Permanent magnet frequency conversion pressure control method and screw air compressor

Abstract

The invention provides a permanent magnet variable-frequency pressure control method and a screw air compressor, and relates to the technical field of pressure control, and the permanent magnet variable-frequency pressure control method comprises the steps that the required pressure and required flow of compressed air are analyzed according to load data; according to the demand pressure and the demand flow, control strategy adaptability analysis is carried out, and an adaptive evaluation value is obtained; performing air pressure staged analysis according to the adaptive evaluation value, and configuring a control period target of air pressure grading; acquiring key parameter data in environment and equipment operation through a sensor based on a control period target; performing compensation analysis on the key parameter data according to a control period target to obtain a control compensation parameter; and performing compensation adjustment on the control period target by using the control compensation parameter. By means of the method and device, the technical problem that in the prior art, the operation state of the screw air compressor is difficult to be accurately matched with the preset control target, and the stability and efficiency of pressure control are affected can be solved, and the technical effect of improving the stability and efficiency of pressure control is achieved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of pressure control, in particular to a permanent magnet variable frequency pressure control method and a screw air compressor. BACKGROUND

[0002] With the continuous improvement of industrial automation and intelligence, compressors, air conditioners, air compressors and other equipment play an increasingly important role in many production processes. These devices adjust the pressure, temperature and flow of air to meet the needs of different links in the production process.

[0003] However, in actual operation, due to factors such as aging of the equipment itself, environmental changes, load fluctuations and other factors, the running state of the equipment often cannot perfectly match the predetermined control target, affecting the stability and efficiency of the system. Therefore, how to accurately control these devices in a complex operating environment and ensure that the system always runs in the best state has become a technical problem to be solved.

[0004] In summary, the existing technology has the technical problem that due to the influence of factors such as aging of the equipment, environmental changes, load fluctuations and other factors, the running state of the screw air compressor is difficult to accurately match the predetermined control target, further affecting the stability and efficiency of the pressure control. SUMMARY

[0005] The purpose of the present application is to provide a permanent magnet variable frequency pressure control method and a screw air compressor to solve the technical problem in the prior art that due to the influence of factors such as aging of the equipment, environmental changes, load fluctuations and other factors, the running state of the screw air compressor is difficult to accurately match the predetermined control target, further affecting the stability and efficiency of the pressure control.

[0006] In view of the above problems, the present application provides a permanent magnet variable frequency pressure control method and a screw air compressor.

[0007] In a first aspect, the present application provides a permanent magnet variable frequency pressure control method, which is realized by a screw air compressor, comprising: analyzing the demand pressure and demand flow of compressed air according to load data; performing control strategy adaptability analysis according to the demand pressure and demand flow to obtain an adaptability value; performing air pressure stage analysis according to the adaptability value to configure control cycle targets of air pressure staging; based on the control cycle targets, collecting key parameter data in the environment and equipment operation through sensors, including air pressure, compression temperature, motor current, voltage, and motor speed; performing compensation analysis on the key parameter data according to the control cycle targets to obtain control compensation parameters; and compensating and adjusting the control cycle targets using the control compensation parameters.

[0008] In a second aspect, the application further provides a screw air compressor for performing the permanent magnet variable frequency pressure control method according to the first aspect, comprising: a demand analysis module configured to analyze the demand pressure and the demand flow of compressed air according to load data; an adaptability analysis module configured to perform control strategy adaptability analysis according to the demand pressure and the demand flow, and obtain an adaptability evaluation value; an air compression stage analysis module configured to perform air compression stage analysis according to the adaptability evaluation value, and configure an air compression stage control cycle target; a parameter acquisition module configured to acquire key parameter data in the environment and equipment operation, including air pressure, compression temperature, motor current, voltage, and motor speed, based on the control cycle target through a sensor; a compensation analysis module configured to perform compensation analysis on the key parameter data according to the control cycle target, and obtain a control compensation parameter; and a compensation adjustment module configured to perform compensation adjustment on the control cycle target by using the control compensation parameter.

[0009] The one or more technical solutions provided in the application have at least the following technical effects or advantages: The demand pressure and the demand flow of compressed air are analyzed according to load data; control strategy adaptability analysis is performed according to the demand pressure and the demand flow, and an adaptability evaluation value is obtained; air compression stage analysis is performed according to the adaptability evaluation value, and an air compression stage control cycle target is configured; key parameter data in the environment and equipment operation, including air pressure, compression temperature, motor current, voltage, and motor speed, are acquired based on the control cycle target through a sensor; compensation analysis is performed on the key parameter data according to the control cycle target, and a control compensation parameter is obtained; and compensation adjustment is performed on the control cycle target by using the control compensation parameter, that is, the technical goal of precisely controlling the operation state of the screw air compressor is achieved, and the technical effect of improving the stability and efficiency of pressure control is achieved.

[0010] The above description is only a summary of the technical solutions of the application. In order to enable the technical means of the application to be more clearly understood, the application can be implemented according to the content of the specification, and in order to enable the above and other purposes, features and advantages of the application to be more obvious and easy to understand, the following specific embodiments of the application are described. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary and, for those skilled in the art, other drawings can be obtained without creative effort on the basis of the provided drawings.

[0012] Figure 1 A flowchart of a permanent magnet variable frequency pressure control method of the application is shown in the figure. Figure 2 A structural diagram of a screw air compressor of the application is shown in the figure.

[0013] Explanation of reference signs: The demand analysis module 11, the adaptability analysis module 12, the air compression stage analysis module 13, the parameter acquisition module 14, the compensation analysis module 15, and the compensation adjustment module 16. DETAILED DESCRIPTION

[0014] The application provides a permanent magnet variable frequency pressure control method and a screw air compressor, which solve the technical problem in the prior art that due to the influence of factors such as equipment aging, environmental changes, and load fluctuations, the running state of the screw air compressor is difficult to accurately match the predetermined control target, which further affects the stability and efficiency of pressure control. The technical target of accurately controlling the running state of the screw air compressor is achieved, and the technical effect of improving the stability and efficiency of pressure control is achieved.

[0015] The technical solutions in the application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the exemplary embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of protection of the application. In addition, it should be noted that, for convenience of description, only parts related to the application are shown in the drawings, not all.

[0016] Embodiment one, please refer to the attached Figure 1 The application provides a permanent magnet variable frequency pressure control method applied to a screw air compressor, which specifically includes the following steps: Step one: according to the load data, analyze the demand pressure and demand flow of compressed air.

[0017] Specifically, by monitoring the operating conditions of the screw air compressor and changes in the use environment in real time, the required compressed air pressure and flow under different time periods and load conditions can be obtained. That is, the demand pressure refers to the gas pressure level that the screw air compressor needs to maintain under the preset application scenario. The demand flow refers to the air flow required by the screw air compressor under the preset application scenario. Through real-time analysis of the load data, the screw air compressor can automatically adjust the demand pressure and demand flow to ensure the efficiency and stability of the compressed air supply.

[0018] Step two: According to the demand pressure and demand flow, perform control strategy adaptability analysis to obtain an adaptability evaluation value.

[0019] Specifically, according to the demand pressure and demand flow, perform control strategy adaptability analysis, such as dividing the pressure control process into different stages and identifying the core evaluation parameters of each stage, and then evaluating the matching control strategy selected based on the demand pressure and demand flow, to finally obtain the adaptability evaluation value of the matching control strategy.

[0020] Step three: According to the adaptability evaluation value, perform air pressure stage analysis and configure air pressure staged control cycle targets.

[0021] Specifically, according to the adaptability evaluation value, perform air pressure stage analysis and configure air pressure staged control cycle targets. The adaptability evaluation value is used to determine whether the control strategy can effectively adapt to the demand pressure and demand flow in actual operation under the current conditions. In the control process, since different operation stages have different control accuracy requirements, the control process needs to be divided into multiple stages for analysis. For example, the oil mixing stage, the temperature rising stage, the reaction stage and the cooling stage, the accuracy requirements and control strategies of each stage may be different. By evaluating the control effect of each stage through the adaptability evaluation value, the corresponding control cycle targets are configured for each stage, so that the system can achieve the best operating state in each stage.

[0022] Step four: Based on the control cycle targets, collect key parameter data in the environment and equipment operation through sensors, including air pressure, compression temperature, motor current, voltage, and motor speed.

[0023] Specifically, based on the configuration of the corresponding control cycle target for each operation stage, key parameter data in the environment and device operation are collected through sensors to help determine whether the current operation state meets the expectations, so as to evaluate and adjust the control strategy. Key parameter data includes air pressure, compression temperature, motor current, voltage, and motor speed. Air pressure refers to the actual pressure of compressed air in the screw air compressor. Compression temperature reflects the heat change in the air compression process, wherein, too high temperature may affect the safety and efficiency of key parameter data. Motor current and voltage are parameters for evaluating the operation state of the motor in the screw air compressor, reflecting the motor load and working efficiency. Motor speed determines the running speed of the screw air compressor, and thus affects the air flow and pressure output.

[0024] Step five: compensating analysis of key parameter data according to the control cycle target to obtain control compensation parameters.

[0025] Specifically, key parameter data refers to actual data collected in the control cycle, such as temperature, pressure, current, etc. Key parameter data may deviate from the predetermined target due to various factors (such as equipment error, environmental change, etc.). Therefore, compensating analysis of key parameter data is needed to find the difference between data and target. For example, if the temperature exceeds the set range, compensating analysis will help calculate the control parameters (such as motor speed or compressor power) that need to be adjusted to correct the deviation.

[0026] Step six: compensating adjustment of the control cycle target using the control compensation parameters.

[0027] Specifically, by compensating analysis, the adjustment value is obtained to correct the deviation between the actual control parameter and the set control cycle target, ensuring that the control cycle target can be accurately achieved, avoiding the influence of system error or external factors on the running state of the device deviating from the predetermined target, and thus maintaining stable operation and ensuring that all control parameters are always within the predetermined range.

[0028] The permanent magnet variable frequency pressure control method can achieve the technical goal of accurately controlling the running state of the screw air compressor, and achieve the technical effect of improving the stability and efficiency of pressure control.

[0029] Further, the present application also includes: According to historical sample data, the parameter change relationship of pressure control is fitted; according to the parameter change relationship of pressure control, the pressure control process is segmented to obtain a control stage cycle; the core evaluation parameter of the control stage cycle is obtained, which is the control parameter with the greatest influence on the control stage cycle; according to the demand pressure and demand flow, a matching control strategy is obtained; the core evaluation parameter is taken as the evaluation target, and the matching control strategy is evaluated to obtain the adaptive evaluation value of the matching control strategy.

[0030] Specifically, based on historical sample data, the parameter variation relationship of pressure control is fitted. Historical sample data refers to the past operating data of the screw air compressor, recording its actual working performance under different operating conditions. Fitting the parameter variation relationship of pressure control involves identifying the variation pattern of parameters during the pressure control process by comparing and analyzing the target values ​​and actual values ​​in the historical data.

[0031] Based on the parameter changes in pressure control, the pressure control process is divided into multiple stages. Each stage may have different control methods and pressure change trends, thus obtaining the control stage cycle. Dividing the control stage cycle helps the screw air compressor adjust its operation to cope with different load conditions.

[0032] The core evaluation parameters for the control phase cycle are identified. These are the control parameters that have the greatest impact on the control phase cycle and can have the greatest influence on the control effect. For example, core evaluation parameters can be obtained by evaluating the impact of abnormal parameters on control accuracy. By identifying these core evaluation parameters, the control strategy can be adjusted more accurately, thereby improving control efficiency and stability.

[0033] Based on the required pressure and flow rate of compressed air, the most suitable control strategy is selected to meet the operating requirements of the screw air compressor. This matched control strategy ensures that the compressor operates while meeting both the required pressure and flow rate. For example, if the required pressure and flow rate are high, a higher speed control strategy is selected to provide sufficient compressed air supply.

[0034] Using core evaluation parameters as evaluation targets, the matched control strategies are evaluated. This involves assessing the control effectiveness of one or more matched control strategies under the given core evaluation parameters in actual operation, generating quantitative values, and ensuring that the selected matched control strategies achieve optimal results in actual operation. The resulting adaptation evaluation value reflects the degree of matching between the matched control strategy and actual operational requirements.

[0035] By analyzing historical sample data, the relationship between pressure control parameters is fitted, the pressure control process is divided into different stages, and the core evaluation parameters of each stage are identified. The matching control strategy selected based on demand pressure and demand flow is evaluated through the core evaluation parameters, and finally the adaptation evaluation value of the matching control strategy is obtained, thereby achieving more accurate and efficient pressure control and ensuring that the screw air compressor operates in the optimal state.

[0036] Furthermore, this application also includes: According to the historical sample data, the pressure control parameter and the actual monitoring parameter are extracted; the target-actual deviation trend is obtained by aligning and fitting the pressure control parameter and the actual monitoring parameter; and the parameter change relationship of the pressure control is obtained according to the target-actual deviation trend.

[0037] Specifically, the historical sample data refers to the relevant data collected in a preset past time period, which contains the parameter records of the pressure and flow of the screw air compressor under different working conditions. The pressure control parameter refers to the preset pressure control target of the screw air compressor, and the actual monitoring parameter refers to the actual measured pressure control parameter in the real-time monitoring of the screw air compressor, such as the actual pressure which may be different from the set value due to environmental changes, load fluctuations and other reasons. By extracting the pressure control parameter and the actual monitoring parameter, basic data is provided for subsequent analysis.

[0038] Aligning and fitting refers to matching and calibrating the pressure control parameter and the actual monitoring parameter in the historical sample data, so as to compare the target value and the actual value, calculate the deviation between the target value and the actual value, obtain the deviation change trend between the target and the actual, and obtain the target-actual deviation trend.

[0039] Through the deviation trend between the target value and the actual value, the change rule of the pressure control parameter is identified. For example, the target-actual deviation trend increases with the increase of the load data, indicating that under high load conditions, more adjustments are made through pressure control to make the screw air compressor more meet the actual demand, and the stability and efficiency of the screw air compressor are maintained.

[0040] By aligning and fitting the pressure control parameter and the actual monitoring parameter in the historical sample data, the deviation trend between the target and the actual is calculated, the change relationship between the pressure control parameter and the actual demand is obtained, which is used for real-time adjustment of the control parameter to adapt to the load change, optimize the operation efficiency of the compressor, and ensure the stability and accuracy of air supply.

[0041] Further, the application also includes: According to the control phase cycle, the historical sample data is periodically divided to construct a phase sample data cluster; for the phase sample data cluster, an abnormal parameter and a promotion parameter are obtained, the abnormal parameter is a parameter for inhibiting control accuracy, and the promotion parameter is a parameter for improving control accuracy; control accuracy influence analysis is performed on the abnormal parameter and the promotion parameter respectively to obtain a parameter influence coefficient; and the core evaluation parameter is determined according to the parameter influence coefficient.

[0042] Specifically, the historical sample data is periodically divided according to the control phase cycle, each cycle has different characteristics and control strategies, and then a phase sample data cluster is constructed.

[0043] The parameters that can have a negative impact on control accuracy are obtained from the stage sample data cluster as abnormal parameters. Abnormal parameters can cause the suppression of control accuracy, such as abnormal parameters that can cause the screw air compressor to be slow or inaccurate in response. The parameters that can have a positive impact on control accuracy are obtained from the stage sample data cluster as promoting parameters. Promoting parameters are factors that can improve control accuracy, such as high-quality sensors, which help to quickly respond and maintain the set pressure target.

[0044] The abnormal parameters and the promoting parameters are respectively subjected to control accuracy influence analysis. The control accuracy influence analysis refers to obtaining parameter influence coefficients by evaluating the influence of each parameter on control accuracy. The parameter influence coefficients reflect the contribution of each parameter to control accuracy. For example, the parameter influence coefficient corresponding to the abnormal parameter is negative, indicating that the abnormal parameter has a suppressing effect on control accuracy; the parameter influence coefficient corresponding to the promoting parameter is positive, indicating that the promoting parameter can improve control accuracy. Exemplarily, the method of control accuracy influence analysis includes introducing an expert evaluation system for evaluation or using the control results in historical sample data to feedback on control accuracy, thereby obtaining the parameter influence coefficients.

[0045] According to the numerical value of the parameter influence coefficient, the parameter corresponding to the maximum numerical value is determined as the core evaluation parameter. The core evaluation parameter is a key factor in guiding the pressure control process, and optimizing the core evaluation parameter improves the control effect.

[0046] By periodically dividing the historical sample data, multiple data clusters are divided according to the control stages. Then, by identifying the abnormal parameters and the promoting parameters in each stage, the influence of control accuracy is analyzed, and finally the influence coefficients of each parameter are calculated to determine the core evaluation parameter, thereby improving the control accuracy and efficiency.

[0047] Further, the present application also includes: The parameter influence coefficient difference of the abnormal parameters and the promoting parameters is obtained; when the parameter influence coefficient difference exceeds a preset threshold, the parameter with the largest parameter influence coefficient is taken as the core evaluation parameter; when the parameter influence coefficient difference does not exceed the preset threshold, the parameter with the largest parameter influence coefficient in the abnormal parameters is taken as the core evaluation parameter.

[0048] Specifically, the parameter influence coefficient difference of the abnormal parameters and the promoting parameters is calculated. The parameter influence coefficient difference represents the difference in the influence degree of the abnormal parameters and the promoting parameters on control accuracy, and then the parameter with the greatest influence in the control process is obtained.

[0049] When the difference in the parameter influence coefficient exceeds a preset threshold, it indicates that the corresponding abnormal parameter and the promoting parameter have significantly different impacts on control accuracy. Therefore, the parameter with the largest influence coefficient is selected as the core evaluation parameter. The preset threshold is determined by a person skilled in the art based on the actual situation.

[0050] When the difference in the parameter influence coefficient does not exceed the preset threshold, it indicates that the difference in the influence of the corresponding abnormal parameter and the promoting parameter on the control accuracy is small, or the difference in the influence of multiple abnormal parameters on the control accuracy is small. At the same time, since abnormal parameters may directly cause abnormal events, the abnormal parameter or promoting parameter with the largest parameter influence coefficient is taken as the core evaluation parameter.

[0051] The impact on control accuracy is assessed by calculating the difference in the influence coefficients of abnormal parameters and promoting parameters. When the difference exceeds a preset threshold, the parameter with the largest influence coefficient is selected as the core evaluation parameter; when the difference does not exceed the threshold, the parameter with the largest influence coefficient among the abnormal parameters is selected, thereby identifying the parameter with the greatest impact on control accuracy and optimizing it to ensure that the system can achieve the best operating effect.

[0052] Furthermore, this application also includes: The process involves: acquiring the monitoring source and monitoring timestamp of the key parameter data; performing correlation and impact analysis on the key parameter data based on the monitoring source and monitoring timestamp to determine monitoring impact prediction information; constructing a monitoring data time series chain using the time alignment relationship between the monitoring timestamp and the monitoring impact prediction information; fitting a control parameter target time series chain according to the control cycle target; identifying differences in the monitoring data time series chain using the control parameter target time series chain to obtain a compensation time series chain; and using the compensation time series chain as the target, performing control parameter matching and analysis using a PID controller to obtain the compensation control parameters.

[0053] Specifically, this refers to the monitoring source and monitoring timestamp that acquire key parameter data through sensors or other devices, such as air pressure, compression temperature, motor current, voltage, and motor speed. The monitoring source is the location or device where the data is collected. When data is collected, a monitoring timestamp is generated, indicating the specific time the data was collected.

[0054] By combining monitoring sources and timestamps with the acquired key parameter data, the interrelationships between various data points are analyzed. For example, air pressure and temperature data collected at different times may be affected by changes in motor speed. Through correlation analysis, the potential impact of parameter changes on other key parameters is predicted, providing a basis for subsequent control decisions.

[0055] Based on the monitoring timestamps, all collected parameter data are arranged in chronological order to form a monitoring data time series chain, thereby obtaining the changing trends and patterns of the parameter data. For example, if changes in motor current always occur after changes in air pressure, this causal relationship can be identified through the monitoring data time series chain.

[0056] By comparing the target control cycle with the actual monitoring data, a target time series chain is fitted to represent the change process of the control parameters under ideal conditions.

[0057] By comparing the target control parameter time series with the monitoring data time series, the discrepancies between the actual control process and the target value are identified. These discrepancies may originate from equipment errors, environmental changes, or other unforeseen factors. After discrepancy identification, a compensation time series is generated, representing the adjustment amount required to correct the discrepancy.

[0058] A PID (Proportional-Integral-Derivative) controller is used to precisely adjust the parameters based on the changes in parameters required by the compensation timing chain. The control parameters are adjusted according to the current error, past cumulative error values, and the rate of error change, ultimately yielding the compensated control parameters. For example, if there is a difference between the target pressure and the actual pressure, the PID controller will adjust the motor speed to compensate for the difference, thus obtaining the compensated control parameters.

[0059] By acquiring the monitoring sources and timestamps of key parameter data and performing correlation analysis, the influence relationships between key parameters are predicted. Subsequently, by constructing a time-series chain, the actual monitoring data is compared with the control cycle target to identify discrepancies and generate a compensation time-series chain. Finally, a PID controller is used to match and analyze control parameters based on the compensation time-series chain, ensuring control accuracy and stability, thereby achieving optimal performance.

[0060] Furthermore, this application also includes: The monitoring location, monitoring parameter attributes, and parameter characteristics are obtained from the monitoring source. Based on the monitoring location, monitoring parameter attributes, and parameter characteristics, a correlation analysis of location, time, and parameter change characteristics is performed on the key parameter data to construct a causal state model. A time lag parameter is introduced into the causal state model, and the time lag parameter determines the lag time of parameter change through timestamp differences. By setting different time windows, the change trends of different parameters within each time window are identified. Based on the time lag parameter and the change trends within the time windows, the correlation strength between different parameters is calculated. Based on the correlation strength between different parameters, a correlation matrix is ​​constructed, which reflects strong correlation, weak correlation, and lag relationships between different parameters. The causal state model and the correlation matrix are fused to identify the correlation between key parameter data, predict the temporal impact of monitoring data, and obtain the monitoring impact prediction information.

[0061] Specifically, the physical location of the sensors is extracted from the monitoring source data to obtain the monitoring location. Monitoring parameter attributes, such as temperature, pressure, or flow rate, are extracted from the data of each monitoring source. Monitoring parameter features are extracted from the data of each monitoring source, such as calculating statistical indicators like average, maximum, minimum, standard deviation, and rate of change, to form a feature vector, providing a basis for subsequent correlation analysis.

[0062] By analyzing the physical location of each monitoring source, spatial correlations between different sensors can be determined. For example, sensors in adjacent locations may exhibit strong correlations, thus their data may show fluctuation patterns. Potential correlations between data are analyzed based on parameter attributes (such as temperature) and feature similarities (such as trends and fluctuation amplitudes). For instance, the fluctuations of two pressure sensors may have similar characteristics. Through correlation analysis results, key parameters under different states (such as temperature increases leading to pressure increases) are defined, and causal relationships between parameters are identified. Based on these causal relationships, a causal state model is then constructed.

[0063] By analyzing the timestamp differences in historical data, the lagged effects between key parameters can be identified. Since parameter changes are not instantaneous but rather have lags—for example, a change in temperature might affect pressure several seconds later—timestamp differences are needed to determine this lag time. Introducing time lag parameters allows causal state models to more realistically reflect the delayed effects between different parameters.

[0064] Setting time windows helps to analyze the changing trends of parameter data at different time scales in greater detail. For example, a five-second window can be set to observe short-term pressure changes, or a larger time window (such as thirty seconds) can be set to analyze the long-term trend of pressure changes. By analyzing the data within different time windows, the trend of parameter changes can be identified, such as whether it is rising, falling, or remaining stable.

[0065] By combining time lag parameters with trends of change within different time windows, the strength of the correlation between different parameters can be assessed. For example, if the trend of temperature change significantly affects the trend of pressure change within a certain time window, then a strong correlation between temperature and pressure can be calculated. Calculating the strength of the correlation helps to identify parameters that have a greater impact on changes in other parameters.

[0066] A correlation matrix is ​​constructed based on the correlation strength between different parameters. The element values ​​of the correlation matrix represent strong, weak, or no correlation between parameters, reflecting the strong, weak, and no correlations between different parameters. A marker for lag relationships is added to the correlation matrix (e.g., a marker can be added if the lag time is set to 10 seconds) to more intuitively represent the lag relationships between parameters.

[0067] By combining causal state models with correlation matrices, the correlations between different parameters can be comprehensively analyzed. Predicting the temporal impact of monitoring data based on these correlations—that is, predicting the future trends of other parameters after a change in one parameter—helps to identify potential problems in advance and optimize control strategies, thereby improving operational efficiency and stability.

[0068] By extracting location information, parameter attributes, and characteristics of monitoring sources, correlation analysis of location, time, and parameter change characteristics is conducted to construct a causal state model, incorporating a time lag parameter. By setting different time windows, parameter change trends are identified, and the correlation strength between parameters is calculated, ultimately constructing a correlation matrix. The fusion of the causal state model and the correlation matrix identifies and predicts the correlation between key parameters, thereby predicting the temporal impact of monitoring data. This provides powerful data analysis support for the system, helping to optimize control strategies and improve operational accuracy and efficiency.

[0069] In summary, the permanent magnet variable frequency pressure control method provided in this application has the following technical effects: Based on load data, the required pressure and flow rate of compressed air are analyzed; based on the required pressure and flow rate, an adaptive analysis of the control strategy is performed to obtain an adaptive evaluation value; based on the adaptive evaluation value, a phased analysis of air compression is conducted to configure a control cycle target for air compression grading; based on the control cycle target, key parameter data of the environment and equipment operation are collected through sensors, including air pressure, compression temperature, motor current, voltage, and motor speed; based on the control cycle target, a compensation analysis is performed on the key parameter data to obtain control compensation parameters; the control compensation parameters are used to compensate and adjust the control cycle target. In other words, by achieving the technical goal of precisely controlling the operating state of the screw air compressor, the stability and efficiency of pressure control are improved.

[0070] Example 2: Based on the same inventive concept as the permanent magnet variable frequency pressure control method in the previous examples, this application also provides a screw air compressor. Please refer to the appendix. Figure 2 ,include: The system comprises the following modules: a demand analysis module 11, used to analyze the demand pressure and demand flow rate of compressed air based on load data; an adaptability analysis module 12, used to perform control strategy adaptability analysis based on the demand pressure and demand flow rate to obtain an adaptation evaluation value; an air-compression stage analysis module 13, used to perform air-compression stage analysis based on the adaptation evaluation value to configure a control cycle target for air-compression grading; a parameter acquisition module 14, used to acquire key parameter data of the environment and equipment operation, including air pressure, compression temperature, motor current, voltage, and motor speed, based on the control cycle target; a compensation analysis module 15, used to perform compensation analysis on the key parameter data based on the control cycle target to obtain control compensation parameters; and a compensation adjustment module 16, used to adjust the control cycle target using the control compensation parameters.

[0071] Furthermore, the screw air compressor is also used for: Based on historical sample data, the parameter variation relationship of pressure control is fitted; based on the parameter variation relationship of pressure control, the pressure control process is segmented to obtain the control stage cycle; the core evaluation parameters of the control stage cycle are obtained, which are the control parameters with the greatest impact on the control stage cycle; based on the demand pressure and demand flow, a matching control strategy is obtained; using the core evaluation parameters as the evaluation target, the matching control strategy is evaluated to obtain the adaptation evaluation value of the matching control strategy.

[0072] Furthermore, the screw air compressor is also used for: Based on the historical sample data, pressure control parameters and actual monitoring parameters are extracted; alignment and fitting are performed on the pressure control parameters and actual monitoring parameters to obtain the target-actual deviation trend; based on the target-actual deviation trend, the parameter change relationship of the pressure control is obtained.

[0073] Furthermore, the screw air compressor is also used for: The historical sample data is periodically segmented according to the control phase cycle to construct a phase sample data cluster; for the phase sample data cluster, abnormal parameters and promoting parameters are obtained, wherein the abnormal parameters are parameters that suppress control accuracy and the promoting parameters are parameters that improve control accuracy; the control accuracy impact analysis is performed on the abnormal parameters and promoting parameters respectively to obtain parameter impact coefficients; and the core evaluation parameters are determined based on the parameter impact coefficients.

[0074] Furthermore, the screw air compressor is also used for: Obtain the difference in the parameter influence coefficients of the abnormal parameters and the promoting parameters; when the difference in the parameter influence coefficients exceeds a preset threshold, the parameter with the largest parameter influence coefficient is taken as the core evaluation parameter; when the difference in the parameter influence coefficients does not exceed the preset threshold, the parameter with the largest parameter influence coefficient among the abnormal parameters is taken as the core evaluation parameter.

[0075] Furthermore, the screw air compressor is also used for: The process involves: acquiring the monitoring source and monitoring timestamp of the key parameter data; performing correlation and impact analysis on the key parameter data based on the monitoring source and monitoring timestamp to determine monitoring impact prediction information; constructing a monitoring data time series chain using the time alignment relationship between the monitoring timestamp and the monitoring impact prediction information; fitting a control parameter target time series chain according to the control cycle target; identifying differences in the monitoring data time series chain using the control parameter target time series chain to obtain a compensation time series chain; and using the compensation time series chain as the target, performing control parameter matching and analysis using a PID controller to obtain the compensation control parameters.

[0076] Furthermore, the screw air compressor is also used for: The monitoring location, monitoring parameter attributes, and parameter characteristics are obtained from the monitoring source. Based on the monitoring location, monitoring parameter attributes, and parameter characteristics, a correlation analysis of location, time, and parameter change characteristics is performed on the key parameter data to construct a causal state model. A time lag parameter is introduced into the causal state model, and the time lag parameter determines the lag time of parameter change through timestamp differences. By setting different time windows, the change trends of different parameters within each time window are identified. Based on the time lag parameter and the change trends within the time windows, the correlation strength between different parameters is calculated. Based on the correlation strength between different parameters, a correlation matrix is ​​constructed, which reflects strong correlation, weak correlation, and lag relationships between different parameters. The causal state model and the correlation matrix are fused to identify the correlation between key parameter data, predict the temporal impact of monitoring data, and obtain the monitoring impact prediction information.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The permanent magnet variable frequency pressure control method and specific examples in the aforementioned embodiment one are also applicable to the screw air compressor in this embodiment. Through the foregoing detailed description of a permanent magnet variable frequency pressure control method, those skilled in the art can clearly understand the screw air compressor in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0078] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A permanent magnet variable frequency pressure control method, characterized by, The permanent magnet variable frequency pressure control method comprises: According to the load data, the demand pressure and the demand flow of compressed air are analyzed; According to the demand pressure and the demand flow, the adaptability of the control strategy is analyzed to obtain an adaptability value; According to the adaptability value, the air pressure is analyzed in stages, and a control cycle target of air pressure grading is configured; Based on the control cycle target, key parameter data in the environment and equipment operation are collected through sensors, including air pressure, compression temperature, motor current, voltage, and motor speed; According to the control cycle target, the key parameter data are compensated and analyzed to obtain control compensation parameters; The control compensation parameters are used to compensate and adjust the control cycle target.

2. The permanent magnet variable frequency pressure control method of claim 1, wherein, According to the demand pressure and the demand flow, the adaptability of the control strategy is analyzed to obtain an adaptability value, and the permanent magnet variable frequency pressure control method comprises: According to historical sample data, a parameter change relationship of pressure control is fitted; According to the parameter change relationship of pressure control, the pressure control process is segmented to obtain a control stage cycle; A core evaluation parameter of the control stage cycle is obtained, which is the control parameter with the greatest impact on the control stage cycle; According to the demand pressure and the demand flow, a matching control strategy is obtained; The core evaluation parameter is taken as an evaluation target to evaluate the matching control strategy to obtain an adaptability value of the matching control strategy.

3. The permanent magnet variable frequency pressure control method of claim 2, wherein, According to the historical sample data, a parameter change relationship of pressure control is fitted, and the permanent magnet variable frequency pressure control method comprises: According to the historical sample data, pressure control parameters and actual monitoring parameters are extracted; According to the pressure control parameters and the actual monitoring parameters, a target-actual deviation trend is obtained by alignment fitting; According to the target-actual deviation trend, the parameter change relationship of pressure control is obtained.

4. The permanent magnet variable frequency pressure control method of claim 2, wherein, A core evaluation parameter of the control stage cycle is obtained, and the permanent magnet variable frequency pressure control method comprises: According to the control stage cycle, the historical sample data are periodically segmented to construct a stage sample data cluster; For the stage sample data cluster, abnormal parameters and promoting parameters are obtained, the abnormal parameters are parameters that inhibit control accuracy, and the promoting parameters are parameters that improve control accuracy; The abnormal parameters and the promoting parameters are respectively analyzed for control accuracy influence to obtain parameter influence coefficients; According to the parameter influence coefficients, the core evaluation parameter is determined.

5. The permanent magnet variable frequency pressure control method of claim 4, wherein, According to the parameter influence coefficients, the core evaluation parameter is determined, and the permanent magnet variable frequency pressure control method comprises: The parameter influence coefficient difference of the abnormal parameters and the promoting parameters is obtained; When the parameter influence coefficient difference exceeds a preset threshold, the parameter with the largest parameter influence coefficient is taken as the core evaluation parameter; When the parameter influence coefficient difference does not exceed the preset threshold, the parameter with the largest parameter influence coefficient in the abnormal parameters is taken as the core evaluation parameter.

6. The permanent magnet variable frequency pressure control method of claim 1, wherein, According to the control cycle target, the key parameter data are compensated and analyzed to obtain control compensation parameters, and the permanent magnet variable frequency pressure control method comprises: The monitoring source and the monitoring timestamp of the key parameter data are obtained; Correlation influence analysis is performed on the key parameter data based on the monitoring source and monitoring timestamp to determine monitoring influence prediction information; A monitoring data time sequence chain is constructed by using the time alignment relationship between the monitoring timestamp and the monitoring influence prediction information; A control parameter target time sequence chain is fitted according to the control cycle target; A compensation time sequence chain is obtained by using the control parameter target time sequence chain to identify differences in the monitoring data time sequence chain; A compensation control parameter is obtained by using a PID controller to perform control parameter matching analysis with the compensation time sequence chain as the target.

7. The permanent magnet variable frequency pressure control method of claim 6, wherein, Correlation influence analysis is performed on the key parameter data based on the monitoring source and monitoring timestamp to determine monitoring influence prediction information, and the permanent magnet variable frequency pressure control method comprises: A monitoring position, monitoring parameter attribute and parameter characteristic are obtained according to the monitoring source; A position, time and parameter change characteristic correlation analysis is performed on the key parameter data based on the monitoring position, monitoring parameter attribute and parameter characteristic to construct a causal state model; A time lag parameter is introduced into the causal state model, and the time lag parameter is used to determine the lag time of parameter change through timestamp difference; Different time windows are set to identify the change trend of different parameters in each time window; The correlation strength between different parameters is calculated according to the time lag parameter and the change trend in the time window; An association matrix is constructed according to the correlation strength between different parameters, and the association matrix is used to reflect strong correlation, weak correlation and lag relationship between different parameters; The correlation between key parameter data is identified, and the time sequence influence of monitoring data is predicted by using the causal state model and the association matrix to fuse, and the monitoring influence prediction information is obtained.

8. A screw air compressor characterized by, The steps for implementing the permanent magnet variable frequency pressure control method in any one of claims 1 to 7 comprise: A demand analysis module is configured to analyze the demand pressure and demand flow of compressed air according to load data; An adaptability analysis module is configured to perform control strategy adaptability analysis according to the demand pressure and demand flow to obtain an adaptability evaluation value; An air pressure stage analysis module is configured to perform air pressure stage analysis according to the adaptability evaluation value to configure control cycle targets for air pressure stages; A parameter acquisition module is configured to acquire key parameter data including air pressure, compression temperature, motor current, voltage and motor speed in the environment and device operation based on the control cycle targets through sensors; A compensation analysis module is configured to perform compensation analysis on the key parameter data according to the control cycle targets to obtain control compensation parameters; A compensation adjustment module is configured to perform compensation adjustment on the control cycle targets by using the control compensation parameters.

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