A cloud service platform-based air compressor management method and system

Abstract

This invention relates to the field of equipment load adjustment technology, and provides an air compressor management method and system based on a cloud service platform. The method includes: acquiring real-time exhaust pressure, real-time air source moisture content, real-time intake flow rate, real-time exhaust flow rate, and real-time gas humidity from a target air compressor, and matching primary load adjustment parameters to the target air compressor based on the real-time exhaust pressure. This invention achieves precise optimization of compressor load adjustment by acquiring the real-time exhaust pressure, real-time air source moisture content, real-time intake flow rate, real-time exhaust flow rate, and real-time gas humidity of the target air compressor, combined with a preset air source moisture content benchmark model and historical operating logs. Through real-time calculated deviation quantification indicators, the load adjustment parameters are gradually corrected, ultimately generating dynamically adapted final load adjustment parameters, thereby achieving efficient management of complex compressor operating conditions.

Description

Technical Field

[0001] This invention belongs to the field of equipment load adjustment technology, and in particular relates to an air compressor management method and system based on a cloud service platform. Background Technology

[0002] Currently, air compressor load adjustment technology mainly relies on real-time monitoring and local control systems at the equipment end. This involves collecting real-time exhaust pressure and dynamically adjusting it using proportional-integral-derivative (PID) control algorithms. While this method is effective in simple scenarios with single-parameter monitoring, it often suffers from insufficient adaptability in complex operating conditions, such as significant humidity fluctuations or changes in equipment operating efficiency. Furthermore, although existing technologies can monitor more parameters using humidity and flow sensors, they often only process single-point data, lacking collaborative analysis and real-time optimization of multi-dimensional parameters.

[0003] With the application of cloud platform services, some technologies have begun to attempt to upload equipment operating data to the cloud, using the cloud's computing power to store and analyze historical operating data. This method provides a foundation for data integration across devices and scenarios. However, the current application of cloud service platforms in air compressor load management is relatively rudimentary, mainly limited to centralized data storage and single-state monitoring. It fails to fully explore the potential of combining real-time data with historical benchmark data, particularly in the analysis and optimization of dynamic parameters such as air source moisture content and air flow rate variation ratios. This limitation makes it difficult for existing technologies to achieve precise management and load adjustment of compressor operating status under complex conditions. Summary of the Invention

[0004] The purpose of this invention is to provide an air compressor management method and system based on a cloud service platform, which aims to solve the problems mentioned in the background art.

[0005] This invention is implemented as follows: an air compressor management method based on a cloud service platform, the method comprising:

[0006] The system acquires real-time discharge pressure, real-time air source moisture content, real-time intake flow rate, real-time discharge flow rate, and real-time gas humidity from the target air compressor, and matches primary load adjustment parameters to the target air compressor based on the real-time discharge pressure.

[0007] Obtain the preset air source moisture content benchmark model of the target air compressor, extract the benchmark air source moisture content corresponding to the real-time gas humidity, and calculate the first deviation quantification index of the real-time air source moisture content compared with the benchmark air source moisture content. Based on the first deviation quantification index, perform preliminary correction on the primary load adjustment parameters to generate secondary load adjustment parameters.

[0008] Retrieve the historical operating log of the target air compressor, find the historical exhaust flow corresponding to the real-time intake flow when the target air compressor is in the initial operating condition of a new machine, and calculate the standard air flow change ratio.

[0009] The real-time air flow rate change ratio of the target air compressor is calculated based on the real-time intake air flow rate and the real-time exhaust air flow rate, and then compared with the standard air flow rate change ratio to obtain the second deviation quantification index. Based on the second deviation quantification index, the secondary load adjustment parameters are corrected to obtain the final load adjustment parameters.

[0010] As a further limitation of the technical solution of the present invention, the preset air source moisture content benchmark model refers to a standard model established based on the relationship between gas humidity and air source moisture content. The preset air source moisture content benchmark model defines the corresponding benchmark air source moisture content for the target air compressor under different gas humidity conditions.

[0011] As a further limitation of the technical solution of this embodiment of the invention, the steps of obtaining a preset air source moisture content benchmark model of the target air compressor, extracting the benchmark air source moisture content corresponding to the real-time gas humidity, calculating a first deviation quantification index of the real-time air source moisture content compared to the benchmark air source moisture content, and performing preliminary correction on the primary load adjustment parameters based on the first deviation quantification index to generate the secondary load adjustment parameters include:

[0012] Obtain the preset air source moisture content benchmark model of the target air compressor, and match the benchmark air source moisture content corresponding to the real-time gas humidity.

[0013] Retrieve the calculation formula for the first indicator, and calculate the first deviation quantification index together with the calculation formula for the first indicator, the baseline gas source moisture content, and the real-time gas source moisture content;

[0014] The secondary load adjustment parameters are generated by multiplying the primary load adjustment parameters by the first deviation quantification index.

[0015] As a further limitation of the technical solution of this embodiment of the invention, the formula for calculating the first index is: Where I1 refers to the first deviation quantification index, R refers to the real-time gas source moisture content, and B refers to the baseline gas source moisture content.

[0016] As a further limitation of the technical solution of the present invention, the initial operating condition of the new machine refers to the initial operating stage of the target air compressor after it leaves the factory and the running time has not exceeded the set cycle. At this time, the target air compressor is in the ideal state of design performance.

[0017] As a further limitation of the technical solution of this invention, the steps of calculating the real-time air flow rate change ratio of the target air compressor based on the real-time intake flow rate and the real-time exhaust flow rate, comparing it with the standard air flow rate change ratio to obtain a second deviation quantification index, and correcting the secondary load adjustment parameters based on the second deviation quantification index to obtain the final load adjustment parameters include:

[0018] Obtain the real-time intake flow rate and real-time exhaust flow rate, and calculate the real-time air flow rate change ratio of the target air compressor by dividing the real-time intake flow rate by the real-time exhaust flow rate;

[0019] The second indicator calculation formula is retrieved, and the second deviation quantification index is calculated together with the real-time intake flow, the historical exhaust flow corresponding to the real-time intake flow, the real-time exhaust flow, and the second indicator calculation formula.

[0020] Multiply the secondary load adjustment parameter by the secondary load adjustment parameter to generate the final load adjustment parameter.

[0021] As a further limitation of the technical solution of this embodiment of the invention, the formula for calculating the second index is: Where I2 refers to the second deviation quantification index, X refers to the real-time air flow rate change ratio, and Y refers to the standard air flow rate change ratio. The standard air flow rate change ratio is calculated by dividing the real-time intake air flow rate by its corresponding historical exhaust air flow rate.

[0022] An air compressor management system based on a cloud service platform, the system comprising: a data acquisition module, a secondary load adjustment parameter generation module, a standard air flow rate change ratio calculation module, and a final load adjustment parameter generation module, wherein:

[0023] The data acquisition module is used to acquire real-time exhaust pressure, real-time air source moisture content, real-time intake flow rate, real-time exhaust flow rate, and real-time gas humidity from the target air compressor, and to match primary load adjustment parameters for the target air compressor based on the real-time exhaust pressure.

[0024] The secondary load adjustment parameter generation module is used to obtain the preset air source moisture content benchmark model of the target air compressor, extract the benchmark air source moisture content corresponding to the real-time gas humidity, calculate the first deviation quantification index of the real-time air source moisture content compared with the benchmark air source moisture content, and perform preliminary correction on the primary load adjustment parameters based on the first deviation quantification index to generate the secondary load adjustment parameters.

[0025] The preset air source moisture content benchmark model refers to a standard model established based on the relationship between gas humidity and air source moisture content. This preset air source moisture content benchmark model defines the corresponding benchmark air source moisture content for the target air compressor under different gas humidity conditions.

[0026] The standard air flow rate change ratio calculation module is used to retrieve the historical operating log of the target air compressor, find the historical exhaust flow corresponding to the real-time intake flow rate when the target air compressor is in the initial operating condition of a new machine, and calculate the standard air flow rate change ratio.

[0027] The initial operating condition of the new machine refers to the initial operating stage after the target air compressor leaves the factory and its operating time has not exceeded the set cycle. At this time, the target air compressor is in the ideal state of its design performance.

[0028] The final load adjustment parameter generation module is used to calculate the real-time air flow change ratio of the target air compressor based on the real-time intake flow and real-time exhaust flow, and compare it with the standard air flow change ratio to obtain the second deviation quantification index. Based on the second deviation quantification index, the secondary load adjustment parameters are corrected to obtain the final load adjustment parameters.

[0029] As a further limitation of the technical solution of this embodiment of the invention, the secondary load adjustment parameter generation module specifically includes:

[0030] The reference air source moisture content acquisition unit is used to acquire the preset air source moisture content reference model of the target air compressor and match the reference air source moisture content corresponding to the real-time gas humidity.

[0031] The first deviation quantification index calculation unit is used to retrieve the first index calculation formula and calculate the first deviation quantification index together with the first index calculation formula, the benchmark gas source moisture content and the real-time gas source moisture content.

[0032] The secondary load adjustment parameter calculation unit is used to multiply the primary load adjustment parameter by the first deviation quantification index to generate the secondary load adjustment parameter.

[0033] The formula for calculating the first indicator is: Where I1 refers to the first deviation quantification index, R refers to the real-time gas source moisture content, and B refers to the baseline gas source moisture content.

[0034] As a further limitation of the technical solution of this embodiment of the invention, the final load adjustment parameter generation module specifically includes:

[0035] The real-time air flow rate change ratio calculation unit is used to obtain the real-time intake air flow and the real-time exhaust air flow, and to calculate the real-time air flow rate change ratio of the target air compressor by dividing the real-time intake air flow by the real-time exhaust air flow.

[0036] The second deviation quantification index calculation unit is used to retrieve the second index calculation formula and calculate the second deviation quantification index together with the real-time intake flow, the historical exhaust flow corresponding to the real-time intake flow, the real-time exhaust flow, and the second index calculation formula.

[0037] The final load adjustment parameter calculation unit is used to multiply the secondary load adjustment parameters by the secondary load adjustment parameters to generate the final load adjustment parameters;

[0038] The formula for calculating the second indicator is: Where I2 refers to the second deviation quantification index, X refers to the real-time air flow rate change ratio, and Y refers to the standard air flow rate change ratio. The standard air flow rate change ratio is calculated by dividing the real-time intake air flow rate by its corresponding historical exhaust air flow rate.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention acquires real-time exhaust pressure, real-time air source moisture content, real-time intake flow rate, real-time exhaust flow rate, and real-time gas humidity of the target air compressor. Combined with a preset air source moisture content benchmark model and historical operating logs, it precisely optimizes compressor load adjustments. By using real-time calculated deviation quantification indicators, the load adjustment parameters are gradually corrected, ultimately generating dynamically adapted final load adjustment parameters, thereby achieving efficient management of complex compressor operating conditions.

[0041] This invention, building upon traditional load adjustment technology, overcomes the limitations of relying solely on single real-time data. By utilizing dynamic deviation analysis of the ratio of changes in air source moisture content and air flow rate, it makes load adjustment parameters more targeted and precise. It solves the load mismatch problem caused by variations in ambient humidity and fluctuations in internal airflow efficiency, significantly improving compressor operating efficiency and stability while reducing energy consumption and operating costs. Furthermore, by incorporating historical benchmark data from the initial operating conditions of a new machine, this method effectively avoids the wear and tear effects of long-term operation, extending the equipment's lifespan.

[0042] This invention is applicable to intelligent management of air compressors under various operating conditions, and has broad application prospects, especially in complex industrial environments with significant humidity changes and frequent load fluctuations. By combining real-time data with historical benchmarks, it achieves multi-dimensional analysis and optimization of equipment operating status, providing a low-energy-consumption, high-efficiency, and long-life equipment load adjustment solution, offering important support for the development of industrial equipment management and energy-saving technologies. Attached Figure Description

[0043] Figure 1 A flowchart of the method provided in the embodiments of the present invention;

[0044] Figure 2This is a flowchart illustrating the calculation of secondary load adjustment parameters in the method provided in this embodiment of the invention;

[0045] Figure 3 This is a flowchart illustrating the calculation of the final load adjustment parameters in the method provided in this embodiment of the invention;

[0046] Figure 4 Application architecture diagram of the system provided in the embodiments of the present invention;

[0047] Figure 5 This is a structural block diagram of the secondary load adjustment parameter generation module in the system provided in the embodiments of the present invention;

[0048] Figure 6 This is a structural block diagram of the final load adjustment parameter generation module in the system provided in the embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0051] Specifically, an air compressor management method based on a cloud service platform includes the following steps:

[0052] Step S100: Obtain the real-time exhaust pressure, real-time air source moisture content, real-time intake flow rate, real-time exhaust flow rate, and real-time gas humidity from the target air compressor, and match the primary load adjustment parameters for the target air compressor based on the real-time exhaust pressure.

[0053] In this embodiment of the invention, during the operation of the target air compressor, real-time exhaust pressure data is monitored by a high-precision pressure sensor deployed on the exhaust pipeline. This sensor captures instantaneous pressure changes, digitizes them, and uploads them to the cloud service platform via an industrial IoT module. Real-time air source moisture content is collected by a humidity sensor installed at the intake end. By detecting the humidity level of the intake air in real time, and combining this with the moisture content calculation model within the cloud platform, specific real-time air source moisture content parameters are generated. Similarly, real-time intake flow rate and real-time exhaust flow rate are continuously monitored by high-precision flow meters embedded in the intake and exhaust pipelines, respectively, ensuring accurate data. These real-time monitoring data are aggregated to the cloud service platform via an industrial IoT gateway. Combined with the real-time gas humidity monitoring results, the data is uniformly analyzed and stored in the cloud to form a comprehensive real-time parameter set of the target air compressor's operating status.

[0054] The process of matching primary load adjustment parameters based on real-time exhaust pressure is achieved using load control technology on a cloud service platform. The cloud-based system utilizes a built-in proportional-integral-derivative (PID) control algorithm module to compare the current real-time exhaust pressure with the target pressure value in real time. It then dynamically calculates and generates primary load adjustment parameters to adjust the intake valve opening or motor power. Furthermore, the cloud service platform can leverage historical operating data and real-time input parameters from the target air compressor to call upon a machine learning-based load prediction model, further optimizing the matching accuracy of the primary load adjustment parameters.

[0055] Most of the technologies mentioned above fall within the scope of existing technologies and have been widely used in modern industrial automation and cloud platform services.

[0056] Furthermore, the air compressor management method based on the cloud service platform also includes the following steps:

[0057] Step S200: Obtain the preset air source moisture content benchmark model of the target air compressor, extract the benchmark air source moisture content corresponding to the real-time gas humidity, and calculate the first deviation quantification index of the real-time air source moisture content compared with the benchmark air source moisture content. Based on the first deviation quantification index, perform preliminary correction on the primary load adjustment parameters to generate secondary load adjustment parameters.

[0058] Specifically, Figure 2 A flowchart for calculating secondary load adjustment parameters is shown.

[0059] The process involves obtaining a preset air source moisture content benchmark model for the target air compressor, extracting the benchmark air source moisture content corresponding to the real-time gas humidity, calculating a first deviation quantification index between the real-time air source moisture content and the benchmark air source moisture content, and, based on this first deviation quantification index, performing preliminary corrections on the primary load adjustment parameters to generate secondary load adjustment parameters. This process specifically includes the following steps:

[0060] Step S201: Obtain the preset air source moisture content benchmark model of the target air compressor, and match the benchmark air source moisture content corresponding to the real-time gas humidity.

[0061] Step S202: Retrieve the calculation formula for the first indicator, and calculate the first deviation quantification index together with the calculation formula for the first indicator, the baseline gas source moisture content, and the real-time gas source moisture content.

[0062] Step S203: Multiply the primary load adjustment parameters by the first deviation quantization index to generate the secondary load adjustment parameters.

[0063] The preset air source moisture content benchmark model refers to a standard model established based on the relationship between gas humidity and air source moisture content. This preset air source moisture content benchmark model defines the corresponding benchmark air source moisture content for the target air compressor under different gas humidity conditions.

[0064] The formula for calculating the first indicator is: Where I1 refers to the first deviation quantification index, R refers to the real-time gas source moisture content, and B refers to the baseline gas source moisture content.

[0065] In this embodiment of the invention, the establishment of the preset air source moisture content benchmark model is based on the historical operating data of the target air compressor under different gas humidity conditions and the actual working environment. By combining the long-term collected humidity data with the corresponding air source moisture content relationship, and using the water vapor saturation calculation model in air physical properties, a standardized reference model is generated. In this model, each gas humidity value has a corresponding ideal moisture content, specifically reflecting the air source moisture content characteristics of the equipment under different humidity conditions. The benchmark model may also incorporate verification data under multiple operating conditions to ensure accuracy and applicability in actual use.

[0066] Once real-time gas humidity data is uploaded to the cloud service platform, the system searches for the benchmark gas source moisture content that best matches the humidity value in a preset gas source moisture content benchmark model. This matching process can be achieved through direct indexing or interpolation calculation. When the real-time humidity value falls between preset points in the model, the system calculates the corresponding moisture content value based on the benchmark moisture content data of adjacent points. This method ensures a high correlation between real-time gas humidity and benchmark gas source moisture content, thus providing reliable standard parameters for subsequent calculations.

[0067] The rationale for adjusting the primary load parameters in this manner is that changes in the moisture content of the air source directly affect the compressor's operating load and efficiency. Higher moisture content increases air density, impacting the compressor's compression ratio and operating temperature, potentially leading to increased equipment load and energy consumption over the long term. By monitoring the moisture content in real time and comparing it with a baseline moisture content, the degree of deviation can be calculated, effectively and dynamically adjusting the compressor's load parameters to better adapt its operation to the current environment. This adjustment strategy not only improves equipment stability but also extends its service life.

[0068] For example, in an operating environment with an air humidity of 50%, a preset air source moisture content benchmark model is used, with a benchmark moisture content of 10 grams per cubic meter. However, the real-time air source moisture content sensor detects an actual value of 12 grams per cubic meter. Calculations show that the actual air source moisture content is 20% higher than the benchmark. This deviation indicates that the current air source humidity is too high, potentially increasing the compressor load. This deviation is quantified and applied to load adjustment; for example, primary load adjustment parameters are multiplied by a deviation correction coefficient to generate secondary load adjustment parameters. This correction allows the load parameters to dynamically reflect current humidity changes, thereby ensuring the equipment's high efficiency and adaptability.

[0069] Through this mechanism, equipment can respond more flexibly to environmental changes, especially in areas with significant humidity variations, such as coastal or seasonally humid environments. Dynamic adjustment strategies based on real-time data can precisely match equipment needs while avoiding performance degradation or overload issues caused by long-term operation, thus achieving more intelligent equipment management and energy efficiency optimization.

[0070] Furthermore, the air compressor management method based on the cloud service platform also includes the following steps:

[0071] Step S300: Retrieve the historical operating log of the target air compressor, find the historical exhaust flow rate corresponding to the real-time intake flow rate when the target air compressor is in the initial operating condition of a new machine, and calculate the standard air flow rate change ratio.

[0072] The initial operating condition of the new machine refers to the initial operating stage when the target air compressor has not exceeded the set cycle after leaving the factory, at which time the target air compressor is in the ideal state of design performance.

[0073] In this embodiment of the invention, during the operation of the target air compressor, historical operating logs typically originate from the equipment's internal monitoring system. These systems include real-time monitoring sensors, PLC controllers, and industrial IoT devices. These devices continuously record various parameters of the compressor under different operating conditions, such as intake flow rate, exhaust flow rate, exhaust pressure, and temperature. All this real-time data is stored in the equipment's local data recording system and periodically uploaded to a central server or cloud service platform for analysis and archiving. Through these logs, complete operating data of the equipment from its manufacturing stage to the present can be obtained, and long-term trend analysis can be performed.

[0074] The setting cycle is typically based on the equipment's factory standards, the manufacturer's recommended maintenance intervals, and the equipment's design performance. For example, for some industrial compressors, their design life and performance are usually maintained at their optimal state within a specific number of operating hours or a specific work cycle. The setting cycle is generally defined according to these standards, with common cycles ranging from several hundred to several thousand hours after the equipment is put into use. The significance of this cycle lies in the fact that it marks the transition of the equipment from a brand-new state to the beginning of wear and tear. Using this cycle as a reference point allows for accurate definition of the ideal performance state of the equipment in the initial operating phase, facilitating subsequent performance comparisons and adjustments.

[0075] Furthermore, the air compressor management method based on the cloud service platform also includes the following steps:

[0076] Step S400: Calculate the real-time air flow rate change ratio of the target air compressor based on the real-time intake flow rate and the real-time exhaust flow rate, and compare it with the standard air flow rate change ratio to obtain the second deviation quantification index. Based on the second deviation quantification index, correct the secondary load adjustment parameters to obtain the final load adjustment parameters.

[0077] Specifically, Figure 3 A flowchart for calculating the final load adjustment parameters is shown.

[0078] The process involves calculating the real-time airflow change ratio of the target air compressor based on the real-time intake and exhaust airflow, comparing it with the standard airflow change ratio to obtain a second deviation quantification index, and then correcting the secondary load adjustment parameters based on this second deviation quantification index to obtain the final load adjustment parameters. The specific steps include:

[0079] Step S401: Obtain the real-time intake flow rate and the real-time exhaust flow rate, and calculate the real-time air flow rate change ratio of the target air compressor by dividing the real-time intake flow rate by the real-time exhaust flow rate.

[0080] Step S402: Retrieve the second indicator calculation formula, and calculate the second deviation quantification index together with the real-time intake flow, the historical exhaust flow corresponding to the real-time intake flow, the real-time exhaust flow, and the second indicator calculation formula.

[0081] Step S403: Multiply the secondary load adjustment parameter by the secondary load adjustment parameter to generate the final load adjustment parameter.

[0082] The formula for calculating the second indicator is: Where I2 refers to the second deviation quantification index, X refers to the real-time air flow rate change ratio, and Y refers to the standard air flow rate change ratio. The standard air flow rate change ratio is calculated by dividing the real-time intake air flow rate by its corresponding historical exhaust air flow rate.

[0083] In this embodiment of the invention, the significance of adjusting the secondary load adjustment parameters based on the difference between the real-time airflow change ratio and the standard airflow change ratio lies in the fact that changes in airflow directly reflect the operating status of the air compressor and its load adaptability. The real-time airflow change ratio is a direct representation of the current operating conditions, while the standard airflow change ratio, as a reference value for the initial operating conditions of a new machine, provides a benchmark for the ideal operating state. The difference between the two can quantify the degree of deviation between the actual operating state and the ideal state of the equipment. This deviation may be caused not only by environmental factors (such as humidity and temperature) but also by the equipment itself due to wear, leakage, or increased resistance. Adjusting the secondary load adjustment parameters through this difference can dynamically compensate for the operating state of the equipment under different conditions, ensuring accurate load adaptation.

[0084] This adjustment is based on the fact that the deviation of the air flow rate change ratio reflects the change in the internal compression efficiency of the equipment. Historical exhaust flow rate, as a key component of the standard air flow rate change ratio, represents the compressor's exhaust efficiency under ideal conditions. Comparing this with the ratio of real-time intake flow rate to real-time exhaust flow rate directly reflects whether the equipment is operating at high efficiency. By correcting the secondary load adjustment parameters, the impact of this deviation on the equipment load can be eliminated, preventing the equipment from operating in excessively high or low load ranges, thereby optimizing energy consumption, reducing wear, and extending equipment lifespan.

[0085] After correcting the primary load adjustment parameters based on moisture content, the correction of the air flow rate change ratio may have a synergistic effect on further adjustments to the secondary load adjustment parameters. Changes in moisture content typically affect gas density, thus indirectly influencing changes in intake and exhaust flow rates. When both factors work together, load parameters can be optimized more comprehensively. For example, high moisture content may lead to changes in compression ratio, while deviations in air flow rate may reveal specific reasons for efficiency decline. Combining the two can more accurately identify and correct load adjustment problems, thereby achieving unexpected optimization results.

[0086] In actual industrial operation, the load adjustment of air compressors needs to consider multiple dynamic factors simultaneously to ensure the efficiency and stability of equipment operation. Among these factors, comparing the ratio of real-time airflow change to the ratio of standard airflow change is one of the important methods for evaluating the equipment's operating status. This method can quantify the deviation between the real-time operating status and the initial operating conditions of a new machine, providing a scientific basis for load adjustment. The following specific example illustrates how to use the difference in the ratio of real-time airflow change to calculate a second deviation quantification index, further refine secondary load adjustment parameters, generate final load adjustment parameters, and optimize the equipment's load adaptability.

[0087] In actual operation, the real-time discharge pressure of the target air compressor is 7.5 bar, while the target discharge pressure is 8 bar. The cloud service platform uses the existing proportional-integral-derivative (PID) control algorithm to calculate the deviation between the real-time discharge pressure and the target discharge pressure, generating primary load adjustment parameters. Assume that the calculated value of the primary load adjustment parameters is 100.

[0088] Subsequently, the primary load adjustment parameters are corrected for the first time based on the real-time gas source moisture content. The real-time gas source moisture content, detected by sensors, is 12 grams per cubic meter; the baseline gas source moisture content, calculated using the cloud platform's gas source moisture content benchmark model, is 10 grams per cubic meter. According to the standard first deviation quantification index formula, the first deviation quantification index is calculated to be 120%. Therefore, the primary load adjustment parameters are multiplied by the first deviation quantification index to generate the secondary load adjustment parameters. The secondary load adjustment parameters are 100 multiplied by 1.2, resulting in 120.

[0089] Next, the secondary load adjustment parameters are corrected a second time based on the real-time air flow rate change ratio. The target air compressor has a real-time intake flow rate of 500 cubic meters per hour and a real-time exhaust flow rate of 450 cubic meters per hour. The real-time air flow rate change ratio is calculated by dividing the real-time intake flow rate by the real-time exhaust flow rate, and is 1.1111. Retrieving the historical operating logs of the target air compressor reveals that under the initial operating conditions of a new machine, when the real-time intake flow rate is 500 cubic meters per hour, the corresponding historical exhaust flow rate is 480 cubic meters per hour. Therefore, the standard air flow rate change ratio is calculated by dividing the real-time intake flow rate by the historical exhaust flow rate, and is 1.0417.

[0090] The deviation between the real-time gas flow rate change ratio and the standard gas flow rate change ratio is calculated as 1.1111 minus 1.0417, resulting in 0.0694. Dividing this deviation by the standard gas flow rate change ratio yields 0.0667. Adding 1 gives 1.0667. Multiplying this by 100% to convert it to a percentage gives 106.67%. This value is the second deviation quantification index. The secondary load adjustment parameters are multiplied by the second deviation quantification index to generate the final load adjustment parameters. The final load adjustment parameters are calculated as 120 multiplied by 1.0667, resulting in 128.004.

[0091] Ultimately, after two revisions, the final load adjustment parameters comprehensively consider the dynamic effects of real-time exhaust pressure, real-time gas source moisture content, and the ratio of gas flow rate changes. This allows for precise adaptation to the current operating conditions, ensuring the compressor's operating efficiency and stability, avoiding overload or inefficient operation, and further optimizing the equipment's energy-saving performance.

[0092] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0093] In another preferred embodiment of the present invention, an air compressor management system based on a cloud service platform includes:

[0094] The data acquisition module 100 is used to acquire real-time exhaust pressure, real-time air source moisture content, real-time intake flow rate, real-time exhaust flow rate, and real-time gas humidity from the target air compressor, and to match primary load adjustment parameters for the target air compressor based on the real-time exhaust pressure.

[0095] In this embodiment of the invention, during the operation of the target air compressor, real-time exhaust pressure data is monitored by a high-precision pressure sensor deployed on the exhaust pipeline. This sensor captures instantaneous pressure changes, digitizes them, and uploads them to the cloud service platform via an industrial IoT module. Real-time air source moisture content is collected by a humidity sensor installed at the intake end. By detecting the humidity level of the intake air in real time, and combining this with the moisture content calculation model within the cloud platform, specific real-time air source moisture content parameters are generated. Similarly, real-time intake flow rate and real-time exhaust flow rate are continuously monitored by high-precision flow meters embedded in the intake and exhaust pipelines, respectively, ensuring accurate data. These real-time monitoring data are aggregated to the cloud service platform via an industrial IoT gateway. Combined with the real-time gas humidity monitoring results, the data is uniformly analyzed and stored in the cloud to form a comprehensive real-time parameter set of the target air compressor's operating status.

[0096] The process of matching primary load adjustment parameters based on real-time exhaust pressure is achieved using load control technology on a cloud service platform. The cloud-based system utilizes a built-in proportional-integral-derivative (PID) control algorithm module to compare the current real-time exhaust pressure with the target pressure value in real time. It then dynamically calculates and generates primary load adjustment parameters to adjust the intake valve opening or motor power. Furthermore, the cloud service platform can leverage historical operating data and real-time input parameters from the target air compressor to call upon a machine learning-based load prediction model, further optimizing the matching accuracy of the primary load adjustment parameters.

[0097] Most of the technologies mentioned above fall within the scope of existing technologies and have been widely used in modern industrial automation and cloud platform services.

[0098] Furthermore, the cloud service platform-based air compressor management system also includes:

[0099] The secondary load adjustment parameter generation module 200 is used to obtain the preset air source moisture content benchmark model of the target air compressor, extract the benchmark air source moisture content corresponding to the real-time gas humidity, calculate the first deviation quantification index of the real-time air source moisture content compared with the benchmark air source moisture content, and perform preliminary correction on the primary load adjustment parameters based on the first deviation quantification index to generate the secondary load adjustment parameters.

[0100] The preset air source moisture content benchmark model refers to a standard model established based on the relationship between gas humidity and air source moisture content. This preset air source moisture content benchmark model defines the corresponding benchmark air source moisture content for the target air compressor under different gas humidity conditions.

[0101] Specifically, Figure 5 The diagram shows a structural block diagram of the secondary load adjustment parameter generation module 200 in the system provided by an embodiment of the present invention.

[0102] In a preferred embodiment of the present invention, the secondary load adjustment parameter generation module 200 specifically includes:

[0103] The reference gas source moisture content acquisition unit 201 is used to acquire the preset gas source moisture content reference model of the target air compressor and match the reference gas source moisture content corresponding to the real-time gas humidity.

[0104] The first deviation quantification index calculation unit 202 is used to retrieve the first index calculation formula and calculate the first deviation quantification index together with the first index calculation formula, the benchmark gas source moisture content and the real-time gas source moisture content.

[0105] The secondary load adjustment parameter calculation unit 203 is used to multiply the primary load adjustment parameter by the first deviation quantification index to generate the secondary load adjustment parameter.

[0106] The formula for calculating the first indicator is: Where I1 refers to the first deviation quantification index, R refers to the real-time gas source moisture content, and B refers to the baseline gas source moisture content.

[0107] In this embodiment of the invention, the establishment of the preset air source moisture content benchmark model is based on the historical operating data of the target air compressor under different gas humidity conditions and the actual working environment. By combining the long-term collected humidity data with the corresponding air source moisture content relationship, and using the water vapor saturation calculation model in air physical properties, a standardized reference model is generated. In this model, each gas humidity value has a corresponding ideal moisture content, specifically reflecting the air source moisture content characteristics of the equipment under different humidity conditions. The benchmark model may also incorporate verification data under multiple operating conditions to ensure accuracy and applicability in actual use.

[0108] Once real-time gas humidity data is uploaded to the cloud service platform, the system searches for the benchmark gas source moisture content that best matches the humidity value in a preset gas source moisture content benchmark model. This matching process can be achieved through direct indexing or interpolation calculation. When the real-time humidity value falls between preset points in the model, the system calculates the corresponding moisture content value based on the benchmark moisture content data of adjacent points. This method ensures a high correlation between real-time gas humidity and benchmark gas source moisture content, thus providing reliable standard parameters for subsequent calculations.

[0109] The rationale for adjusting the primary load parameters in this manner is that changes in the moisture content of the air source directly affect the compressor's operating load and efficiency. Higher moisture content increases air density, impacting the compressor's compression ratio and operating temperature, potentially leading to increased equipment load and energy consumption over the long term. By monitoring the moisture content in real time and comparing it with a baseline moisture content, the degree of deviation can be calculated, effectively and dynamically adjusting the compressor's load parameters to better adapt its operation to the current environment. This adjustment strategy not only improves equipment stability but also extends its service life.

[0110] For example, in an operating environment with an air humidity of 50%, a preset air source moisture content benchmark model is used, with a benchmark moisture content of 10 grams per cubic meter. However, the real-time air source moisture content sensor detects an actual value of 12 grams per cubic meter. Calculations show that the actual air source moisture content is 20% higher than the benchmark. This deviation indicates that the current air source humidity is too high, potentially increasing the compressor load. This deviation is quantified and applied to load adjustment; for example, primary load adjustment parameters are multiplied by a deviation correction coefficient to generate secondary load adjustment parameters. This correction allows the load parameters to dynamically reflect current humidity changes, thereby ensuring the equipment's high efficiency and adaptability.

[0111] Through this mechanism, equipment can respond more flexibly to environmental changes, especially in areas with significant humidity variations, such as coastal or seasonally humid environments. Dynamic adjustment strategies based on real-time data can precisely match equipment needs while avoiding performance degradation or overload issues caused by long-term operation, thus achieving more intelligent equipment management and energy efficiency optimization.

[0112] Furthermore, the cloud service platform-based air compressor management system also includes:

[0113] The standard air flow rate change ratio calculation module 300 is used to retrieve the historical operation log of the target air compressor, find the historical exhaust flow corresponding to the real-time intake flow when the target air compressor is in the initial working condition of a new machine, and calculate the standard air flow rate change ratio.

[0114] The initial operating condition of the new machine refers to the initial operating stage when the target air compressor has not exceeded the set cycle after leaving the factory, at which time the target air compressor is in the ideal state of design performance.

[0115] In this embodiment of the invention, during the operation of the target air compressor, historical operating logs typically originate from the equipment's internal monitoring system. These systems include real-time monitoring sensors, PLC controllers, and industrial IoT devices. These devices continuously record various parameters of the compressor under different operating conditions, such as intake flow rate, exhaust flow rate, exhaust pressure, and temperature. All this real-time data is stored in the equipment's local data recording system and periodically uploaded to a central server or cloud service platform for analysis and archiving. Through these logs, complete operating data of the equipment from its manufacturing stage to the present can be obtained, and long-term trend analysis can be performed.

[0116] The setting cycle is typically based on the equipment's factory standards, the manufacturer's recommended maintenance intervals, and the equipment's design performance. For example, for some industrial compressors, their design life and performance are usually maintained at their optimal state within a specific number of operating hours or a specific work cycle. The setting cycle is generally defined according to these standards, with common cycles ranging from several hundred to several thousand hours after the equipment is put into use. The significance of this cycle lies in the fact that it marks the transition of the equipment from a brand-new state to the beginning of wear and tear. Using this cycle as a reference point allows for accurate definition of the ideal performance state of the equipment in the initial operating phase, facilitating subsequent performance comparisons and adjustments.

[0117] Furthermore, the cloud service platform-based air compressor management system also includes:

[0118] The final load adjustment parameter generation module 400 is used to calculate the real-time air flow change ratio of the target air compressor based on the real-time intake flow and the real-time exhaust flow, and compare it with the standard air flow change ratio to obtain a second deviation quantification index. Based on the second deviation quantification index, the secondary load adjustment parameters are corrected to obtain the final load adjustment parameters.

[0119] Specifically, Figure 6 The diagram shows a structural block diagram of the final load adjustment parameter generation module 400 in the system provided by an embodiment of the present invention.

[0120] In a preferred embodiment of the present invention, the final load adjustment parameter generation module 400 specifically includes:

[0121] The real-time air flow rate change ratio calculation unit 401 is used to obtain the real-time intake air flow and the real-time exhaust air flow, and to calculate the real-time air flow rate change ratio of the target air compressor by dividing the real-time intake air flow by the real-time exhaust air flow.

[0122] The second deviation quantification index calculation unit 402 is used to retrieve the second index calculation formula and calculate the second deviation quantification index together with the real-time intake flow, the historical exhaust flow corresponding to the real-time intake flow, the real-time exhaust flow and the second index calculation formula.

[0123] The final load adjustment parameter calculation unit 403 is used to multiply the secondary load adjustment parameter by the secondary load adjustment parameter to generate the final load adjustment parameter;

[0124] The formula for calculating the second indicator is: Where I2 refers to the second deviation quantification index, X refers to the real-time air flow rate change ratio, and Y refers to the standard air flow rate change ratio. The standard air flow rate change ratio is calculated by dividing the real-time intake air flow rate by its corresponding historical exhaust air flow rate.

[0125] In this embodiment of the invention, the significance of adjusting the secondary load adjustment parameters based on the difference between the real-time airflow change ratio and the standard airflow change ratio lies in the fact that changes in airflow directly reflect the operating status of the air compressor and its load adaptability. The real-time airflow change ratio is a direct representation of the current operating conditions, while the standard airflow change ratio, as a reference value for the initial operating conditions of a new machine, provides a benchmark for the ideal operating state. The difference between the two can quantify the degree of deviation between the actual operating state and the ideal state of the equipment. This deviation may be caused not only by environmental factors (such as humidity and temperature) but also by the equipment itself due to wear, leakage, or increased resistance. Adjusting the secondary load adjustment parameters through this difference can dynamically compensate for the operating state of the equipment under different conditions, ensuring accurate load adaptation.

[0126] This adjustment is based on the fact that the deviation of the air flow rate change ratio reflects the change in the internal compression efficiency of the equipment. Historical exhaust flow rate, as a key component of the standard air flow rate change ratio, represents the compressor's exhaust efficiency under ideal conditions. Comparing this with the ratio of real-time intake flow rate to real-time exhaust flow rate directly reflects whether the equipment is operating at high efficiency. By correcting the secondary load adjustment parameters, the impact of this deviation on the equipment load can be eliminated, preventing the equipment from operating in excessively high or low load ranges, thereby optimizing energy consumption, reducing wear, and extending equipment lifespan.

[0127] After correcting the primary load adjustment parameters based on moisture content, the correction of the air flow rate change ratio may have a synergistic effect on further adjustments to the secondary load adjustment parameters. Changes in moisture content typically affect gas density, thus indirectly influencing changes in intake and exhaust flow rates. When both factors work together, load parameters can be optimized more comprehensively. For example, high moisture content may lead to changes in compression ratio, while deviations in air flow rate may reveal specific reasons for efficiency decline. Combining the two can more accurately identify and correct load adjustment problems, thereby achieving unexpected optimization results.

[0128] In actual industrial operation, the load adjustment of air compressors needs to consider multiple dynamic factors simultaneously to ensure the efficiency and stability of equipment operation. Among these factors, comparing the ratio of real-time airflow change to the ratio of standard airflow change is one of the important methods for evaluating the equipment's operating status. This method can quantify the deviation between the real-time operating status and the initial operating conditions of a new machine, providing a scientific basis for load adjustment. The following specific example illustrates how to use the difference in the ratio of real-time airflow change to calculate a second deviation quantification index, further refine secondary load adjustment parameters, generate final load adjustment parameters, and optimize the equipment's load adaptability.

[0129] In actual operation, the real-time discharge pressure of the target air compressor is 7.5 bar, while the target discharge pressure is 8 bar. The cloud service platform uses the existing proportional-integral-derivative (PID) control algorithm to calculate the deviation between the real-time discharge pressure and the target discharge pressure, generating primary load adjustment parameters. Assume that the calculated value of the primary load adjustment parameters is 100.

[0130] Subsequently, the primary load adjustment parameters are corrected for the first time based on the real-time gas source moisture content. The real-time gas source moisture content, detected by sensors, is 12 grams per cubic meter; the baseline gas source moisture content, calculated using the cloud platform's gas source moisture content benchmark model, is 10 grams per cubic meter. According to the standard first deviation quantification index formula, the first deviation quantification index is calculated to be 120%. Therefore, the primary load adjustment parameters are multiplied by the first deviation quantification index to generate the secondary load adjustment parameters. The secondary load adjustment parameters are 100 multiplied by 1.2, resulting in 120.

[0131] Next, the secondary load adjustment parameters are corrected a second time based on the real-time air flow rate change ratio. The target air compressor has a real-time intake flow rate of 500 cubic meters per hour and a real-time exhaust flow rate of 450 cubic meters per hour. The real-time air flow rate change ratio is calculated by dividing the real-time intake flow rate by the real-time exhaust flow rate, and is 1.1111. Retrieving the historical operating logs of the target air compressor reveals that under the initial operating conditions of a new machine, when the real-time intake flow rate is 500 cubic meters per hour, the corresponding historical exhaust flow rate is 480 cubic meters per hour. Therefore, the standard air flow rate change ratio is calculated by dividing the real-time intake flow rate by the historical exhaust flow rate, and is 1.0417.

[0132] The deviation between the real-time gas flow rate change ratio and the standard gas flow rate change ratio is calculated as 1.1111 minus 1.0417, resulting in 0.0694. Dividing this deviation by the standard gas flow rate change ratio yields 0.0667. Adding 1 gives 1.0667. Multiplying this by 100% to convert it to a percentage gives 106.67%. This value is the second deviation quantification index. The secondary load adjustment parameters are multiplied by the second deviation quantification index to generate the final load adjustment parameters. The final load adjustment parameters are calculated as 120 multiplied by 1.0667, resulting in 128.004.

[0133] Ultimately, after two revisions, the final load adjustment parameters comprehensively consider the dynamic effects of real-time exhaust pressure, real-time gas source moisture content, and the ratio of gas flow rate changes. This allows for precise adaptation to the current operating conditions, ensuring the compressor's operating efficiency and stability, avoiding overload or inefficient operation, and further optimizing the equipment's energy-saving performance.

[0134] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0135] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0136] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for managing air compressors based on a cloud service platform, characterized in that, The method includes: The system acquires real-time discharge pressure, real-time air source moisture content, real-time intake flow rate, real-time discharge flow rate, and real-time gas humidity from the target air compressor, and matches primary load adjustment parameters to the target air compressor based on the real-time discharge pressure. Obtain the preset air source moisture content benchmark model of the target air compressor, extract the benchmark air source moisture content corresponding to the real-time gas humidity, and calculate the first deviation quantification index of the real-time air source moisture content compared with the benchmark air source moisture content. Based on the first deviation quantification index, perform preliminary correction on the primary load adjustment parameters to generate secondary load adjustment parameters. Retrieve the historical operating log of the target air compressor, find the historical exhaust flow corresponding to the real-time intake flow when the target air compressor is in the initial operating condition of a new machine, and calculate the standard air flow change ratio. The real-time air flow rate change ratio of the target air compressor is calculated based on the real-time intake air flow rate and the real-time exhaust air flow rate, and then compared with the standard air flow rate change ratio to obtain the second deviation quantification index. Based on the second deviation quantification index, the secondary load adjustment parameters are corrected to obtain the final load adjustment parameters.

2. The air compressor management method based on a cloud service platform according to claim 1, characterized in that, The preset air source moisture content benchmark model refers to a standard model established based on the relationship between gas humidity and air source moisture content. This preset air source moisture content benchmark model defines the corresponding benchmark air source moisture content for the target air compressor under different gas humidity conditions.

3. The air compressor management method based on a cloud service platform according to claim 2, characterized in that, The steps for obtaining a preset air source moisture content benchmark model for the target air compressor, extracting the benchmark air source moisture content corresponding to the real-time gas humidity, calculating the first deviation quantification index of the real-time air source moisture content compared to the benchmark air source moisture content, and performing preliminary corrections on the primary load adjustment parameters based on this first deviation quantification index to generate the secondary load adjustment parameters include: Obtain the preset air source moisture content benchmark model of the target air compressor, and match the benchmark air source moisture content corresponding to the real-time gas humidity. Retrieve the calculation formula for the first indicator, and calculate the first deviation quantification index together with the calculation formula for the first indicator, the baseline gas source moisture content, and the real-time gas source moisture content; The secondary load adjustment parameters are generated by multiplying the primary load adjustment parameters by the first deviation quantification index.

4. The air compressor management method based on a cloud service platform according to claim 3, characterized in that, The formula for calculating the first indicator is: Where I1 refers to the first deviation quantification index, R refers to the real-time gas source moisture content, and B refers to the baseline gas source moisture content.

5. The air compressor management method based on a cloud service platform according to claim 1, characterized in that, The initial operating condition of the new machine refers to the initial operating stage when the target air compressor has not exceeded the set cycle after leaving the factory, at which time the target air compressor is in the ideal state of design performance.

6. The air compressor management method based on a cloud service platform according to claim 5, characterized in that, The steps for calculating the real-time air flow rate change ratio of the target air compressor based on the real-time intake air flow rate and the real-time exhaust air flow rate, comparing it with the standard air flow rate change ratio to obtain a second deviation quantification index, and then correcting the secondary load adjustment parameters based on this second deviation quantification index to obtain the final load adjustment parameters include: Obtain the real-time intake flow rate and real-time exhaust flow rate, and calculate the real-time air flow rate change ratio of the target air compressor by dividing the real-time intake flow rate by the real-time exhaust flow rate; The second indicator calculation formula is retrieved, and the second deviation quantification index is calculated together with the real-time intake flow, the historical exhaust flow corresponding to the real-time intake flow, the real-time exhaust flow, and the second indicator calculation formula. Multiply the secondary load adjustment parameter by the secondary load adjustment parameter to generate the final load adjustment parameter.

7. The air compressor management method based on a cloud service platform according to claim 6, characterized in that, The formula for calculating the second indicator is: Where I2 refers to the second deviation quantification index, X refers to the real-time air flow rate change ratio, and Y refers to the standard air flow rate change ratio. The standard air flow rate change ratio is calculated by dividing the real-time intake air flow rate by its corresponding historical exhaust air flow rate.

8. An air compressor management system based on a cloud service platform, characterized in that, The system includes: a data acquisition module, a secondary load adjustment parameter generation module, a standard gas flow rate change ratio calculation module, and a final load adjustment parameter generation module, wherein: The data acquisition module is used to acquire real-time exhaust pressure, real-time air source moisture content, real-time intake flow rate, real-time exhaust flow rate, and real-time gas humidity from the target air compressor, and to match primary load adjustment parameters for the target air compressor based on the real-time exhaust pressure. The secondary load adjustment parameter generation module is used to obtain the preset air source moisture content benchmark model of the target air compressor, extract the benchmark air source moisture content corresponding to the real-time gas humidity, calculate the first deviation quantification index of the real-time air source moisture content compared with the benchmark air source moisture content, and perform preliminary correction on the primary load adjustment parameters based on the first deviation quantification index to generate the secondary load adjustment parameters. The preset air source moisture content benchmark model refers to a standard model established based on the relationship between gas humidity and air source moisture content. This preset air source moisture content benchmark model defines the corresponding benchmark air source moisture content for the target air compressor under different gas humidity conditions. The standard air flow rate change ratio calculation module is used to retrieve the historical operating log of the target air compressor, find the historical exhaust flow corresponding to the real-time intake flow rate when the target air compressor is in the initial operating condition of a new machine, and calculate the standard air flow rate change ratio. The initial operating condition of the new machine refers to the initial operating stage after the target air compressor leaves the factory and its operating time has not exceeded the set cycle. At this time, the target air compressor is in the ideal state of its design performance. The final load adjustment parameter generation module is used to calculate the real-time air flow change ratio of the target air compressor based on the real-time intake flow and real-time exhaust flow, and compare it with the standard air flow change ratio to obtain the second deviation quantification index. Based on the second deviation quantification index, the secondary load adjustment parameters are corrected to obtain the final load adjustment parameters.

9. The air compressor management system based on a cloud service platform according to claim 8, characterized in that, The secondary load adjustment parameter generation module specifically includes: The reference air source moisture content acquisition unit is used to acquire the preset air source moisture content reference model of the target air compressor and match the reference air source moisture content corresponding to the real-time gas humidity. The first deviation quantification index calculation unit is used to retrieve the first index calculation formula and calculate the first deviation quantification index together with the first index calculation formula, the benchmark gas source moisture content and the real-time gas source moisture content. The secondary load adjustment parameter calculation unit is used to multiply the primary load adjustment parameter by the first deviation quantification index to generate the secondary load adjustment parameter. The formula for calculating the first indicator is: Where I1 refers to the first deviation quantification index, R refers to the real-time gas source moisture content, and B refers to the baseline gas source moisture content.

10. The air compressor management system based on a cloud service platform according to claim 9, characterized in that, The final load adjustment parameter generation module specifically includes: The real-time air flow rate change ratio calculation unit is used to obtain the real-time intake air flow and the real-time exhaust air flow, and to calculate the real-time air flow rate change ratio of the target air compressor by dividing the real-time intake air flow by the real-time exhaust air flow. The second deviation quantification index calculation unit is used to retrieve the second index calculation formula and calculate the second deviation quantification index together with the real-time intake flow, the historical exhaust flow corresponding to the real-time intake flow, the real-time exhaust flow, and the second index calculation formula. The final load adjustment parameter calculation unit is used to multiply the secondary load adjustment parameters by the secondary load adjustment parameters to generate the final load adjustment parameters; The formula for calculating the second indicator is: Where I2 refers to the second deviation quantification index, X refers to the real-time air flow rate change ratio, and Y refers to the standard air flow rate change ratio. The standard air flow rate change ratio is calculated by dividing the real-time intake air flow rate by its corresponding historical exhaust air flow rate.

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