Air compressor management method and system based on cloud service platform

Abstract

The invention is suitable for the technical field of equipment load adjustment, and provides an air compressor management method and system based on a cloud service platform, and the method comprises the steps: obtaining the real-time exhaust pressure, the real-time air source water content, the real-time air inlet flow, the real-time exhaust flow and the real-time air humidity from a target air compressor; and a primary load adjustment parameter is matched for the target air compressor according to the real-time exhaust pressure. According to the method, the real-time exhaust pressure, the real-time air source water content, the real-time air inlet flow, the real-time exhaust flow and the real-time air humidity of the target air compressor are obtained, the preset air source water content reference model and the historical operation log are combined, and compressor load adjustment is precisely optimized. Through the deviation quantitative index calculated in real time, the load adjustment parameters are corrected step by step, and finally dynamic adaptive final load adjustment parameters are generated, so that efficient management of complex operation conditions of the compressor is realized.

Description

Technical Field

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

[0002] At present, the load adjustment technology of air compressors is mainly based on real-time monitoring and local control systems on the equipment side, which collects real-time exhaust pressure and uses the proportional integral differential control (PID control) algorithm for dynamic adjustment. This adjustment method can play a certain role in simple scenarios where a single parameter is monitored, but in complex working conditions, such as when humidity fluctuates significantly or equipment operating efficiency changes, it often has problems with insufficient adaptability. At the same time, although existing technologies can monitor more parameters through humidity sensors and flow sensors, they often only stay at the level of single-point data processing and lack collaborative analysis and real-time optimization of multi-dimensional parameters.

[0003] With the application of cloud platform services, some technologies have begun to try to upload the operating data of the equipment to the cloud, and store and analyze the historical operating data through the computing power of the cloud. This method provides a basis 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, and fails to fully tap the potential of combining real-time data with historical benchmark data, especially in the analysis and optimization adjustment of dynamic parameters such as air source moisture content and air flow change ratio. This limitation makes it difficult for existing technologies to achieve accurate management and load adjustment of compressor operating status under complex working conditions. Summary of the invention

[0004] The purpose of the present invention is to provide an air compressor management method and system based on a cloud service platform, aiming to solve the problems raised in the background technology.

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

[0006] Obtaining 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 for the target air compressor according to the real-time exhaust pressure;

[0007] Obtaining a preset air source moisture content benchmark model of the target air compressor, extracting a benchmark air source moisture content corresponding to the real-time gas humidity therefrom, and calculating a first deviation quantitative index of the real-time air source moisture content compared to the benchmark air source moisture content, and based on the first deviation quantitative index, performing a preliminary correction on the primary load adjustment parameter to generate a secondary load adjustment parameter;

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

[0009] The real-time air flow change ratio of the target air compressor is calculated according to the real-time intake flow and the real-time exhaust flow, and compared 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 parameter is corrected to obtain the final load adjustment parameter.

[0010] As a further limitation of the technical solution of the embodiment 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 the embodiment of the present invention, a preset air source moisture content reference model of the target air compressor is obtained, a reference air source moisture content corresponding to the real-time gas humidity is extracted therefrom, and a first deviation quantitative index of the real-time air source moisture content compared to the reference air source moisture content is calculated, and based on the first deviation quantitative index, a preliminary correction is made to the primary load adjustment parameter, and the steps of generating the secondary load adjustment parameter include:

[0012] Obtaining a preset air source moisture content benchmark model of a target air compressor, and matching the benchmark air source moisture content corresponding to the real-time gas humidity therefrom;

[0013] Retrieving the first indicator calculation formula, and jointly calculating the first deviation quantitative indicator according to the first indicator calculation formula, the reference gas source moisture content and the real-time gas source moisture content;

[0014] The primary load adjustment parameter is multiplied by the first deviation quantization indicator to generate a secondary load adjustment parameter.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the first indicator calculation formula is: , where I1 refers to the first deviation quantitative index, R refers to the real-time gas source moisture content, and B refers to the benchmark gas source moisture content.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the initial operating condition of the new machine refers to the initial operating stage in which the operating time of the target air compressor does not exceed the set cycle after leaving the factory. At this time, the target air compressor is in an ideal state of design performance.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the real-time air flow change ratio of the target air compressor is calculated according to the real-time intake air flow and the real-time exhaust air flow, and the real-time air flow change ratio is compared with the standard air flow change ratio to obtain a second deviation quantification index, and based on the second deviation quantification index, the secondary load adjustment parameter is corrected to obtain the final load adjustment parameter. The step includes:

[0018] The real-time intake flow rate and the real-time exhaust flow rate are obtained, and the real-time intake flow rate is divided by the real-time exhaust flow rate to calculate the real-time air flow rate change ratio of the target air compressor;

[0019] Retrieving a second index calculation formula, and jointly calculating a second deviation quantification index according to 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;

[0020] The secondary load adjustment parameter is multiplied by the secondary load adjustment parameter to generate a final load adjustment parameter.

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

[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] A data acquisition module is used to 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 according to the real-time exhaust pressure;

[0024] A secondary load adjustment parameter generation module is used to obtain a preset air source moisture content reference model of a target air compressor, extract a reference air source moisture content corresponding to the real-time gas humidity therefrom, and calculate a first deviation quantitative index of the real-time air source moisture content compared to the reference air source moisture content, and based on the first deviation quantitative index, perform a preliminary correction on the primary load adjustment parameter to generate a secondary load adjustment parameter;

[0025] The preset air source moisture content reference model refers to a standard model established based on the relationship between gas humidity and air source moisture content, and the preset air source moisture content reference model defines the corresponding reference 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 operation log of the target air compressor, and find the historical exhaust flow rate corresponding to the real-time intake flow rate when the target air compressor is in the initial working condition of the new machine, and calculate the standard air flow rate change ratio;

[0027] The new machine initial operating condition refers to the initial operating stage when the target air compressor does not exceed the set period after leaving the factory, and the target air compressor is in an ideal state of design performance at this time;

[0028] The final load adjustment parameter generation module is used to calculate the real-time air flow change ratio of the target air compressor according to 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 parameter is corrected to obtain the final load adjustment parameter.

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

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

[0031] A first deviation quantitative index calculation unit, used to retrieve a first index calculation formula, and calculate the first deviation quantitative index according to the first index calculation formula, a reference gas source moisture content, and a real-time gas source moisture content;

[0032] A secondary load adjustment parameter calculation unit, configured to multiply the primary load adjustment parameter by the first deviation quantization index to generate a secondary load adjustment parameter;

[0033] The calculation formula of the first indicator is: , where I1 refers to the first deviation quantitative index, R refers to the real-time gas source moisture content, and B refers to the benchmark gas source moisture content.

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

[0035] A real-time air flow change ratio calculation unit is used to obtain the real-time intake flow and the real-time exhaust flow, and divide the real-time intake flow by the real-time exhaust flow to calculate the real-time air flow change ratio of the target air compressor;

[0036] a second deviation quantitative index calculation unit, used to retrieve a second index calculation formula, and calculate the second deviation quantitative index according to 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] a final load adjustment parameter calculation unit, configured to multiply the secondary load adjustment parameter by the secondary load adjustment parameter to generate a final load adjustment parameter;

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

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

[0040] The present invention obtains the real-time exhaust pressure, real-time air source moisture content, real-time intake flow, real-time exhaust flow and real-time gas humidity of the target air compressor, and combines the preset air source moisture content benchmark model and historical operation log to accurately optimize the compressor load adjustment. Through the deviation quantitative index calculated in real time, the load adjustment parameters are gradually corrected, and finally the dynamic adaptive final load adjustment parameters are generated, thereby realizing efficient management of the complex operating conditions of the compressor.

[0041] Based on the traditional load adjustment technology, this invention breaks through the limitation of relying on a single real-time data, and uses the dynamic deviation analysis of the ratio of the moisture content of the air source and the air flow rate change to make the load adjustment parameters more targeted and refined. It solves the load mismatch problem caused by changes in ambient humidity and fluctuations in the air flow efficiency inside the equipment, significantly improves the operating efficiency and stability of the compressor, and reduces energy consumption and operating costs. At the same time, by combining the historical benchmark data of the initial operating conditions of the new machine, this method effectively avoids the wear and tear caused by long-term operation and extends the service life of the equipment.

[0042] The present invention is applicable to the intelligent management of air compressors under various working conditions, and has broad application prospects, especially in complex industrial environments with significant humidity changes and frequent load fluctuations. It realizes multi-dimensional analysis and optimization of equipment operation status through the combination of real-time data and historical benchmarks, and provides a low-energy, high-efficiency, and long-life equipment load adjustment solution, providing important support for the development of industrial equipment management and energy-saving technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0044] Figure 2A flow chart of calculating a secondary load adjustment parameter in a method provided in an embodiment of the present invention;

[0045] Figure 3 A flow chart of calculating the final load adjustment parameter in the method provided in an embodiment of the present invention;

[0046] Figure 4 An application architecture diagram of a system provided by an embodiment of the present invention;

[0047] Figure 5 A structural block diagram of a secondary load adjustment parameter generation module in a system provided by an embodiment of the present invention;

[0048] Figure 6 This is a structural block diagram of a final load adjustment parameter generating module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

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

[0051] Specifically, a method for managing an air compressor based on a cloud service platform comprises the following steps:

[0052] Step S100, obtaining the real-time exhaust pressure, real-time air source moisture content, real-time intake flow, real-time exhaust flow and real-time gas humidity from the target air compressor, and matching the primary load adjustment parameters for the target air compressor according to the real-time exhaust pressure.

[0053] In an embodiment of the present invention, during the operation of the target air compressor, the real-time exhaust pressure data is monitored by a high-precision pressure sensor deployed on the exhaust pipeline. The sensor can capture instantaneous pressure changes and digitize them, and then upload them to the cloud service platform through the industrial Internet of Things module. The real-time air source moisture content is collected by the humidity sensor installed at the air intake end, and the humidity level of the intake air is detected in real time, and then converted into a specific real-time air source moisture content parameter in combination with the moisture content calculation model in the cloud platform. Similarly, the real-time intake flow rate and the real-time exhaust flow rate are continuously monitored by high-precision flow meters embedded in the intake pipeline and the exhaust pipeline, respectively, to ensure accurate data. These real-time monitoring data are aggregated to the cloud service platform through the industrial Internet of Things gateway, and are uniformly parsed and stored by the cloud in combination with the real-time gas humidity monitoring results to form a comprehensive real-time parameter set of the target air compressor's operating status.

[0054] The process of matching the primary load adjustment parameters according to the real-time exhaust pressure is realized by relying on the load control technology of the cloud service platform. The cloud calls the built-in proportional integral differential control (PID control) algorithm module to compare the current real-time exhaust pressure with the target pressure value in real time, and generates the primary load adjustment parameters through dynamic calculation to adjust the intake valve opening or motor power. In addition, the cloud service platform can also use the historical operating data and real-time input parameters of the target air compressor to call the load prediction model based on machine learning to further optimize the matching accuracy of the primary load adjustment parameters.

[0055] Most of the technologies involved in the above content belong to the category 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 a 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 therefrom, and calculate a first deviation quantification index of the real-time air source moisture content compared to the benchmark air source moisture content, and based on the first deviation quantification index, perform a preliminary correction on the primary load adjustment parameter to generate a secondary load adjustment parameter.

[0058] Specifically, Figure 2 A flow chart for calculating secondary load regulation parameters is shown.

[0059] Among them, 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, and calculating a first deviation quantitative index of the real-time air source moisture content compared to the benchmark air source moisture content, and based on the first deviation quantitative index, implementing a preliminary correction on the primary load adjustment parameter to generate the secondary load adjustment parameter specifically includes the following steps:

[0060] Step S201, obtaining a preset air source moisture content reference model of a target air compressor, and matching the reference air source moisture content corresponding to the real-time gas humidity therefrom;

[0061] Step S202, calling a first indicator calculation formula, and calculating a first deviation quantification indicator based on the first indicator calculation formula, a reference gas source moisture content, and a real-time gas source moisture content;

[0062] Step S203: multiply the primary load adjustment parameter by the first deviation quantization index to generate a secondary load adjustment parameter.

[0063] The preset air source moisture content reference 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 reference model defines corresponding reference air source moisture content for a target air compressor under different gas humidity conditions.

[0064] The calculation formula of the first indicator is: , where I1 refers to the first deviation quantitative index, R refers to the real-time gas source moisture content, and B refers to the benchmark gas source moisture content.

[0065] In an embodiment of the present invention, the establishment of a preset air source moisture content benchmark model is based on the historical operating data and actual working environment of the target air compressor under different gas humidity conditions. A standardized reference model is generated by combining the relationship between the humidity data collected over a long period of time and the corresponding air source moisture content, combined with the water vapor saturation calculation model in the physical properties of air. In this model, each gas humidity value has a corresponding ideal moisture content, which specifically reflects the air source moisture characteristics of the equipment under different humidity conditions. The benchmark model may also combine verification data under multiple working conditions to ensure accuracy and applicability in actual use.

[0066] When the real-time gas humidity data is uploaded to the cloud service platform, the system will search for the reference gas source moisture content that best matches the humidity value in the 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 is between the preset points of the model, the system will calculate the corresponding moisture content value based on the benchmark moisture content data of the adjacent points. This method ensures a high correlation between the real-time gas humidity and the benchmark gas source moisture content, thereby providing reliable standard parameters for subsequent calculations.

[0067] The basis for adjusting the primary load adjustment parameters in this way is that changes in the moisture content of the air source directly affect the operating load and efficiency of the compressor. The increased density of the air source with a high moisture content will affect the compression ratio and operating temperature of the compressor, and long-term operation may cause an increase in equipment load and energy consumption. By monitoring the moisture content in real time and comparing it with the baseline moisture content, calculating the degree of deviation, the load parameters of the compressor can be effectively and dynamically adjusted to make its operating state more suitable for the current environment. This adjustment strategy can not only improve the stability of equipment operation, but also extend the service life of the equipment.

[0068] For example, in an operating environment, the air humidity is 50%. Through the preset air source moisture content benchmark model matching, the benchmark air source moisture content is 10 grams per cubic meter. The actual value detected by the real-time air source moisture content sensor is 12 grams per cubic meter. Through calculation, it is found that the actual air source moisture content is 20% higher than the benchmark moisture content. This deviation indicates that the humidity of the current air source is too high, which may cause the compressor load to increase. This deviation is quantified and applied to the load adjustment. For example, the primary load adjustment parameter is multiplied by a deviation correction coefficient to generate a secondary load adjustment parameter. This correction enables the load parameter to dynamically reflect the current humidity changes, thereby ensuring the efficiency and adaptability of equipment operation.

[0069] Through such a mechanism, the equipment can respond more flexibly to environmental changes, especially in areas with significant humidity changes, such as coastal areas or seasonally humid environments. Dynamic adjustment strategies based on real-time data can accurately match equipment needs while avoiding performance degradation or overload problems caused by long-term operation, thereby 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 operation log of the target air compressor, and search therefrom for the historical exhaust flow corresponding to the real-time intake flow when the target air compressor is in the initial working condition of the new machine, and calculate the standard air flow change ratio.

[0072] The new machine initial operating condition refers to the initial operating stage in which the operating time of the target air compressor does not exceed the set period after leaving the factory. At this time, the target air compressor is in an ideal state of design performance.

[0073] In an embodiment of the present invention, during the operation of the target air compressor, the historical operation log is usually derived from the monitoring system inside the equipment, which includes real-time monitoring sensors, PLC controllers, and industrial Internet of Things devices. These devices continuously record various parameters of the compressor under different operating conditions, such as intake flow, exhaust flow, exhaust pressure, temperature, etc. All these real-time data will be stored in the local data recording system of the device and regularly uploaded to the central server or cloud service platform for analysis and archiving. Through these logs, the complete operation data of the equipment from the factory to the present can be obtained, and long-term trend analysis can be performed.

[0074] The basis for setting the cycle is usually based on the factory standards of the equipment, the manufacturer's recommended maintenance cycle, and the design performance of the equipment. For example, for some industrial compressors, their design service life and performance are usually maintained at the optimal state within a specific number of operating hours or a specific working cycle. The setting cycle is generally defined according to these standards, and a common cycle is hundreds to thousands of hours after the equipment is put into use. The significance of this cycle is 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 can accurately define the ideal performance state of the equipment in the initial operation stage for subsequent performance comparison and adjustment.

[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 change ratio of the target air compressor according to 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, and based on the second deviation quantification index, correct the secondary load adjustment parameter to obtain the final load adjustment parameter.

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

[0078] The real-time air flow change ratio of the target air compressor is calculated according to the real-time intake air flow and the real-time exhaust air flow, and compared 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 parameter is corrected to obtain the final load adjustment parameter, which specifically includes the following steps:

[0079] Step S401, obtaining a real-time intake flow rate and a real-time exhaust flow rate, and dividing the real-time intake flow rate by the real-time exhaust flow rate to calculate a real-time air flow rate change ratio of the target air compressor;

[0080] Step S402, calling a second index calculation formula, and calculating a second deviation quantification index based on 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;

[0081] Step S403 , multiplying the secondary load adjustment parameter by the secondary load adjustment parameter to generate a final load adjustment parameter.

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

[0083] In an embodiment of the present invention, the significance of adjusting the secondary load adjustment parameters by the difference between the real-time air flow change ratio and the standard air flow change ratio is that the change in air flow directly reflects the operating state of the air compressor and its load adaptability. The real-time air flow change ratio is a direct manifestation of the current operating conditions, while the standard air flow change ratio, as a reference value for the initial operating conditions of the new machine, can provide a benchmark for the ideal operating state. The difference between the two can quantify the degree of deviation between the actual operating state of the equipment and the ideal state. This deviation may not only be caused by environmental factors (such as humidity and temperature), but may also be caused by wear, leakage or increased resistance of the equipment itself. By adjusting the secondary load adjustment parameters through this difference, the operating state of the equipment under different conditions can be dynamically compensated to ensure accurate adaptation of the equipment load.

[0084] The basis for this adjustment is that the degree of deviation of the air flow change ratio reflects the change in the compression efficiency inside the equipment. As a key component of the standard air flow change ratio, the historical exhaust flow represents the exhaust efficiency of the compressor under ideal conditions. The ratio of the real-time intake flow and the real-time exhaust flow is compared with it, which can directly reflect whether the equipment is in an efficient operating state. By correcting the secondary load adjustment parameters, the impact of this deviation on the equipment load can be eliminated, and the equipment can be prevented from operating in a load range that is too high or too low, thereby optimizing energy consumption, reducing wear and increasing equipment life.

[0085] After the primary load adjustment parameters are corrected by the water content factor, the correction of the airflow change ratio may have a linkage effect on the further adjustment of the secondary load adjustment parameters. Changes in water content usually affect the gas density, which indirectly affects the changes in intake and exhaust flow. When the two factors work together, the load parameters can be optimized more comprehensively. For example, high water content may cause compression ratio changes, while airflow deviations may reveal specific reasons for efficiency reduction. The combination of 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 at the same time to ensure the efficiency and stability of equipment operation. Among them, the comparison between the real-time air flow change ratio and the standard air flow change ratio is one of the important methods to evaluate the operating status of the equipment. This method can quantify the deviation between the real-time operating status and the initial operating conditions of the new equipment, providing a scientific basis for load adjustment. The following is a specific example to illustrate how to use the difference in the real-time air flow change ratio to calculate the second deviation quantification index, and further correct the secondary load adjustment parameters to generate the final load adjustment parameters to optimize the load adaptability of the equipment.

[0087] In actual operation, the real-time exhaust pressure of the target air compressor is 7.5 bar, while the target exhaust pressure is 8 bar. The cloud service platform calculates the deviation between the real-time exhaust pressure and the target exhaust pressure through the existing proportional integral differential control algorithm (PID control) to generate the primary load adjustment parameter. Assume that after calculation, the value of the primary load adjustment parameter is 100.

[0088] Subsequently, the primary load adjustment parameter is corrected for the first time based on the real-time air source moisture content. The real-time air source moisture content is detected by the sensor and is 12 grams per cubic meter; the reference air source moisture content is calculated by the cloud platform's air source moisture content reference model and 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 percent. Therefore, the primary load adjustment parameter is multiplied by the first deviation quantification index to generate the secondary load adjustment parameter. The secondary load adjustment parameter is 100 times 1.2, which is 120.

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

[0090] The deviation between the real-time airflow change ratio and the standard airflow change ratio is calculated as 1.1111 minus 1.0417, which is 0.0694. Divide this deviation by the standard airflow change ratio, which is 0.0667. Add 1 to get 1.0667. Multiply it by 100% to convert it to a percentage, which is 106.67%. This value is the second deviation quantification index. The secondary load adjustment parameter is multiplied by the second deviation quantification index to generate the final load adjustment parameter. The final load adjustment parameter is calculated as 120 times 1.0667, which is 128.004.

[0091] Finally, after two corrections, the final load adjustment parameters generated comprehensively consider the dynamic influence of real-time exhaust pressure, real-time air source moisture content and air flow change ratio. They can accurately adapt to the current working conditions, ensure the operating efficiency and stability of the compressor, avoid overload or inefficient operation, and further optimize the energy-saving performance of the equipment.

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

[0093] Among them, in another preferred embodiment provided by the present invention, an air compressor management system based on a cloud service platform includes:

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

[0095] In an embodiment of the present invention, during the operation of the target air compressor, the real-time exhaust pressure data is monitored by a high-precision pressure sensor deployed on the exhaust pipeline. The sensor can capture instantaneous pressure changes and digitize them, and then upload them to the cloud service platform through the industrial Internet of Things module. The real-time air source moisture content is collected by the humidity sensor installed at the air intake end, and the humidity level of the intake air is detected in real time, and then converted into a specific real-time air source moisture content parameter in combination with the moisture content calculation model in the cloud platform. Similarly, the real-time intake flow rate and the real-time exhaust flow rate are continuously monitored by high-precision flow meters embedded in the intake pipeline and the exhaust pipeline, respectively, to ensure accurate data. These real-time monitoring data are aggregated to the cloud service platform through the industrial Internet of Things gateway, and are uniformly parsed and stored by the cloud in combination with the real-time gas humidity monitoring results to form a comprehensive real-time parameter set of the target air compressor's operating status.

[0096] The process of matching the primary load adjustment parameters according to the real-time exhaust pressure is realized by relying on the load control technology of the cloud service platform. The cloud calls the built-in proportional integral differential control (PID control) algorithm module to compare the current real-time exhaust pressure with the target pressure value in real time, and generates the primary load adjustment parameters through dynamic calculation to adjust the intake valve opening or motor power. In addition, the cloud service platform can also use the historical operating data and real-time input parameters of the target air compressor to call the load prediction model based on machine learning to further optimize the matching accuracy of the primary load adjustment parameters.

[0097] Most of the technologies involved in the above content belong to the category of existing technologies and have been widely used in modern industrial automation and cloud platform services.

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

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

[0100] The preset air source moisture content reference 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 reference model defines corresponding reference air source moisture content for a target air compressor under different gas humidity conditions.

[0101] Specifically, Figure 5 It shows a structural block diagram of the secondary load adjustment parameter generating module 200 in the system provided by the 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] A reference air source moisture content acquisition unit 201 is used to acquire a preset air source moisture content reference model of a target air compressor, and match the reference air source moisture content corresponding to the real-time gas humidity therefrom;

[0104] A first deviation quantitative index calculation unit 202, used to retrieve a first index calculation formula, and calculate a first deviation quantitative index according to the first index calculation formula, a reference gas source moisture content, and a real-time gas source moisture content;

[0105] A secondary load adjustment parameter calculation unit 203, configured to multiply the primary load adjustment parameter by the first deviation quantization index to generate a secondary load adjustment parameter;

[0106] The calculation formula of the first indicator is: , where I1 refers to the first deviation quantitative index, R refers to the real-time gas source moisture content, and B refers to the benchmark gas source moisture content.

[0107] In an embodiment of the present invention, the establishment of a preset air source moisture content benchmark model is based on the historical operating data and actual working environment of the target air compressor under different gas humidity conditions. A standardized reference model is generated by combining the relationship between the humidity data collected over a long period of time and the corresponding air source moisture content, combined with the water vapor saturation calculation model in the physical properties of air. In this model, each gas humidity value has a corresponding ideal moisture content, which specifically reflects the air source moisture characteristics of the equipment under different humidity conditions. The benchmark model may also combine verification data under multiple working conditions to ensure accuracy and applicability in actual use.

[0108] When the real-time gas humidity data is uploaded to the cloud service platform, the system will search for the reference gas source moisture content that best matches the humidity value in the 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 is between the preset points of the model, the system will calculate the corresponding moisture content value based on the benchmark moisture content data of the adjacent points. This method ensures a high correlation between the real-time gas humidity and the benchmark gas source moisture content, thereby providing reliable standard parameters for subsequent calculations.

[0109] The basis for adjusting the primary load adjustment parameters in this way is that changes in the moisture content of the air source directly affect the operating load and efficiency of the compressor. The increased density of the air source with a high moisture content will affect the compression ratio and operating temperature of the compressor, and long-term operation may cause an increase in equipment load and energy consumption. By monitoring the moisture content in real time and comparing it with the baseline moisture content, calculating the degree of deviation, the load parameters of the compressor can be effectively and dynamically adjusted to make its operating state more suitable for the current environment. This adjustment strategy can not only improve the stability of equipment operation, but also extend the service life of the equipment.

[0110] For example, in an operating environment, the air humidity is 50%. Through the preset air source moisture content benchmark model matching, the benchmark air source moisture content is 10 grams per cubic meter. The actual value detected by the real-time air source moisture content sensor is 12 grams per cubic meter. Through calculation, it is found that the actual air source moisture content is 20% higher than the benchmark moisture content. This deviation indicates that the humidity of the current air source is too high, which may cause the compressor load to increase. This deviation is quantified and applied to the load adjustment. For example, the primary load adjustment parameter is multiplied by a deviation correction coefficient to generate a secondary load adjustment parameter. This correction enables the load parameter to dynamically reflect the current humidity changes, thereby ensuring the efficiency and adaptability of equipment operation.

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

[0112] Furthermore, the air compressor management system based on the cloud service platform 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, and find the historical exhaust flow rate corresponding to the real-time intake flow rate when the target air compressor is in the initial working condition of the new machine, and calculate the standard air flow rate change ratio;

[0114] The new machine initial operating condition refers to the initial operating stage in which the operating time of the target air compressor does not exceed the set period after leaving the factory. At this time, the target air compressor is in an ideal state of design performance.

[0115] In an embodiment of the present invention, during the operation of the target air compressor, the historical operation log is usually derived from the monitoring system inside the equipment, which includes real-time monitoring sensors, PLC controllers, and industrial Internet of Things devices. These devices continuously record various parameters of the compressor under different operating conditions, such as intake flow, exhaust flow, exhaust pressure, temperature, etc. All these real-time data will be stored in the local data recording system of the device and regularly uploaded to the central server or cloud service platform for analysis and archiving. Through these logs, the complete operation data of the equipment from the factory to the present can be obtained, and long-term trend analysis can be performed.

[0116] The basis for setting the cycle is usually based on the factory standards of the equipment, the manufacturer's recommended maintenance cycle, and the design performance of the equipment. For example, for some industrial compressors, their design service life and performance are usually maintained at the optimal state within a specific number of operating hours or a specific working cycle. The setting cycle is generally defined according to these standards, and a common cycle is hundreds to thousands of hours after the equipment is put into use. The significance of this cycle is 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 can accurately define the ideal performance state of the equipment in the initial operation stage for subsequent performance comparison and adjustment.

[0117] Furthermore, the air compressor management system based on the cloud service platform 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 parameter is corrected to obtain the final load adjustment parameter.

[0119] Specifically, Figure 6 It shows a structural block diagram of the final load adjustment parameter generating 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 generating module 400 specifically includes:

[0121] A real-time air flow change ratio calculation unit 401 is used to obtain the real-time intake flow and the real-time exhaust flow, and divide the real-time intake flow by the real-time exhaust flow to calculate the real-time air flow change ratio of the target air compressor;

[0122] A second deviation quantitative index calculation unit 402 is used to retrieve a second index calculation formula and calculate a second deviation quantitative index based on 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] a final load adjustment parameter calculation unit 403, configured to multiply the secondary load adjustment parameter by the secondary load adjustment parameter to generate a final load adjustment parameter;

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

[0125] In an embodiment of the present invention, the significance of adjusting the secondary load adjustment parameters by the difference between the real-time air flow change ratio and the standard air flow change ratio is that the change in air flow directly reflects the operating state of the air compressor and its load adaptability. The real-time air flow change ratio is a direct manifestation of the current operating conditions, while the standard air flow change ratio, as a reference value for the initial operating conditions of the new machine, can provide a benchmark for the ideal operating state. The difference between the two can quantify the degree of deviation between the actual operating state of the equipment and the ideal state. This deviation may not only be caused by environmental factors (such as humidity and temperature), but may also be caused by wear, leakage or increased resistance of the equipment itself. By adjusting the secondary load adjustment parameters through this difference, the operating state of the equipment under different conditions can be dynamically compensated to ensure accurate adaptation of the equipment load.

[0126] The basis for this adjustment is that the degree of deviation of the air flow change ratio reflects the change in the compression efficiency inside the equipment. As a key component of the standard air flow change ratio, the historical exhaust flow represents the exhaust efficiency of the compressor under ideal conditions. The ratio of the real-time intake flow and the real-time exhaust flow is compared with it, which can directly reflect whether the equipment is in an efficient operating state. By correcting the secondary load adjustment parameters, the impact of this deviation on the equipment load can be eliminated, and the equipment can be prevented from operating in a load range that is too high or too low, thereby optimizing energy consumption, reducing wear and increasing equipment life.

[0127] After the primary load adjustment parameters are corrected by the water content factor, the correction of the airflow change ratio may have a linkage effect on the further adjustment of the secondary load adjustment parameters. Changes in water content usually affect the gas density, which indirectly affects the changes in intake and exhaust flow. When the two factors work together, the load parameters can be optimized more comprehensively. For example, high water content may cause compression ratio changes, while airflow deviations may reveal specific reasons for efficiency reduction. The combination of 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 at the same time to ensure the efficiency and stability of equipment operation. Among them, the comparison between the real-time air flow change ratio and the standard air flow change ratio is one of the important methods to evaluate the operating status of the equipment. This method can quantify the deviation between the real-time operating status and the initial operating conditions of the new equipment, providing a scientific basis for load adjustment. The following is a specific example to illustrate how to use the difference in the real-time air flow change ratio to calculate the second deviation quantification index, and further correct the secondary load adjustment parameters to generate the final load adjustment parameters to optimize the load adaptability of the equipment.

[0129] In actual operation, the real-time exhaust pressure of the target air compressor is 7.5 bar, while the target exhaust pressure is 8 bar. The cloud service platform calculates the deviation between the real-time exhaust pressure and the target exhaust pressure through the existing proportional integral differential control algorithm (PID control) to generate the primary load adjustment parameter. Assume that after calculation, the value of the primary load adjustment parameter is 100.

[0130] Subsequently, the primary load adjustment parameter is corrected for the first time based on the real-time air source moisture content. The real-time air source moisture content is detected by the sensor and is 12 grams per cubic meter; the reference air source moisture content is calculated by the cloud platform's air source moisture content reference model and 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 percent. Therefore, the primary load adjustment parameter is multiplied by the first deviation quantification index to generate the secondary load adjustment parameter. The secondary load adjustment parameter is 100 times 1.2, which is 120.

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

[0132] The deviation between the real-time airflow change ratio and the standard airflow change ratio is calculated as 1.1111 minus 1.0417, which is 0.0694. Divide this deviation by the standard airflow change ratio, which is 0.0667. Add 1 to get 1.0667. Multiply it by 100% to convert it to a percentage, which is 106.67%. This value is the second deviation quantification index. The secondary load adjustment parameter is multiplied by the second deviation quantification index to generate the final load adjustment parameter. The final load adjustment parameter is calculated as 120 times 1.0667, which is 128.004.

[0133] Finally, after two corrections, the final load adjustment parameters generated comprehensively consider the dynamic influence of real-time exhaust pressure, real-time air source moisture content and air flow change ratio. They can accurately adapt to the current working conditions, ensure the operating efficiency and stability of the compressor, avoid overload or inefficient operation, and further optimize the energy-saving performance of the equipment.

[0134] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0135] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0136] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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 above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached 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 in the protection scope of the present invention.

Claims

1. An air compressor management method based on a cloud service platform, characterized in that: The method comprises: Obtaining 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 for the target air compressor according to the real-time exhaust pressure; Obtaining a preset air source moisture content benchmark model of the target air compressor, extracting a benchmark air source moisture content corresponding to the real-time gas humidity therefrom, and calculating a first deviation quantitative index of the real-time air source moisture content compared to the benchmark air source moisture content, and based on the first deviation quantitative index, performing a preliminary correction on the primary load adjustment parameter to generate a secondary load adjustment parameter; Retrieve the historical operation log of the target air compressor, and find the historical exhaust flow rate corresponding to the real-time intake flow rate when the target air compressor is in the initial working condition of the new machine, and calculate the standard air flow rate change ratio; The real-time air flow change ratio of the target air compressor is calculated according to the real-time intake flow and the real-time exhaust flow, and compared 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 parameter is corrected to obtain the final load adjustment parameter.

2. The air compressor management method based on the cloud service platform according to claim 1, characterized in that: The preset air source moisture content reference 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 reference model defines corresponding reference air source moisture content for a target air compressor under different gas humidity conditions.

3. The air compressor management method based on the cloud service platform according to claim 2 is characterized in that: The preset air source moisture content benchmark model of the target air compressor is obtained, and the benchmark air source moisture content corresponding to the real-time gas humidity is extracted therefrom, and a first deviation quantitative index of the real-time air source moisture content compared to the benchmark air source moisture content is calculated. Based on the first deviation quantitative index, a preliminary correction is made to the primary load adjustment parameter. The steps of generating the secondary load adjustment parameter include: Obtaining a preset air source moisture content benchmark model of a target air compressor, and matching the benchmark air source moisture content corresponding to the real-time gas humidity therefrom; Retrieving the first indicator calculation formula, and jointly calculating the first deviation quantitative indicator according to the first indicator calculation formula, the reference gas source moisture content and the real-time gas source moisture content; The primary load adjustment parameter is multiplied by the first deviation quantization indicator to generate a secondary load adjustment parameter.

4. The air compressor management method based on the cloud service platform according to claim 3 is characterized in that: The calculation formula of the first indicator is: , where I1 refers to the first deviation quantitative index, R refers to the real-time gas source moisture content, and B refers to the benchmark gas source moisture content.

5. The air compressor management method based on the cloud service platform according to claim 1, characterized in that: The new machine initial operating condition refers to the initial operating stage in which the operating time of the target air compressor does not exceed the set period after leaving the factory. At this time, the target air compressor is in an ideal state of design performance.

6. The air compressor management method based on the cloud service platform according to claim 5, characterized in that: The steps of calculating a real-time air flow change ratio of a target air compressor according to the real-time intake air flow and the real-time exhaust air flow, and comparing the real-time air flow change ratio with the standard air flow change ratio to obtain a second deviation quantification index, and correcting the secondary load adjustment parameter based on the second deviation quantification index to obtain the final load adjustment parameter include: The real-time intake flow rate and the real-time exhaust flow rate are obtained, and the real-time intake flow rate is divided by the real-time exhaust flow rate to calculate the real-time air flow rate change ratio of the target air compressor; Retrieving a second index calculation formula, and jointly calculating a second deviation quantification index according to 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 secondary load adjustment parameter is multiplied by the secondary load adjustment parameter to generate a final load adjustment parameter.

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

8. An air compressor management system based on a cloud service platform, characterized in that: The system comprises: 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: A data acquisition module is used to 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 according to the real-time exhaust pressure; A secondary load adjustment parameter generation module is used to obtain a preset air source moisture content reference model of a target air compressor, extract a reference air source moisture content corresponding to the real-time gas humidity therefrom, and calculate a first deviation quantitative index of the real-time air source moisture content compared to the reference air source moisture content, and based on the first deviation quantitative index, perform a preliminary correction on the primary load adjustment parameter to generate a secondary load adjustment parameter; The preset air source moisture content reference model refers to a standard model established based on the relationship between gas humidity and air source moisture content, and the preset air source moisture content reference model defines the corresponding reference 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 operation log of the target air compressor, and find the historical exhaust flow rate corresponding to the real-time intake flow rate when the target air compressor is in the initial working condition of the new machine, and calculate the standard air flow rate change ratio; The new machine initial operating condition refers to the initial operating stage when the target air compressor does not exceed the set period after leaving the factory, and the target air compressor is in an ideal state of design performance at this time; The final load adjustment parameter generation module is used to calculate the real-time air flow change ratio of the target air compressor according to 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 parameter is corrected to obtain the final load adjustment parameter.

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

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

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