A pressure stabilizing control system for a pressurized device

Abstract

The application discloses a kind of pressure stabilizing control systems for pressurizing equipment, belongs to pressure control technical field, comprising: sensor module, prediction module and control module;The sensor module is for using high-precision sensor, real-time acquisition equipment in the process of running key parameters;The prediction module is for using nonlinear regression analysis method, constructs pressure relationship model, the pressure relationship model is used to describe the quantitative relationship between multiple key parameters and pressure of pressurizing equipment in the process of running, captures the complex mechanism of pressure fluctuation;The control module is for self-adaptive adjustment of key parameters according to actual parameter value of pressurizing equipment, by comparing actual parameter value obtained in real time with preset parameter stable range, judging and updating key parameters, generating pressure prediction value, fine tuning key parameters, flexibly cope with various unpredictable factors in production, improve the stability and controllability of production.

Description

A voltage stabilization control system for pressurization equipment Technical Field

[0001] This invention relates to a pressure stabilization control system for pressurization equipment, belonging to the field of pressure control technology. Background Technology

[0002] Pressurization equipment, such as water pumps, compressors, and booster pumps, efficiently delivers fluids such as water and air to designated locations or meets specific process requirements by applying pressure. However, during operation, the internal pressure of pressurization equipment is often affected by various factors and fluctuates. These fluctuations not only affect the performance of the equipment and lead to a decrease in production efficiency, but may also damage the equipment itself and even cause safety hazards.

[0003] To address this issue, pressure changes in the pressurizing equipment can be monitored in real time, and the output pressure can be adjusted according to a preset pressure range. This ensures that the pressurizing equipment operates in optimal condition, improving production efficiency and product quality, effectively extending equipment lifespan, and reducing maintenance costs. Existing pressure monitoring and adjustment control systems already possess functions such as remote monitoring, fault diagnosis, and data analysis. Operators can use mobile phones, computers, and other terminal devices to understand the equipment's operating status and pressure changes in real time, promptly identify and address potential problems, and ensure the stable operation of the production line.

[0004] Chinese patent application CN118317659A discloses a pressurizing device, pressurizing equipment, and pressurizing method. The pressurizing device includes at least one pressurizing body, a pressurizing assembly, an exhaust assembly, and a sealing assembly. The pressurizing body has a pressurizing chamber containing a plate assembly and an inlet communicating with the pressurizing chamber. The pressurizing assembly includes a first air box, a pressurizing pump, and a pressurizing valve. The pressurizing pump is connected to the first air box and the pressurizing body. The exhaust assembly includes an exhaust valve connected to the pressurizing body. The sealing assembly includes a sealing element disposed in the pressurizing chamber. The sealing element can move relative to the pressurizing body between a first position and a second position. When the sealing element is in the first position, it covers the inlet, and the pressurizing chamber is in a sealed state. When the sealing element is in the second position, it moves away from the inlet, and the pressurizing chamber is in an open state.

[0005] While existing technologies introduce gas into the pressurization chamber via an exhaust assembly, and the gas acts on the plate assembly to expel air bubbles between the plate assemblies, thus removing them, they do not consider the adaptability and scalability of pressure control. In particular, they do not consider the various factors influencing pressure fluctuations during the operation of the pressurization equipment, nor how to control these factors through real-time monitoring and adjustment to ensure stable operation of the pressurization equipment under optimal conditions. Therefore, this application provides a pressure stabilization control system for pressurization equipment. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a pressure stabilization control system for pressurizing equipment. By constructing a relationship model between multiple key parameters and pressure in the pressurizing equipment, the required pressure value is predicted, and the actual parameter values ​​of the pressurizing equipment are compared with the parameter stability range. The operating parameters are then adaptively adjusted, thereby enhancing the flexibility and adaptability of production.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A voltage stabilization control system for a pressurizing device includes: a sensor module, a prediction module, and a control module;

[0009] The sensor module is used to acquire key parameters during the operation of the equipment in real time using sensors;

[0010] The prediction module is used to construct a pressure relationship model using nonlinear regression analysis. The pressure relationship model is used to describe the quantitative relationship between multiple key parameters and pressure during the operation of the pressurizing equipment.

[0011] The control module is used to adaptively adjust key parameters based on the actual parameter values ​​of the pressurizing equipment. By comparing the actual parameter values ​​with the preset parameter stability range, the key parameters are judged and updated, pressure prediction values ​​are generated, and the key parameters are adjusted.

[0012] Specifically, the steps for constructing a stress relationship model include:

[0013] Pressure is defined as the dependent variable, and key parameters affecting pressure changes are defined as independent variables. Interaction terms are introduced, dynamic interaction coefficients are configured in combination with dynamic coupling strength coefficients, and nonlinear regression analysis is used to construct a pressure relationship model.

[0014] Acquire historical operational data and preprocess it to generate a dependent variable matrix and an independent variable matrix;

[0015] Calculate the Euclidean distance between each pair of sample sequences in the independent variable matrix to generate a distance matrix;

[0016] Calculate the covariance matrix of the distance matrix and perform eigenvalue decomposition to obtain eigenvalues ​​and corresponding eigenvectors.

[0017] Specifically, the steps for constructing a stress relationship model also include:

[0018] Obtain historical grid voltage and pipeline pressure difference, calculate grid voltage deviation and pipeline pressure difference deviation, and select the maximum value as the interference intensity;

[0019] Set a secondary interference threshold, determine the category of interference intensity, and call the corresponding filtering quantity. ;

[0020] Sort the eigenvalues ​​in descending order and select the top ones. Generate a distance component matrix by taking the largest eigenvalue and its corresponding eigenvector;

[0021] For each independent variable, the difference between the time the adjustment command is issued and the time the pressure stabilizes is calculated to obtain the lag time, and the samples in the distance component matrix are time-shifted to correct the distance components.

[0022] Quasi-linear regression was performed on the corrected distance component, and the coefficients between the distance component and the dependent variable were solved using the least squares method.

[0023] Specifically, the steps for constructing a stress relationship model also include:

[0024] The residuals and cross-validation coefficients are calculated using the regression equation to determine whether the pressure relationship model meets the convergence condition.

[0025] If the convergence condition is met, the distance component is output; if not, new distance components are extracted and quasi-linear regression is performed repeatedly until the convergence condition is met.

[0026] Once the model converges, correlation operations are performed on the distance component matrix and the independent variable matrix, and the standard coefficients of the objective function are calculated.

[0027] The standard coefficients are destandardized to convert them into nonstandard coefficients, and a pressure relationship model is output.

[0028] Specifically, the steps for adaptive adjustment include:

[0029] Set the basic stability range, including the basic temperature range, the basic flow rate range, and the basic pressure range;

[0030] Obtain the rated lifespan and rated power of the equipment, combine the operating time and actual power, calculate the equipment health and thermal management health, and solve for the average value to obtain the health index;

[0031] Calculate the pressure deviation, combine the parameter fluctuation amplitude and the preset fluctuation ratio, classify the working condition type, and call the corresponding working condition coefficient;

[0032] Based on the health index and operating condition type, the basic stability range is corrected to generate parameter stability ranges, including temperature stability range, flow rate stability range, and pressure stability range.

[0033] Specifically, the steps for adaptive adjustment also include:

[0034] The system acquires real-time parameter values ​​and makes judgments and updates to key parameters based on these values.

[0035] The lag time of each parameter is obtained, and the actual parameter values ​​are corrected to obtain the predicted parameter values;

[0036] Using the predicted parameter values ​​and the pressure relationship model, pressure prediction values ​​are generated. Determine whether the predicted pressure value deviates from the stable pressure range;

[0037] like Apply a preset pressure value to the pressurizing device; if or Adjust the key parameters.

[0038] Specifically, the steps for judging and updating key parameters include:

[0039] For temperature ,like The temperature did not exceed the stable temperature range;

[0040] like or If the temperature exceeds the stable temperature range, the ambient temperature is obtained, and a preset ambient standard temperature is called to perform out-of-limit tracking.

[0041] If the ambient temperature is lower than the standard ambient temperature, temporarily relax the temperature stability range; if the ambient temperature is not lower than the standard ambient temperature, start the heating or cooling device to adjust the temperature to the rated temperature. ;

[0042] For flow rate ,like The flow velocity did not exceed the stable flow velocity range;

[0043] like or If the flow rate exceeds the stable flow rate range, adjust the pump speed or valve to bring the flow rate back to the rated flow rate. ;

[0044] After adjustment, the actual parameter values ​​are updated in real time, and a new stability assessment is performed until the actual parameter values ​​do not exceed the parameter stability range.

[0045] Specifically, the steps for adjusting key parameters include:

[0046] The key parameters are defined in the following order for adjustment: solid fraction, temperature, flow rate, and rated pressure. The upper limit threshold for adjusting the solid fraction is set as follows: Adjust the lower threshold to ;

[0047] Based on the rated pressure and pressure forecast values Calculate the adjustment amount for the solid fraction. and target solid fraction ;

[0048] like Adjust the solid fraction to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment;

[0049] like or Adjust the solid fraction to or Update the pressure forecast value And determine the predicted pressure value. Check if the pressure exceeds the stable range. If not, adjust the predicted pressure value. Apply pressure to the pressurizing equipment and stop the adjustment. If the pressure exceeds the limit, adjust the temperature.

[0050] Specifically, the steps for adjusting key parameters also include:

[0051] Based on pressure predictions Calculate the temperature adjustment amount and target temperature ;

[0052] like Adjust the temperature to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment;

[0053] like or Adjust the temperature to or Update pressure forecast values And determine the predicted pressure value. Check if the pressure exceeds the stable range. If not, adjust the predicted pressure value. Apply the pressure to the pressurizing equipment and end the adjustment. If the flow rate is exceeded, adjust the flow rate.

[0054] Based on pressure predictions Calculate the adjustment amount of the flow rate and target flow rate ;

[0055] like Adjust the flow rate to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment;

[0056] like or Adjust the flow rate to or Update pressure forecast values And determine the predicted pressure value. Check if the pressure exceeds the stable range. If not, adjust the predicted pressure value. Apply pressure to the pressurizing equipment, then stop the adjustment. If the pressure exceeds the limit, a warning signal will be generated.

[0057] Specifically, it also includes: an execution module and a communication module;

[0058] The execution module is used to adjust the working state of the pressurizing equipment according to the adjustment amount generated by the control module. The execution module is configured with a multi-axis linkage control strategy to simultaneously adjust multiple key parameters of the pressurizing equipment.

[0059] The communication module is used to transmit the collected actual parameter values ​​to each module using wireless communication technology.

[0060] The beneficial effects of this invention are:

[0061] 1. By constructing an accurate pressure relationship model, it is possible to predict and adjust pressure in real time, significantly improving the accuracy of pressure prediction during pressurization. Furthermore, the pressure relationship model comprehensively considers multiple key parameters and their interactions, effectively capturing the complex mechanism of pressure fluctuations. Through steps such as nonlinear regression analysis, data standardization, distance component extraction, and quasi-linear regression, the model is continuously optimized until convergence, ensuring the reliability of the prediction results, improving the stability and controllability of the production process, reducing the product defect rate caused by pressure fluctuations, and optimizing resource utilization, reducing unnecessary energy consumption and maintenance costs.

[0062] 2. By monitoring and adaptively adjusting key parameters in real time, product defects caused by parameter fluctuations are effectively avoided, improving product yield and quality. By setting a stable range for parameters, various unpredictable factors in the production process can be flexibly addressed, ensuring the continuity and stability of the production process and improving production flexibility and adaptability. At the same time, the optimization of parameter adjustment reduces over-adjustment, extends equipment life, reduces maintenance costs, enhances the flexibility and response speed of the production line, and significantly improves production efficiency and economic benefits. Attached Figure Description

[0063] Figure 1 is a structural diagram of a pressure stabilization control system for a pressurizing device;

[0064] Figure 2 is a flowchart of the pressure relationship model constructed in this application;

[0065] Figure 3 is a flowchart of the adaptive adjustment process in this application;

[0066] Figure 4 is a flowchart of the adjustment of key parameters in this application. Detailed Implementation

[0067] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0068] Example 1

[0069] Referring to Figures 1 to 4, this embodiment introduces a pressure stabilization control system for pressurizing equipment, taking a semi-solid die-casting machine as an example, including: a sensor module, a prediction module, a control module, an execution module, and a communication module;

[0070] The sensor module is used to acquire key parameters in the semi-solid die-casting process in real time through various high-precision sensors configured within the semi-solid die-casting machine, such as the operating environment of the semi-solid die-casting machine. This allows for comprehensive monitoring of the metal slurry's state, ensuring that even minute changes in key parameters can be immediately captured. The monitored data is converted into electrical signals and transmitted to the prediction module for processing. The high-precision sensors include pressure sensors, temperature sensors, solid-liquid ratio sensors, and flow state sensors. The pressure sensor monitors the internal pressure state of the semi-solid die-casting machine to ensure that the pressure remains within a preset range, thereby guaranteeing the quality stability of the die-cast parts and ensuring that any minute pressure fluctuations are captured promptly, avoiding adverse effects on the product's microstructure and performance. The temperature sensor monitors the temperature changes of the metal slurry and die-casting mold to precisely control the temperature and optimize the die-casting process. The solid-liquid ratio sensor monitors the solid-liquid ratio of the metal slurry to ensure that the die-cast parts have ideal mechanical properties and surface quality. The flow state sensor monitors the flow rate and direction of the metal slurry in real time to ensure that the metal slurry is evenly distributed in the mold, avoiding die-casting defects.

[0071] The prediction module processes and analyzes the data collected by the sensor module to construct a pressure relationship model, describing the quantitative relationship between multiple key parameters and pressure during the operation of the pressurizing equipment. Using nonlinear regression analysis, it considers key parameters affecting pressure changes during semi-solid die casting, including the solid fraction, temperature, viscosity, flow rate, and flow direction of the slurry, as well as the interactions between these key parameters. The pressure relationship model is then continuously optimized until convergence through data standardization, distance component extraction, and quasi-linear regression. The standard coefficients are then converted into non-standard coefficients before being output as the pressure relationship model. This achieves an accurate description of the relationship between solid fraction and pressure during semi-solid die casting. The pressure relationship model is then used to predict the pressure value of the semi-solid die casting equipment in real time, providing precise guidance for pressure control during production.

[0072] The control module is used to adaptively adjust the operating parameters of the semi-solid die-casting equipment based on actual parameter values. It compares key parameters acquired in real time by high-precision sensors with preset parameter stability ranges. When a key parameter exceeds the range, it judges and updates the key parameter to achieve timely parameter adjustment. It also generates pressure prediction values ​​using actual parameter values ​​and a pressure relationship model, compares them with the pressure stability range, and adjusts key parameters based on the results to ensure the stability of the pressure prediction values. This achieves fine control and optimization of the die-casting process.

[0073] The execution module is used to adjust the working state of the pressurizing equipment according to the adjustment amount generated by the control module. The execution module is configured with a multi-axis linkage control strategy, which can adjust multiple key parameters at the same time, achieve precise synchronization and coordination between multiple axes, ensure that the metal slurry is evenly distributed in the mold, reduce the generation of bubbles and defects, and achieve precise control of the die casting process.

[0074] The communication module is used to transmit the actual parameter values ​​collected during system operation to each module through wireless communication technology, enabling the voltage regulation control system to communicate with other devices or systems, improving the system's integration and flexibility, and allowing the system to better adapt to different application scenarios.

[0075] Specifically, the steps for constructing a stress relationship model include:

[0076] A pressure relationship model for semi-solid die-casting equipment was constructed using nonlinear regression analysis. The pressure value at different times was defined as the dependent variable to reflect the real-time pressure changes during the die-casting process. Key parameters affecting pressure changes were defined as independent variables, including the solid fraction of the slurry, temperature, viscosity, flow rate, and flow direction, with corresponding regression coefficients configured. An interaction term was introduced into the pressure relationship model, representing the interaction relationship between any two parameters. Corresponding interaction coefficients were configured to expand the independent variables. Since the parameters in the independent variables are all static values, they cannot adapt to the time-varying nature of parameter correlations. For the interaction term, the dynamic coupling strength coefficient was calculated by the ratio of the product of the covariance of two parameters to the global standard deviation to quantify the real-time correlation of the parameters. The dynamic interaction coefficient was obtained by multiplying the dynamic coupling strength coefficient by the initial static interaction coefficient of historical data. Integrating the expanded independent variables and the dynamic coupling coefficient, a model expression that covers all influencing factors and adapts to time-varying correlations was formed, as shown below:

[0077]

[0078]

[0079]

[0080] In the formula, This is the functional expression for the pressure relationship model. The pressure values ​​at different times, For timestamps, , , The independent variables at different times are, in order, the solid fraction, temperature, viscosity, flow rate, and flow direction of the slurry. Both the dependent and independent variables change with time. change, The regression coefficients for each independent variable represent the direct impact of that variable on stress. For interactive items, The interaction coefficients between independent variables represent the impact of their interactions on stress. These interactions can be positive (increasing stress) or negative (reducing stress). In some cases, interaction terms are omitted to simplify the model or reduce computational complexity, causing the model to fail to accurately capture the interactions between independent variables, thus affecting the accuracy of predictions. The random error represents the random fluctuations that the stress-relationship model failed to capture completely. For parameters With parameters The dynamic coupling strength coefficient, For parameters With parameters covariance, , Parameters With parameters global standard deviation ,and ;

[0081] The system acquires historical operating data collected by the sensor module, preprocesses the historical operating data to ensure data quality and consistency, and constructs an initial dependent variable matrix and an initial independent variable matrix. To facilitate the solution of interaction coefficients, the interaction terms are treated as independent variables and extended to the initial independent variable matrix.

[0082] The data in the initial dependent variable matrix and the initial independent variable matrix are normalized to generate standardized dependent variable matrix and independent variable matrix, so as to eliminate the difference in the dimensions between different independent variables and accelerate the convergence speed of parameter solution and optimization.

[0083] To capture the similarities or differences between samples and facilitate subsequent distance component extraction, the Euclidean distance between each pair of sample sequences in the independent variable matrix is ​​calculated to generate the distance matrix of the independent variables. ;

[0084] To extract the key components that best reflect the characteristics of the data from the distance matrix, the distance matrix is ​​calculated. covariance matrix The covariance matrix is ​​used to measure the correlation between different independent variables, and to analyze the covariance matrix... Perform eigenvalue decomposition to obtain eigenvalues. and the corresponding feature vector ;in, The number of samples collected;

[0085] Historical grid voltage and pipeline pressure difference are obtained. Based on the sampling interval, the grid voltage deviation is calculated by the difference between the grid voltage at the current time and the previous time. At the same time, the pipeline pressure difference deviation is calculated by the difference between the pipeline pressure difference at the current time and the previous time. The maximum value between the grid voltage deviation and the pipeline pressure difference deviation is selected as the interference intensity.

[0086] Two levels of interference thresholds are set, including weak interference threshold and strong interference threshold, to determine the category of interference intensity, including steady state, weak interference, and strong interference. Different categories correspond to different distance component screening quantities. Based on the specific interference intensity category, the corresponding screening quantity is applied. And sort the feature values ​​in descending order, and select the top ones. Generate the distance component matrix by finding the largest eigenvalue and its corresponding eigenvector. Each column of the matrix represents a distance component, which is extracted from the original data and can explain the main direction of change in the data. Each row corresponds to a sample, representing the projection or score of the sample on each distance component.

[0087] Based on historical data, for each independent variable, the difference between the time the adjustment command was issued and the time the pressure stabilized was calculated to obtain the lag time. The lag time was then used to time-shift the samples in the distance component matrix to obtain the corrected distance components. Quasi-linear regression was performed on the extracted distance components, and the coefficients between the distance components and the dependent variable were solved using the least squares method. The coefficients include regression coefficients and interaction coefficients; the expression for the regression equation is as follows:

[0088]

[0089]

[0090] In the formula, The dependent variable matrix is ​​standardized.

[0091] Based on the regression equation, the residuals and cross-validation coefficients for each sample are calculated to determine whether the pressure relationship model meets the preset dynamic convergence conditions. If the convergence conditions are met, the distance component is output; otherwise, the screening quantity is readjusted. Extract new distance components and perform quasi-linear regression until the convergence condition is met;

[0092] Once the model converges, by analyzing the distance component matrix... and independent variable matrix Relevant calculations were performed to demonstrate the relationship between distance components and the original variables, facilitating a deeper understanding of the data, and the standard coefficients of the objective function were calculated. The standardized coefficients include the standardized regression coefficients and the standardized interaction coefficients, as shown in the following expressions:

[0093]

[0094] In the formula, The standard coefficient between the independent and dependent variables;

[0095] The standard coefficients are destandardized to restore the dimensions of the initial variables, and the standard coefficients are transformed into nonstandard coefficients to obtain the specific values ​​of the regression coefficients and interaction coefficients in the objective function, and output the stress relationship model.

[0096] Specifically, the steps for adaptive adjustment include:

[0097] In the operation of semi-solid die casting equipment, in order to ensure that the metal slurry maintains stable physical and chemical properties during the die casting process, and to ensure that the slurry can uniformly fill the mold and avoid the generation of defects, a series of basic stability ranges are set. Among them, the basic stability range includes the basic temperature range. Basic flow velocity range and the basic pressure range is , , These are the lower and upper limits of the base temperature, respectively. , These are the lower and upper limits of the basic slurry flow rate, respectively. , These are the upper and lower limits of the basic pressure, respectively.

[0098] Since the equipment will experience performance degradation after long-term operation, the rated life and rated power of the equipment are obtained, and the running time and actual power of the equipment are obtained in real time. Based on the difference between the rated life and the running time, the ratio of the difference to the rated life is calculated to obtain the equipment health. Based on the ratio of the actual power to the rated power, the thermal management health is calculated. By averaging the equipment health and the thermal management health, the health index is obtained.

[0099] Because the parameter fluctuation characteristics differ significantly at different stages, using a uniform standard for judgment may lead to misjudgment of anomalies during the start-up and shutdown stages and omission of anomalies during the steady-state stage. The pressure deviation is calculated by the absolute difference between the real-time pressure value and the rated pressure value. Combined with the parameter fluctuation amplitude and the preset fluctuation ratio, the operating condition type is classified, including start-up and shutdown conditions, transition conditions, and steady-state conditions. The corresponding operating condition coefficient is called according to the identified operating condition type. Among them, the parameter fluctuation amplitude is the standard deviation of the parameter within the preset sliding window.

[0100] Based on the health index and operating condition type, the basic stability range is corrected to generate real-time applicable parameter stability ranges, including temperature stability range, flow rate stability range, and pressure stability range, as shown in the following expressions:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] In the formula, , , , , , These are the corrected thresholds, , , These are correction factors for temperature, flow rate, and pressure, respectively. This is the operating condition coefficient. Health index;

[0108] Using high-precision sensors, real-time parameter values, including solid fraction, are acquired. ,temperature Viscosity Flow rate of slurry in the mold and flow direction These actual parameter values ​​reflect the real-time status of the current die-casting process. Based on these actual parameter values, key parameters are judged and updated. By judging whether each actual parameter value exceeds the preset parameter stability range, and updating and re-judging key parameters that exceed the range, it is easier to deal with unpredictable factors in the production process, such as the impact of ambient temperature changes on the actual die-casting temperature. Different batches of aluminum alloys contain different trace elements, which affect the fluidity and solidification process of the slurry, thereby affecting the actual flow rate and temperature.

[0109] The lag time of each parameter is obtained, and the actual parameter values ​​are corrected for time offset to obtain the corrected predicted parameter values. Using the predicted parameter values ​​and the pressure relationship model, pressure prediction values ​​are generated. The predicted pressure value is then compared with the stable pressure range to determine whether the predicted pressure value deviates from the stable pressure range; if... This indicates that the pressure has not deviated from the stable pressure range, and the preset pressure value is applied during the die-casting process; if or This indicates that the pressure deviates from the stable pressure range. The key parameters are adjusted immediately to ensure that the actual parameter values ​​are all within the stable range while adaptively adjusting the key parameters to stabilize the pressure prediction value.

[0110] Specifically, the steps for judging and updating key parameters include:

[0111] For temperature ,like The temperature did not exceed the stable temperature range;

[0112] like or If the temperature exceeds the stable temperature range, the ambient temperature is acquired, and a preset environmental standard temperature is invoked for over-limit tracing. If the ambient temperature is lower than the environmental standard temperature, the over-limit is determined to be caused by low ambient temperature. The corresponding stable temperature range is temporarily widened by setting a stable relaxation step size, such as widening the lower limit. After a temporary interval, the original stable range is restored. If the ambient temperature is not lower than the environmental standard temperature, the heating or cooling device is activated to adjust the temperature to the rated temperature. ;

[0113] For flow rate ,like The flow velocity did not exceed the stable flow velocity range;

[0114] like or If the flow rate exceeds the stable flow rate range, adjust the pump speed or valve to bring the flow rate back to the rated flow rate. ;

[0115] After adjustment, the actual parameter values ​​are updated in real time, and a new stability assessment is performed until the actual parameter values ​​do not exceed the parameter stability range.

[0116] Specifically, the steps for adjusting key parameters include:

[0117] The key parameters are defined in the following order for adjustment: solid fraction, temperature, flow rate, and rated pressure. The upper limit threshold for adjusting the solid fraction is set according to the specific die-casting process, material properties, and required product performance requirements. Adjust the lower threshold to This ensures that parameter adjustments are neither excessive nor insufficient, thereby maintaining the stability and controllability of the production process;

[0118] Adjust the solid fraction based on the rated pressure. and pressure forecast values Adjustment for calculating solid fraction and target solid fraction To determine whether the target solid fraction exceeds the solid fraction adjustment threshold, the expression is as follows:

[0119]

[0120]

[0121] In the formula, , These are the corresponding interaction and regression coefficients in the stress relationship model. This represents the actual solid fraction;

[0122] like Adjust the solid fraction to and rated pressure Apply during the die-casting process and then end the adjustment.

[0123] like or Adjust the solid fraction to ( (time) or ( (At that time), the updated pressure prediction value is calculated using the pressure relationship model. Determine the updated pressure forecast value Check if the pressure exceeds the stable pressure range. If not, update the pressure prediction value. Apply the temperature during the die-casting process and then stop adjusting. If the temperature exceeds the limit, adjust the temperature accordingly.

[0124] Adjust the temperature based on the rated pressure. and pressure forecast values Calculate the temperature adjustment amount and target temperature To determine whether the target temperature exceeds the temperature stability range, the expression is as follows:

[0125]

[0126]

[0127] In the formula, , These are the corresponding interaction and regression coefficients in the stress relationship model. This refers to the actual temperature.

[0128] like Adjust the temperature to and rated pressure Apply during the die-casting process and then end the adjustment.

[0129] like or Adjust the temperature to ( (time) or ( (At that time), the updated pressure prediction value is calculated using the pressure relationship model. Determine the updated pressure forecast value Check if the pressure exceeds the stable pressure range. If not, update the pressure prediction value. During the die-casting process, the adjustment is completed; if the flow rate is exceeded, it is adjusted.

[0130] Adjust the flow rate based on the rated pressure. and pressure forecast values Calculate the adjustment amount of the flow rate and target flow rate To determine whether the target flow velocity exceeds the stable flow velocity range, the expression is as follows:

[0131]

[0132]

[0133] In the formula, , These are the corresponding interaction and regression coefficients in the stress relationship model. This refers to the actual flow rate;

[0134] like Adjust the flow rate to and rated pressure Apply during the die-casting process and then end the adjustment.

[0135] like or Adjust the flow rate to ( (time) or ( (At that time), the updated pressure prediction value is calculated using the pressure relationship model. Determine the updated pressure forecast value Check if the pressure exceeds the stable pressure range. If not, update the pressure prediction value. During the die-casting process, the adjustment is completed. If the error exceeds the limit, a warning signal is generated to alert the operator and prompt them to take appropriate measures.

[0136] When the pressure forecast meets expectations and all key parameters have been finely adjusted and reached a stable state, the parameter values ​​are locked to ensure the continuity and stability of the subsequent production process. At the same time, all records and data during the adjustment process are organized and saved for subsequent analysis and improvement. The saved data includes the starting value, target value, adjustment amount, pressure forecast value, and the final locked parameter value for each parameter adjustment.

[0137] In summary, this invention uses high-precision sensors to monitor key parameters in the die-casting process in real time, comprehensively monitoring the internal state of the die-casting machine. A pressure relationship model is established using nonlinear regression analysis, considering various key parameters affecting pressure changes and their interactions, and continuously optimized until the model converges. Based on a comparison of the actual detected operating parameters with preset ranges, key parameters are adjusted promptly when they exceed the preset range. Predicted values ​​are generated using the actual parameter values ​​and the pressure relationship model, and compared with the stable pressure range, allowing for fine-tuning of parameters to achieve precise control and optimization. Furthermore, based on the calculated adjustment amount, a multi-axis linkage control strategy is used to adjust the working state of the pressurizing equipment, ensuring uniform distribution of the metal slurry.

[0138] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A voltage stabilizing control system for pressurizing equipment, characterized in that, include: The system comprises a sensor module, a prediction module, and a control module. The sensor module acquires key parameters during equipment operation in real time using sensors. The prediction module constructs a pressure relationship model using nonlinear regression analysis, describing the quantitative relationship between multiple key parameters and pressure during the pressurization process. The control module adaptively adjusts the key parameters based on their actual values. This is achieved by comparing the actual parameter values ​​with preset parameter stability ranges, judging and updating the key parameters, generating predicted pressure values, and adjusting the key parameters. The specific steps of the adaptive adjustment include: setting a basic stability range, including a basic temperature range, a basic flow rate range, and a basic pressure range. The system involves: acquiring the rated lifespan and rated power of the equipment; calculating the equipment health and thermal management health based on the operating time and actual power, and averaging these values ​​to obtain the health index; calculating the pressure deviation, classifying the operating conditions based on parameter fluctuation amplitude and a preset fluctuation ratio, and calling the corresponding operating condition coefficients; correcting the basic stability range based on the health index and operating condition type to generate parameter stability ranges, including temperature stability range, flow rate stability range, and pressure stability range; acquiring actual parameter values ​​in real time, and judging and updating key parameters based on these values; acquiring the lag time of each key parameter, and correcting the actual parameter values ​​to obtain predicted parameter values; and using the predicted parameter values ​​and the pressure relationship model to generate predicted pressure values. Determine whether the predicted pressure value deviates from the stable pressure range; if Apply a preset pressure value to the pressurizing device; if or Adjust key parameters; among them, 、 These are the revised upper and lower pressure limits.

2. A voltage stabilizing control system for a pressurizing device according to claim 1, characterized in that, The specific steps for constructing a pressure relationship model include: defining pressure as the dependent variable and key parameters affecting pressure changes as independent variables; introducing interaction terms; configuring dynamic interaction coefficients by combining dynamic coupling strength coefficients; and constructing a pressure relationship model using nonlinear regression analysis; acquiring historical operational data and preprocessing it to generate a dependent variable matrix and an independent variable matrix; calculating the Euclidean distance between each pair of sample sequences in the independent variable matrix to generate a distance matrix; calculating the covariance matrix of the distance matrix and performing eigenvalue decomposition to obtain eigenvalues ​​and corresponding eigenvectors.

3. A voltage stabilizing control system for a pressurizing device according to claim 2, characterized in that, The specific steps for constructing the pressure relationship model also include: obtaining historical grid voltage and pipeline pressure difference, calculating grid voltage deviation and pipeline pressure difference deviation, and selecting the maximum value as the interference intensity; setting a secondary interference threshold, determining the category of interference intensity, and calling the corresponding filtering quantity. Sort the eigenvalues ​​in descending order and select the top ones. The largest eigenvalue and its corresponding eigenvector are used to generate a distance component matrix. For each independent variable, the difference between the time the adjustment command is issued and the time the pressure stabilizes is calculated to obtain the lag time. The samples in the distance component matrix are then time-shifted to correct the distance components. Quasi-linear regression is performed on the corrected distance components, and the coefficients between the distance components and the dependent variable are solved using the least squares method.

4. A voltage stabilizing control system for a pressurizing device according to claim 3, characterized in that, The specific steps for constructing the stress relationship model also include: calculating the residuals and cross-validation coefficients using the regression equation to determine whether the stress relationship model meets the convergence condition; if the convergence condition is met, outputting the distance component; if not, repeatedly adjusting the screening quantity. The process involves extracting new distance components and performing quasi-linear regression until the convergence condition is met. Once the stress relationship model converges, the relationship between the distance components and the original variables is demonstrated by performing correlation operations on the distance component matrix and the independent variable matrix. The standard coefficients between the independent and dependent variables of the objective function in the stress relationship model are then calculated. The standard coefficients are destandardized to transform them into non-standard coefficients, and the stress relationship model is output.

5. A voltage stabilizing control system for a pressurizing device according to claim 4, characterized in that, The specific steps for judging and updating key parameters include: for temperature ,like The temperature did not exceed the stable temperature range; among them, 、 These are the corrected lower and upper temperature limits; if or If the temperature exceeds the stable temperature range, the ambient temperature is obtained, and a preset environmental standard temperature is invoked for over-limit tracing. If the ambient temperature is lower than the environmental standard temperature, the stable temperature range is temporarily relaxed. If the ambient temperature is not lower than the environmental standard temperature, the heating or cooling device is activated to adjust the temperature to the rated temperature. For flow rate ,like The flow velocity did not exceed the stable flow velocity range; among them, 、 These are the corrected lower and upper limits for slurry flow rate; if or If the flow rate exceeds the stable flow rate range, adjust the pump speed or valve to bring the flow rate back to the rated flow rate. After adjustment, the actual parameter values ​​are updated in real time, and a new stability assessment is performed until the actual parameter values ​​do not exceed the parameter stability range.

6. A voltage stabilizing control system for a pressurizing device according to claim 5, characterized in that, The specific steps for adjusting key parameters include: defining the adjustment order of key parameters as solid fraction, temperature, and flow rate, and defining the rated pressure as... The upper limit threshold for adjusting the solid fraction is set as follows: Adjust the lower threshold to Based on the rated pressure and pressure forecast values Calculate the adjustment amount for the solid fraction. and target solid fraction The expression is as follows: ； In the formula, 、 These are the corresponding interaction and regression coefficients in the stress relationship model. This is the actual solid fraction; if Adjust the solid fraction to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment; if or Adjust the solid fraction to or Update the pressure forecast value And determine the predicted pressure value. Check if the pressure exceeds the stable range. If not, adjust the predicted pressure value. Apply pressure to the pressurizing equipment and stop the adjustment. If the pressure exceeds the limit, adjust the temperature.

7. A voltage stabilizing control system for a pressurizing device according to claim 6, characterized in that, The specific steps for adjusting key parameters also include: based on predicted pressure values. Calculate the temperature adjustment amount and target temperature The expression is as follows: ； In the formula, 、 These are the corresponding interaction and regression coefficients in the stress relationship model. This is the actual temperature; if Adjust the temperature to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment; if or Adjust the temperature to or Update pressure forecast values And determine the predicted pressure value. Check if the pressure exceeds the stable range. If not, adjust the predicted pressure value. Apply pressure to the pressurization equipment, then stop the adjustment. If the pressure exceeds the limit, adjust the flow rate accordingly; based on the predicted pressure value. Calculate the adjustment amount of the flow rate and target flow rate The expression is as follows: ； In the formula, 、 These are the corresponding interaction and regression coefficients in the stress relationship model. This is the actual flow rate; if Adjust the flow rate to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment; if or Adjust the flow rate to or Update pressure forecast values And determine the predicted pressure value. Check if the pressure exceeds the stable range. If not, adjust the predicted pressure value. Apply pressure to the pressurizing equipment, then stop the adjustment. If the pressure exceeds the limit, a warning signal will be generated.

8. A voltage stabilizing control system for a pressurizing device according to claim 7, characterized in that, Also includes: The system includes an execution module and a communication module. The execution module adjusts the working state of the pressurizing equipment according to the adjustment amount generated by the control module. The execution module is configured with a multi-axis linkage control strategy to simultaneously adjust multiple key parameters of the pressurizing equipment. The communication module uses wireless communication technology to transmit the collected actual parameter values ​​to each module.

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