Pressure stabilization control system for pressurization equipment

Abstract

The invention discloses a pressure stabilization control system for pressurization equipment, which belongs to the technical field of pressure control and comprises a sensor module, a prediction module and a control module. The sensor module is used for acquiring key parameters in the equipment operation process in real time by using a high-precision sensor; the prediction module is used for constructing a pressure relation model by using a nonlinear regression analysis method, and the pressure relation model is used for describing a quantitative relation between a plurality of key parameters and pressure in the operation process of pressurization equipment and capturing a complex mechanism of pressure fluctuation; the control module is used for adaptively adjusting the key parameters according to the actual parameter values of the pressurizing equipment, comparing the actual parameter values obtained in real time with a preset parameter stability range, judging and updating the key parameters, generating a pressure prediction value, finely adjusting the key parameters, and outputting the pressure prediction value. Various unpredictable factors in production are flexibly coped with, and the stability and controllability of production are improved.

Description

TECHNICAL FIELD

[0001] The application relates to a pressure stabilizing control system for a pressurizing device, and belongs to the technical field of pressure control. BACKGROUND

[0002] Pressurizing devices, such as water pumps, compressors, and booster pumps, efficiently deliver fluids such as water and air to designated locations or meet specific process requirements by applying pressure. However, the internal pressure of the pressurizing device often fluctuates during operation due to various factors, which not only affects the performance of the device, leading to a decrease in production efficiency, but also can cause damage to the device itself, and even lead to safety hazards.

[0003] To address this problem, the pressure change of the pressurizing device can be monitored in real time, and the output pressure can be adjusted according to the preset pressure range, so that the pressurizing device operates in the best state, improves production efficiency and product quality, effectively prolongs the service life of the device, and reduces maintenance costs. The existing pressure monitoring and adjustment control system already has functions such as remote monitoring, fault diagnosis, and data analysis. Operators can use mobile phones, computers, and other terminal devices to monitor the running state and pressure changes of the device in real time, discover and handle potential problems in a timely manner, and ensure the stable operation of the production line.

[0004] The Chinese patent application with the publication number CN118317659A discloses a pressurizing device, a pressurizing device, and a 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 cavity accommodating the plate assembly and is provided with an inlet communicating with the pressurizing cavity. The pressurizing assembly includes a first air tank, a pressurizing pump, and a pressurizing valve. The pressurizing pump is connected with the first air tank and the pressurizing body. The exhaust assembly includes an exhaust valve connected with the pressurizing body. The sealing assembly includes a sealing element arranged in the pressurizing cavity. The sealing element is movable relative to the pressurizing body between a first position and a second position. When the sealing element is located at the first position, it covers the inlet, and the pressurizing cavity is in a sealed state. When the sealing element is located at the second position, it is away from the inlet, and the pressurizing cavity is in an open state.

[0005] Although the prior art uses the exhaust assembly to input gas into the pressurizing cavity, the gas acts on the plate assembly, and the bubbles between the plate assemblies are extruded to achieve bubble removal, but it does not consider the adaptability and scalability of pressure control, especially the various influencing factors of pressure fluctuation during the operation of the pressurizing device, and how to control these influencing factors through real-time monitoring and adjustment to ensure the stable operation of the pressurizing device in the best state. Therefore, the present application provides a pressure stabilizing control system for a pressurizing device. SUMMARY

[0006] In view of the deficiencies of the prior art, the purpose of the present application is to provide a stable pressure control system for a pressurizing device, which predicts the required pressure value by constructing a relationship model between multiple key parameters and pressure in the pressurizing device, compares the actual parameter value of the pressurizing device with the parameter stable range, and adaptively adjusts the operating parameters, thereby enhancing the flexibility and adaptability of production.

[0007] To achieve the above object, the present application provides the following technical solutions.

[0008] A stable pressure control system for a pressurizing device comprises a sensor module, a prediction module and a control module.

[0009] The sensor module is configured to acquire key parameters in real time during the operation of the device by using sensors.

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

[0011] The control module is configured to adaptively adjust the key parameters according to the actual parameter value of the pressurizing device, compare the actual parameter value with the preset parameter stable range, judge and update the key parameters, generate a pressure prediction value, and adjust the key parameters.

[0012] Specifically, the specific steps of constructing the pressure relationship model comprise:

[0013] Defining pressure as the dependent variable and the key parameters affecting the change of pressure as the independent variable, introducing an interaction term, configuring a dynamic interaction coefficient in combination with a dynamic coupling strength coefficient, and constructing a pressure relationship model by using a nonlinear regression analysis method.

[0014] Obtain historical operation data and pre-process them 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 specific steps of constructing the pressure relationship model further comprise:

[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 strength.

[0019] Set a two-level interference threshold, determine the category of the interference strength, and call the corresponding screening quantity. ​

[0020] arranging the characteristic values in descending order, and selecting the first largest characteristic value and its corresponding eigenvector to generate a distance component matrix;

[0021] For each independent variable, calculate the difference between the adjustment instruction issuing time and the pressure stabilization time to obtain the lag time, and perform time offset on the samples in the distance component matrix to correct the distance component;

[0022] Performing quasi-linear regression on the corrected distance component, and using the least squares method to solve the coefficients between the distance component and the dependent variable.

[0023] Specifically, the specific steps of constructing the pressure relationship model further include:

[0024] Calculate the residual error and cross validity coefficient using the regression equation to determine whether the pressure relationship model meets the convergence condition;

[0025] If the convergence condition is met, output the distance component; if not, repeat the extraction of new distance components and quasi-linear regression until the convergence condition is met;

[0026] Once the model converges, perform correlation operation on the distance component matrix and the independent variable matrix, and calculate the standard coefficient of the objective function;

[0027] Perform inverse standardization on the standard coefficient to convert the standard coefficient to a non-standard coefficient, and output the pressure relationship model.

[0028] Specifically, the specific steps of adaptive adjustment include:

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

[0030] Obtain the rated service life and rated power of the device, combine the running time and actual power, calculate the device health degree, thermal management health degree, and perform average value solving to obtain the health degree index;

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

[0032] Based on the health degree index and the working condition type, correct the basic stabilization range to generate a parameter stabilization range, including a temperature stabilization range, a flow rate stabilization range and a pressure stabilization range.

[0033] Specifically, the specific steps of adaptive adjustment further include:

[0034] Real-time acquisition of actual parameter values, and judgment and update of key parameters based on actual parameter values;

[0035] acquire the lag time of each parameter, and correct the actual parameter value to obtain a predicted parameter value;

[0036] generate a pressure predicted value using the predicted parameter value and a pressure relationship model , determine whether the pressure predicted value deviates from a pressure stable range;

[0037] If , a pressure preset value is applied to the pressurizing device; if or , the key parameters are adjusted.

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

[0039] For temperature , if , the temperature does not exceed a temperature stable range;

[0040] If or , the temperature exceeds the temperature stable range, the ambient temperature is acquired, and a preset ambient standard temperature is called to perform over-limit tracing;

[0041] If the ambient temperature is less than the ambient standard temperature, the temperature stable range is temporarily relaxed; if the ambient temperature is not less than the ambient standard temperature, a heating device or a cooling device is started to adjust the temperature to a rated temperature ;

[0042] For flow rate , if , the flow rate does not exceed a stable flow rate range;

[0043] If or , the flow rate exceeds the stable flow rate range, and the pump speed or the valve is adjusted to adjust the flow rate to a rated flow rate ;

[0044] The actual parameter value is updated in real time after adjustment, and the stability judgment is performed again until the actual parameter values do not exceed the parameter stable range.

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

[0046] The adjustment order of the key parameters is defined as the solid phase fraction, the temperature, the flow rate, and the rated pressure is defined as , the upper limit threshold of the adjustment of the solid phase fraction is defined as , and the lower limit threshold is defined as ;

[0047] Based on the rated pressure and the pressure prediction value , calculate the adjustment amount of the solid phase fraction and the target solid phase fraction ;

[0048] If , adjust the solid phase fraction to , and apply the rated pressure to the pressurizing device, and end the adjustment;

[0049] If or , adjust the solid phase fraction to or , update the pressure prediction value to , and determine whether the pressure prediction value is out of the pressure stable range, if not, apply the pressure prediction value to the pressurizing device, and end the adjustment, if yes, adjust the temperature.

[0050] Specifically, the specific steps of adjusting the key parameters further comprise:

[0051] Based on the pressure prediction value , calculate the adjustment amount of the temperature and the target temperature ;

[0052] If , adjust the temperature to , and apply the rated pressure to the pressurizing device, and end the adjustment;

[0053] If or , adjust the temperature to or , update the pressure prediction value , and determine whether the pressure prediction value is out of the pressure stable range, if not, apply the pressure prediction value to the pressurizing device, and end the adjustment, if yes, adjust the flow rate;

[0054] Based on the pressure prediction value , calculate the adjustment amount of the flow rate and the target flow rate ;

[0055] If , adjust the flow rate to , and apply the rated pressure to the pressurizing device, and end the adjustment;

[0056] If or , the flow rate is adjusted to or , the pressure prediction value is updated , and it is determined whether the pressure prediction value is out of the pressure stable range, if not, the pressure prediction value is applied to the pressurizing equipment, the adjustment is ended, and if yes, a warning signal is generated.

[0057] Specifically, it further comprises 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, and a multi-axis linkage control strategy is configured in the execution module 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 by using wireless communication technology.

[0060] The beneficial effects of the present application are as follows:

[0061] 1. By constructing an accurate pressure relationship model, the pressure can be predicted and adjusted in real time, which significantly improves the accuracy of pressure prediction in the pressurizing process, and the pressure relationship model comprehensively considers multiple key parameters and their interactions, effectively capturing the complex mechanism of pressure fluctuation; 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 fluctuation, optimizing resource utilization, and reducing unnecessary energy consumption and maintenance costs.

[0062] 2. By real-time monitoring and self-adaptive adjustment of key parameters, product defects caused by parameter fluctuations are effectively avoided, and product yield and quality are improved; by setting the stable range of parameters, various unpredictable factors in the production process are flexibly responded to, ensuring the continuity and stability of the production process, improving the flexibility and adaptability of production; at the same time, the optimization of parameter adjustment reduces excessive adjustment, prolongs the service life of the equipment, reduces maintenance costs, enhances the flexibility and response speed of the production line, significantly improves production efficiency and economic benefits. BRIEF DESCRIPTION OF DRAWINGS

[0063] Fig. 1 It is a pressure stabilizing control system structure diagram for a pressurizing equipment;

[0064] Fig. 2 It is a pressure relationship model construction flowchart in the present application;

[0065] Fig. 3Adaptive adjustment flowchart in the present application;

[0066] Fig. 4 Adjustment flowchart for key parameters in the present application. DETAILED DESCRIPTION

[0067] The technical solutions of the present application will be described in detail below with the help of the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solutions of the present application, rather than limitations of the technical solutions of the present application. In the case of no conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0068] Embodiment 1

[0069] Reference Figs. 1 to 4 As shown in the figure, the present embodiment introduces a pressure stabilizing control system for a pressurizing device, taking a semi-solid die casting machine as an example, which includes a sensor module, a prediction module, a control module, an execution module and a communication module.

[0070] The sensor module is used to obtain key parameters in the semi-solid die casting process in real time through a variety of high-precision sensors configured in the semi-solid die casting machine, such as the operating environment of the semi-solid die casting machine, to comprehensively monitor the state of the metal slurry and ensure that the slight changes in the key parameters can be captured immediately. The data monitored is converted into an electrical signal and transmitted to the prediction module for processing. The high-precision sensors include a pressure sensor, a temperature sensor, a solid-liquid ratio sensor and a flow state sensor. The pressure sensor is used to monitor the pressure state inside the semi-solid die casting machine to ensure that the pressure is always maintained within a preset range, thereby ensuring the quality stability of the die casting parts and ensuring that any slight pressure fluctuations can be captured in time to avoid adverse effects on the microstructure and performance of the product. The temperature sensor is used to monitor the temperature changes of the metal slurry and the die casting mold to accurately control the temperature and optimize the die casting process. The solid-liquid ratio sensor is used to monitor the solid-liquid ratio of the metal slurry to ensure that the die casting parts have ideal mechanical properties and surface quality. The flow state sensor is used to monitor the flow rate and flow direction of the metal slurry in real time to ensure that the metal slurry is evenly distributed in the mold and avoid the generation of die casting defects.

[0071] The prediction module is used to process and analyze the data collected in the sensor module, construct a pressure relationship model, and describe the quantitative relationship between the key parameters of the pressurizing equipment during operation and the pressure; using nonlinear regression analysis method, considering the key parameters affecting the pressure change in the semi-solid die casting process, including the solid phase fraction, temperature, viscosity, flow rate and flow direction of the slurry, as well as the interaction between each key parameter, a pressure relationship model is established, through data standardization, distance component extraction and linear regression, the pressure relationship model is continuously optimized until convergence, and the standard coefficient is converted into a non-standard coefficient, and the pressure relationship model is output, realizing the accurate description of the relationship between the solid phase fraction and the pressure in the semi-solid die casting process, at this time the pressure relationship model is used to predict the pressure value of the semi-solid die casting equipment in real time, providing accurate guidance for pressure control in the production process.

[0072] The control module is used to adaptively adjust the operating parameters of the semi-solid die casting equipment according to the actual parameter values; the key parameters obtained by the high-precision sensor in real time are compared with the preset parameter stable range, when the key parameters exceed the range, the key parameters are judged and updated, realizing the timely adjustment of the parameters, and using the actual parameter values and the pressure relationship model to generate the pressure prediction value, compared with the pressure stable range, the key parameters are adjusted according to the result, to ensure the stability of the pressure prediction value, realizing the 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, and a multi-axis linkage control strategy is configured in the execution module, which can adjust multiple key parameters at the same time, realize accurate synchronization and coordination between multiple axes, ensure uniform distribution of metal slurry in the mold, reduce the generation of bubbles and defects, and realize accurate control of the die casting process.

[0074] The communication module is used to transmit the actual parameter values collected during the system operation to each module through wireless communication technology, realize the communication between the pressure control system and other devices or systems, improve the integration and flexibility of the system, and make the system better adapt to different application scenarios.

[0075] Specifically, the specific steps of constructing the pressure relationship model include:

[0076] The nonlinear regression analysis method is used to construct a pressure relationship model of the semi-solid die casting equipment, wherein the pressure values at different times are defined as dependent variables to reflect the real-time changes of the pressure in the die casting process, the key parameters influencing the pressure changes are defined as independent variables, including the solid phase fraction, temperature, viscosity, flow rate and flow direction of the slurry, and the corresponding regression coefficients are configured, the interaction terms are introduced into the pressure relationship model to represent the interaction between the independent variables by the product of any two parameters, and the corresponding interaction coefficients are configured to expand the independent variables, since the parameters in the independent variables are static values and cannot adapt to the time-varying nature of the parameter correlation, for the interaction terms, the dynamic coupling strength coefficient is calculated by the ratio of the covariance of the two parameters to the product of the global standard deviation, to quantify the real-time correlation degree of the parameters, and the dynamic interaction coefficient is obtained by the product of the dynamic coupling strength coefficient and the initial static interaction coefficient of the historical data, the expanded independent variables and the dynamic coupling coefficients are integrated to form a model expression that can cover all influencing factors and adapt to the time-varying correlation, and the expression is as follows:

[0077]

[0078]

[0079]

[0080] In the formula, is a function expression of the pressure relationship model, is the pressure value at different times, is a time stamp, , , is the independent variable value at different times, which represents the solid phase fraction, temperature, viscosity, flow rate and flow direction of the slurry in turn, and the dependent variable and the independent variable change with time , is the regression coefficient of each independent variable, which represents the direct influence of each independent variable on the pressure, is the interaction term, is the dynamic interaction coefficient between the independent variables, which represents the influence of the interaction between the independent variables on the pressure, and the interaction includes positive (increasing the pressure) and negative (decreasing the pressure), in some cases, in order to simplify the model or reduce the calculation complexity, the interaction term is not included, which leads to the model being unable to accurately capture the interaction between the independent variables, thereby affecting the prediction accuracy, is a random error, which represents the random fluctuations that cannot be completely captured in the pressure relationship model, is the dynamic coupling strength coefficient of the parameter and the parameter , is the covariance of the parameter and the parameter , , are the global standard deviations of the parameters and , , and ;

[0081] The historical operation data collected by the sensor module is acquired, the historical operation data is preprocessed to ensure the quality and consistency of the data, and an initial dependent variable matrix and an initial independent variable matrix are constructed. In order to facilitate the solution of the interaction coefficient, the interaction term is regarded as an independent variable, and is expanded into the initial independent variable matrix;

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

[0083] In order to capture the similarity or difference between samples, 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 variable;

[0084] In order to extract the main component from the distance matrix which can best reflect the characteristics of the data, the covariance matrix of the distance matrix is calculated, the covariance matrix is used to measure the correlation between different independent variables, and the eigenvalue and the corresponding eigenvector of the covariance matrix are obtained by eigenvalue decomposition; wherein, is the number of samples collected;

[0085] The historical power grid voltage and pipeline pressure difference are acquired, based on the sampling interval, the power grid voltage deviation is calculated through the difference between the current time and the previous time of the power grid voltage, and the pipeline pressure difference deviation is calculated through the difference between the current time and the previous time of the pipeline pressure difference. The maximum value of the power grid voltage deviation and the pipeline pressure difference deviation is selected as the interference intensity;

[0086] The secondary interference threshold is set, including the weak interference threshold and the strong interference threshold, to determine the category of the interference intensity, including steady state, weak interference and strong interference. Different categories correspond to different distance component screening quantities. According to the specific interference intensity category, the corresponding screening quantity is called, and the eigenvalues are arranged in descending order, and the first largest eigenvalues and the corresponding eigenvectors are selected to generate the distance component matrix wherein each column of the matrix represents a distance component, the distance components being extracted from the original data and capable of explaining the main variation direction of the data, and each row corresponds to a sample, representing the projection or score of the sample on each distance component;

[0087] Based on the historical data, for each independent variable, the difference between the adjusted command issuance time and the pressure stabilization time is calculated to obtain the lag time, and the lag time is used to time-shift the samples in the distance component matrix to obtain the corrected distance components. Linear regression is performed on the extracted distance components, and the least squares method is used to solve the coefficients between the distance components and the dependent variable wherein the coefficients include regression coefficients and interaction coefficients; at this time, the expression of the regression equation is as follows:

[0088]

[0089]

[0090] wherein, is the standardized dependent variable matrix;

[0091] Based on the regression equation, the residual error and the cross-effectiveness coefficient of each sample are calculated to determine whether the pressure relationship model meets the preset dynamic convergence condition; if the convergence condition is met, the distance components are output; if not, the screening amount is adjusted again , new distance components are extracted and linear regression is performed until the convergence condition is met;

[0092] Once the model converges, the relationship between the distance components and the original variables is shown by performing correlation operations on the distance component matrix and the independent variable matrix , which facilitates in-depth understanding of the data, and the standard coefficient of the objective function is calculated wherein the standard coefficient includes a standard regression coefficient and a standard interaction coefficient, and the expression is as follows:

[0093]

[0094] wherein, is the standard coefficient between the independent variable and the dependent variable;

[0095] The standard coefficient is anti-standardized to restore the dimension of the initial variable, convert the standard coefficient to a non-standard coefficient, obtain the specific values of the regression coefficients and the interaction coefficients in the objective function, and output the pressure relationship model.

[0096] Specifically, the specific steps of adaptive adjustment include:

[0097] In the operation of the 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 enables the slurry to uniformly fill the mold and avoid defects, a series of basic stability ranges are set, wherein the basic stability ranges include a basic temperature range , a basic flow rate range and a basic pressure range , , are respectively a basic temperature lower limit and an upper limit, , are respectively a basic slurry flow rate lower limit and an upper limit, , are respectively a basic pressure upper limit and a lower limit;

[0098] Since the performance of the equipment will decay over a long period of operation, the rated service 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 service life and the running time, the ratio of the difference to the rated service life is calculated to obtain the equipment health degree. Based on the ratio of the actual power to the rated power, the thermal management health degree is calculated. The health degree index is obtained by averaging the equipment health degree and the thermal management health degree.

[0099] Since the parameter fluctuation characteristics are significantly different at different stages, if a unified standard is used for judgment, it may lead to misjudgment of the start-stop stage as abnormal and missed judgment of the steady-state stage as abnormal. The pressure deviation is calculated based on 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 working condition type is divided into start-stop working condition, transition working condition and steady-state working condition. According to the identified working condition type, the corresponding working condition coefficient is called. The parameter fluctuation amplitude is the standard deviation of the parameter in the preset sliding window.

[0100] Based on the health degree index and the working condition type, the basic stability range is corrected to generate a real-time applicable parameter stability range, including a temperature stability range, a flow rate stability range and a pressure stability range, which are expressed as follows:

[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 principle 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: Sensor module, prediction module, and control module; The sensor module is used to acquire key parameters during the operation of the equipment in real time using sensors; 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. 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.

2. A voltage stabilizing control system for a pressurizing device according to claim 1, characterized in that, The specific steps for constructing a stress relationship model include: 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. Acquire historical operational data and preprocess it to generate a dependent variable matrix and an independent variable matrix; Calculate the Euclidean distance between each pair of sample sequences in the independent variable matrix to generate a distance matrix; Calculate the covariance matrix of the distance matrix and perform 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 in constructing a stress relationship model also include: 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; Set a secondary interference threshold, determine the category of interference intensity, and call the corresponding filtering quantity. ; 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; 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. 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.

4. A voltage stabilizing control system for a pressurizing device according to claim 3, characterized in that, The specific steps in constructing a stress relationship model also include: The residuals and cross-validation coefficients are calculated using the regression equation to determine whether the pressure relationship model meets the convergence condition. If the convergence condition is met, the distance component is output; if not, the selection criteria are repeatedly adjusted to extract new distance components and perform quasi-linear regression until the convergence condition is met. 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. The standard coefficients are destandardized to convert them into nonstandard coefficients, and a pressure relationship model is output.

5. A voltage stabilizing control system for a pressurizing device according to claim 4, characterized in that, The specific steps of adaptive adjustment include: Set the basic stability range, including the basic temperature range, the basic flow rate range, and the basic pressure range; 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; 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; 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.

6. A voltage stabilizing control system for a pressurizing device according to claim 5, characterized in that, The specific steps of adaptive adjustment also include: The system acquires real-time parameter values ​​and makes judgments and updates to key parameters based on these values. The lag time of each parameter is obtained, and the actual parameter values ​​are corrected to obtain the predicted parameter values; 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; like Apply a preset pressure value to the pressurizing device; if or Adjust the key parameters.

7. A voltage stabilizing control system for a pressurizing device according to claim 6, 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; 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. 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. ; For flow rate ,like The flow velocity did not exceed the stable flow velocity range; 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. ; 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.

8. A voltage stabilizing control system for a pressurizing device according to claim 7, characterized in that, The specific steps for adjusting key parameters include: 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 ; Based on the rated pressure and pressure forecast values Calculate the adjustment amount for the solid fraction. and target solid fraction ; like Adjust the solid fraction to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment; 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.

9. A voltage stabilizing control system for a pressurizing device according to claim 8, characterized in that, The specific steps for adjusting key parameters also include: Based on pressure predictions Calculate the temperature adjustment amount and target temperature ; like Adjust the temperature to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment; 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. Based on pressure predictions Calculate the adjustment amount of the flow rate and target flow rate ; like Adjust the flow rate to and the rated pressure Apply pressure to the pressurizing equipment and end the adjustment; 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.

10. A voltage stabilizing control system for a pressurizing device according to claim 9, characterized in that, It also includes: an execution module and a communication module; 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. The communication module is used to transmit the collected actual parameter values ​​to each module using wireless communication technology.

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