Real-time humidity monitoring system for air compressor pipeline

Abstract

The invention relates to the technical field of humidity monitoring, in particular to a real-time humidity monitoring system for an air compressor pipeline, which is characterized in that a signal acquisition module acquires humidity, pressure and temperature parameters in the air compressor pipeline in real time; the data processing module utilizes a dynamic compensation mechanism to generate an instantaneous error correction value according to pressure and temperature fluctuation so as to suppress instantaneous interference, meanwhile utilizes a long-term drift suppression mechanism to analyze historical data to generate a long-term drift correction value so as to compensate slow change of sensor performance, and fuses the instantaneous error correction value and the long-term drift correction value to obtain a high-precision correction humidity parameter; and the prediction and maintenance module predicts the system state and generates a predictive maintenance instruction based on the correction parameter and the long-term trend data, so that the problem of humidity measurement misalignment caused by environmental fluctuation and sensor drift is effectively solved, and the whole-process guarantee from accurate monitoring to prospective maintenance is realized.

Description

Technical Field

[0001] This invention relates to the field of humidity monitoring technology, specifically to a real-time humidity monitoring system for air compressor pipelines. Background Technology

[0002] In the industrial sector, air compressors are critical power equipment, and humidity monitoring of their piping systems is crucial for ensuring system reliability and preventing condensation, corrosion, and equipment damage. Traditional humidity monitoring solutions primarily rely on humidity sensors directly installed within the piping for measurement, using the readings directly for condition assessment and maintenance decisions. However, the interior of an air compressor piping is a dynamically changing environment. Pressure and temperature parameters fluctuate frequently due to the compressor's operating cycle, load changes, and external factors. These fluctuations significantly affect the measurement accuracy of humidity sensors. For example, pressure changes may alter the physical properties of the sensor's sensitive elements, while temperature fluctuations can cause the sensor material to expand or contract, introducing deviations in instantaneous measurements and preventing humidity readings from accurately reflecting actual conditions.

[0003] Furthermore, humidity sensors inevitably experience performance drift during long-term continuous operation. This is due to aging, contamination, or cumulative damage to sensor elements. Even if the actual humidity is stable, the measured value will slowly deviate from the true value over time, forming a trend error. Existing technologies typically address these issues using periodic calibration or simple static compensation methods. However, periodic calibration requires interrupting system operation, impacting production efficiency, while static compensation cannot adapt to dynamic operating conditions and cannot effectively handle the combined effects of instantaneous fluctuations and long-term drift. This results in poor real-time performance and insufficient accuracy in humidity monitoring data. Maintenance decisions based on inaccurate data are often delayed or erroneous, failing to achieve true predictive maintenance and increasing system failure risks and maintenance costs. Therefore, existing technologies have significant shortcomings in humidity monitoring of air compressor pipelines and cannot meet the high-reliability monitoring requirements of industrial intelligence. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time humidity monitoring system for air compressor pipelines to solve the problems mentioned in the background art. The specific technical problems include how to use dynamic compensation mechanisms and long-term drift suppression mechanisms to correct humidity parameters in real time, so as to solve the measurement error problems caused by pressure and temperature fluctuations and long-term sensor drift in air compressor pipeline humidity monitoring.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This real-time humidity monitoring system for air compressor pipelines includes a signal acquisition module, a data processing module, and a prediction and maintenance module, wherein: The signal acquisition module acquires humidity, pressure, and temperature parameters within the air compressor piping. Humidity is obtained using a capacitive humidity sensor; pressure using a piezoresistive pressure sensor; and temperature using a platinum resistance temperature sensor. This module not only obtains the core parameter to be calibrated (humidity) but also accurately captures real-time data from the two main sources of interference (pressure and temperature parameters) that cause instantaneous errors in humidity measurement. This provides an indispensable and interconnected raw input dataset for the dynamic compensation and long-term drift suppression mechanisms in the backend data processing module, laying a solid foundation for the entire system to achieve high-precision humidity calibration.

[0006] The dynamic compensation mechanism unit in the data processing module uses a dynamic compensation mechanism to generate an instantaneous error correction amount for the humidity parameter based on the fluctuations in pressure and temperature parameters. The specific process includes: Identify fluctuations in pressure and temperature parameters, including both the direction and magnitude of the fluctuations. Based on the fluctuation of the pressure parameter, the pressure error component of the humidity parameter is calculated; Based on the fluctuation of temperature parameters, the temperature error component of humidity parameters is calculated; the calculation process of the pressure error component of humidity parameters is to read the instantaneous value of the current pressure parameter, compare it with the set reference pressure baseline, determine the direction and amplitude of fluctuation, and output the pressure error component of humidity parameters according to the preset mapping relationship in the internal model of the dynamic compensation mechanism. The pressure error component of the humidity parameter and the temperature error component of the humidity parameter are algebraically superimposed, and a preset coupling factor is applied to quantify the interaction between the two. The value obtained after superposition and coupling correction is used as the instantaneous error correction amount.

[0007] The core function of the dynamic compensation mechanism unit is to solve the interference problem caused by instantaneous fluctuations in pressure and temperature parameters within the air compressor pipeline on humidity measurement. This unit identifies the direction and amplitude of pressure and temperature parameter fluctuations in real time, and calculates the corresponding pressure error components and temperature error components of the humidity parameter according to an internally preset mapping model. Subsequently, it algebraically superimposes these two error components and introduces a coupling factor to quantify the interaction between pressure and temperature fluctuations, ultimately generating a comprehensive instantaneous error correction amount. The effect of this mechanism is that it can offset the instantaneous measurement deviation introduced by dynamic changes in environmental parameters in real time, significantly improving the instantaneous response accuracy and measurement authenticity of humidity parameters under dynamic operating conditions.

[0008] The long-term drift suppression mechanism unit in the data processing module utilizes the long-term drift suppression mechanism to perform trend analysis based on stored historical sensor signal data, generating a long-term drift correction amount for the humidity parameter. The specific process includes: Trend analysis is performed on stored historical sensor signal data containing humidity, pressure, and temperature parameters. Short-term changes in humidity parameters caused by daily fluctuations in pressure and temperature parameters are removed, and the slow, over-time trend of humidity parameters is extracted as long-term humidity trend data. The generation process of long-term humidity trend data involves constructing a dynamic model to predict short-term theoretical values ​​of humidity parameters based on instantaneous values ​​of pressure and temperature parameters; comparing actual humidity parameter values ​​with short-term theoretical values ​​to obtain a difference sequence; and applying a moving average to the difference sequence to obtain the long-term humidity trend data. Based on long-term humidity variation trend data, a long-term drift curve is established using mathematical fitting methods. Based on the current shape and slope of the long-term drift curve, the long-term drift correction amount is calculated in reverse.

[0009] The long-term drift suppression mechanism unit aims to address the measurement benchmark inaccuracy caused by the slow performance degradation (i.e., long-term drift) of humidity sensors due to long-term use. This unit performs trend analysis on stored historical sensor data (including humidity, pressure, and temperature). First, it constructs a dynamic model to predict short-term humidity values ​​based on instantaneous pressure and temperature. By comparing the actual humidity values ​​with the model predictions, a difference sequence is obtained. This sequence is then processed using a moving average to remove short-term fluctuations and accurately separate the long-term humidity trend data, which reflects only the slow changes in the sensor's own performance. Based on this trend data, the unit establishes a long-term drift curve through mathematical fitting and inversely calculates the long-term drift correction amount. The effect is to effectively compensate for the slow drift of the sensor over time, restoring the long-term accuracy and benchmark reliability of humidity measurements.

[0010] The correction unit in the data processing module merges the instantaneous error correction and the long-term drift correction to correct the humidity parameters, resulting in corrected humidity parameters, specifically including: First, the long-term drift correction is algebraically added to the original measured humidity parameter to obtain an intermediate humidity parameter; Then, the instantaneous error correction amount is algebraically added to the intermediate humidity parameter to obtain the corrected humidity parameter.

[0011] The calibration unit plays a crucial role in fusing the outputs of dynamic compensation and long-term drift suppression mechanisms to achieve the final calibration of the original humidity parameter. This unit performs calculations in a specific order: first, it adds the long-term drift correction calculated by the long-term drift suppression mechanism unit to the original humidity parameter to correct the reference drift, obtaining an intermediate humidity parameter; then, it adds the instantaneous error correction generated by the dynamic compensation mechanism unit to this intermediate humidity parameter to correct for instantaneous fluctuations. This series calibration method effectively corrects both the systematic errors caused by the slow aging of the sensor and the random errors introduced by environmental fluctuations in real time, ultimately outputting a high-precision calibrated humidity parameter that simultaneously reflects the actual operating conditions and possesses long-term stability.

[0012] The prediction and maintenance module uses the long-term drift suppression mechanism to perform trend analysis and obtains long-term humidity trend data. Based on the corrected humidity parameters and the long-term humidity trend data, it predicts the status of the piping system and generates predictive maintenance instructions, specifically including: A deviation threshold and a fixed monitoring time window are preset; The current corrected humidity parameter is compared with the expected value inferred from the long-term humidity change trend data at the current moment, and the real-time deviation between the two is calculated. Monitor the behavior of real-time deviation within the monitoring time window; If the real-time deviation is lower than the deviation threshold, the pipeline system is predicted to be in stable operation. If the real-time deviation exceeds the deviation threshold for a period of time equal to or exceeding the entire monitoring time window, the pipeline system is predicted to be in an abnormal state. Calculate the rate of change of the long-term humidity trend data itself. If the rate of change is found to be increasing or the trend direction is reversed, it is predicted that the pipeline system is experiencing performance degradation. If the real-time deviation exceeds the deviation threshold for a period of time equal to or exceeding the entire monitoring time window, and the rate of change shows an increasing trend or the trend direction reverses, then a specific fault is predicted in the pipeline system. When a specific fault is predicted in the pipeline system, a corresponding predictive maintenance instruction is generated based on the fault-instruction mapping rule base.

[0013] The predictive maintenance module utilizes precisely calibrated humidity data (calibrated humidity parameters) and their long-term trends to predict the condition of the piping system and perform proactive maintenance. This module continuously compares the real-time calibrated humidity parameters with the expected values ​​inferred from long-term trend data, calculates the real-time deviation, and monitors the behavior of this deviation within a preset time window. By analyzing whether the deviation continuously exceeds a threshold and the rate and direction of change in the long-term trend, the module can intelligently determine whether the system is in a stable operating state, an abnormal state, performance degradation, or a specific fault, and trigger corresponding predictive maintenance commands. Its technical effect is to transform high-precision calibration data into valuable system health insights, thereby achieving early warning and precise maintenance of potential faults, fundamentally solving the problem of delayed or misjudged maintenance decisions caused by inaccurate measurement data.

[0014] Compared with the prior art, the beneficial effects of the present invention are: By integrating a dual correction mechanism of dynamic compensation and long-term drift suppression, it can automatically and in real time offset instantaneous measurement errors caused by fluctuations in pipeline pressure and temperature, and simultaneously compensate for long-term drift caused by the slow degradation of sensor performance. This solves the problem of inaccurate humidity data in traditional monitoring methods, and ultimately outputs continuously high-precision corrected humidity parameters. This provides key data support for achieving reliable predictive maintenance based on real data, and improves the reliability, safety and maintenance efficiency of the air compressor pipeline system. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall modules of the present invention; Figure 2 This is a schematic diagram of the core process of the data processing module of the present invention; Figure 3 This is a schematic diagram of the data processing module of the present invention.

[0016] In the diagram: 100, signal acquisition module; 200, data processing module; 201, dynamic compensation mechanism unit; 202, long-term drift suppression mechanism unit; 203, correction unit; 300, prediction and maintenance module. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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] Next, please refer to Figure 1The present invention provides a technical solution: a real-time humidity monitoring system for air compressor pipelines, including a signal acquisition module 100, a data processing module 200, and a prediction and maintenance module 300.

[0019] The signal acquisition module 100 acquires humidity, pressure, and temperature parameters within the air compressor piping, specifically including: For acquiring humidity parameters, a humidity sensor based on the capacitive measurement principle is used. This sensor features fast response and good stability. It is directly installed in an appropriate position in the pipeline, such as through a flange connection or embedded interface, so that the sensor element is in full contact with the airflow in the pipeline. It can detect changes in the water vapor content in the air in real time. The humidity sensor outputs an analog electrical signal that is proportional to the relative humidity value. This signal is transmitted to the signal conditioning circuit through a shielded cable for preliminary amplification and filtering to eliminate environmental noise and interference and improve signal quality. For obtaining pressure parameters, an integrated piezoresistive pressure sensor is used. This sensor is manufactured using microelectromechanical technology and can sensitively sense fluctuations in gas pressure in the pipeline. The pressure sensor is fixed to the pressure measuring point in the pipeline by threads or flanges. Its sensing diaphragm deforms under pressure and outputs a corresponding electrical signal. The signal is then differentially amplified and linearized to ensure the accuracy of the pressure data and its anti-interference ability. Temperature parameters are obtained by relying on a platinum resistance temperature sensor, which is placed in a representative area of ​​the pipeline, such as near the humidity monitoring point, to accurately reflect changes in gas temperature. The temperature sensor outputs an analog signal based on the correspondence between resistance value or thermoelectric potential and temperature. The signal is preprocessed by cold junction compensation and calibration circuits to reduce measurement errors. The output signals of all sensors are acquired synchronously. The system is equipped with a multiplexer and a high-speed analog-to-digital converter to convert analog signals into digital quantities. It also features self-testing and drift correction functions and performs automatic calibration of the sensors periodically to maintain long-term stability. The acquired humidity, pressure, and temperature parameters are output in real time in digital form, forming a complete data stream that provides a reliable foundation for subsequent processing modules. The entire signal acquisition process emphasizes real-time performance, synchronization, and anti-interference capabilities to ensure high-quality monitoring data can be obtained even in complex industrial environments.

[0020] Please see Figure 2-3 The dynamic compensation mechanism unit 201 in the data processing module 200 uses a dynamic compensation mechanism to generate an instantaneous error correction amount for the humidity parameter based on the fluctuations of pressure and temperature parameters. Specifically, this includes: The dynamic compensation mechanism has a built-in internal model that defines the instantaneous impact of pressure and temperature parameter fluctuations on humidity readings. The mechanism first synchronously and continuously monitors the pressure and temperature parameters and identifies their fluctuations, including the direction and amplitude of the fluctuations. Based on the parallel execution of this internal model, on the one hand, a corresponding humidity parameter pressure error component is calculated according to the fluctuation of the pressure parameter; on the other hand, another humidity parameter temperature error component is calculated according to the fluctuation of the temperature parameter. When calculating the humidity parameter pressure error component, the dynamic compensation mechanism first reads the instantaneous value of the current pressure parameter and compares it with a set reference pressure baseline to determine whether the pressure fluctuation is positive or negative, and the specific amplitude of the fluctuation. Subsequently, according to the mapping relationship of the internal model, this specific fluctuation direction and amplitude are used as input, and through a built-in calculation rule, a numerical value with a corresponding sign and magnitude is directly output; this numerical value is the humidity parameter pressure error component. Similarly, when calculating the humidity parameter temperature error component, the same logical process is used: the current temperature parameter is read, its fluctuation direction and amplitude relative to the temperature reference baseline are determined, and according to the mapping relationship in the model specifically for temperature influence, another numerical value with a corresponding sign and magnitude is calculated; this is the humidity parameter temperature error component. The calculation processes of these two components are parallel and independent, quantifying the purely theoretical impact of single physical parameter fluctuations on humidity measurement.

[0021] Subsequently, the data fusion step is performed, where the pressure error component and the temperature error component of the humidity parameter are algebraically superimposed, and a preset coupling factor is applied to quantify the interaction between the two. The value obtained after superposition and coupling correction is the instantaneous error correction amount. Since the changes in pressure and temperature within the pipeline are not completely independent, there may be interrelated physical effects. Therefore, simple algebraic superposition may not fully reflect the true comprehensive error. To address this, the dynamic compensation mechanism introduces a preset coupling factor to accurately quantify this interaction. This coupling factor is a coefficient predetermined based on numerous experiments or deep physical laws. It describes the additional or offsetting effects on humidity measurement when pressure and temperature fluctuations act together under specific operating conditions. The specific operation of applying this coupling factor involves multiplying the aforementioned preliminary composite correction amount with the coupling factor. The result of this operation is equivalent to a weighted correction of the preliminary composite correction amount, thereby quantifying the synergistic or antagonistic effects between pressure and temperature fluctuations and incorporating them into the total correction amount. Finally, the value obtained after this coupling correction is the instantaneous error correction amount that can comprehensively and accurately offset the current comprehensive dynamic interference. The algebraic sign of the instantaneous error correction determines the direction of compensation, and its absolute value determines the compensation amount. Its function is to directly and in real time offset the instantaneous measurement deviation of the humidity parameter caused by the fluctuation of the current pressure and temperature parameters.

[0022] The long-term drift suppression mechanism unit 202 in the data processing module 200 utilizes a long-term drift suppression mechanism to perform trend analysis based on stored historical sensor signal data, generating a long-term drift correction amount for the humidity parameter, specifically including: The long-term drift suppression mechanism calls upon stored historical sensor signal data, which includes historical records of humidity, pressure, and temperature parameters over long-term operation. Trend analysis is then performed on this data. The trend analysis first separates short-term changes in humidity parameters caused by daily fluctuations in pressure and temperature parameters from the historical sensor signal data, thus revealing the slow, long-term trend of humidity changes over time. The specific steps of the trend analysis process include: First, the mechanism calls upon time-series data of humidity, pressure, and temperature parameters recorded synchronously over a historical period. Then, it constructs a dynamic model to predict short-term theoretical values ​​of humidity based on instantaneous pressure and temperature values. This model reflects reasonable fluctuations in humidity readings caused only by changes in the gas state, under ideal sensor performance. Next, the analysis compares the actual recorded humidity parameter value at each moment with the short-term theoretical value calculated by the dynamic model at that moment; the difference sequence is the residual signal after excluding the influence of short-term pressure and temperature fluctuations. Finally, a low-pass filter or moving average is applied to this residual signal sequence to filter out all high-frequency random noise and short-term fluctuations. The resulting low-frequency, smooth signal change trajectory represents the long-term humidity trend data, stripped of external interference and purely driven by sensor drift.

[0023] Subsequently, based on the extracted long-term humidity trend data, a long-term drift curve describing its unidirectional, slow shift is established using mathematical fitting methods. This curve clearly quantifies the degree and direction of the humidity sensor's measurement value deviating from its initial reference. Specifically, a predefined function library (e.g., linear, polynomial, or exponential functions) is traversed to calculate the overall deviation between each function form and the long-term humidity trend data. Through iterative optimization, the function with the highest fit is finally selected, and the graph of this function is the long-term drift curve. This curve no longer contains random fluctuations in the data but clearly reveals, in the form of a defined mathematical expression, whether the overall direction of the humidity parameter drift is unidirectionally increasing, decreasing, or accelerating, and quantifies the rate of drift, thus transforming the historical trend into a predictable mathematical model.

[0024] Next, based on the current shape and slope of the long-term drift curve, a compensation value is calculated in reverse. This value is the long-term drift correction. The algebraic sign of the long-term drift correction is opposite to the drift direction, and its value is set to exactly offset the drift error accumulated to date. The current shape of the long-term drift curve indicates the cumulative drift of the sensor measurement value relative to the initial reference, while the slope of the curve indicates the instantaneous rate of drift at the current moment. In the calculation, an initial calibration point is first determined as the reference zero value. Then, the current time point is substituted into the mathematical expression of the long-term drift curve to calculate the function value corresponding to the curve at that moment. This value is the estimated cumulative deviation of the sensor due to long-term drift. Finally, the long-term drift correction is set to a value that is equal in magnitude but opposite in sign to this estimated cumulative deviation. In this way, when this correction is algebraically added to the currently measured humidity parameter, it can, in principle, directly offset all the systematic drift errors accumulated from the initial moment to the current moment, allowing the measured value to return to the initial calibration reference.

[0025] Ultimately, this long-term drift correction is output and used to systematically correct the baseline value of the humidity parameter in subsequent corrections, thereby combating the slow degradation of sensor performance and ensuring the long-term accuracy of measurement data.

[0026] The correction unit 203 in the data processing module 200 integrates the instantaneous error correction amount with the long-term drift correction amount to correct the humidity parameter, obtaining the corrected humidity parameter, specifically including: First, a long-term drift correction is applied to the humidity parameter. This step aims to correct for reference point shifts caused by sensor material aging or slow contamination. Specifically, the long-term drift correction is algebraically added to the originally measured humidity parameter to obtain an intermediate humidity parameter that eliminates long-term systematic bias. Subsequently, an instantaneous error correction is applied to this intermediate humidity parameter. This step is specifically designed to counteract dynamic disturbances introduced by rapid fluctuations in pressure and temperature parameters. The instantaneous error correction is algebraically added to the intermediate humidity parameter. This sequence ensures that a correct long-term measurement reference is established first, and then the effects of short-term fluctuations are eliminated on this basis. Finally, the value obtained after these two consecutive algebraic superposition corrections is the corrected humidity parameter. This corrected humidity parameter has both instantaneous accuracy and long-term stability, accurately reflecting the true humidity conditions inside the pipeline.

[0027] The prediction and maintenance module 300 uses the long-term drift suppression mechanism to perform trend analysis and obtains long-term humidity change trend data. Based on the corrected humidity parameters and the long-term humidity change trend data, it predicts the status of the piping system and generates predictive maintenance instructions, specifically including: A deviation threshold and a fixed monitoring time window are preset. The currently obtained corrected humidity parameter is compared with the expected value inferred from the long-term humidity change trend data at the current moment, and the real-time deviation between the two is calculated. The behavior of this real-time deviation is monitored within the monitoring time window. If the real-time deviation is below the deviation threshold, the pipeline system is predicted to be in a stable state. If the real-time deviation exceeds the deviation threshold for a period of time equal to or exceeding the entire monitoring time window, the pipeline system is predicted to be in an abnormal state. At the same time, analyze the rate of change of the long-term humidity trend data itself. If the rate of change is found to be increasing or the trend direction is reversed, it is predicted that the pipeline system is experiencing performance degradation. By combining the anomaly assessment of real-time deviations with the rate of change analysis of trend data, the status of the pipeline system is predicted. When both real-time deviation anomalies and trend data indicating performance degradation are met, the predicted status is a specific fault in the pipeline system, such as desiccant failure or interface leakage. Based on this predicted status, corresponding specific predictive maintenance instructions are generated. The instructions are directly associated with the predicted specific fault, such as "replace the desiccant" or "tighten the leaking interface bolts." The core of this process lies in a pre-defined and unambiguous fault-instruction mapping rule base. After predicting the pipeline system status and outputting a specific predicted status, the mapping rule base is immediately queried. The rule base pre-stores the unique, executable operation instructions corresponding to each specific predicted status. This mapping relationship is direct and deterministic. For example, when the predicted status is "desiccant failure", the instruction bound to it in the rule base is "replace desiccant"; when the predicted status is "interface leakage", the bound instruction is "tighten the leaking interface bolts". After the query and matching are completed, the module automatically outputs the specific text instruction as the final predictive maintenance instruction. The whole process does not require manual intervention, ensuring a seamless connection from status identification to maintenance action suggestions. The instruction content is directly derived from and completely corresponds to the specific fault diagnosed, thereby achieving accurate predictive maintenance.

[0028] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time humidity monitoring system for air compressor pipelines, characterized in that, It includes a signal acquisition module (100), a data processing module (200), and a prediction and maintenance module (300), wherein: The signal acquisition module (100) acquires the humidity, pressure and temperature parameters in the air compressor pipeline; The data processing module (200) uses a dynamic compensation mechanism to generate an instantaneous error correction amount for the humidity parameter based on the fluctuation of the pressure and temperature parameters; it uses a long-term drift suppression mechanism to perform trend analysis based on stored historical sensor signal data to generate a long-term drift correction amount for the humidity parameter; it merges the instantaneous error correction amount with the long-term drift correction amount to correct the humidity parameter and obtain the corrected humidity parameter. The prediction and maintenance module (300) calls the long-term drift suppression mechanism to perform trend analysis and obtains the long-term humidity change trend data. Based on the corrected humidity parameter and the long-term humidity change trend data, it predicts the status of the pipeline system and generates predictive maintenance instructions.

2. The real-time humidity monitoring system for air compressor pipelines according to claim 1, characterized in that, The humidity parameter is acquired using a capacitive humidity sensor; the pressure parameter is acquired using a piezoresistive pressure sensor; and the temperature parameter is acquired using a platinum resistance temperature sensor.

3. The real-time humidity monitoring system for air compressor pipelines according to claim 1, characterized in that, The data processing module (200) includes a dynamic compensation mechanism unit (201), and the process by which the dynamic compensation mechanism unit (201) generates instantaneous error correction amounts specifically includes: Identify the fluctuations in the pressure and temperature parameters, wherein the fluctuations include the direction and amplitude of the fluctuations; Based on the fluctuation of the pressure parameters, the pressure error component of the humidity parameters is calculated; Based on the fluctuation of the temperature parameter, the temperature error component of the humidity parameter is calculated; The pressure error component of the humidity parameter and the temperature error component of the humidity parameter are algebraically superimposed, and a preset coupling factor is applied to quantify the interaction between the two. The value obtained after superposition and coupling correction is used as the instantaneous error correction amount.

4. The real-time humidity monitoring system for air compressor pipelines according to claim 1, characterized in that, The data processing module (200) includes a long-term drift suppression mechanism unit (202). The long-term drift suppression mechanism unit (202) generates a long-term drift correction amount for the humidity parameter using a long-term drift suppression mechanism, specifically including: Trend analysis is performed on the stored historical sensor signal data containing humidity, pressure, and temperature parameters. Short-term changes in humidity parameters caused by daily fluctuations in pressure and temperature parameters are removed, and the slow change trend of humidity parameters over time is separated as the long-term humidity change trend data. Based on the long-term humidity change trend data, a long-term drift curve is established using mathematical fitting methods. The long-term drift correction amount is calculated in reverse based on the current shape and slope of the long-term drift curve.

5. The real-time humidity monitoring system for air compressor pipelines according to claim 1, characterized in that, The data processing module (200) includes a correction unit (203), and the process by which the correction unit (203) generates corrected humidity parameters specifically includes: First, the long-term drift correction is algebraically added to the originally measured humidity parameter to obtain an intermediate humidity parameter; The instantaneous error correction amount is then algebraically added to the intermediate humidity parameter to obtain the corrected humidity parameter.

6. The real-time humidity monitoring system for air compressor pipelines according to claim 1, characterized in that, The prediction and maintenance module (300) predicts the status of the pipeline system and generates predictive maintenance instructions, specifically including: A deviation threshold and a fixed monitoring time window are preset; The currently obtained corrected humidity parameter is compared with the expected value inferred from the long-term humidity change trend data at the current moment, and the real-time deviation between the two is calculated. Monitor the behavior of the real-time deviation within the monitoring time window.

7. The real-time humidity monitoring system for air compressor pipelines according to claim 1, characterized in that, The predicted pipeline system status includes: If the real-time deviation is lower than the deviation threshold, the pipeline system is predicted to be in stable operation. If the real-time deviation exceeds the deviation threshold for a period of time equal to or exceeding the entire monitoring time window, the pipeline system state is predicted to be abnormal. Calculate the rate of change of the long-term humidity trend data itself. If the rate of change is found to be increasing or the trend direction is reversed, it is predicted that the pipeline system is experiencing performance degradation. If the real-time deviation exceeds the deviation threshold for a period of time equal to or exceeding the entire monitoring time window, and the rate of change shows an increasing trend or the trend direction reverses, then it is predicted that there is a specific fault in the pipeline system.

8. The real-time humidity monitoring system for air compressor pipelines according to claim 1, characterized in that, When a specific fault exists in the predicted pipeline system, a corresponding predictive maintenance instruction is generated based on the fault-instruction mapping rule base.

9. The real-time humidity monitoring system for air compressor pipelines according to claim 4, characterized in that, The process of generating the long-term humidity change trend data specifically includes: Construct a dynamic model to predict short-term theoretical values ​​of humidity parameters based on instantaneous values ​​of pressure and temperature parameters; By comparing actual humidity parameter values ​​with short-term theoretical values, a difference sequence is obtained; The long-term humidity trend data is obtained by performing a moving average on the difference series.

10. The real-time humidity monitoring system for air compressor pipelines according to claim 3, characterized in that, The calculation process for the pressure error component of the humidity parameter specifically includes: The instantaneous value of the current pressure parameter is read and compared with the set reference pressure baseline to determine the fluctuation direction and fluctuation amplitude. The humidity parameter pressure error component is output according to the preset mapping relationship in the internal model of the dynamic compensation mechanism.

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