Air compressor frequency conversion energy-saving control system based on sensor data acquisition

Abstract

The invention belongs to the field of air compressor frequency conversion control, relates to a data analysis technology, and aims to solve the problem that the pressure mutation probability of a main pipeline cannot be analyzed by combining state parameters of an air compressor in the prior art, in particular to an air compressor frequency conversion energy-saving control system based on sensor data acquisition. Comprising a real-time detection module, a deviation analysis module and a control optimization module which are in communication connection in sequence, and the real-time detection module, the deviation analysis module and the control optimization module are all in communication connection with a database; through the strategy of actively and dynamically adjusting the control threshold value, the basic control module can respond to smaller pressure deviation, so that the control sensitivity and the response speed of the system are remarkably improved; through earlier and more positive frequency adjustment, sudden increase of pressure deviation can be effectively inhibited, and the air compressor is prevented from entering an unstable operation state.

Description

Technical Field

[0001] This invention belongs to the field of variable frequency control of air compressors and involves data analysis technology, specifically a variable frequency energy-saving control system for air compressors based on sensor data acquisition. Background Technology

[0002] The variable frequency energy-saving control system for air compressors collects key sensor data from the air compressor system in real time. The intelligent controller (usually a PLC + frequency converter, or a dedicated energy-saving controller) analyzes and makes decisions to dynamically adjust the operating frequency of the air compressor motor. This allows the actual air output of the air compressor to accurately match the real-time air demand of the downstream, eliminating unnecessary unloading energy consumption and achieving significant energy-saving effects.

[0003] The invention patent with publication number CN118934571B discloses an air compressor power control method and system based on output demand detection. This control method inputs the time information of various air consumption parameters into the air compression system model, calculates the terminal air consumption and transmission loss in different time periods, and then calculates the air demand value to generate air demand time information. However, this control method cannot analyze the probability of pressure change in the main pipeline by combining the air compressor's own state parameters. This results in a lag when the air compressor is running unstable and the frequency conversion control based on the fixed pressure deviation threshold is not timely.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an air compressor variable frequency energy-saving control system based on sensor data acquisition, which solves the problem that existing technologies cannot analyze the probability of pressure change in the main pipeline by combining the air compressor's own state parameters; The technical problem to be solved by this invention is: how to provide an air compressor variable frequency energy-saving control system based on sensor data acquisition that can analyze the probability of pressure change in the main pipeline by combining the air compressor's own state parameters.

[0006] The objective of this invention can be achieved through the following technical solutions: The variable frequency energy-saving control system for air compressors based on sensor data acquisition includes a real-time detection module, a deviation analysis module, and a control optimization module that are connected in sequence. The real-time detection module, the deviation analysis module, and the control optimization module are all connected in communication with a database. The real-time detection module is used to detect and analyze the operating status of the air compressor: it generates a detection cycle and acquires the main pressure data, power data, opening degree data, and winding temperature data of the air compressor in real time within the detection cycle; it performs basic control of the air compressor based on the main pressure data. The deviation analysis module is used to analyze the pressure deviation pattern of the air compressor: at the end of the detection cycle, a sliding window with a duration of L1 seconds is generated and slides from the start to the end of the detection cycle to obtain the deviation growth rate of the sliding window. Abnormal windows and abnormal time periods are marked by the deviation growth rate; power anomaly set, opening degree anomaly set, and winding temperature anomaly set are generated; the power anomaly set is cleaned to obtain the power anomaly range; the opening degree anomaly set and winding temperature anomaly set are cleaned in the same way as the power anomaly set to obtain the opening degree anomaly range and winding temperature anomaly range, respectively. The control optimization module is used to perform optimized control analysis on the air compressor: based on the basic control in the new detection cycle, the power data, opening data, and winding temperature data of the air compressor are compared with the abnormal ranges of power, opening, and winding temperature in real time, and optimized control is performed.

[0007] Furthermore, the main pressure data is the pressure value of the main pipeline of the air compressor, the power data is the operating power of the air compressor, the opening degree data is the opening degree of the air compressor's intake valve, and the winding temperature data is the temperature of the air compressor's motor windings.

[0008] Furthermore, the specific process of basic control of the air compressor includes: retrieving the target pressure value and pressure deviation threshold from the database, marking the absolute value of the difference between the main pressure data and the target pressure value as the pressure deviation value, and comparing the pressure deviation value with the pressure deviation threshold: if the pressure deviation value is less than the pressure deviation threshold, it is determined that the air compressor does not have frequency regulation characteristics; if the pressure deviation value is greater than or equal to the pressure deviation threshold, it is determined that the air compressor has frequency regulation characteristics, and the frequency of the air compressor is adjusted by the PID controller.

[0009] Furthermore, the process of obtaining the deviation growth rate of the sliding window includes: obtaining the difference between the maximum and minimum pressure deviation values ​​within the sliding window and marking it as the deviation mutation value; marking the time interval corresponding to the maximum and minimum pressure deviation values ​​as the mutation period; and marking the ratio of the deviation mutation value to the duration of the mutation period as the deviation growth rate.

[0010] Furthermore, the specific process of marking abnormal windows and abnormal periods includes: obtaining the growth rate threshold from the database, comparing the deviation growth rate with the growth rate threshold; if the deviation growth rate is less than the growth rate threshold, the corresponding time window is marked as a normal window; if the deviation growth rate is greater than or equal to the growth rate threshold, the corresponding time window is marked as an abnormal window, and the sudden change period corresponding to the abnormal window is marked as an abnormal period.

[0011] Furthermore, the generation process of the power anomaly set, the opening degree anomaly set, and the winding temperature anomaly set includes: selecting several abnormal time points within the abnormal period, acquiring the power data, opening degree data, and winding temperature data of the air compressor at the abnormal time points, and marking them as power anomaly data, opening degree anomaly data, and winding temperature anomaly data, respectively. The power anomaly set, the opening degree anomaly set, and the winding temperature anomaly set are respectively formed by the power anomaly data, the opening degree anomaly data, and the winding temperature anomaly data corresponding to all abnormal time points within the detection cycle.

[0012] Furthermore, the specific process of data cleaning for the power anomaly set includes: calculating the variance of all elements in the power anomaly set to obtain the power distribution coefficient; obtaining the power distribution threshold from the database; comparing the power distribution coefficient with the preset power distribution threshold; if the power distribution coefficient is greater than or equal to the power distribution threshold, then the largest and smallest elements in the power anomaly set are removed, and the power distribution coefficient is recalculated, and so on, until the power distribution coefficient is less than the power distribution threshold; if the power distribution coefficient is less than the power distribution threshold, then the largest and smallest elements retained in the power anomaly set constitute the power anomaly range.

[0013] Furthermore, the specific process of optimizing control includes: if the power data, opening degree data, and winding temperature data are within the abnormal range of power, opening degree, and winding temperature respectively, then it is determined that the air compressor has entered a state of sudden increase in deviation. The pressure deviation replacement value YPx is obtained by the formula YPx=t1×YP, where YP is the value of the pressure deviation threshold, t1 is the proportional coefficient, and 0.85≤t1≤0.95. The pressure deviation replacement value YPx is then used to replace the pressure deviation threshold.

[0014] The present invention has the following beneficial effects: 1. This application can accurately determine whether frequency adjustment is needed based on real-time changes in the main pressure data of the air compressor, combined with preset target pressure values ​​and pressure deviation thresholds. This judgment mechanism based on a clear threshold avoids the problems of over-adjustment or under-adjustment that may occur in traditional control, making the basic control of the air compressor more refined and intelligent. When the pressure deviation is within the allowable range, the system does not perform frequency adjustment, effectively reducing the frequent start-stop and adjustment of the frequency converter and motor, reducing equipment wear and instantaneous energy consumption. When the pressure deviation exceeds the threshold, precise frequency adjustment is performed through the PID controller, which can quickly and effectively pull the system pressure back to the target range, ensuring the stability of the production process and product quality. This control strategy, combined with the data acquisition capability of the real-time detection module, enables the air compressor to achieve significant energy-saving effects while maintaining stable operation, improving the overall operating efficiency and reliability of the system. 2. Based on the comparison between the pressure deviation growth rate and the preset growth rate threshold, the operating status of the air compressor is refined and clearly distinguished between normal fluctuations and abnormal sudden changes. This classification mechanism enables the system to accurately identify abnormal windows and periods of severe pressure fluctuations, avoiding blind processing of all data and thus improving the accuracy and efficiency of subsequent abnormal data set generation. In particular, based on the deviation growth rate already calculated by the deviation analysis module, the introduction of a growth rate threshold for judgment allows the system to extract meaningful abnormal events from the quantified rate of change, providing a more accurate range of abnormal occurrence times for subsequent optimization control. This ensures that the control strategy can be applied specifically to abnormal operating conditions that truly require intervention, avoiding unnecessary adjustments and improving the overall system response speed and energy-saving effect. 3. Iterative cleaning of power anomaly data collected from air compressors during abnormal periods effectively identifies and removes extreme outliers and noise data from the dataset. This method, based on variance judgment and iterative removal of the maximum and minimum elements, ensures that the final power anomaly range has higher accuracy and representativeness, and can more realistically reflect the power fluctuation range of the air compressor under abnormal operating conditions. This avoids misjudgments caused by randomness or measurement errors in the original data, allowing the control optimization module to make decisions based on a refined and more reliable anomaly range when performing optimization control. This improves the robustness and control accuracy of the variable frequency energy-saving control system, and further optimizes the operating efficiency and energy-saving effect of the air compressor. 4. By determining that the air compressor has entered a state of sudden increase in deviation when the power data, opening degree data, and winding temperature data are all within their respective abnormal ranges, potential system instability risks can be identified more comprehensively and accurately. This proactive and dynamic adjustment strategy of the control threshold enables the basic control module to respond to smaller pressure deviations, thereby significantly improving the system's control sensitivity and response speed. By adjusting the frequency earlier and more actively, this solution can effectively suppress sudden increases in pressure deviation and prevent the air compressor from entering an unstable operating state, thereby ensuring that the system can maintain efficient and stable operation under complex working conditions, further improving energy saving and operational reliability. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In traditional variable frequency energy-saving control systems for air compressors, the lack of correlation analysis between the compressor's own state parameters and the probability of pressure surges leads to lag when relying solely on fixed pressure deviation thresholds for variable frequency control during unstable operation. This lag stems from the system's failure to comprehensively evaluate main pressure data, power data, opening degree data, and winding temperature data, thus hindering its ability to dynamically adapt to pressure surges. This further impacts the timeliness and accuracy of frequency adjustment, thereby reducing the system's ability to match real-time air demand.

[0019] For example, in an automobile manufacturing plant's assembly line, when multiple pneumatic tightening devices start simultaneously, the air demand changes abruptly, causing rapid fluctuations in the main pipeline pressure. Furthermore, because the air compressor motor winding temperature rises and the intake valve opening is at a critical state, the system fails to analyze the probability of pressure surges based on these state parameters. The fixed pressure deviation threshold cannot identify the abnormal characteristics of the current operating condition, resulting in the frequency converter failing to adjust the motor frequency in time. The system is forced into an unloaded operation mode, causing energy loss and interrupting the continuity of the production process.

[0020] If the above problems are not addressed, control lag will cause the air compressor to frequently switch between loading and unloading states, increasing ineffective operating time. In particular, the persistent pressure fluctuations will cause the system to deviate from its stable operating range, accelerating mechanical wear of the equipment over long-term operation, reducing the overall system reliability and lifespan, thus having a systemic negative impact on energy efficiency management in industrial scenarios. Example 1: Figure 1 As shown, the variable frequency energy-saving control system for air compressors based on sensor data acquisition includes a real-time detection module, a deviation analysis module, and a control optimization module that are connected in sequence. The real-time detection module, the deviation analysis module, and the control optimization module are all connected to a database.

[0021] For ease of understanding, the following explains some key terms in this embodiment: Database: This database stores various types of data generated during the operation of the air compressor, including real-time acquired data, historical operating data, preset parameters (such as target pressure values, thresholds, etc.), and analysis results (such as anomaly ranges). This database provides a unified platform for data sharing and information exchange between various modules.

[0022] Detection cycle: This detection cycle refers to the time interval between one complete data acquisition and status analysis by the real-time detection module. During this cycle, the system continuously monitors the operating parameters of the air compressor and performs a periodic summary at the end of the cycle.

[0023] Main pressure data: This refers to the pressure value within the main pipeline of the air compressor. This data is a key indicator for measuring the degree of matching between the air compressor's gas production capacity and downstream gas demand.

[0024] Power data: This power data refers to the electrical power consumed by the air compressor during operation. This data directly reflects the energy consumption level of the air compressor.

[0025] Opening degree data: This opening degree data refers to the degree to which the air compressor's intake valve is open. The size of the intake valve opening directly affects the air compressor's intake volume and output volume.

[0026] Winding temperature data: This refers to the temperature of the air compressor motor windings. This data is an important indicator for assessing the motor's operating status and whether there are any abnormalities such as overload or poor heat dissipation.

[0027] Basic control: This refers to the initial frequency adjustment or start / stop control of the air compressor based on the main pressure data. This control aims to maintain the main pipeline pressure within the set range to meet basic air usage needs.

[0028] Sliding window: This data processing technique is used for localized analysis of time series data. By sliding the data stream at fixed lengths of L1 seconds, it is possible to capture data characteristics over a specific time period, such as trends in pressure deviations.

[0029] Deviation Rate of Change: This refers to the rate at which the pressure deviation in the main pipeline of the air compressor changes over time within a sliding window. This indicator is used to quantify the severity of pressure fluctuations and is a key parameter for identifying potential anomalies.

[0030] Anomaly Window: This anomaly window refers to the time period marked when the internal data characteristics (such as the rate of deviation increase) of the sliding window exceed a preset threshold during the sliding process. This anomaly window indicates that the air compressor may be in an unstable or abnormal operating state.

[0031] Abnormal Period: This abnormal period refers to the specific time interval within the abnormal window where the pressure deviation value changes drastically. This abnormal period is the focus of further analysis of the causes of the abnormality and optimization control.

[0032] Example 1: An air compressor variable frequency energy-saving control system based on sensor data acquisition includes a real-time detection module, a deviation analysis module, and a control optimization module that are connected in sequence via communication. The real-time detection module, the deviation analysis module, and the control optimization module are all connected to a database via communication.

[0033] The real-time monitoring module is used to monitor and analyze the operating status of the air compressor: It generates a monitoring cycle and acquires real-time data on the air compressor's main pressure, power, opening degree, and winding temperature within that cycle. The main pressure data is the pressure value of the air compressor's main pipeline; the power data is the operating power of the air compressor; the opening degree data is the opening degree of the air compressor's intake valve; and the winding temperature data is the temperature of the air compressor's motor windings. Basic control of the air compressor is performed: The target pressure value and pressure deviation threshold are retrieved from the database. The absolute value of the difference between the main pressure data and the target pressure value is marked as the pressure deviation value. The pressure deviation value is compared with the pressure deviation threshold: if the pressure deviation value is less than the pressure deviation threshold, the air compressor is determined not to have frequency regulation characteristics; if the pressure deviation value is greater than or equal to the pressure deviation threshold, the air compressor is determined to have frequency regulation characteristics, and the frequency of the air compressor is adjusted using a PID controller.

[0034] Specifically, main pressure data is explicitly defined as the pressure value of the air compressor's main pipeline. This pressure value is a key parameter for measuring the air compressor's output performance and system load, and is typically monitored in real time by a pressure sensor installed on the main pipeline. This sensor can be of the piezoresistive, capacitive, or strain gauge type, and its function is to convert the air pressure signal in the pipeline into an electrical signal that can be processed by the system. Power data is explicitly defined as the air compressor's operating power. Operating power is a core indicator reflecting the air compressor's energy consumption level, and is usually measured in the air compressor's power supply circuit using a power sensor or smart meter. These devices can collect parameters such as voltage and current in real time and calculate instantaneous active power, providing a basis for energy-saving control. Opening degree data is explicitly defined as the air compressor's intake valve opening degree. The intake valve opening degree directly controls the airflow entering the compressor and is an important actuator parameter for adjusting the compressor's load and output. Its opening degree value is usually obtained by a position sensor (e.g., potentiometer, Hall sensor, or linear displacement sensor) installed on the intake valve actuator to accurately reflect the actual degree of valve opening. Winding temperature data is explicitly defined as the air compressor's motor winding temperature. Motor winding temperature is a key indicator for assessing the motor's operating status and health. Excessively high winding temperatures can lead to insulation aging or even burnout. This temperature is typically measured by embedding or placing temperature sensors, such as resistance temperature detectors (RTDs) (e.g., PT100, PT1000) or thermocouples, directly into or near the motor windings to monitor the motor's thermal load in real time.

[0035] The deviation analysis module is used to analyze the pressure deviation pattern of the air compressor: At the end of the detection cycle, a sliding window with a duration of L1 seconds is generated and slides from the start to the end of the detection cycle. The difference between the maximum and minimum pressure deviation values ​​within the sliding window is obtained and marked as the deviation mutation value. The time interval corresponding to the maximum and minimum pressure deviation values ​​is marked as the mutation period. The ratio of the deviation mutation value to the duration of the mutation period is marked as the deviation growth rate. The growth rate threshold is obtained from the database and compared with the growth rate threshold: if the deviation growth rate is less than the growth rate threshold, the corresponding time window is marked as a normal window; if the deviation growth rate is greater than or equal to the growth rate threshold, the corresponding time window is marked as an abnormal window, and the mutation period corresponding to the abnormal window is marked as an abnormal period. Within the abnormal period, several abnormal time points are selected, and the power data, opening data, and winding temperature data of the air compressor at the abnormal time points are obtained and marked as abnormal power data, abnormal opening data, etc. The abnormal data for the surrounding temperature is generated by constructing sets for power, opening, and surrounding temperature, respectively, based on power anomaly data, opening degree anomaly data, and surrounding temperature anomaly data corresponding to all abnormal time points within the detection period. The power anomaly sets are cleaned to obtain the power anomaly range. The variance of all elements within the power anomaly set is calculated to obtain the power distribution coefficient. A power distribution threshold is obtained from the database, and the power distribution coefficient is compared with a preset threshold. If the power distribution coefficient is greater than or equal to the threshold, the largest and smallest elements in the power anomaly set are removed, and the power distribution coefficient is recalculated. This process is repeated until the power distribution coefficient is less than the threshold. If the power distribution coefficient is less than the threshold, the largest and smallest elements retained in the power anomaly set constitute the power anomaly range. The opening degree anomaly set and surrounding temperature anomaly set are cleaned in the same way to obtain the opening degree anomaly range and surrounding temperature anomaly range, respectively.

[0036] This application's solution first determines the maximum and minimum values ​​of the pressure deviation within a sliding window, thereby quantifying the fluctuation range of the pressure deviation and defining it as the deviation abrupt change value. Subsequently, the time interval between these two extreme values, i.e., the abrupt change period, is precisely calculated to reflect the time required for a significant change in the pressure deviation. Finally, the deviation abrupt change value is compared to the duration of the abrupt change period to obtain the deviation growth rate. This calculation method ensures that the deviation growth rate not only reflects the absolute change in pressure deviation but, more importantly, the rate of change. In the variable frequency energy-saving control system of an air compressor, the real-time detection module acquires the main pressure data, and the deviation analysis module uses this method to calculate the deviation growth rate, enabling more accurate capture of the dynamic characteristics of pressure changes during air compressor operation. For example, when the main pressure data of the air compressor fluctuates drastically within a short period, even if the pressure deviation value itself may still be within a certain range, its deviation growth rate will increase significantly, thus enabling timely identification of this rapidly changing abnormal state and providing accurate quantitative basis for subsequent marking of abnormal windows and abnormal periods.

[0037] The rate of increase threshold is a preset critical value used to determine whether the rate of increase in pressure deviation has reached an abnormal level. It represents the upper limit of the pressure change rate that the system can tolerate. This threshold is the criterion for distinguishing between normal pressure fluctuations and abnormal pressure abrupt changes, providing a basis for subsequent anomaly identification and control strategies. This rate of increase threshold can be set based on the air compressor's design parameters, historical operating data analysis, and expert experience, and stored in a database for dynamic retrieval during system operation; alternatively, it can be trained on a large amount of historical operating data using machine learning algorithms to automatically learn and generate an optimal rate of increase threshold to adapt to the operating characteristics under different conditions. The deviation rate of increase is the ratio of the difference between the maximum and minimum pressure deviation values ​​within a sliding window (deviation abrupt change value) to the corresponding time interval (abrupt change period), reflecting the severity of pressure deviation changes within a specific time period. It quantifies the speed of pressure fluctuation and is a key indicator for assessing system stability and identifying potential faults or sudden changes in operating conditions. The rate of increase in deviation can be calculated by monitoring the main pressure data of the air compressor in real time and combining it with the target pressure value. Then, within a preset sliding window, the maximum and minimum values ​​of the pressure deviation and their corresponding time points are continuously calculated to obtain the deviation mutation value and mutation period, and finally calculate the rate of increase in deviation. Alternatively, differential or derivative calculation methods can be used to process the pressure deviation value sequence within the sliding window to estimate its rate of change more smoothly or accurately. A normal window refers to the time period within the sliding window where the rate of increase in the air compressor's pressure deviation is lower than a preset rate of increase threshold. Marking it as a normal window indicates that the air compressor's operating status is relatively stable within this time period, and pressure fluctuations are within an acceptable range, requiring no special anomaly handling. When the calculated rate of increase in deviation is less than the rate of increase threshold, the system marks the current sliding window's time range and related data as normal and can store it in the database for subsequent operating status analysis or model training. Alternatively, a specific status code or label can be assigned to the normal window for differentiation in data processing and control logic. An abnormal window refers to the time period within the sliding window where the rate of increase in the air compressor's pressure deviation reaches or exceeds the preset rate of increase threshold. Marking an abnormal window indicates that the air compressor pressure fluctuates abnormally drastically within this time period, potentially indicating a system malfunction, a sudden change in operating conditions, or the need for intervention. When the calculated rate of increase in deviation is greater than or equal to the rate of increase threshold, the system marks the current sliding window's time range and its related data as abnormal and triggers the corresponding abnormal handling process, such as recording the abnormal event, issuing an alarm, or preparing for optimized control; alternatively, a specific status code or label can be assigned to the abnormal window, and its start and end times can be recorded for subsequent traceability and analysis. The abnormal period is the time interval within the abnormal window where the pressure deviation value abruptly changes from its minimum to its maximum value or from its maximum value to its minimum value; that is, the abrupt change period.The abnormal time period precisely identifies the specific time range in which the pressure deviation changes drastically, providing more precise time positioning for subsequent fault diagnosis and optimized control. Once a sliding window is marked as an abnormal window, the system will further extract the abrupt change period within that abnormal window used to calculate the deviation rate increase and mark it as an abnormal time period; alternatively, the start and end timestamps of the abnormal time period can be recorded and associated with the abnormal window, so that data can be accurately selected in the subsequent generation of power anomaly sets, opening degree anomaly sets, and surrounding temperature anomaly sets.

[0038] The control optimization module is used to optimize and analyze the control of the air compressor. Based on the basic control in the new detection cycle, the power data, opening data, and winding temperature data of the air compressor are compared with the abnormal power range, abnormal opening range, and abnormal winding temperature range in real time. If the power data, opening data, and winding temperature data are within the abnormal power range, abnormal opening range, and abnormal winding temperature range, respectively, it is determined that the air compressor has entered a state of sudden increase in deviation. The pressure deviation replacement value YPx is obtained by the formula YPx=t1×YP, where YP is the value of the pressure deviation threshold, t1 is the proportional coefficient, and 0.85≤t1≤0.95. The pressure deviation replacement value YPx replaces the pressure deviation threshold value.

[0039] This application, based on real-time monitoring and deviation analysis of the air compressor and the generation of abnormal power, opening, and winding temperature ranges, further proposes an optimized control strategy to address the sudden increase in pressure deviation that may be caused by simultaneous anomalies in multiple parameters. When the control optimization module performs basic control within a new monitoring cycle, it monitors the air compressor's power, opening, and winding temperature data in real time. Once these three key operating parameters are detected to be simultaneously within their pre-defined abnormal ranges, the system immediately determines that the air compressor has entered a state of sudden deviation increase. This synchronous anomaly detection of multiple parameters, compared to single-parameter anomaly detection, provides a more comprehensive and accurate warning of potential system instability risks. To effectively address this sudden deviation increase, the control optimization module does not simply use the original pressure deviation threshold YP for basic control. Instead, it calculates a new, smaller pressure deviation replacement value YPx using a preset formula YPx=t1×YP. Here, t1 is a proportionality coefficient between 0.85 and 0.95, ensuring that YPx is always less than YP. Subsequently, the system replaces the original pressure deviation threshold YP with this calculated YPx. Through this numerical substitution, the basic control module (such as a PID controller) will use a more stringent YPx as the judgment criterion during subsequent frequency adjustment. This means that even a small pressure deviation, as long as it reaches or exceeds YPx, will immediately trigger frequency adjustment, allowing the air compressor to respond to pressure fluctuations earlier and more sensitively. This proactive and precise control adjustment can effectively suppress further expansion of pressure deviation, prevent the system from entering an unstable state, and thus improve the operational stability and energy-saving effect of the air compressor.

[0040] Example 2: Figure 2 As shown, the variable frequency energy-saving control method for air compressors based on sensor data acquisition includes the following steps: Step 1: Perform operational status detection and analysis on the air compressor: generate a detection cycle, perform basic control on the air compressor within the detection cycle, and adjust the frequency of the air compressor based on the pressure deviation value; Step 2: Analyze the pressure deviation pattern of the air compressor: At the end of the detection cycle, generate a sliding window with a duration of L1 seconds that slides from the beginning to the end of the detection cycle to analyze the abnormal range of power, abnormal range of opening degree, and abnormal range of winding temperature. Step 3: Optimize and analyze the control of the air compressor: Based on the basic control in the new detection cycle, compare the power data, opening data, and winding temperature data of the air compressor with the abnormal power range, abnormal opening range, and abnormal winding temperature range in real time, and adjust the pressure deviation threshold based on the comparison results.

[0041] The variable frequency energy-saving control system for air compressors, based on sensor data acquisition, generates a detection cycle during operation. Within this cycle, basic control is performed on the air compressor, adjusting its frequency according to pressure deviation values. At the end of the detection cycle, a sliding window of duration L1 seconds is generated and slides from the start to the end of the cycle to monitor abnormal power, opening, and winding temperature ranges. In the new detection cycle, based on the basic control, the system compares the air compressor's power, opening, and winding temperature data with the abnormal power, opening, and winding temperature ranges in real time, and adjusts the pressure deviation threshold based on the comparison results.

[0042] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0043] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0044] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An air compressor variable frequency energy-saving control system based on sensor data acquisition, characterized in that, It includes a real-time detection module, a deviation analysis module, and a control optimization module that are connected in sequence via communication. The real-time detection module, the deviation analysis module, and the control optimization module are all connected in communication with a database. The real-time detection module is used to detect and analyze the operating status of the air compressor: generate a detection cycle, and acquire the main pressure data, power data, opening degree data and winding temperature data of the air compressor in real time within the detection cycle; The air compressor is controlled based on the main pressure data. The deviation analysis module is used to analyze the pressure deviation pattern of the air compressor: at the end of the detection cycle, a sliding window with a duration of L1 seconds is generated and slides from the start to the end of the detection cycle to obtain the deviation growth rate of the sliding window. Abnormal windows and abnormal time periods are marked by the deviation growth rate; power anomaly set, opening degree anomaly set, and winding temperature anomaly set are generated; the power anomaly set is cleaned to obtain the power anomaly range; the opening degree anomaly set and winding temperature anomaly set are cleaned in the same way as the power anomaly set to obtain the opening degree anomaly range and winding temperature anomaly range, respectively. The control optimization module is used to perform optimized control analysis on the air compressor: based on the basic control in the new detection cycle, the power data, opening data, and winding temperature data of the air compressor are compared with the abnormal ranges of power, opening, and winding temperature in real time, and optimized control is performed accordingly.

2. The variable frequency energy-saving control system for air compressors based on sensor data acquisition according to claim 1, characterized in that, The main pressure data is the pressure value of the main pipeline of the air compressor, the power data is the operating power of the air compressor, the opening data is the opening degree of the air compressor's intake valve, and the winding temperature data is the temperature of the air compressor's motor windings.

3. The variable frequency energy-saving control system for air compressors based on sensor data acquisition according to claim 2, characterized in that, The specific process of basic control of the air compressor includes: retrieving the target pressure value and pressure deviation threshold from the database, marking the absolute value of the difference between the main pressure data and the target pressure value as the pressure deviation value, and comparing the pressure deviation value with the pressure deviation threshold: if the pressure deviation value is less than the pressure deviation threshold, it is determined that the air compressor does not have frequency regulation characteristics; if the pressure deviation value is greater than or equal to the pressure deviation threshold, it is determined that the air compressor has frequency regulation characteristics, and the frequency of the air compressor is adjusted by the PID controller.

4. The variable frequency energy-saving control system for air compressors based on sensor data acquisition according to claim 3, characterized in that, The process of obtaining the deviation growth rate of the sliding window includes: obtaining the difference between the maximum and minimum pressure deviation values ​​within the sliding window and marking it as the deviation mutation value; marking the time interval between the maximum and minimum pressure deviation values ​​as the mutation period; and marking the ratio of the deviation mutation value to the duration of the mutation period as the deviation growth rate.

5. The variable frequency energy-saving control system for air compressors based on sensor data acquisition according to claim 4, characterized in that, The specific process of marking abnormal windows and abnormal periods includes: obtaining the growth rate threshold from the database, comparing the deviation growth rate with the growth rate threshold; if the deviation growth rate is less than the growth rate threshold, the corresponding time window is marked as a normal window; if the deviation growth rate is greater than or equal to the growth rate threshold, the corresponding time window is marked as an abnormal window, and the sudden change period corresponding to the abnormal window is marked as an abnormal period.

6. The variable frequency energy-saving control system for air compressors based on sensor data acquisition according to claim 5, characterized in that, The generation process of the power anomaly set, the opening degree anomaly set, and the winding temperature anomaly set includes: selecting several abnormal time points within the abnormal period, acquiring the power data, opening degree data, and winding temperature data of the air compressor at the abnormal time points, and marking them as power anomaly data, opening degree anomaly data, and winding temperature anomaly data, respectively. The power anomaly set, the opening degree anomaly set, and the winding temperature anomaly set are respectively formed by the power anomaly data, the opening degree anomaly data, and the winding temperature anomaly data corresponding to all abnormal time points within the detection cycle.

7. The variable frequency energy-saving control system for air compressors based on sensor data acquisition according to claim 6, characterized in that, The specific process of data cleaning for the power anomaly set includes: calculating the variance of all elements in the power anomaly set to obtain the power distribution coefficient; obtaining the power distribution threshold from the database; comparing the power distribution coefficient with the preset power distribution threshold; if the power distribution coefficient is greater than or equal to the power distribution threshold, removing the largest and smallest elements in the power anomaly set, and then recalculating the power distribution coefficient, and so on, until the power distribution coefficient is less than the power distribution threshold; if the power distribution coefficient is less than the power distribution threshold, the largest and smallest elements retained in the power anomaly set constitute the power anomaly range.

8. The variable frequency energy-saving control system for air compressors based on sensor data acquisition according to claim 7, characterized in that, The specific process of optimized control includes: if the power data, opening degree data, and winding temperature data are within the abnormal range of power, opening degree, and winding temperature respectively, it is determined that the air compressor has entered a state of sudden increase in deviation. The pressure deviation replacement value YPx is obtained by the formula YPx=t1×YP, where YP is the value of the pressure deviation threshold, t1 is the proportional coefficient, and 0.85≤t1≤0.

95. The pressure deviation replacement value YPx is used to replace the pressure deviation threshold.

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