smoothness data of the object to be bundled; performing surface regular shape analysis on the three-dimensional modeling data of the object to be bundled to generate surface shape analysis data of the object to be bundled; Step S142: Calculate the required bundling strength based on the surface smoothness data and the surface shape analysis data of the objects to be bundled, and generate required bundling strength data of the objects to be bundled; wherein the formula for calculating the required bundling strength is as follows:

[0009] In the formula, Expressed as the bundle strength requirement, Expressed as the weight of the object, Expressed as the weight of the object in the calculation of the binding strength requirement, Expressed as the surface smoothness coefficient, Expressed as the weight of surface smoothness in the calculation of the bundle strength requirement, Expressed as the object shape complexity coefficient, It is expressed as the weight of the object shape complexity in the calculation of the bundling strength requirement, Expressed as the surface roughness coefficient, It is expressed as the weight of the surface roughness coefficient in the calculation of the binding strength requirement; Step S143: comparing the bundling strength requirement data of the object to be bundled with a preset standard bundling strength requirement threshold, and when the bundling strength requirement data of the object to be bundled is greater than the preset standard bundling strength requirement threshold, confirming the multi-layer bundling mode of the bundling machine, thereby generating a multi-layer bundling mode; Step S144: When the bundling strength requirement data of the objects to be bundled is less than or equal to the preset standard bundling strength requirement threshold, the single-layer bundling mode is confirmed for the bundling machine, thereby generating a single-layer bundling mode; the multi-layer bundling mode and the single-layer bundling mode are integrated to generate bundling mode data.

[0010] The present invention can obtain the physical properties of the object more comprehensively by analyzing the surface smoothness and shape of the object's three-dimensional modeling data. , combining factors such as weight, smoothness, shape complexity and surface unevenness to calculate the bundling strength requirement, making the calculation of the bundling strength requirement more accurate and scientific. By comparing with the preset standard bundling strength requirement threshold, the system can automatically judge and select the single-layer or multi-layer bundling mode. This adaptive selection mechanism ensures the optimal bundling mode under different strength requirements, meets the strength requirements of different objects, and improves the adaptability and stability of bundling. Based on factors such as the smoothness and shape complexity of the object surface, the calculation of the bundling strength requirement is ensured to be more accurate. Especially when multi-layer bundling is required, this method can ensure the stability of the bundling, avoid the situation where the bundling is not firm or damages the object, and improve the safety of the bundling process. Through the automatic confirmation of the bundling mode, the time and error of manual judgment of the bundling strength requirement are reduced. Automated mode selection improves operational efficiency, especially in a large number of bundling tasks, it significantly saves time and optimizes the overall bundling process.

[0011] Preferably, step S2 comprises the following steps: Step S21: setting an initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode to obtain single-layer bundling initial pressure data; performing bundling pressure feedback synchronization on the three-dimensional modeling data of the object to be bundled based on the single-layer bundling initial pressure data to generate bundling pressure feedback data; Step S22: using the bundling pressure feedback data to perform object deformation detection on the three-dimensional modeling data of the object to be bundled, and generating object deformation detection data; using the object deformation detection data to perform single-layer bundling pressure adjustment on the single-layer bundling initial pressure data, and generating single-layer bundling pressure adjustment data; Step S23: setting multi-layer bundling pressure for the information data of the object to be bundled based on the multi-layer bundling mode to obtain multi-layer bundling initial pressure data; simulating bundling for the three-dimensional modeling data of the object to be bundled according to the multi-layer bundling initial pressure data to generate multi-layer bundling simulation data; Step S24: performing pressure aggregation point analysis on the multi-layer bundling simulation data to generate multi-layer bundling pressure aggregation points; performing bundling pressure strain range analysis on the multi-layer bundling simulation data based on the multi-layer bundling pressure aggregation points to generate multi-layer bundling pressure strain range data.

[0012] The present invention can adjust the initial bundling pressure according to the single-layer bundling mode through the initial bundling pressure setting, so that the pressure control in the bundling process is more accurate. By synchronizing the bundling pressure feedback of the three-dimensional modeling data of the object to be bundled, the real-time nature of the feedback data is ensured, which helps to adjust the bundling pressure in time and avoid the occurrence of excessive or insufficient bundling. Object deformation detection can monitor the deformation of the object in the bundling process in real time and generate deformation detection data. Based on these data, the single-layer bundling pressure is adjusted to ensure that the pressure in the bundling process can adapt to the actual changes of the object, thereby effectively preventing the object from being damaged and improving the safety and reliability of the bundling. The initial pressure setting of the multi-layer bundling mode and the simulated bundling can better evaluate the impact of the multi-layer bundling on the object. By generating simulated data, the rationality of the bundling pressure is ensured, thereby providing a valuable reference for subsequent actual operations. Pressure aggregation point analysis and pressure strain range analysis provide a scientific basis for the safety of multi-layer bundling. By analyzing the multi-layer bundling simulation data, potential pressure concentration and strain problems can be predicted and avoided, the rationality of the bundling design is improved, and the risk of object damage is reduced. The integration of real-time feedback and pressure adjustment mechanisms makes the strapping operation more intelligent and automated, reducing the need for manual intervention. Through data-driven decision-making, it improves operational efficiency and ensures standardization and consistency of the strapping process.

[0013] Preferably, step S22 includes the following steps: Step S221: using the bundling pressure feedback data to filter the dynamic data of the three-dimensional modeling data of the object to be bundled, to obtain the three-dimensional modeling dynamic data of the object to be bundled; Step S222: Scale change analysis is performed on the 3D modeling dynamic data of the objects to be bundled and the 3D modeling data of the objects to be bundled to generate scale change data of the objects to be bundled; object deformation is confirmed on the bundling pressure feedback data according to the scale change data of the objects to be bundled to obtain object deformation detection data; Step S223: performing deformation detection judgment on the object deformation detection data. When the judgment result of the object deformation detection data is true, reducing the bundling pressure of the single-layer bundling initial pressure data according to the object deformation detection data, thereby generating single-layer bundling first pressure adjustment data; Step S224: when the judgment result of the object deformation detection data is false, the strapping pressure feedback data is analyzed according to the object deformation detection data to generate strapping pressure form data; the strapping pressure is increased according to the strapping pressure form data for the single-layer strapping initial pressure data to generate single-layer strapping second pressure adjustment data; Step S225: integrating the single-layer bundling first pressure adjustment data and the single-layer bundling second pressure adjustment data to generate single-layer bundling pressure adjustment data.

[0014] The present invention uses the bundling pressure feedback data to dynamically screen the three-dimensional modeling data of the object to be bundled, so that the data used is more in line with the current bundling conditions. This dynamic screening can reflect the state change of the object in real time and improve the accuracy of subsequent analysis. The scale change data of the three-dimensional modeling dynamic data and the original data of the object to be bundled can be analyzed to generate the scale change data of the object to be bundled. This process can effectively identify the deformation of the object, provide a reliable basis for the confirmation of the object deformation, and ensure the adaptability of the bundling process. The deformation detection and discrimination mechanism can automatically determine the authenticity of the object deformation. When the deformation is detected, the bundling pressure is adjusted according to the detection data to ensure the safety and stability of the object during the bundling process. This intelligent adjustment mechanism can respond quickly, reduce manual intervention, and improve work efficiency. According to the object deformation detection result, the system can flexibly select the strategy of reducing or increasing the pressure. When the object deformation is significant, the pressure is reduced in time to avoid damage; when the object deformation is not obvious, the pressure can be appropriately increased to ensure the tightness and effectiveness of the bundling. This flexibility improves the safety and effectiveness of the bundling. The different pressure adjustment data of single-layer strapping are integrated to generate comprehensive single-layer strapping pressure adjustment data. This comprehensive adjustment data can not only optimize the pressure application during the strapping process, but also effectively reduce the operational errors caused by multiple adjustments, making the strapping process more stable and consistent. Through the implementation of this series of steps, the entire strapping process can achieve a higher degree of automation and intelligence, reduce manual intervention, and improve operational efficiency and accuracy, which not only improves the efficiency of the operation, but also ensures the quality and safety of the final product.

[0015] Preferably, step S224 includes the following steps: When the judgment result of the object deformation detection data is false, the key feature extraction of the strapping pressure feedback data is performed to obtain the key feature data of the strapping pressure, wherein the key feature extraction of the strapping pressure includes stress distribution extraction, stress concentration area extraction and tension uniformity extraction; According to the key characteristic data of strapping pressure, strapping contact surface analysis is performed on the object deformation detection data to generate strapping contact surface data; strapping fit is calculated on the key characteristic data of strapping pressure based on the strapping contact surface data to obtain strapping fit data; The strapping belt pressure morphology analysis is performed based on the strapping belt contact surface data and strapping belt fit data to generate strapping belt pressure morphology data; the strapping belt-object porosity is calculated based on the strapping belt pressure morphology data and the three-dimensional modeling data of the object to be strapped to obtain the strapping belt-object porosity; the strapping belt-object porosity calculation formula is as follows:

[0016] In the formula, Expressed as porosity, It is expressed as the void volume between the strapping tape and the surface of the object, It is expressed as the contact area between the strapping tape and the object; The bundling pressure of the single-layer bundling initial pressure data is increased based on the bundling band-object porosity to generate the single-layer bundling second pressure adjustment data.

[0017] The present invention extracts key features of strapping pressure, including stress distribution, stress concentration area and tension uniformity, so that the mechanical characteristics in the strapping process can be fully understood. This multi-dimensional feature extraction can better reflect the effect of the strapping on the object and ensure refined control of the strapping process. Based on the key feature data of strapping pressure, the strapping contact surface analysis of the object deformation detection data can generate strapping contact surface data. This analysis can evaluate the contact between the strapping and the surface of the object, ensure the tightness of the strapping, and avoid strapping failure due to poor contact. The strapping fit data generated by the strapping contact surface data can quantify the contact quality between the strapping and the object. This fit calculation helps to maintain a uniform pressure effect during the strapping process and reduce deformation and damage caused by uneven contact. The analysis of the pressure form of the strapping belt in the step, combined with the contact surface data and fit data of the strapping belt, can more comprehensively understand the actual state of the pressure applied by the strapping belt. This comprehensive analysis helps to adjust the strapping method in time and improve the strapping effect. By calculating the porosity of the strapping belt-object, the porosity between the strapping belt and the surface of the object can be quantitatively analyzed. In the porosity formula, the quantification of volume and contact area provides an important basis for the evaluation of the pressure effect, and this quantitative evaluation can effectively guide the adjustment of pressure. The pressure increase decision based on the strapping belt-object porosity for the initial pressure data of the single-layer strapping can ensure that the pressure is increased in time when the contact quality between the strapping belt and the object is insufficient, thereby enhancing the firmness and safety of the strapping. This dynamic adjustment mechanism effectively improves the flexibility and adaptability of the strapping. Through the comprehensive analysis of the key characteristics of pressure and porosity, the pressure state in the strapping process can be more effectively controlled, the safety and stability of the object in the strapping process can be ensured, and the risk of damage caused by improper strapping can be reduced, thereby improving the overall efficiency and quality of the operation.

[0018] Preferably, step S24 includes the following steps: Step S241: extracting the pressure application data of each layer of the multi-layer bundling simulation data to obtain the layer pressure application data; marking the pressure application area of ​​the multi-layer bundling simulation data based on the layer pressure application data to generate the pressure application area marking data; Step S242: performing regional pressure peak calculation on the pressure application area mark data to generate regional pressure peak calculation data; performing pressure aggregation point identification on the layer pressure application data based on the regional pressure peak calculation data to generate multi-layer bundling pressure aggregation points; Step S243: marking the stress concentration area of ​​the pressure area marking data according to the multi-layer bundling pressure aggregation point to generate the stress concentration area; performing multi-layer bundling pressure gradient analysis on the stress concentration area to generate a hierarchical bundling pressure gradient map; extracting the pressure change amplitude of the hierarchical bundling pressure gradient map to obtain hierarchical pressure change amplitude data; Step S244: Calculate the multi-layer bundling pressure strain range of the multi-layer bundling simulation data using the layer pressure variation amplitude data to generate multi-layer bundling pressure strain range data; wherein the formula for calculating the multi-layer bundling pressure strain range is as follows:

[0019] In the formula, Expressed as pressure-strain range, Expressed as The pressure applied by the strapping tape, Expressed as the contact area over which pressure is applied, Expressed as the internal stress of the object in the untied state.

[0020] The present invention can clearly understand the specific pressure applied by each layer of strapping tape by extracting the hierarchical pressure application data of the multi-layer strapping simulation data. The acquisition of such data is helpful to evaluate the role of each layer of strapping tape in the entire strapping system, and provide a basis for subsequent optimization and adjustment. The pressure application area of ​​the multi-layer strapping simulation data is marked based on the hierarchical pressure application data to ensure the clear definition of each pressure application area during the strapping process. Such marking can effectively guide the strapping operation and avoid damage to objects or poor strapping effect caused by improper pressure application. The regional pressure peak calculation of the pressure application area marking data provides a specific quantitative analysis of the pressure applied by the strapping tape. This calculation result can help identify the pressure concentration area that exists during the pressure application process, thereby providing an important basis for subsequent pressure adjustment. The pressure aggregation point identification of the hierarchical pressure application data based on the regional pressure peak calculation data can effectively find the key positions of pressure concentration and dispersion. This identification ability is crucial to optimizing the strapping strategy and improving the strapping effect. By marking the stress concentration area in the pressure area, the area with greater stress during the bundling process can be identified, and the multi-layer bundling pressure gradient analysis can be performed. This analysis helps to gain a deeper understanding of the distribution of pressure applied by the strapping belt, and facilitates the identification of potential weaknesses and risk areas of excessive stress. The extraction of the pressure change amplitude of the hierarchical bundling pressure gradient diagram provides data support for the dynamic changes in the pressure applied by the strapping belt. This data can help design a more reasonable bundling strategy to cope with changes in the actual pressure application process. By applying the multi-layer bundling pressure strain range calculation formula, the internal stress changes of the object under the bundling state can be quantified. This calculation result provides an important basis for evaluating the bundling effect and the safety of the object, so that the bundling operation can be appropriately adjusted according to the stress state. Through a comprehensive analysis of the multi-layer bundling simulation data, the impact of bundling on the object can be predicted and evaluated before bundling, thereby optimizing the bundling scheme. This method effectively improves the bundling effect, enhances the safety of the object during transportation and storage, and reduces the risk of damage. Combined with the hierarchical pressure application data, regional pressure peak and stress concentration area analysis, the entire bundling process can be dynamically optimized and adaptively adjusted. By monitoring the strapping status in real time, the pressure strategy can be quickly adjusted according to the changing situation to adapt to the needs of different objects and environments. By implementing this meticulous strapping pressure management strategy, rework and losses caused by improper strapping can be reduced, thereby improving overall production efficiency and ensuring the integrity of objects during transportation and storage.

[0021] Preferably, step S3 comprises the following steps: Step S31: predicting the time series stress change of the multi-layer bundling pressure strain range data to generate multi-layer bundling stress prediction data; Step S32: performing a multi-layer bundling pressure strength demand analysis according to the multi-layer bundling stress prediction data to generate multi-layer bundling pressure strength demand data; Step S33: performing multi-layer strapping dynamic pressure control adjustment on the multi-layer strapping initial pressure data based on the multi-layer strapping pressure intensity requirement data to generate multi-layer strapping dynamic pressure control adjustment data; Step S34: constructing a pressure control strategy for the strapping mode data using the multi-layer strapping dynamic pressure control adjustment data and the single-layer strapping pressure adjustment data to generate a strapping mode pressure control strategy.

[0022] The present invention can identify the stress change trend occurring in the bundling process in advance by predicting the time series stress change of the multi-layer bundling pressure strain range data. This prediction capability enables the stress distribution at different time nodes to be evaluated before the bundling operation, thereby effectively preventing damage to the object caused by excessive stress. According to the multi-layer bundling stress prediction data, the pressure intensity required to be applied at different stages can be determined. This analysis ensures that the pressure applied during the bundling process can adapt to the actual needs of the object, and improves the accuracy and effectiveness of the bundling operation. Based on the multi-layer bundling pressure intensity demand data, the initial pressure is dynamically controlled and adjusted, which can adapt to the deformation and stress changes of the object during the bundling process in real time. This dynamic adjustment mechanism improves the flexibility of bundling and effectively reduces the risk of damage to the object during the bundling process. By combining the multi-layer bundling dynamic pressure control adjustment data with the single-layer bundling pressure adjustment data, the generated bundling mode pressure control strategy provides systematic guidance for the bundling process. This strategy ensures the consistency and reliability of pressure application under different bundling modes and improves the overall bundling effect. Through timely stress prediction and dynamic pressure adjustment, the objects to be bundled can be better protected and the risk of damage caused by improper pressure during the bundling process can be reduced. This protection mechanism improves the safety of objects during transportation and storage. Through accurate pressure intensity demand analysis and dynamic control adjustment, unnecessary damage to objects can be avoided, thereby reducing rework and costs caused by damage, and further improving the overall efficiency and safety of the bundling operation. The entire process has strong adaptability and can be intelligently adjusted according to actual conditions to meet the bundling needs of different types of objects. This flexibility allows the bundling operation to more efficiently cope with complex environments and changeable object characteristics. Through the comprehensive analysis of multi-layer bundling pressure strain range data and dynamic pressure control data, decision makers can make more reasonable and scientific bundling scheme choices based on data, improving the accuracy and effectiveness of decision-making. The construction of the bundling mode pressure control strategy enables the entire bundling process to be integrated and managed, and the coordination and unification of each link is achieved through the system pressure control strategy to ensure the smooth progress of the bundling work.

[0023] Preferably, step S31 includes the following steps: Step S311: dividing the data set of the multi-layer bundling pressure-strain range data to generate a model training set and a model test set; training the model training set by using a long short-term memory neural network algorithm to generate a multi-layer bundling pressure-strain prediction pre-model; Step S312: performing model test iterations on the multi-layer bundling pressure-strain prediction pre-model through the model test set, thereby generating a multi-layer bundling pressure-strain prediction model; Step S313: importing the multi-layer bundling pressure-strain range data into the multi-layer bundling pressure-strain prediction model to perform time-series stress change prediction and generate multi-layer bundling stress prediction data.

[0024] The present invention can ensure the effectiveness and scientificity of model training and testing by dividing the data set of multi-layer bundling pressure strain range data. The separation of training set and test set makes the training process of the model not interfered by the test data, thereby improving the generalization ability and accuracy of the model. The long short-term memory (LSTM) neural network algorithm is used for model training, which can fully capture the time series characteristics in the multi-layer bundling pressure strain range data. This advanced algorithm is particularly suitable for processing time series data, so that the prediction model can accurately identify and learn the complex pattern of pressure change, and improve the prediction accuracy. The multi-layer bundling pressure strain prediction pre-model is tested and iterated through the model test set, and the model parameters can be continuously optimized and adjusted. This process helps to improve the robustness and accuracy of the model and ensure that it can perform well under different conditions. Importing the multi-layer bundling pressure strain range data into the multi-layer bundling pressure strain prediction model for time series stress change prediction can provide accurate stress change data for subsequent bundling operations. This prediction ability helps to adjust the bundling strategy in time to prevent damage to objects caused by excessive or insufficient stress. Through the generated multi-layer bundling stress prediction data, real-time monitoring and feedback of the bundling process can be achieved. When potential stress anomalies are detected, the system can respond immediately and optimize the strapping strategy to ensure the safety and integrity of the object. Multi-layer strapping stress prediction data provides decision makers with data-based scientific support, enabling them to better plan and implement strapping solutions. By analyzing the prediction data, decision makers can more reasonably allocate pressure and adjust the strapping method. Accurate stress prediction can help reduce object damage and resource waste caused by improper strapping, thereby reducing operational risks and costs. This benefit can significantly improve economic benefits in the long run. The model based on the LSTM neural network has strong adaptability and can be adjusted according to different strapping requirements and environmental changes to ensure that it can still provide stable prediction results under changing conditions. Through data-driven prediction models, the intelligent upgrade of the strapping process can be promoted, so that the strapping system has the ability to learn and adjust autonomously to adapt to more complex application scenarios in the future.

[0025] In this specification, a dynamic pressure control system of a strapping machine is provided, which is used to execute the above-mentioned dynamic pressure control method of the strapping machine. The dynamic pressure control system of the strapping machine includes: The bundling mode recognition module is used to obtain information data of the object to be bundled; perform three-dimensional modeling of the object to be bundled based on the information data of the object to be bundled to generate three-dimensional modeling data of the object to be bundled; confirm the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled to obtain the bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode; The single-layer bundling pressure adjustment module is used to set the initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode, and obtain the single-layer bundling initial pressure data; perform object deformation bundling pressure adjustment on the single-layer bundling initial pressure data, and generate the single-layer bundling pressure adjustment data; perform multi-layer bundling pressure setting on the information data of the object to be bundled based on the multi-layer bundling mode, and obtain the multi-layer bundling initial pressure data; perform bundling pressure strain range analysis on the multi-layer bundling initial pressure data, and generate the multi-layer bundling pressure strain range data; The multi-layer bundling pressure adjustment module is used to predict the time-series stress change of the multi-layer bundling pressure strain range data and generate the multi-layer bundling stress prediction data; perform multi-layer bundling dynamic pressure control adjustment on the multi-layer bundling initial pressure data according to the multi-layer bundling stress prediction data and generate the multi-layer bundling dynamic pressure control adjustment data; construct the pressure control strategy for the bundling mode data through the multi-layer bundling dynamic pressure control adjustment data and the single-layer bundling pressure adjustment data and generate the bundling mode pressure control strategy; The feedback adjustment module is used to collect pressure control adjustment feedback data for the strapping mode pressure control strategy to obtain a pressure control adjustment feedback data set; perform strapping efficiency evaluation on the pressure control adjustment feedback data set to generate strapping efficiency evaluation data for the strapping machine; and adaptively adjust the strapping mode pressure control strategy through the strapping efficiency evaluation data of the strapping machine, thereby generating a dynamic pressure control optimization strategy to perform strapping operations.

[0026] The beneficial effect of the present invention is that by accurately obtaining object information, a comprehensive understanding of the characteristics of the object is ensured during the bundling process, the risk of bundling errors is reduced, and a three-dimensional model of the object to be bundled is generated, so that the bundling machine can perform precise operations based on the real object shape and improve the bundling adaptability. The bundling mode (single layer or multi-layer) is intelligently confirmed according to the object characteristics, so that the bundling method is more targeted and the efficiency and effect are improved. In the single-layer mode, the initial pressure is set for the object to be bundled to ensure that the initial bundling effect meets the actual needs of the object and reduces damage. The bundling pressure is dynamically adjusted according to the deformation characteristics of the object to ensure that the pressure distribution during the bundling process is uniform and avoids being too tight or too loose. Special pressure settings are performed in the multi-layer bundling mode to provide a more reliable bundling solution for complex objects. By analyzing the strain range, the bundling risk is identified to ensure the scientificity and effectiveness of the pressure control. By analyzing the time series of the pressure strain range, the stress changes in the multi-layer bundling process are predicted, and potential problems are identified in advance. The initial pressure is adjusted according to the stress prediction data, so that the changes of the object can be responded to in real time and the bundling quality can be improved. Based on the dynamic adjustment data, a scientific bundling pressure control strategy is constructed to achieve an optimized combination of multi-layer and single-layer bundling to ensure the best bundling effect. Through the real-time collection of feedback data, the bundling effect is evaluated to provide a basis for subsequent adjustments and ensure process traceability. The bundling efficiency of the bundling machine is evaluated to provide data support for the optimization strategy and promote continuous improvement of the equipment. By adaptively adjusting the control strategy according to the evaluation data, the bundling machine can continuously optimize the bundling effect under different environmental and object conditions to achieve intelligence and efficiency. Therefore, the present invention improves the intelligence and accuracy of the dynamic pressure control of the bundling machine through three-dimensional modeling, dynamic pressure adjustment and feedback control mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic flow chart of the steps of a dynamic pressure control method for a strapping machine; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0030] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0031] To achieve this, please refer to Figures 1 to 3 , a dynamic pressure control method of a strapping machine, the method comprising the following steps: Step S1: obtaining information data of the object to be bundled; performing three-dimensional modeling of the object to be bundled based on the information data of the object to be bundled to generate three-dimensional modeling data of the object to be bundled; confirming the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled to obtain bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode; Step S2: setting an initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode to obtain the single-layer bundling initial pressure data; adjusting the object deformation bundling pressure for the single-layer bundling initial pressure data to generate the single-layer bundling pressure adjustment data; setting multi-layer bundling pressure for the information data of the object to be bundled based on the multi-layer bundling mode to obtain the multi-layer bundling initial pressure data; performing bundling pressure strain range analysis on the multi-layer bundling initial pressure data to generate the multi-layer bundling pressure strain range data; Step S3: predicting the time-series stress change of the multi-layer bundling pressure strain range data to generate multi-layer bundling stress prediction data; performing multi-layer bundling dynamic pressure control adjustment on the multi-layer bundling initial pressure data according to the multi-layer bundling stress prediction data to generate multi-layer bundling dynamic pressure control adjustment data; constructing a pressure control strategy for the bundling mode data through the multi-layer bundling dynamic pressure control adjustment data and the single-layer bundling pressure adjustment data to generate a bundling mode pressure control strategy; Step S4: collecting pressure control adjustment feedback data of the strapping mode pressure control strategy to obtain a pressure control adjustment feedback data set; evaluating the strapping efficiency of the pressure control adjustment feedback data set to generate strapping machine strapping efficiency evaluation data; adaptively adjusting the strapping mode pressure control strategy through the strapping machine strapping efficiency evaluation data, thereby generating a dynamic pressure control optimization strategy to perform strapping operations.

[0032] The present invention can fully understand the size, shape and material properties of the object by acquiring the information data of the object to be bundled, and provide basic data for subsequent modeling and bundling. The object to be bundled is three-dimensionally modeled, and the generated three-dimensional modeling data can truly reflect the geometric characteristics of the object and support more accurate bundling operations. Confirming the bundling mode (single layer or multi-layer) according to the three-dimensional modeling data can ensure that the bundling method matches the characteristics of the object, thereby improving the bundling quality and efficiency. The initial pressure setting of the object based on the single-layer bundling mode can achieve the best bundling effect under the premise of ensuring the safety of the object. The bundling pressure is adjusted according to the deformation of the object, so that the bundling process can adapt to the deformation of the object during the bundling process and reduce the risk of damage. The bundling pressure is adjusted according to the deformation of the object, so that the bundling process can adapt to the deformation of the object during the bundling process and reduce the risk of damage. The time-series stress change prediction of the multi-layer bundling pressure strain range data can identify potential bundling problems in advance and ensure that the optimal pressure state is maintained during the bundling process. The initial pressure of the multi-layer bundling is dynamically adjusted according to the stress prediction result, which can more effectively cope with the pressure changes occurring during the bundling process and enhance the bundling stability. By constructing a pressure control strategy for the bundling mode, it is possible to provide systematic management and control for the bundling process, optimize the operating process, and improve efficiency. Pressure control adjustment feedback data collection can monitor the pressure changes in the bundling process in real time, providing a basis for further optimization. Evaluating the bundling efficiency of the bundling machine can quantify the effect of the bundling process, help identify efficiency bottlenecks, and support improvement strategies. Adaptive adjustment of the pressure control strategy based on the efficiency evaluation data of the bundling machine can continuously improve the efficiency and safety of the bundling operation and make the system more intelligent. The combination of the above steps makes the bundling process more flexible and can quickly adapt to different objects and bundling conditions. Through precise pressure control and dynamic adjustment, the damage to objects caused by improper bundling is reduced, and the loss and overall cost are reduced. The entire process optimizes the bundling operation in a data-driven manner, ensuring the improvement of work efficiency and the safety of the operation. Therefore, the present invention improves the intelligence and accuracy of the dynamic pressure control of the bundling machine through three-dimensional modeling, dynamic pressure adjustment and feedback control mechanism.

[0033] In the embodiment of the present invention, reference Figure 1The above is a schematic flow chart of the steps of a dynamic pressure control method of a strapping machine of the present invention. In this example, the dynamic pressure control method of a strapping machine includes the following steps: Step S1: obtaining information data of the object to be bundled; performing three-dimensional modeling of the object to be bundled based on the information data of the object to be bundled to generate three-dimensional modeling data of the object to be bundled; confirming the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled to obtain bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode; In an embodiment of the present invention, the appearance features, size, shape and material information of the objects to be bundled, including physical properties such as color, texture, and surface smoothness, are collected by devices such as sensors, cameras or scanners. The collected information is sorted and stored to form standardized information data of the objects to be bundled. The information data includes geometric features, material properties and other related parameters of the objects. The information data of the objects to be bundled is cleaned and formatted to ensure data quality and remove outliers and redundant information. Three-dimensional modeling software or algorithms (such as CAD software, 3D scanning and reconstruction technology, etc.) are used to perform three-dimensional modeling of the objects to be bundled. In the modeling process, the various dimensional features of the objects are considered to generate high-precision three-dimensional model data. The generated three-dimensional model is optimized and the model details are adjusted to ensure its accuracy and practicality in the bundling process. According to the three-dimensional modeling data of the objects to be bundled, the geometric characteristics and actual application requirements of the objects are analyzed to determine the appropriate bundling mode. Factors such as the size, shape, load-bearing capacity of the objects and the characteristics of the strapping belt are considered during the analysis. On the basis of confirming the single-layer bundling mode and the multi-layer bundling mode, the bundling mode data is generated. The strapping pattern data includes the selected strapping method, the layout of the strapping belt, and the strategy for applying pressure. The strapping pattern data and related 3D modeling data are integrated to generate the final strapping pattern confirmation report. The confirmed strapping pattern data and 3D modeling data are saved to the database or cloud platform for subsequent operations and real-time sharing. Prepare to enter the subsequent strapping pressure setting and dynamic adjustment stage, laying the foundation for the optimization of the entire strapping process.

[0034] Step S2: setting an initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode to obtain the single-layer bundling initial pressure data; adjusting the object deformation bundling pressure for the single-layer bundling initial pressure data to generate the single-layer bundling pressure adjustment data; setting multi-layer bundling pressure for the information data of the object to be bundled based on the multi-layer bundling mode to obtain the multi-layer bundling initial pressure data; performing bundling pressure strain range analysis on the multi-layer bundling initial pressure data to generate the multi-layer bundling pressure strain range data; In an embodiment of the present invention, the initial bundling pressure of the single-layer bundling mode is set by using a physical model or an empirical formula according to the information data of the object to be bundled and its three-dimensional modeling data. The initial pressure calculation formula can be based on the material properties and geometric characteristics of the object, taking into account the friction coefficient, the weight of the object and the carrying capacity of the bundling belt. The calculated single-layer bundling initial pressure value is recorded, and a single-layer bundling initial pressure data set is generated. During the single-layer bundling process, the pressure applied by the bundling belt is monitored in real time, and pressure feedback data is collected. The pressure feedback data is used to detect the object deformation of the three-dimensional modeling data of the object to be bundled, and whether the object is deformed. The object deformation detection method can use optical monitoring, sensor data analysis and other technologies. Pressure adjustment is performed according to the object deformation detection result: if the object deformation is detected, the single-layer bundling initial pressure data is correspondingly reduced according to the degree of deformation, and the single-layer bundling pressure adjustment data is generated. If no deformation is detected, the current pressure setting is maintained. Based on the information data of the object to be bundled and its three-dimensional modeling data, the initial pressure setting of the multi-layer bundling mode is performed by using a physical model or an empirical formula. The initial pressure of each layer of strapping can be calculated separately according to its position, function and the force characteristics of the object. The calculated initial pressure value of each layer of strapping is recorded to form a multi-layer strapping initial pressure data set. The strain range analysis of the multi-layer strapping initial pressure data is performed to evaluate the strain performance of multi-layer strapping under different load conditions.

[0035] The strain range analysis can include the stress concentration and strain distribution of each layer of strapping. A multi-layer strapping pressure strain range data set is generated based on the analysis results to record the strain range of each layer of strapping during the pressure application process. The generated single-layer strapping pressure adjustment data and multi-layer strapping pressure strain range data are stored for subsequent pressure control and optimization adjustment.

[0036] Step S3: predicting the time-series stress change of the multi-layer bundling pressure strain range data to generate multi-layer bundling stress prediction data; performing multi-layer bundling dynamic pressure control adjustment on the multi-layer bundling initial pressure data according to the multi-layer bundling stress prediction data to generate multi-layer bundling dynamic pressure control adjustment data; constructing a pressure control strategy for the bundling mode data through the multi-layer bundling dynamic pressure control adjustment data and the single-layer bundling pressure adjustment data to generate a bundling mode pressure control strategy; In an embodiment of the present invention, the multi-layer bundling pressure strain range data is obtained as an input data set for training and testing the prediction model. The multi-layer bundling pressure strain range data is divided into a training set and a test set. The training set is used for model training, and the test set is used for model verification. A time series prediction model is constructed using a long short-term memory (LSTM) neural network algorithm to capture the time series characteristics of stress changes. Model parameters, including the number of hidden layers, learning rate, number of training rounds, etc., are set to configure the model. The constructed LSTM model is trained using the training set, and the model parameters are optimized to improve the prediction accuracy. The loss function and prediction accuracy during the training process are recorded to evaluate the model performance. The trained model is evaluated using the test set, the prediction error is calculated, and the model test iteration is performed to optimize the model performance. The multi-layer bundling pressure strain range data is input into the trained LSTM model to predict the time series stress change and generate multi-layer bundling stress prediction data. Based on the generated multi-layer bundling stress prediction data, the strength requirements of each layer of bundling belt under different applied pressures are analyzed. The bearing capacity of the multi-layer bundling is evaluated to ensure that it does not break under load. Record and store the pressure intensity demand data of multi-layer strapping as the basis for subsequent dynamic pressure control. According to the pressure intensity demand data of multi-layer strapping, analyze the difference with the initial pressure data to determine whether pressure adjustment is needed. Use an automated control system or manual adjustment method to dynamically adjust the initial pressure data of multi-layer strapping. Record the adjusted pressure value and generate dynamic pressure control adjustment data for multi-layer strapping to ensure that the pressure applied by each layer of strapping meets the requirements. Combine the dynamic pressure control adjustment data of multi-layer strapping with the pressure adjustment data of single-layer strapping to analyze the relationship and influence between the two. Determine the best pressure control scheme under different strapping modes. Based on the analysis results, formulate a strapping mode pressure control strategy to ensure that each layer of strapping is uniformly stressed during the pressure application process. The strategy should include specific methods, monitoring indicators and implementation plans for pressure adjustment. Verify the effectiveness and feasibility of the constructed pressure control strategy through actual strapping experiments or simulation tests to ensure that it can achieve the expected strapping effect.

[0037] Step S4: collecting pressure control adjustment feedback data of the strapping mode pressure control strategy to obtain a pressure control adjustment feedback data set; evaluating the strapping efficiency of the pressure control adjustment feedback data set to generate strapping machine strapping efficiency evaluation data; adaptively adjusting the strapping mode pressure control strategy through the strapping machine strapping efficiency evaluation data, thereby generating a dynamic pressure control optimization strategy to perform strapping operations.

[0038] In an embodiment of the present invention, during the bundling process, the applied pressure and the bundling state are continuously monitored to collect the pressure control adjustment feedback data. The data should include: the actual applied pressure value, the displacement and deformation data of the bundling belt, and any abnormal conditions or adjustment records that occur during the bundling process. The collected feedback data is sorted into a data set, including timestamps, pressure data, bundling states, and related parameters for subsequent analysis. Determine the indicators for bundling efficiency evaluation, including but not limited to: bundling time, bundling strength, material utilization, and bundling consistency and uniformity. Analyze the pressure control adjustment feedback data set, calculate the above indicators, and evaluate the bundling efficiency of the current bundling mode. Use statistical analysis methods (such as regression analysis, variance analysis, etc.) to determine the impact of different parameters on the bundling efficiency. Organize the evaluation results to form the bundling efficiency evaluation data of the bundling machine, including efficiency scores, evaluation conclusions, and improvement suggestions. Based on the bundling efficiency evaluation data of the bundling machine, analyze the advantages and disadvantages of the current pressure control strategy and identify areas that need improvement. Based on the evaluation data, propose targeted adjustment plans and optimize the pressure control strategy, such as adjusting the time and method of applying the strapping pressure, optimizing the selection or arrangement of strapping tape, and changing the strapping order or strategy. Organize the adaptive adjustment plan to form a set of dynamic pressure control optimization strategies to be implemented in future strapping operations. Establish a continuous feedback mechanism to incorporate the implementation effect of the optimization strategy into the evaluation system so that the strategy can be updated and adjusted in a timely manner. In subsequent strapping operations, apply the generated dynamic pressure control optimization strategy to ensure the stability and efficiency of the strapping operation. Record data such as applied pressure, strapping effect and efficiency to form a new feedback data set. Regularly evaluate the strapping efficiency and the execution effect of the pressure control strategy, continuously optimize the strapping process and strategy, and improve the overall strapping efficiency.

[0039] Preferably, step S1 comprises the following steps: Step S11: Acquire information data of the object to be bundled; Step S12: preprocessing the information data of the objects to be bundled to generate standard information data of the objects to be bundled, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: performing object surface feature recognition on the standard information data of the object to be bundled to obtain object surface feature data; performing three-dimensional modeling of the object to be bundled based on the object surface feature data to generate three-dimensional modeling data of the object to be bundled; Step S14: confirming the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled, and obtaining the bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode.

[0040] In an embodiment of the present invention, by collecting relevant information of the objects to be bundled, such as object size, shape, weight, material properties, etc., these data can be obtained by sensors, cameras or manual input, and stored in a database for subsequent processing. The acquired object information data is cleaned to remove redundant and irrelevant data items to ensure the accuracy and consistency of the data. Appropriate algorithms (such as filters) are applied to reduce noise in the data to ensure the quality of subsequent analysis and modeling. Check whether there are missing values ​​in the data set, and use appropriate methods (such as mean interpolation, nearest neighbor interpolation, etc.) to fill them to ensure data integrity. Standardize the data so that different features are on the same scale to improve the efficiency and accuracy of model training. Use computer vision and machine learning technology to identify the surface features of the standardized information data of the objects to be bundled, which can include edge detection, corner point extraction, texture analysis, etc., to generate object surface feature data. Based on the identified object surface feature data, three-dimensional modeling software (such as CAD software) is used to perform three-dimensional modeling to generate three-dimensional model data of the object to be bundled. This process involves a variety of modeling techniques, such as polygonal modeling, NURBS modeling, etc., to ensure the accuracy and availability of the model. Based on the generated 3D modeling data of the object to be bundled, the strapping mode is confirmed using the strapping machine's control system. The system can analyze the geometric shape and size of the object, and the confirmed strapping mode data includes two types: Single-layer strapping mode: suitable for smaller and lighter objects, and the strapping process is relatively simple. Multi-layer strapping mode: suitable for larger and heavier objects, and the strapping process needs to consider the stability and safety between layers.

[0041] Preferably, step S14 includes the following steps: Step S141: performing surface smoothness analysis on the three-dimensional modeling data of the object to be bundled to generate surface smoothness data of the object to be bundled; performing surface regular shape analysis on the three-dimensional modeling data of the object to be bundled to generate surface shape analysis data of the object to be bundled; Step S142: Calculate the required bundling strength based on the surface smoothness data and the surface shape analysis data of the objects to be bundled, and generate required bundling strength data of the objects to be bundled; wherein the formula for calculating the required bundling strength is as follows:

[0042] In the formula, Expressed as the bundle strength requirement, Expressed as the weight of the object, Expressed as the weight of the object in the calculation of the binding strength requirement, Expressed as the surface smoothness coefficient, Expressed as the weight of surface smoothness in the calculation of the bundle strength requirement, Expressed as the object shape complexity coefficient, It is expressed as the weight of the object shape complexity in the calculation of the bundling strength requirement, Expressed as the surface roughness coefficient, It is expressed as the weight of the surface roughness coefficient in the calculation of the binding strength requirement; Step S143: comparing the bundling strength requirement data of the object to be bundled with a preset standard bundling strength requirement threshold, and when the bundling strength requirement data of the object to be bundled is greater than the preset standard bundling strength requirement threshold, confirming the multi-layer bundling mode of the bundling machine, thereby generating a multi-layer bundling mode; Step S144: When the bundling strength requirement data of the objects to be bundled is less than or equal to the preset standard bundling strength requirement threshold, the single-layer bundling mode is confirmed for the bundling machine, thereby generating a single-layer bundling mode; the multi-layer bundling mode and the single-layer bundling mode are integrated to generate bundling mode data.

[0043] In an embodiment of the present invention, a surface smoothness analysis is performed on the three-dimensional modeling data of the object to be bundled. An algorithm (such as curvature analysis or normal vector calculation) is used to evaluate the smoothness of the object surface and generate surface smoothness data of the object to be bundled. The smoothness coefficient is recorded as one of the parameters for the subsequent calculation of the bundling strength requirement. The surface shape regularity analysis is performed on the three-dimensional modeling data to evaluate whether the shape of the object is regular. Geometric shape feature extraction technology (such as boundary detection and shape matching) can be used to generate surface shape analysis data of the object to be bundled. The shape complexity coefficient is determined and stored for subsequent calculations. The bundling strength requirement of the object to be bundled is calculated using the formula: In the formula, Expressed as the bundle strength requirement, Expressed as the weight of the object, Expressed as the weight of the object in the calculation of the binding strength requirement, Expressed as the surface smoothness coefficient, Expressed as the weight of surface smoothness in the calculation of the bundle strength requirement, Expressed as the object shape complexity coefficient, It is expressed as the weight of the object shape complexity in the calculation of the bundling strength requirement, Expressed as the surface roughness coefficient, It is expressed as the weight of the surface roughness coefficient in the calculation of the bundling strength requirement; based on the above calculation, the bundling strength requirement data of the object to be bundled is generated and recorded in the system. The bundling strength requirement data of the object to be bundled is compared with the preset standard bundling strength requirement threshold. If the bundling strength requirement data is greater than the standard threshold, it means that a stronger bundling mode is required. When the strength requirement exceeds the threshold, the bundling machine is confirmed to be in a multi-layer bundling mode. The system generates the configuration parameters of the multi-layer bundling mode according to the requirements and saves them to the bundling mode data. If the bundling strength requirement data is less than or equal to the standard threshold, the bundling machine is confirmed to be in a single-layer bundling mode. Similarly, the configuration parameters of the single-layer bundling mode are generated and saved to the bundling mode data. The multi-layer bundling mode and the single-layer bundling mode are integrated to form the final bundling mode data. Ensure that the integrated bundling mode data can support the implementation of the actual bundling operation.

[0044] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: setting an initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode to obtain single-layer bundling initial pressure data; performing bundling pressure feedback synchronization on the three-dimensional modeling data of the object to be bundled based on the single-layer bundling initial pressure data to generate bundling pressure feedback data; Step S22: using the bundling pressure feedback data to perform object deformation detection on the three-dimensional modeling data of the object to be bundled, and generating object deformation detection data; using the object deformation detection data to perform single-layer bundling pressure adjustment on the single-layer bundling initial pressure data, and generating single-layer bundling pressure adjustment data; Step S23: setting multi-layer bundling pressure for the information data of the object to be bundled based on the multi-layer bundling mode to obtain multi-layer bundling initial pressure data; simulating bundling for the three-dimensional modeling data of the object to be bundled according to the multi-layer bundling initial pressure data to generate multi-layer bundling simulation data; Step S24: performing pressure aggregation point analysis on the multi-layer bundling simulation data to generate multi-layer bundling pressure aggregation points; performing bundling pressure strain range analysis on the multi-layer bundling simulation data based on the multi-layer bundling pressure aggregation points to generate multi-layer bundling pressure strain range data.

[0045] In an embodiment of the present invention, the initial pressure range (e.g., 200-300N) is set according to the size, material properties, etc. of the object. The initial pressure value is input in the control panel to set the bundling pressure in the single-layer bundling mode, and the single-layer bundling initial pressure data is generated. The three-dimensional modeling data of the object is loaded into the simulation software, and the initial pressure data is applied to the three-dimensional model using the simulation tool. During the simulation process, the pressure changes and distribution in each area of ​​the model are recorded in real time. By setting a pressure sensor or a stress detection node, the pressure feedback information in different areas is captured to generate the bundling pressure feedback data. For example, if the pressure detected at the edge of the model is too high, the pressure value and position coordinates of the area are recorded in the feedback data. The generated bundling pressure feedback data is input into the deformation detection module to perform deformation detection on the surface of the object. In the simulation, the deformation information of the model under the action of the bundling pressure, such as the deformation amplitude and deformation area, is recorded. The detection output includes the local expansion or contraction data of the object, for example, an expansion of 0.3mm is recorded in the long side area of ​​the model to generate the object deformation detection data. The pressure condition of the deformation area is analyzed using the deformation detection results. If it is found that the deformation of a certain area exceeds the set threshold (such as 0.2mm), the pressure value of the area is reduced, for example, from 280N to 240N, to reduce deformation. The adjusted pressure data is saved as the single-layer bundling pressure adjustment data. According to the requirements of the multi-layer bundling mode, the initial pressure range of each layer is set, such as 300N for the first layer, 350N for the second layer, and 250N for the third layer. The pressure setting takes into account the gradual decrease of the pressure of each layer to ensure that the bottom layer bears the maximum support force, and the initial pressure data of the multi-layer bundling is generated. The initial pressure data of the multi-layer bundling is applied to the three-dimensional model in the simulation environment. For the multi-layer structure, the pressure distribution of each layer is monitored through simulation to generate multi-layer bundling simulation data. The pressure value of each layer and the model change during the multi-layer bundling process are recorded, such as the local offset of 0.4mm detected in the third layer. In the multi-layer bundling simulation data, the algorithm is applied to detect the bundling pressure concentration area and identify the aggregation point. The aggregation point is the location where the pressure value is significantly higher, for example, a 450N aggregation point is detected at the top edge of the model, and the multi-layer bundling pressure aggregation point data is generated. Based on the pressure aggregation point data, the strain range of the model is analyzed. By setting the strain analysis node near the pressure aggregation point, the stress-strain changes at different levels are recorded, and the multi-layer bundling pressure-strain range data is generated. For example, the strain range of the bottom layer of the model is recorded to be 0.1-0.3mm, the middle layer is 0.2-0.4mm, and the top layer is 0.3-0.5mm.

[0046] Preferably, step S22 includes the following steps: Step S221: using the bundling pressure feedback data to filter the dynamic data of the three-dimensional modeling data of the object to be bundled, to obtain the three-dimensional modeling dynamic data of the object to be bundled; Step S222: Scale change analysis is performed on the 3D modeling dynamic data of the objects to be bundled and the 3D modeling data of the objects to be bundled to generate scale change data of the objects to be bundled; object deformation is confirmed on the bundling pressure feedback data according to the scale change data of the objects to be bundled to obtain object deformation detection data; Step S223: performing deformation detection judgment on the object deformation detection data. When the judgment result of the object deformation detection data is true, reducing the bundling pressure of the single-layer bundling initial pressure data according to the object deformation detection data, thereby generating single-layer bundling first pressure adjustment data; Step S224: when the judgment result of the object deformation detection data is false, the strapping pressure feedback data is analyzed according to the object deformation detection data to generate strapping pressure form data; the strapping pressure is increased according to the strapping pressure form data for the single-layer strapping initial pressure data to generate single-layer strapping second pressure adjustment data; Step S225: integrating the single-layer bundling first pressure adjustment data and the single-layer bundling second pressure adjustment data to generate single-layer bundling pressure adjustment data.

[0047] In an embodiment of the present invention, by using the bundling pressure feedback data, the three-dimensional modeling data of the object to be bundled is analyzed in real time and dynamically, and the area where the force changes significantly during the bundling process is screened out to obtain the three-dimensional modeling dynamic data of the object to be bundled. For example, the pressure changes greatly at the edge position or the bending area of ​​the object, and the dynamic data of this part of the area is marked after screening. The three-dimensional modeling dynamic data of the object to be bundled is compared with the original three-dimensional modeling data, and the scale change of the object under the action of the bundling pressure, such as the increase or decrease of the size, the change of the curvature, etc., is analyzed to generate the scale change data of the object to be bundled. For example, the width in a specific area increases by 0.2mm or the thickness decreases by 0.15mm. According to the scale change data, the deformation of the object is confirmed for the bundling pressure feedback data. If the scale change exceeds the preset deformation threshold (such as 0.1mm), the area is marked as a deformation area, and the object deformation detection data is generated. The object deformation detection data is analyzed to determine whether there is obvious deformation. When the deformation data meets the judgment condition (that is, the detection data is "true"), it means that the deformation is caused by excessive pressure. When the judgment is true, the bundling pressure is adjusted according to the object deformation detection data. By reducing the initial pressure value of the single-layer bundling (for example, from 300N to 250N), the pressure in the deformation area is reduced, and the first pressure adjustment data of the single-layer bundling is generated. When the judgment result of the object deformation detection data is "false", it indicates that the current pressure is not enough to produce deformation. Based on the bundling pressure feedback data, the pressure form of the bundling belt on the surface of the object is analyzed, such as the contact area and pressure uniformity between the bundling belt and the object, and the bundling belt pressure form data is generated. According to the bundling belt pressure form data, the initial pressure of the single-layer bundling is appropriately increased (for example, from 250N to 300N) to ensure that the bundling belt applies sufficient pressure in the target area, and the second pressure adjustment data of the single-layer bundling is generated. The first pressure adjustment data of the single-layer bundling and the second pressure adjustment data of the single-layer bundling are integrated to generate the final single-layer bundling pressure adjustment data. The integrated data is used for real-time pressure control in the subsequent bundling process to ensure the bundling quality and deformation control effect.

[0048] Preferably, step S224 includes the following steps: When the judgment result of the object deformation detection data is false, the key feature extraction of the strapping pressure feedback data is performed to obtain the key feature data of the strapping pressure, wherein the key feature extraction of the strapping pressure includes stress distribution extraction, stress concentration area extraction and tension uniformity extraction; According to the key characteristic data of strapping pressure, strapping contact surface analysis is performed on the object deformation detection data to generate strapping contact surface data; strapping fit is calculated on the key characteristic data of strapping pressure based on the strapping contact surface data to obtain strapping fit data; The strapping belt pressure morphology analysis is performed based on the strapping belt contact surface data and strapping belt fit data to generate strapping belt pressure morphology data; the strapping belt-object porosity is calculated based on the strapping belt pressure morphology data and the three-dimensional modeling data of the object to be strapped to obtain the strapping belt-object porosity; the strapping belt-object porosity calculation formula is as follows:

[0049] In the formula, Expressed as porosity, It is expressed as the void volume between the strapping tape and the surface of the object, It is expressed as the contact area between the strapping tape and the object; The bundling pressure of the single-layer bundling initial pressure data is increased based on the bundling band-object porosity to generate the single-layer bundling second pressure adjustment data.

[0050] In an embodiment of the present invention, the stress distribution of the strapping tape on the surface of the object is extracted from the strapping pressure feedback data. The distribution of the stress value is mapped to the surface model of the object to determine the stress difference in different parts. The stress distribution data is analyzed to identify the stress concentration area on the strapping tape. For example, the stress area is located at the corners or protrusions of the object. The tension uniformity index of the strapping tape is calculated to analyze whether the tension of the strapping tape on the surface of the object is uniform. According to the key characteristic data of the strapping tape pressure, the contact surface between the strapping tape and the surface of the object is analyzed. For example, the contact area and the contact angle are determined to generate the strapping tape contact surface data. Based on the strapping tape contact surface data, the degree of fit of the strapping tape on the surface of the object is analyzed to calculate the fit of the strapping tape and generate the strapping tape fit data. The higher the fit, the closer the strapping tape is to the surface of the object, which helps to stabilize the strapping effect. By combining the strapping tape contact surface data and the fit data, the pressure form of the strapping tape on the surface of the object is analyzed to generate the strapping tape pressure form data. For example, determine whether the strapping belt has deviation, wrinkles or uneven pressure. According to the strapping belt pressure shape data and the 3D modeling data of the object to be strapped, calculate the porosity between the strapping belt and the surface of the object. The porosity formula is as follows: In the formula, Expressed as porosity, It is expressed as the void volume between the strapping tape and the surface of the object, It is expressed as the contact area between the strapping belt and the object. Based on the porosity of the strapping belt and the object, when the porosity is large, it means that the fit is not tight, and the initial pressure of the single-layer strapping needs to be appropriately increased to ensure that the strapping belt fits tightly on the surface of the object. The single-layer strapping second pressure adjustment data is generated by adjusting the pressure to further optimize the strapping effect.

[0051] Preferably, step S24 includes the following steps: Step S241: extracting the pressure application data of each layer of the multi-layer bundling simulation data to obtain the layer pressure application data; marking the pressure application area of ​​the multi-layer bundling simulation data based on the layer pressure application data to generate the pressure application area marking data; Step S242: performing regional pressure peak calculation on the pressure application area mark data to generate regional pressure peak calculation data; performing pressure aggregation point identification on the layer pressure application data based on the regional pressure peak calculation data to generate multi-layer bundling pressure aggregation points; Step S243: marking the stress concentration area of ​​the pressure area marking data according to the multi-layer bundling pressure aggregation point to generate the stress concentration area; performing multi-layer bundling pressure gradient analysis on the stress concentration area to generate a hierarchical bundling pressure gradient map; extracting the pressure change amplitude of the hierarchical bundling pressure gradient map to obtain hierarchical pressure change amplitude data; Step S244: Calculate the multi-layer bundling pressure strain range of the multi-layer bundling simulation data using the layer pressure variation amplitude data to generate multi-layer bundling pressure strain range data; wherein the formula for calculating the multi-layer bundling pressure strain range is as follows:

[0052] In the formula, Expressed as pressure-strain range, Expressed as The pressure applied by the strapping tape, Expressed as the contact area over which pressure is applied, Expressed as the internal stress of the object in the untied state.

[0053] In an embodiment of the present invention, by extracting the pressure data applied by each layer of strapping tape from the multi-layer strapping simulation data, information containing the pressure of each layer is generated, which is called layer pressure application data. The pressure data of each layer is used for subsequent layered analysis and marking. Based on the layer pressure application data, the pressure application area of ​​each layer of strapping tape in the multi-layer strapping simulation data is marked to generate pressure application area marking data. The marking data is used to identify and analyze the pressure distribution of each layer of pressure application area. According to the pressure application area marking data, the pressure peak values ​​of different pressure application areas in each layer are calculated to obtain regional pressure peak calculation data. This data helps to identify the local maximum pressure generated when the strapping tape acts on the surface of the object. Using the regional pressure peak calculation data, the area where pressure is concentrated during the multi-layer strapping process is identified, which is called the multi-layer strapping pressure aggregation point. These aggregation points represent the location where pressure is concentrated after multiple strapping layers are superimposed, providing a basis for subsequent analysis. According to the multi-layer strapping pressure aggregation point, the stress concentration area is marked on the pressure application area marking data to generate a stress concentration area. The stress concentration area represents the relatively high pressure area during the strapping process, and these areas are more prone to deformation or damage. In the stress concentration area, the pressure gradient of each layer of the multi-layer strapping is analyzed to generate a layered strapping pressure gradient map. The pressure gradient map reflects the pressure change trend of the strapping tape between different layers. The layered strapping pressure gradient map is analyzed to extract the pressure change amplitude of each layer and generate the layered pressure change amplitude data. This data is used to measure the difference in pressure between the layers of strapping tape, which helps to evaluate the uniformity of the strapping effect. Using the layered pressure change amplitude data, the strain range of the applied pressure in the multi-layer strapping simulation data is calculated to obtain the multi-layer strapping pressure strain range data. The pressure strain range calculation formula is as follows: In the formula, Expressed as pressure-strain range, Expressed as The pressure applied by the strapping tape, Expressed as the contact area over which pressure is applied, It is expressed as the internal stress of the object in the unbundled state. Through this formula, the stress changes inside the object after multi-layer bundling can be quantified, and the pressure effect of the strapping belt at different levels can be evaluated to optimize the strapping process parameters.

[0054] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: predicting the time series stress change of the multi-layer bundling pressure strain range data to generate multi-layer bundling stress prediction data; Step S32: performing a multi-layer bundling pressure strength demand analysis according to the multi-layer bundling stress prediction data to generate multi-layer bundling pressure strength demand data; Step S33: performing multi-layer strapping dynamic pressure control adjustment on the multi-layer strapping initial pressure data based on the multi-layer strapping pressure intensity requirement data to generate multi-layer strapping dynamic pressure control adjustment data; Step S34: constructing a pressure control strategy for the strapping mode data using the multi-layer strapping dynamic pressure control adjustment data and the single-layer strapping pressure adjustment data to generate a strapping mode pressure control strategy.

[0055] In the embodiment of the present invention, by collecting multi-layer bundling pressure strain range data, analyzing the time series characteristics thereof, especially the law of pressure change over time, potential stress fluctuation trends are identified. A time series prediction algorithm (such as ARIMA model, LSTM, etc.) is used to predict the pressure change at future moments based on the multi-layer bundling pressure strain range data, and generate multi-layer bundling stress prediction data. The data predicts the pressure distribution at different time nodes during the bundling process, which is helpful for subsequent pressure intensity adjustment and control. According to the multi-layer bundling stress prediction data, the pressure intensity generated by each layer of bundling belt during the bundling process is evaluated to identify the pressure level to be applied at each stage of the bundling. According to the stress change occurring during the bundling process, the minimum and maximum pressure intensity requirements to meet the bundling stability are calculated, and multi-layer bundling pressure intensity requirement data is generated. The data is used to ensure that the bundling belt can provide sufficient pressure intensity to resist external forces or maintain the stability of the bundling object. According to the multi-layer bundling pressure intensity requirement data, the multi-layer bundling initial pressure data is adjusted in real time to ensure that the pressure of each layer of bundling belt meets the requirements without excessive increase, and multi-layer bundling dynamic pressure control adjustment data is generated. Monitor the pressure of the strapping tape in real time, and make subtle adjustments to the strapping pressure in combination with the strapping stress prediction data to achieve dynamic pressure control of the strapping tape. Adjust the pressure distribution of the strapping tape through the feedback mechanism to ensure the stability of the overall strapping. Integrate the dynamic pressure control adjustment data of multi-layer strapping with the pressure adjustment data of single-layer strapping to construct a strapping mode pressure control strategy that adapts to different pressure requirements. The strategy includes the pressure distribution settings and adjustment criteria for single-layer and multi-layer strapping, as well as operating specifications in different pressure ranges. The resulting strapping mode pressure control strategy is used to guide the actual strapping process, ensuring that the pressure can be reasonably controlled in single-layer and multi-layer modes, avoiding over-tightening or over-loosening, and ensuring the fit and safety of the strapping tape to the surface of the object.

[0056] Preferably, step S31 includes the following steps: Step S311: dividing the data set of the multi-layer bundling pressure-strain range data to generate a model training set and a model test set; training the model training set by using a long short-term memory neural network algorithm to generate a multi-layer bundling pressure-strain prediction pre-model; Step S312: performing model test iterations on the multi-layer bundling pressure-strain prediction pre-model through the model test set, thereby generating a multi-layer bundling pressure-strain prediction model; Step S313: importing the multi-layer bundling pressure-strain range data into the multi-layer bundling pressure-strain prediction model to perform time-series stress change prediction and generate multi-layer bundling stress prediction data.

[0057] In the embodiment of the present invention, the multi-layer bundling pressure-strain range data is preprocessed, including data cleaning and normalization. The processed data set is divided into a model training set and a model test set. Usually, the training set accounts for 80% of the total data set, and the test set accounts for 20%. The long short-term memory neural network (LSTM) algorithm is selected to capture the temporal characteristics of the pressure-strain range. The LSTM model is preliminarily trained using the model training set to identify the temporal change pattern of pressure during the bundling process and generate a preliminary multi-layer bundling pressure-strain prediction pre-model. The multi-layer bundling pressure-strain prediction pre-model is tested using the model test set to evaluate the prediction accuracy and temporal feature fitting ability of the model. Error analysis is performed based on the test results, and the model is parameter optimized and iteratively trained, and the model hyperparameters (such as learning rate, number of layers, etc.) are continuously adjusted to improve the model prediction accuracy. After multiple iterative tests and optimizations, a multi-layer bundling pressure-strain prediction model is finally generated. The multi-layer bundling pressure-strain range data is imported into the optimized multi-layer bundling pressure-strain prediction model to obtain accurate pressure change trend prediction. The pressure and strain at future time points are predicted by the model to generate multi-layer bundling stress prediction data. The predicted data is used to guide the pressure adjustment and control strategy in subsequent steps to ensure that the pressure during the strapping process meets the safety and stability requirements.

[0058] In this specification, a dynamic pressure control system of a strapping machine is provided, which is used to execute the above-mentioned dynamic pressure control method of the strapping machine. The dynamic pressure control system of the strapping machine includes: The bundling mode recognition module is used to obtain information data of the object to be bundled; perform three-dimensional modeling of the object to be bundled based on the information data of the object to be bundled to generate three-dimensional modeling data of the object to be bundled; confirm the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled to obtain the bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode; The single-layer bundling pressure adjustment module is used to set the initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode, and obtain the single-layer bundling initial pressure data; perform object deformation bundling pressure adjustment on the single-layer bundling initial pressure data, and generate the single-layer bundling pressure adjustment data; perform multi-layer bundling pressure setting on the information data of the object to be bundled based on the multi-layer bundling mode, and obtain the multi-layer bundling initial pressure data; perform bundling pressure strain range analysis on the multi-layer bundling initial pressure data, and generate the multi-layer bundling pressure strain range data; The multi-layer bundling pressure adjustment module is used to predict the time-series stress change of the multi-layer bundling pressure strain range data and generate the multi-layer bundling stress prediction data; perform multi-layer bundling dynamic pressure control adjustment on the multi-layer bundling initial pressure data according to the multi-layer bundling stress prediction data and generate the multi-layer bundling dynamic pressure control adjustment data; construct the pressure control strategy for the bundling mode data through the multi-layer bundling dynamic pressure control adjustment data and the single-layer bundling pressure adjustment data and generate the bundling mode pressure control strategy; The feedback adjustment module is used to collect pressure control adjustment feedback data for the strapping mode pressure control strategy to obtain a pressure control adjustment feedback data set; perform strapping efficiency evaluation on the pressure control adjustment feedback data set to generate strapping efficiency evaluation data for the strapping machine; and adaptively adjust the strapping mode pressure control strategy through the strapping efficiency evaluation data of the strapping machine, thereby generating a dynamic pressure control optimization strategy to perform strapping operations.

[0059] The beneficial effect of the present invention is that by accurately obtaining object information, a comprehensive understanding of the characteristics of the object is ensured during the bundling process, the risk of bundling errors is reduced, and a three-dimensional model of the object to be bundled is generated, so that the bundling machine can perform precise operations based on the real object shape and improve the bundling adaptability. The bundling mode (single layer or multi-layer) is intelligently confirmed according to the object characteristics, so that the bundling method is more targeted and the efficiency and effect are improved. In the single-layer mode, the initial pressure is set for the object to be bundled to ensure that the initial bundling effect meets the actual needs of the object and reduces damage. The bundling pressure is dynamically adjusted according to the deformation characteristics of the object to ensure that the pressure distribution during the bundling process is uniform and avoids being too tight or too loose. Special pressure settings are performed in the multi-layer bundling mode to provide a more reliable bundling solution for complex objects. By analyzing the strain range, the bundling risk is identified to ensure the scientificity and effectiveness of the pressure control. By analyzing the time series of the pressure strain range, the stress changes in the multi-layer bundling process are predicted, and potential problems are identified in advance. The initial pressure is adjusted according to the stress prediction data, so that the changes of the object can be responded to in real time and the bundling quality can be improved. Based on the dynamic adjustment data, a scientific bundling pressure control strategy is constructed to achieve an optimized combination of multi-layer and single-layer bundling to ensure the best bundling effect. Through the real-time collection of feedback data, the bundling effect is evaluated to provide a basis for subsequent adjustments and ensure process traceability. The bundling efficiency of the bundling machine is evaluated to provide data support for the optimization strategy and promote continuous improvement of the equipment. By adaptively adjusting the control strategy according to the evaluation data, the bundling machine can continuously optimize the bundling effect under different environmental and object conditions to achieve intelligence and efficiency. Therefore, the present invention improves the intelligence and accuracy of the dynamic pressure control of the bundling machine through three-dimensional modeling, dynamic pressure adjustment and feedback control mechanism.

[0060] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0061] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A dynamic pressure control method for a strapping machine, characterized in that: The following steps are involved: Step S1: Obtain information data of the object to be bundled; Perform three-dimensional modeling of the objects to be bundled based on the information data of the objects to be bundled, and generate three-dimensional modeling data of the objects to be bundled; Confirming the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled, and obtaining the bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode; Step S2: setting the initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode to obtain the single-layer bundling initial pressure data; The object deformation bundling pressure is adjusted for the single-layer bundling initial pressure data to generate single-layer bundling pressure adjustment data; multi-layer bundling pressure is set for the information data of the object to be bundled based on the multi-layer bundling mode to obtain multi-layer bundling initial pressure data; Performing bundling pressure strain range analysis on multi-layer bundling initial pressure data to generate multi-layer bundling pressure strain range data; Step S3: predicting the time-series stress change of the multi-layer bundling pressure strain range data to generate multi-layer bundling stress prediction data; performing multi-layer bundling dynamic pressure control adjustment on the multi-layer bundling initial pressure data according to the multi-layer bundling stress prediction data to generate multi-layer bundling dynamic pressure control adjustment data; constructing a pressure control strategy for the bundling mode data through the multi-layer bundling dynamic pressure control adjustment data and the single-layer bundling pressure adjustment data to generate a bundling mode pressure control strategy; Step S4: collecting pressure control adjustment feedback data of the strapping mode pressure control strategy to obtain a pressure control adjustment feedback data set; evaluating the strapping efficiency of the pressure control adjustment feedback data set to generate strapping machine strapping efficiency evaluation data; adaptively adjusting the strapping mode pressure control strategy through the strapping machine strapping efficiency evaluation data, thereby generating a dynamic pressure control optimization strategy to perform strapping operations.

2. The dynamic pressure control method of the strapping machine according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire information data of the object to be bundled; Step S12: preprocessing the information data of the objects to be bundled to generate standard information data of the objects to be bundled, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: performing object surface feature recognition on the standard information data of the object to be bundled to obtain object surface feature data; performing three-dimensional modeling of the object to be bundled based on the object surface feature data to generate three-dimensional modeling data of the object to be bundled; Step S14: confirming the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled, and obtaining the bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode.

3. The dynamic pressure control method of the strapping machine according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: performing surface smoothness analysis on the three-dimensional modeling data of the object to be bundled to generate surface smoothness data of the object to be bundled; performing surface regular shape analysis on the three-dimensional modeling data of the object to be bundled to generate surface shape analysis data of the object to be bundled; Step S142: Calculate the required bundling strength based on the surface smoothness data and the surface shape analysis data of the objects to be bundled, and generate required bundling strength data of the objects to be bundled; wherein the formula for calculating the required bundling strength is as follows: ； In the formula, Expressed as the bundle strength requirement, Expressed as the weight of the object, Expressed as the weight of the object in the calculation of the binding strength requirement, Expressed as the surface smoothness coefficient, Expressed as the weight of surface smoothness in the calculation of the bundle strength requirement, Expressed as the object shape complexity coefficient, It is expressed as the weight of the object shape complexity in the calculation of the bundling strength requirement, Expressed as the surface roughness coefficient, It is expressed as the weight of the surface roughness coefficient in the calculation of the binding strength requirement; Step S143: comparing the bundling strength requirement data of the object to be bundled with a preset standard bundling strength requirement threshold, and when the bundling strength requirement data of the object to be bundled is greater than the preset standard bundling strength requirement threshold, confirming the multi-layer bundling mode of the bundling machine, thereby generating a multi-layer bundling mode; Step S144: When the bundling strength requirement data of the objects to be bundled is less than or equal to the preset standard bundling strength requirement threshold, the single-layer bundling mode is confirmed for the bundling machine, thereby generating a single-layer bundling mode; the multi-layer bundling mode and the single-layer bundling mode are integrated to generate bundling mode data.

4. The dynamic pressure control method of the strapping machine according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: setting an initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode to obtain single-layer bundling initial pressure data; performing bundling pressure feedback synchronization on the three-dimensional modeling data of the object to be bundled based on the single-layer bundling initial pressure data to generate bundling pressure feedback data; Step S22: using the bundling pressure feedback data to perform object deformation detection on the three-dimensional modeling data of the object to be bundled, and generating object deformation detection data; using the object deformation detection data to perform single-layer bundling pressure adjustment on the single-layer bundling initial pressure data, and generating single-layer bundling pressure adjustment data; Step S23: setting multi-layer bundling pressure for the information data of the object to be bundled based on the multi-layer bundling mode to obtain multi-layer bundling initial pressure data; simulating bundling for the three-dimensional modeling data of the object to be bundled according to the multi-layer bundling initial pressure data to generate multi-layer bundling simulation data; Step S24: performing pressure aggregation point analysis on the multi-layer bundling simulation data to generate multi-layer bundling pressure aggregation points; performing bundling pressure strain range analysis on the multi-layer bundling simulation data based on the multi-layer bundling pressure aggregation points to generate multi-layer bundling pressure strain range data.

5. The dynamic pressure control method of the strapping machine according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: using the bundling pressure feedback data to filter the dynamic data of the three-dimensional modeling data of the object to be bundled, to obtain the three-dimensional modeling dynamic data of the object to be bundled; Step S222: Scale change analysis is performed on the 3D modeling dynamic data of the objects to be bundled and the 3D modeling data of the objects to be bundled to generate scale change data of the objects to be bundled; object deformation is confirmed on the bundling pressure feedback data according to the scale change data of the objects to be bundled to obtain object deformation detection data; Step S223: performing deformation detection judgment on the object deformation detection data. When the judgment result of the object deformation detection data is true, reducing the bundling pressure of the single-layer bundling initial pressure data according to the object deformation detection data, thereby generating single-layer bundling first pressure adjustment data; Step S224: when the judgment result of the object deformation detection data is false, the strapping pressure feedback data is analyzed according to the object deformation detection data to generate strapping pressure form data; the strapping pressure is increased according to the strapping pressure form data for the single-layer strapping initial pressure data to generate single-layer strapping second pressure adjustment data; Step S225: integrating the single-layer bundling first pressure adjustment data and the single-layer bundling second pressure adjustment data to generate single-layer bundling pressure adjustment data.

6. The dynamic pressure control method of the strapping machine according to claim 5, characterized in that: Step S224 includes the following steps: When the judgment result of the object deformation detection data is false, the key feature extraction of the strapping pressure feedback data is performed to obtain the key feature data of the strapping pressure, wherein the key feature extraction of the strapping pressure includes stress distribution extraction, stress concentration area extraction and tension uniformity extraction; According to the key characteristic data of strapping pressure, strapping contact surface analysis is performed on the object deformation detection data to generate strapping contact surface data; strapping fit is calculated on the key characteristic data of strapping pressure based on the strapping contact surface data to obtain strapping fit data; The strapping belt pressure morphology analysis is performed based on the strapping belt contact surface data and strapping belt fit data to generate strapping belt pressure morphology data; the strapping belt-object porosity is calculated based on the strapping belt pressure morphology data and the three-dimensional modeling data of the object to be strapped to obtain the strapping belt-object porosity; the strapping belt-object porosity calculation formula is as follows: ； In the formula, Expressed as porosity, It is expressed as the void volume between the strapping tape and the surface of the object, It is expressed as the contact area between the strapping tape and the object; The bundling pressure of the single-layer bundling initial pressure data is increased based on the bundling band-object porosity to generate the single-layer bundling second pressure adjustment data.

7. The dynamic pressure control method of a strapping machine according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: extracting the pressure application data of each layer of the multi-layer bundling simulation data to obtain the layer pressure application data; marking the pressure application area of ​​the multi-layer bundling simulation data based on the layer pressure application data to generate the pressure application area marking data; Step S242: performing regional pressure peak calculation on the pressure application area mark data to generate regional pressure peak calculation data; performing pressure aggregation point identification on the layer pressure application data based on the regional pressure peak calculation data to generate multi-layer bundling pressure aggregation points; Step S243: marking the stress concentration area of ​​the pressure area marking data according to the multi-layer bundling pressure aggregation point to generate the stress concentration area; performing multi-layer bundling pressure gradient analysis on the stress concentration area to generate a hierarchical bundling pressure gradient map; extracting the pressure change amplitude of the hierarchical bundling pressure gradient map to obtain hierarchical pressure change amplitude data; Step S244: Calculate the multi-layer bundling pressure strain range of the multi-layer bundling simulation data using the layer pressure variation amplitude data to generate multi-layer bundling pressure strain range data; wherein the formula for calculating the multi-layer bundling pressure strain range is as follows: ； In the formula, Expressed as pressure-strain range, Expressed as The pressure applied by the strapping tape, Expressed as the contact area over which pressure is applied, Expressed as the internal stress of the object in the untied state.

8. The dynamic pressure control method of a strapping machine according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: predicting the time series stress change of the multi-layer bundling pressure strain range data to generate multi-layer bundling stress prediction data; Step S32: performing a multi-layer bundling pressure strength demand analysis according to the multi-layer bundling stress prediction data to generate multi-layer bundling pressure strength demand data; Step S33: performing multi-layer strapping dynamic pressure control adjustment on the multi-layer strapping initial pressure data based on the multi-layer strapping pressure intensity requirement data to generate multi-layer strapping dynamic pressure control adjustment data; Step S34: constructing a pressure control strategy for the strapping mode data using the multi-layer strapping dynamic pressure control adjustment data and the single-layer strapping pressure adjustment data to generate a strapping mode pressure control strategy.

9. The dynamic pressure control method of the strapping machine according to claim 8, characterized in that: Step S31 includes the following steps: Step S311: dividing the data set of the multi-layer bundling pressure-strain range data to generate a model training set and a model test set; training the model training set by using a long short-term memory neural network algorithm to generate a multi-layer bundling pressure-strain prediction pre-model; Step S312: performing model test iterations on the multi-layer bundling pressure-strain prediction pre-model through the model test set, thereby generating a multi-layer bundling pressure-strain prediction model; Step S313: importing the multi-layer bundling pressure-strain range data into the multi-layer bundling pressure-strain prediction model to perform time-series stress change prediction and generate multi-layer bundling stress prediction data.

10. A dynamic pressure control system for a strapping machine, characterized in that: The method for controlling the dynamic pressure of a strapping machine according to claim 1 is used to control the dynamic pressure of the strapping machine, wherein the dynamic pressure control system of the strapping machine comprises: The bundling mode recognition module is used to obtain information data of the object to be bundled; perform three-dimensional modeling of the object to be bundled based on the information data of the object to be bundled to generate three-dimensional modeling data of the object to be bundled; confirm the bundling mode of the bundling machine according to the three-dimensional modeling data of the object to be bundled to obtain the bundling mode data, wherein the bundling mode data includes a single-layer bundling mode and a multi-layer bundling mode; The single-layer bundling pressure adjustment module is used to set the initial bundling pressure for the information data of the object to be bundled based on the single-layer bundling mode, and obtain the single-layer bundling initial pressure data; perform object deformation bundling pressure adjustment on the single-layer bundling initial pressure data, and generate the single-layer bundling pressure adjustment data; perform multi-layer bundling pressure setting on the information data of the object to be bundled based on the multi-layer bundling mode, and obtain the multi-layer bundling initial pressure data; perform bundling pressure strain range analysis on the multi-layer bundling initial pressure data, and generate the multi-layer bundling pressure strain range data; The multi-layer bundling pressure adjustment module is used to predict the time-series stress change of the multi-layer bundling pressure strain range data and generate the multi-layer bundling stress prediction data; perform multi-layer bundling dynamic pressure control adjustment on the multi-layer bundling initial pressure data according to the multi-layer bundling stress prediction data and generate the multi-layer bundling dynamic pressure control adjustment data; construct the pressure control strategy for the bundling mode data through the multi-layer bundling dynamic pressure control adjustment data and the single-layer bundling pressure adjustment data and generate the bundling mode pressure control strategy; The feedback adjustment module is used to collect pressure control adjustment feedback data for the strapping mode pressure control strategy to obtain a pressure control adjustment feedback data set; perform strapping efficiency evaluation on the pressure control adjustment feedback data set to generate strapping efficiency evaluation data for the strapping machine; and adaptively adjust the strapping mode pressure control strategy through the strapping efficiency evaluation data of the strapping machine, thereby generating a dynamic pressure control optimization strategy to perform strapping operations.

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