Intelligent powder vacuum roller self-adaptive charging and homogenizing method and system

Abstract

The intelligent powder vacuum roller self-adaptive charging and homogenizing method comprises the steps that powder flowing in a roller, the state of the roller and the external environment are monitored in real time through a sensor, and parameter data are collected; real-time data are analyzed, the influence of the electrode position on the charging capacity and powder distribution is evaluated, and the charging problem is predicted; the rotating speed, the feeding amount and the vibration amplitude of the roller are automatically adjusted according to the prediction result so as to optimize powder distribution and filling efficiency; ultrasonic vibration and an electromagnetic field are automatically adjusted according to real-time feedback; the loading process of the roller is simulated in real time through the digital twinning technology, and the powder flowing and filling state is optimized; charging parameters are automatically adjusted according to real-time feedback, the charging effect is evaluated, and an optimization report is generated. According to the invention, feedback information can be continuously collected, an adjustment strategy is continuously optimized according to real-time data, long-term stable operation and self-optimization of the system are realized, and the intelligent level of the production process is improved.

Description

Technical Field

[0001] The present invention relates to a field, and in particular to an intelligent powder vacuum drum adaptive charging and homogenization method and system. Background Art

[0002] In modern industrial production, the powder charging process is one of the key steps in many manufacturing and processing links. In order to improve production efficiency and product quality, many companies have adopted automated equipment and control systems. However, the existing technology still has many problems in the field of powder charging and fails to fully meet the needs of modern production.

[0003] Most existing powder loading systems rely on traditional mechanical devices or simple automated control, which usually do not have real-time data feedback functions. Most equipment uses fixed speeds and feed rates and lacks a dynamic adjustment mechanism. This single control method cannot adapt to changes in different production environments, such as the different properties of powder particles, changes in humidity, etc., resulting in insufficient accuracy in the loading process, and prone to problems such as uneven powder distribution, accumulation, and particle loss. These problems not only affect the quality of the final product, but also greatly reduce production efficiency.

[0004] Another major problem is that existing technologies do not adequately monitor powder flowability and distribution during the loading process. Due to the lack of efficient real-time monitoring methods, traditional systems usually rely on manual or simple detection equipment to determine the flow state of powder. As a result, when powder accumulation or uneven distribution occurs, it is impossible to make timely adjustments, resulting in waste and instability in the production process.

[0005] Many existing technologies have not yet fully utilized advanced control methods to optimize energy consumption. Traditional systems often operate in a fixed manner and fail to adjust the speed and other operating parameters according to the actual needs of the production process, which not only wastes energy but also makes it impossible to maximize the operating efficiency of the equipment.

[0006] Due to the lack of sufficient intelligent design, existing technologies cannot support comprehensive digital and information management, and the management and processing capabilities of data flow are weak. Especially in large-scale production, the scheduling and management methods of traditional equipment often rely on manual operation, and cannot achieve automated full-process monitoring and optimized decision-making, resulting in a high error rate in the production process.

[0007] In summary, a more advanced, intelligent, precise and efficient solution is urgently needed to improve existing technology, enhance production efficiency, ensure consistency of product quality and reduce resource waste. Summary of the invention

[0008] In order to overcome the shortcomings of the prior art, the present invention proposes an intelligent powder vacuum drum adaptive loading and homogenization method and system, which can continuously collect feedback information and continuously optimize and adjust strategies according to real-time data, so as to achieve long-term stable operation and self-optimization of the system and improve the intelligence level of the production process.

[0009] To achieve the above object, the present invention proposes an intelligent powder vacuum drum adaptive charging and homogenization method, comprising: Step 1: Use sensors to monitor the powder flow in the drum, drum status and external environment in real time and collect key parameter data; Step 2: Analyze real-time data and evaluate the impact of electrode position on charging capacity and powder distribution to predict possible charging problems; Step 3: Automatically adjust the drum speed, feed amount and vibration amplitude based on the prediction results to optimize powder distribution and filling efficiency; Step 4: Automatically adjust ultrasonic vibration and electromagnetic field based on real-time feedback to improve powder flowability and uniform distribution; Step 5: Use digital twin technology to simulate the drum loading process in real time to optimize powder flow and filling status; Step 6: Automatically adjust charging parameters based on real-time feedback, evaluate charging results and generate optimization reports.

[0010] Furthermore, step 1 is as follows: Step 1.1: Install multiple sensors inside and outside the drum, including a pressure sensor, a temperature sensor, a vibration sensor, and a powder flow sensor; Step 1.2: The sensor monitors key data such as the flow state of the powder in the drum, the drum speed, the vibration amplitude, and the amount of powder in real time; Step 1.3: The collected data is transmitted to the central control system in real time and stored in the data management module to ensure the real-time and integrity of the data.

[0011] Furthermore, step 2 is as follows: Step 2.1: Based on the new electrode installation position, at the upper 1 / 3 of the drum diameter, the system establishes a dynamic simulation model of the powder flow and filling process; Step 2.2: Evaluate the effect of electrode position on charging capacity, powder distribution and accumulation to ensure that the new structure increases the effective charging capacity (from 1 / 3 to 1 / 2), such as Figure 2 As shown; Step 2.3: Analyze sensor data, paying special attention to the flow trajectory and distribution of powder, and predict uneven loading or accumulation problems that may occur due to the new design; Step 2.4: Identify potential problems in the loading process, such as uneven distribution and excessive accumulation, by comparing real-time data with model results.

[0012] Furthermore, step 3 is as follows: Step 3.1: Automatically adjust the drum speed based on real-time feedback and prediction models to ensure that the powder can flow effectively and be evenly distributed; Step 3.2: According to the powder distribution state, dynamically adjust the feed amount to ensure that each area is filled evenly to avoid over- or under-feeding; Step 3.3: According to the fluidity and filling uniformity of the powder, adjust the vibration frequency and amplitude of the drum to enhance the powder fluidity and improve the distribution; Step 3.4: Verify the adjusted loading effect based on real-time feedback data to ensure efficient filling and uniform distribution during the loading process.

[0013] Furthermore, step 4 is as follows: Step 4.1: Automatically control the frequency and amplitude of ultrasonic vibrations based on real-time feedback during the loading process to promote the fluidity of powder particles and reduce accumulation; Step 4.2: By adjusting the intensity and frequency of the electromagnetic field, the fluidity between the powder particles is enhanced to ensure uniform distribution; Step 4.3: Monitor the control effect of ultrasonic vibration and electromagnetic field in real time to evaluate their influence on the uniform distribution and filling effect of powder.

[0014] Furthermore, step 5 is as follows: Step 5.1: Based on the new structure and real-time data, a digital twin model of the drum loading process is constructed to simulate the flow trajectory, accumulation and filling effect of the powder; Step 5.2: Real-time synchronization of the physical state of the drum and the simulation model, tracking and adjusting the powder flow state to ensure that the loading process is always carried out in the best state; Step 5.3: Use the digital twin model to simulate the loading process in real time, simulate the powder distribution of the roller after adjustment, and further optimize the adjustment parameters based on the simulation results.

[0015] Furthermore, step 6 is as follows: Step 6.1: Based on real-time feedback, the system automatically adjusts parameters such as drum speed, feed amount, vibration frequency, etc. to continuously optimize the loading process; Step 6.2: Evaluate the powder distribution, stacking and filling uniformity of each charging process to ensure that each batch of powder charging meets the predetermined standards; Step 6.3: After loading is completed, the system generates a detailed production report, recording key data, adjustment parameters and effect evaluation during the loading process, and provides subsequent optimization suggestions.

[0016] An intelligent powder vacuum roller adaptive loading and homogenization system is applicable to the above-mentioned intelligent powder vacuum roller adaptive loading and homogenization method, and includes a sensor acquisition module, a data analysis and prediction module, a roller control and adjustment module, a digital twin and simulation module, an optimization decision and reporting module, and an optimization feedback and adjustment module.

[0017] Furthermore, the sensor acquisition module is responsible for real-time monitoring of key parameters inside and outside the drum through various sensors to provide real-time data support during the loading process. By accurately collecting this data, the system can analyze the current state of the loading process, thus providing a basis for subsequent optimization decisions.

[0018] The data analysis and prediction module is responsible for processing the data collected by the sensors, establishing a dynamic loading process model, and performing predictive analysis through the model to identify potential problems such as uneven distribution or accumulation, and provide a basis for subsequent adjustments and optimization.

[0019] The drum control and adjustment module automatically adjusts the operating parameters of the drum, such as speed, feed amount, etc., based on data analysis and prediction results to ensure uniform distribution of powder and improve loading efficiency. It is the execution unit in the system and directly controls the operating status of the drum.

[0020] The digital twin and simulation module builds a digital twin model based on the real-time collected equipment status and charging process data to simulate and optimize the actual charging process. It helps predict and optimize the powder flow and filling conditions during the charging process through a virtual simulation environment.

[0021] The optimization decision and reporting module is responsible for making comprehensive optimization decisions for the entire loading process and generating detailed production reports. It automatically adjusts the loading process based on real-time data, simulation results, and loading effect evaluation, and provides improvement suggestions and optimization solutions.

[0022] The optimization feedback and adjustment module compares the optimization adjustment results with the actual loading process through a real-time feedback mechanism, and continues to optimize the loading parameters based on the feedback to ensure that the loading process always remains in the best state throughout the entire production cycle.

[0023] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system. Through real-time data collection and dynamic adjustment of the drum's operating parameters (such as rotation speed and feed amount), the system can automatically optimize the loading process at different production stages, significantly improve loading efficiency, and reduce time waste.

[0024] 2. The present invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system. By utilizing ultrasonic vibration and electromagnetic field control technology, the system effectively avoids the problem of powder accumulation in the drum, ensures the uniform distribution of powder, and thus improves the quality and consistency of the product.

[0025] 3. The present invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system. Through the dynamic model established by the data analysis and prediction module, the system can predict the changing trend in the loading process in real time, and automatically adjust the operating parameters according to the prediction results, thereby reducing manual intervention and improving the system's adaptive ability.

[0026] 4. The present invention provides an intelligent powder vacuum drum adaptive loading and homogenization method and system, which optimizes the drum rotation speed and feed amount, reduces unnecessary energy consumption, and the system can efficiently utilize energy, reduce overall production costs, and avoid energy waste through precise control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 It is a schematic diagram of the steps of the present invention Figure 2 This is the position diagram of the electrode inside the drum Figure 3 This is the flow chart of this system DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be more clearly and completely explained below through description of preferred embodiments of the present invention in combination with the accompanying drawings.

[0030] like Figure 1 As shown, the present invention is specifically: Step 1: Use sensors to monitor the powder flow in the drum, drum status and external environment in real time and collect parameter data; Step 2: Analyze real-time data and evaluate the impact of electrode position on charging capacity and powder distribution to predict charging problems; Step 3: Automatically adjust the drum speed, feed amount and vibration amplitude based on the prediction results to optimize powder distribution and filling efficiency; Step 4: Automatically adjust ultrasonic vibration and electromagnetic field based on real-time feedback; Step 5: Use digital twin technology to simulate the drum loading process in real time to optimize powder flow and filling status; Step 6: Automatically adjust charging parameters based on real-time feedback, evaluate charging results and generate optimization reports.

[0031] As a specific implementation method, Figure 3 As shown, the system includes the following modules: sensor acquisition module, data analysis and prediction module, roller control and adjustment module, digital twin and simulation module and optimization decision and reporting module; The sensor acquisition module functions include: The powder status in the drum, including the fluidity and accumulation of powder, is monitored in real time through pressure sensors, vibration sensors, etc.

[0032] Monitor the external environment of the drum and the working status of the equipment itself, such as drum speed and feed volume.

[0033] The collected sensor data is transmitted to the data analysis module in real time for further processing and analysis.

[0034] The functions of the data analysis and prediction module include: Clean and normalize sensor data to remove noise and ensure data accuracy and consistency.

[0035] A dynamic model of powder charging is established to describe the powder flow, accumulation, filling and other processes. The model is based on fluid mechanics and particle dynamics, and takes into account factors such as drum speed, feed amount, and vibration frequency.

[0036] Dynamic models are used to predict powder flow trends, accumulation, and possible problems during the charging process, providing a basis for optimization decisions.

[0037] The functions of the drum control and adjustment module include: The drum speed is automatically adjusted based on the prediction results to optimize powder fluidity and distribution, making charging more uniform and efficient.

[0038] The feeding rate is automatically adjusted to ensure that the amount of powder flowing into the drum matches the current loading state to avoid excessive or insufficient feeding. The system can also further improve the powder flowability and avoid accumulation and uneven distribution through control means such as ultrasonic vibration and electromagnetic field.

[0039] Digital Twin and Simulation Module features include: Based on digital twin technology, the physical state of the drum is synchronized in real time, and the powder flow, accumulation, filling and other processes are simulated in a virtual environment.

[0040] Based on the simulation results, the impact of different operating parameters on the charging effect is evaluated, and optimization and adjustment suggestions are put forward to further improve the efficiency and uniformity of charging.

[0041] Predict the loading process under different conditions in a simulation environment, optimize parameters such as drum speed and feed rate, and reduce the trial and error cost in actual operation.

[0042] The optimized decision-making and reporting module features include: Combining real-time data, dynamic models, simulation results, etc., the key parameters in the charging process are automatically optimized to ensure uniform distribution of powder and charging efficiency.

[0043] Evaluate the effectiveness of each loading process, analyze key indicators such as powder distribution and filling efficiency, and adjust operating parameters based on these evaluation results.

[0044] Generate a detailed report of the loading process, record the key operating parameters of each loading, optimization adjustment process, final effect, etc., to provide data support and reference for subsequent optimization.

[0045] Optimized feedback and adjustment module functions include: Based on the data collected in real time by the sensor module, changes in the loading process are evaluated and real-time feedback is provided.

[0046] Adjust the roller speed, feed rate and other control parameters based on the feedback information to ensure uniform powder flow and achieve the best loading effect.

[0047] Through the feedback and adjustment mechanism, a closed-loop system is formed to continuously optimize the loading process and ensure long-term high efficiency and stability.

[0048] The sensor acquisition module in this system provides data input for the entire system and provides a basis for analysis and control through precise real-time monitoring; the data analysis and prediction module helps identify problems and provides a basis for system adjustment through modeling and trend prediction; the roller control and adjustment module performs real-time roller operation adjustment to ensure that the loading process meets the optimization goals; the digital twin and simulation module optimizes control decisions through virtual simulation to improve loading accuracy and efficiency; the optimization decision and reporting module is responsible for making adjustment decisions and generating reports to further improve system operation; the optimization feedback and adjustment module continuously optimizes the loading process through a closed-loop control mechanism to ensure efficiency and stability.

[0049] The function of the sensor acquisition module is to monitor the key parameters inside and outside the drum in real time, including powder flow, stacking status, drum speed, vibration frequency, etc.

[0050] The sensors include pressure sensor: used to monitor the pressure changes of powder filling in the drum and provide dynamic feedback of the filling process; vibration sensor: used to monitor the frequency and amplitude of drum vibration to help control powder fluidity; temperature sensor: used to monitor the temperature inside and outside the drum to ensure stability during the filling process; flow sensor: used to detect the powder feed amount to ensure real-time adjustment of the feed amount.

[0051] The data analysis and prediction module uses real-time data to build dynamic models and predict the loading process.

[0052] In order to accurately describe the flow, accumulation and filling process of powder, a hybrid model based on fluid mechanics and particle dynamics is used. The model takes into account the powder flow in the drum, the interaction between particles and the effect of drum rotation on the powder.

[0053] Assumptions: Powder particles are rigid and satisfy the law of particle size distribution; the powder flow in the drum is laminar or turbulent, depending on the rotation speed of the drum and the characteristics of the particles; the interaction between powder particles can be represented by the Coulomb friction and van der Waals force models.

[0054] Momentum equation (a simplified form of the Navier-Stokes equations):

[0055] Where v is the fluid velocity, ρ is the density of the powder, p is the pressure, μ is the viscosity, and F is the external force (such as gravity, vibration, etc.).

[0056] Interaction forces between particles: The contact force between particles is composed of elastic force, viscous force and friction force, which can be described by the Hertz model and the Coulomb friction model.

[0057] Hertz contact force model:

[0058] Among them, F elastic is the elastic force, k is the contact stiffness, and δ is the displacement between particles.

[0059] Coulomb friction model:

[0060] Among them, F friction is the friction force, μ is the friction coefficient, and N is the normal force.

[0061] Using the above physical model, key parameters in the charging process can be predicted, such as filling time, powder distribution, stacking degree, etc. Assuming that the charging process is a dynamic system, the model is:

[0062] Where M is the mass of powder in the drum, is the powder flow rate, It is the powder outflow rate caused by vibration or gravity.

[0063] According to the fluidity of the powder and the rotation speed of the drum, the accumulation and flow process of the powder in the drum are predicted, and the drum speed and feed amount are adjusted accordingly.

[0064] Based on the prediction results, the roller speed, feed amount and other control parameters are automatically adjusted to ensure uniformity and efficiency of the loading process.

[0065] The speed control of the drum is the key, affecting the fluidity and uniformity of the powder. Assume that the effect of the drum speed on the powder flow conforms to the following relationship:

[0066] Among them, v flow is the flow rate of the powder, k speed is a constant related to the powder properties and the drum structure, and ω is the rotation speed of the drum.

[0067] According to the predicted powder flow trend, the drum speed is automatically adjusted to make the powder more evenly distributed and avoid accumulation.

[0068] Feeding amount control is achieved by adjusting the feeding rate. The feeding rate is closely related to the powder filling degree and flow state in the drum. The feed amount adjustment formula is:

[0069] in, is the powder feed amount, M is the current load, M max is the maximum loading capacity of the drum, k feed is a constant related to charging efficiency and powder characteristics.

[0070] Based on the dynamic model, a digital twin model is established to simulate the flow, accumulation and filling process of powder in the drum through simulation technology. The digital twin model is synchronized with the actual drum in real time to ensure that the simulation results are consistent with the actual situation.

[0071] Simulate the charging process using Finite Element Analysis (FEA) and Discrete Element Method (DEM), including: Finite element analysis (FEA) is used to simulate the structural stress, vibration and physical interaction of the drum. Discrete element method (DEM) is used to simulate the flow and interaction of powder particles. The control parameters such as drum speed and feed amount are optimized and adjusted based on the simulation results.

[0072] The loading process is automatically optimized based on real-time data and simulation results. After each loading, the system generates a detailed production report that records the operating parameters, adjustment process and effect evaluation to ensure continuous process improvement.

[0073] As a specific implementation method, the sensor acquisition module starts working to monitor the key parameters inside and outside the drum in real time, including the fluidity and accumulation of the powder, the speed, temperature, pressure and feed amount of the drum. The sensor continuously collects and transmits data, providing accurate environmental and charging status information for subsequent analysis and control. The data of each sensor is transmitted to the data analysis and prediction module.

[0074] The sensor data received by the data analysis and prediction module will be preprocessed to remove noise and inconsistency to ensure the accuracy and effectiveness of the data.

[0075] The system uses these real-time data and pre-established powder charging process models to perform dynamic modeling. The model calculates the flowability, stacking state, and filling conditions of the powder based on the principles of fluid mechanics and particle dynamics. Through these dynamic models, the system can predict the changing trends that may occur during the charging process, such as uneven distribution, stacking, and other problems, and can identify and predict possible failures or bottlenecks in advance.

[0076] Based on the prediction results, the data analysis and prediction module generates a control strategy and transmits it to the drum control and adjustment module. At this time, the drum control and adjustment module receives the instruction from the data analysis module and starts to adjust the operating parameters of the drum.

[0077] The speed of the drum will be automatically adjusted according to the predicted powder flow trend to ensure the fluidity and uniform distribution of the powder in the drum. The speed adjustment is determined by the characteristics of the powder and the loading state. If the system predicts that the powder has poor fluidity, the drum speed will increase; conversely, if the fluidity is good, the speed will be appropriately reduced.

[0078] The feed amount is also dynamically adjusted based on the prediction model. The system adjusts the feed rate based on the current powder amount and flow state in the drum to ensure that the powder matches the current loading state when entering the drum, avoiding excessive or insufficient feeding.

[0079] During the adjustment process, the control of the drum is not limited to the speed and feed amount, but also involves other auxiliary control means, including the application of ultrasonic vibration and electromagnetic field. The role of these control means is to further enhance the fluidity of the powder particles, prevent the powder from accumulating in the drum, and ensure the uniform distribution of the powder. By precisely controlling the frequency and amplitude of ultrasonic vibration, the fluidity of the powder particles is enhanced, thereby achieving uniform filling; similarly, by adjusting the electromagnetic field, the distribution of the powder can be optimized and the loading effect can be improved.

[0080] The digital twin and simulation module establishes a virtual simulation environment by synchronizing the working status of the drum in real time. The simulation technology simulates the flow process of powder in the drum and compares the actual operation with the simulation results. During this process, the digital twin model continuously updates the simulation parameters based on sensor data and real-time adjustments to optimize the simulation results. Through simulation, operators can see the state of powder flow in real time and can adjust the speed and feed amount of the drum in time to achieve the best loading effect.

[0081] During the entire loading process, the optimization decision and reporting module is responsible for summarizing all adjustments and optimization decisions and generating production reports. The key operating parameters, adjustment process and final effect of each loading link are recorded in detail. The report not only contains the specific adjustment strategy for each operation, but also provides evaluations of key indicators such as loading efficiency and powder distribution uniformity. These evaluation results provide a basis for subsequent improvements and adjustments, enabling the system to continuously optimize the loading process and improve overall production efficiency.

[0082] The optimization feedback and adjustment module forms a closed-loop optimization mechanism based on production reports and real-time feedback. When deviations or unexpected situations occur during the loading process, the system will automatically adjust parameters such as drum speed and feed rate based on feedback information to restore to the optimal state. At the same time, the system will gradually optimize control parameters based on feedback data to ensure efficient and stable operation during long-term production.

[0083] The entire system ensures the uniformity and efficiency of the powder charging process through real-time data monitoring, dynamic adjustment and optimized decision-making, thereby greatly improving production efficiency and product quality.

[0084] The above specific implementations are only descriptions of the preferred implementations of the present invention, and do not limit the protection scope of the present invention. Without departing from the design concept and spirit of the present invention, various modifications, substitutions and improvements made by ordinary technicians in this field to the technical solution of the present invention based on the text description and drawings provided by the present invention should all fall within the protection scope of the present invention. The protection scope of the present invention is determined by the claims.

Claims

1. An intelligent powder vacuum drum adaptive loading and homogenization method, characterized in that: include: Step 1: Use sensors to monitor the powder flow in the drum, drum status and external environment in real time and collect parameter data; Step 2: Analyze real-time data and evaluate the impact of electrode position on charging capacity and powder distribution to predict charging problems; Step 3: Automatically adjust the drum speed, feed amount and vibration amplitude based on the prediction results to optimize powder distribution and filling efficiency; Step 4: Automatically adjust ultrasonic vibration and electromagnetic field based on real-time feedback; Step 5: Use digital twin technology to simulate the drum loading process in real time to optimize powder flow and filling status; Step 6: Automatically adjust charging parameters based on real-time feedback, evaluate charging results and generate optimization reports.

2. The method for adaptively charging and homogenizing a smart powder vacuum drum according to claim 1, characterized in that: Step 1 is as follows: Step 1.1: Install sensors inside and outside the drum, including pressure sensors, temperature sensors, vibration sensors, and powder flow sensors; Step 1.2: The sensor monitors the flow state of the powder in the drum, the drum speed, the vibration amplitude and the powder quantity data in real time; Step 1.3: The collected data is transmitted to the central control system in real time and stored to ensure the real-time and integrity of the data.

3. The method for adaptively charging and homogenizing a smart powder vacuum drum according to claim 1, characterized in that: Step 2 is as follows: Step 2.1: Based on the electrode installation position, at the upper 1 / 3 of the drum diameter, the system establishes a dynamic simulation model of the powder flow and filling process; Step 2.2: Evaluate the effect of electrode position on charge capacity, powder distribution and accumulation; Step 2.3: Analyze sensor data to predict possible uneven loading or accumulation problems; Step 2.4: Identify potential problems in the loading process by comparing real-time data with model results.

4. The method for adaptively charging and homogenizing a smart powder vacuum drum according to claim 1, characterized in that: Step 3 is as follows: Step 3.1: Automatically adjust the drum speed based on real-time feedback and prediction model; Step 3.2: Dynamically adjust the feed amount according to the powder distribution state; Step 3.3: Adjust the vibration frequency and amplitude of the drum according to the fluidity and filling uniformity of the powder; Step 3.4: Verify the adjusted loading effect based on real-time feedback data.

5. The method for adaptively charging and homogenizing a smart powder vacuum drum according to claim 1, characterized in that: Step 4 is as follows: Step 4.1: Automatically control the frequency and amplitude of ultrasonic vibration according to real-time feedback during the loading process; Step 4.2: By adjusting the intensity and frequency of the electromagnetic field, the fluidity between the powder particles is enhanced; Step 4.3: Monitor the control effect of ultrasonic vibration and electromagnetic field in real time to evaluate their influence on the uniform distribution and filling effect of powder.

6. The method for adaptively charging and homogenizing a smart powder vacuum drum according to claim 1, characterized in that: Step 5 is as follows: Step 5.1: Based on the structure and real-time data, a digital twin model of the drum loading process is constructed to simulate the flow trajectory, accumulation and filling effect of the powder; Step 5.2: Real-time synchronization of the physical state of the drum and the simulation model to track and adjust the powder flow state; Step 5.3: Use the digital twin model to simulate the loading process in real time, simulate the powder distribution of the roller after adjustment, and further optimize the adjustment parameters based on the simulation results.

7. The method for adaptively charging and homogenizing a smart powder vacuum drum according to claim 1, characterized in that: Step 6 is as follows: Step 6.1: Based on real-time feedback, the system automatically adjusts parameters to continuously optimize the loading process; Step 6.2: Evaluate the powder distribution, accumulation and filling uniformity of each loading process; Step 6.3: After loading is completed, the system generates a production report, records the data, adjustment parameters and effect evaluation during the loading process, and provides subsequent optimization suggestions.

8. An intelligent powder vacuum drum adaptive charging and homogenizing system, applicable to an intelligent powder vacuum drum adaptive charging and homogenizing method according to any one of claims 1 to 7, characterized in that: It includes sensor acquisition module, data analysis and prediction module, roller control and adjustment module, digital twin and simulation module, optimization decision and reporting module and optimization feedback and adjustment module.

9. The intelligent powder vacuum drum adaptive loading and homogenization system according to claim 8, characterized in that: The sensor acquisition module is responsible for real-time monitoring of the internal and external parameters of the drum through sensors; The data analysis and prediction module is responsible for processing the data collected by the sensors, establishing a dynamic loading process model, and performing predictive analysis through the model; The drum control and adjustment module automatically adjusts the drum operating parameters based on data analysis and prediction results; The digital twin and simulation module builds a digital twin model based on the real-time collected equipment status and loading process data to simulate and optimize the actual loading process; The optimization decision and report module is responsible for making comprehensive optimization decisions for the entire loading process and generating detailed production reports; The optimization feedback and adjustment module compares the optimization adjustment results with the actual charging process through a real-time feedback mechanism, and continues to optimize the charging parameters based on the feedback.

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