An optimization scheduling method, medium, and device for an air compressor system

Abstract

The present invention relates to an optimized scheduling method, medium, and device for an air compressor system. The method includes the following steps: obtaining the air flow rate values of each air compressor within a certain operation cycle, obtaining the parameter data Yn related to the air flow rate, calculating the correlation between the data X and Yn, and selecting the parameter data with strong correlation; S200, the parameter data with strong correlation is predicted through a prediction model to obtain the predicted air flow rate values of each air compressor, and the predicted power of each air compressor is calculated based on the predicted air flow rate values; calculating the similarity index according to the predicted power and the load power of each air compressor, and when the similarity index is positive, determining the air compressor that can meet the load usage requirements. The present invention adopts an improved prediction algorithm, selects the air compressor that best matches the load demand by calculating the similarity index, and makes accurate predictions based on the operating mechanism and historical data of the air compressor, improving the accuracy and reliability of the prediction algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of air compressor control, and in particular to an optimization scheduling method, medium and equipment for an air compressor system. Background Art

[0002] An air compressor is a device that converts the mechanical energy of a prime mover (such as an electric motor or an internal combustion engine) into gas pressure energy. It is widely used in various fields such as industrial production, mining, construction, textiles, food, and medical treatment. With the development of industry and the need to save energy, the energy efficiency and automatic control of air compressors have become a hot topic of research. The existing air compressor control system mainly adjusts the operating status of the air compressor by detecting the operating parameters of the air compressor (such as pressure, temperature, current, etc.) to meet production needs. At the same time, some advanced control systems also use prediction algorithms to predict future air compressor operating parameters by learning from historical data, thereby adjusting the operating status of the air compressor in advance and improving energy efficiency.

[0003] However, in the existing air compressor control system, the accuracy and reliability of the prediction algorithm are still the main factors restricting its performance. The existing prediction algorithm is mainly based on the statistical analysis of historical data, lacking an in-depth understanding and consideration of the operating mechanism of the air compressor. Therefore, the accuracy and reliability of the prediction results are often poor, and it is difficult to meet the needs of actual production. In addition, when selecting air compressors, the existing air compressor control system mainly matches the parameters of the air compressor (such as flow, pressure, power, etc.), lacking comprehensive consideration of the operating status and load requirements of the air compressor. Therefore, in the actual operation process, it is easy to have excess or insufficient air compressor capacity, resulting in energy waste or low production efficiency. Therefore, how to improve the accuracy and reliability of the air compressor prediction algorithm, and how to reasonably select and dispatch air compressors to meet production needs and reduce energy consumption are problems that need to be solved in the current air compressor technology field. Summary of the invention

[0004] The object of the present invention is to provide an optimized scheduling method, medium and equipment for an air compressor system to improve the accuracy and reliability of an air compressor prediction algorithm.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An optimization scheduling method for an air compressor system comprises the following steps:

[0007] S100, obtain the air flow value of each air compressor in a certain operating cycle, the data is X{x 1 , x 2 , x 3 …x n}, and obtain parameter data Y related to air flown where n = 1, 2, 3…, and the specific data of the parameter data is Y n {Y n1 , Y n2 , Y n3 …Y nn}, calculate the correlation between the data X {x 1 , x 2 , x 3 …x n} and Y n {Y n1 , Y n2 , Y n3 …Y nn}, and select the parameter data with strong correlation;

[0008] S200. The parameter data with strong correlation is predicted through the POA-LSTM prediction model to obtain the predicted air flow value of each air compressor, and the predicted power of each air compressor is calculated based on the predicted air flow value;

[0009] S300. Calculate the similarity index according to the predicted power and the load power of each air compressor. When the similarity index is positive, determine the air compressor that can meet the load usage requirements.

[0010] The present invention is further provided that the parameter data Y related to the air flow in step S100 n includes compressed air leakage, exhaust pressure, operating current of the air compressor, pressure of the air storage tank, and exhaust temperature of the air compressor.

[0011] The present invention is further provided that the calculation formula for the correlation coefficient between the air flow value X and the parameter data Y related to the air flow n is:

[0012] ;

[0013] where has a value range of [0, 1.0], the range with strong correlation is [0.9 - 1.0], the range with relatively strong correlation is [0.6 - 0.9), the range with medium correlation is [0.4 - 0.6), and the range with no correlation is [0 - 0.4).

[0014] The present invention is further provided that after obtaining the predicted air flow value, the predicted power of each air compressor is calculated according to the formula: P = k×Q×s / 60; where P represents the output power of the air compressor, in kilowatts; k is a constant, with a value of 1.471; Q represents the air flow, in cubic meters per minute; s represents the rotational speed of the air compressor, in revolutions per minute.

[0015] The present invention is further configured such that the calculation of the similarity index between the predicted power and the load power of each air compressor is as follows: Determine the operating cycle T, and the predicted power of each air compressor within the operating cycle is denoted as P N {P N1 , P N2 …, P Ni}, where P Ni is the predicted power of the air compressor in the i-th time period, and the load power within one operating cycle is P L {P L1 , P L2 …, P Li}. Then the similarity index R(P N , P L ) is calculated according to the following formula:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] In the above formula, is the covariance between the predicted power and the load power of each air compressor; and are the variances of the predicted power and the load power of each air compressor, is the average value of the predicted power of the air compressor within one operating cycle; is the average value of the load power within one operating cycle.

[0021] The present invention is further configured to determine the air compressors that can meet the load usage requirements according to the similarity index. The number of them is Z, and a mathematical model of the energy consumption of the air compressor system is established. Its objective function is:

[0022] ;

[0023] In the above formula, EC is the operating energy consumption of the air compressor unit; is a binary variable representing the operating state of the j-th air compressor in the time period; is the predicted air flow value of the j-th air compressor at time t; is the operating energy consumption of the j-th air compressor at time t; ω is the operating energy consumption generated by the j-th air compressor from shutdown to startup; is the operating time of the air compressor; Z is the air compressor that can meet the load usage requirements.

[0024] The present invention is further configured, where The relational expression for predicting the air flow rate value is as follows:

[0025] ;

[0026] a j 、b j 、c j ——Fitting parameters of the energy consumption characteristic curve of the jth air compressor.

[0027] The present invention further sets that constraint conditions are established during scheduling, and the constraint conditions include load balance constraint, gas production constraint, pipeline network pressure constraint, air compressor start-stop times constraint, and binary variable constraint.

[0028] The present invention also provides a computer device, which includes a memory and a processor. The memory is used to store computer instructions, and the processor executes the above-mentioned optimal scheduling method of an air compressor system by executing the computer instructions.

[0029] The present invention also provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute the above-mentioned optimal scheduling method of an air compressor system.

[0030] Advantages of the present invention:

[0031] Compared with the existing technologies, the present invention has the following beneficial effects including at least one of the following: 1. Improving prediction accuracy: The present invention adopts an improved prediction algorithm. By calculating similarity indexes, it selects the air compressor that best matches the load demand, and makes accurate predictions based on the operating mechanism and historical data of the air compressor, thereby improving the accuracy and reliability of the prediction algorithm. 2. Optimizing resource allocation: When selecting an air compressor, the present invention not only considers the parameters of the air compressor, but also comprehensively considers the operating state and load demand of the air compressor, thereby realizing the reasonable selection and scheduling of the air compressor, avoiding the situation of overcapacity or undercapacity of the air compressor, optimizing resource allocation, and improving production efficiency. 3. Reducing energy consumption: On the premise of ensuring the load usage requirements, the present invention minimizes the operating energy consumption of the air compressor unit. By optimizing the selection and scheduling of the air compressor, it realizes the efficient utilization of energy and reduces energy consumption. 4. Improving system energy efficiency: The mathematical model of the energy consumption of the air compressor system of the present invention considers the energy consumption characteristic curve and operating state of the air compressor. By optimizing the start-stop and operating state of the air compressor, it realizes the improvement of the energy efficiency of the air compressor system. 5. High flexibility: The scheduling method of the present invention can flexibly adjust the selection and scheduling strategy of the air compressor according to different load demands and environmental conditions, improving the adaptability and flexibility of the system. In summary, compared with the existing technologies, the present invention has higher prediction accuracy, better resource allocation, lower energy consumption, higher system energy efficiency and higher flexibility. It is an advanced air compressor control method and has important practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0033] Figure 1 It is a schematic flowchart of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The following will describe in detail the embodiments of the present application in conjunction with the drawings and embodiments, so as to fully understand how the present application uses technical means to solve technical problems and achieve the realization process of technical effects and implement accordingly.

[0035] Embodiment 1:

[0036] As Figure 1 shown, this embodiment provides an optimized scheduling method for an air compressor system, including the following steps:

[0037] S100. Obtain the air flow rate values of each air compressor within a certain operating cycle, and the data is X{x 1 , x 2 , x3 …x n}, where certain operating settings are from 0:00 to 24:00; and obtain parameter data Y related to air flow n where n = 1, 2, 3…, and the specific data of the parameter data is Y n {Y n1 , Y n2 , Y n3 …Y nn}}, calculate data X {x 1 , x 2 , x 3 …x n} and the correlation between Y n {Y n1 , Y n2 , Y n3 …Y nn}, select the parameter data with strong correlation; the parameter data Y related to air flow n includes compressed air leakage, exhaust pressure, air compressor operating current, air storage tank pressure, air compressor exhaust temperature. In this embodiment, the specifically adopted parameter data with strong correlation are: air compressor exhaust temperature, air compressor operating current, exhaust pressure, and air storage tank pressure. These four data are used as the input values of the POA-LSTM prediction model;

[0038] S200. The parameter data with strong correlation are predicted through the POA-LSTM prediction model to obtain the predicted air flow values of each air compressor, and the predicted power of each air compressor is calculated according to the predicted air flow values;

[0039] To establish a prediction model, the LSTM network needs to be trained first. The training process includes two parts: the forward propagation of signals and the backpropagation of errors. Calculate the values of the hidden layer and the output layer according to the forward propagation of signals, and optimize the connection weights through the backpropagation of the error between the actual output value and the theoretical output value until the error is infinitesimal or less than the given reference error value, complete the training and save the network model parameters.

[0040] The steps when the POA-LSTM prediction model makes a prediction are as follows:

[0041] The air compressor exhaust temperature, air compressor operating current, exhaust pressure, and air storage tank pressure. These four data are used as the input values of the POA-LSTM prediction model and are normalized to make the input features have the same measurement scale and eliminate the adverse effects caused by bad sample data. The normalization formula is:

[0042] ;

[0043] In the above formula, is the normalized value; is the value to be normalized; , are the minimum value and the maximum value respectively.

[0044] Subsequently, it is solved for the optimal value through the POA algorithm (Pelican algorithm), and the obtained output value is the predicted air flow rate value;

[0045] The specific method of the POA algorithm is as follows:

[0046] The first step is to randomly initialize the population individuals according to the upper and lower limits of the given problem variables (i.e., the exhaust temperature of the air compressor, the operating current of the air compressor, the exhaust pressure, and the pressure of the air storage tank) using formula (a);

[0047] , , β = 1, 2, 3, 4…M, (a - 1);

[0048] In formula (a - 1), is the position of the β-th dimension of the -th pelican; is the lower limit of the β-th dimension of the problem to be solved; rand is a random number within the range of [0, 1]; is the upper limit of the β-th dimension of the problem to be solved; S is the population size of the pelicans; M is the dimension of the problem to be solved.

[0049] The population individuals of the pelicans are shown in formula (a - 2). Each row of the matrix represents the position of each pelican, and each column represents the value of the problem variable to be solved.

[0050] (a - 2);

[0051] In the formula, X is the population matrix of the pelicans; is the position of the -th pelican.

[0052] Additionally, in the POA algorithm, the objective function value of the pelican population can be represented by the objective function value vector:

[0053] (a - 3);

[0054] In the above formula, F is the objective function vector of the pelican population; is the objective function value of the -th pelican.

[0055] Subsequently, it enters the position update. The position update includes an exploration stage and a exploitation stage, and the exploration stage follows the following formula:

[0056] (a - 4);

[0057] In the formula, is the position of the th pelican after updating in the β-th dimension in the first stage; is the position of the prey in the β-th dimension; I is a random number with a value of 1 or 2; is the objective function value of the prey. The parameter I affects the global search ability of the POA algorithm. After the exploration ends, compare the objective function value of the new position with that of the prey to determine which is larger and which is smaller, and select the smaller one. The update method is as follows:

[0058] ; (a - 5)

[0059] In the formula, is the new position of the th pelican, is its objective function value in the second stage.

[0060] The formula in the exploitation stage is:

[0061] ; (a - 6)

[0062] is the position of the th pelican after updating in the β-th dimension in the second stage; R is a constant, R = 0.2; is 's neighborhood radius, e is the current iteration number; E is the maximum iteration number. In this stage, effective update is also used to accept or reject the new pelican position, and this process can be described by Equation (a - 7):

[0063] ; (a - 7)

[0064] In the formula, is the new position of the th pelican, is its objective function value in the second stage.

[0065] After all the population individuals are updated through the above two stages, the optimal solution of the objective function is obtained. Repeat the iteration until it is completely finished, and finally the global optimal solution of the problem to be solved is obtained.

[0066] In addition, in this embodiment, the correlation coefficient calculation formula between the air flow rate value X and the parameter data Y related to the air flow rate n is:

[0067] ;

[0068] Among them The value range of [[ ]] is [0, 1.0]. The range with strong correlation is [0.9 - 1.0], the range with relatively strong correlation is [0.6 - 0.9), the range with medium correlation is [0.4 - 0.6), and the range with no correlation is [0 - 0.4); According to the calculation, in this embodiment, the data with strong correlation are the exhaust temperature of the air compressor, the operating current of the air compressor, the exhaust pressure, and the pressure of the air storage tank.

[0069] After obtaining the predicted air flow value in this embodiment, the predicted power of each air compressor is calculated according to the formula: P = k×Q×s / 60; where P represents the output power of the air compressor, with the unit of kilowatt; k is a constant, and its value is 1.471; Q represents the air flow, with the unit of cubic meters per minute; s is the rotational speed of the air compressor, with the unit of revolutions per minute.

[0070] S300. Calculate the similarity index based on the predicted power and the load power of each air compressor. When the similarity index is positive, determine the air compressor that can meet the load usage requirements.

[0071] The calculation of the similarity index between the predicted power and the load power of each air compressor is as follows: Determine the operation cycle T. The predicted power of each air compressor within the operation cycle is denoted as P N {P N1 , P N2 …, P Ni}, where P Ni is the predicted power of the air compressor at time period i. The load power within one operation cycle is P L {P L1 , P L2 …, P Li}. Then the calculation formula for the similarity index R(P N , P L ) is as follows:

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] In the above formula, is the covariance between the predicted power and the load power of each air compressor; and are the variances of the predicted power and the load power of each air compressor, is the average value of the predicted power of the air compressor within one operation cycle; is the average value of the load power within one operation cycle; Since R(PN , P L ) ranges from [-1, 1], and its value can accurately characterize the tracking and matching degree between the load and the air compressor: when the changing trends of the load curve and the air compressor curve are the same, R(P N , P L ) is a positive value; when the changing trends of the load curve and the air compressor curve are opposite, R(P N , P L ) is a negative value; when the changing trends of the load curve and the air compressor curve are nearly completely monotonically correlated, |R(P N , P L )| will increase, and when they are completely monotonically correlated, |R(P N , P L )| = 1; when the changing trends of the load curve and the air compressor curve are not correlated, R(P N , P L )) = 0. Therefore, in this embodiment, during the same period, the air compressor when the changing trends of the load curve and the air compressor curve are the same is selected for use by the load, that is, this part of the air compressors can meet the requirements of the load for use.

[0077] After determining the air compressors that can be used, select the corresponding air compressors for operation according to the operating states of the air compressors and the load. The specific means are as follows:

[0078] Determine the air compressors that can meet the requirements of the load for use according to the similarity index. The number of them is Z, and establish a mathematical model for the energy consumption of the air compressor system. Its objective function is:

[0079] ;

[0080] In the above formula, EC is the operating energy consumption of the air compressor unit; is a binary variable, indicating the operating state of the jth air compressor in the period; is the predicted air flow value of the jth air compressor at time t; is the operating energy consumption of the jth air compressor at time t; ω is the operating energy consumption generated by the jth air compressor when starting from shutdown; is the operating time of the air compressor; Z is the air compressors that can meet the requirements of the load for use.

[0081] Among them The relational formula with the predicted air flow value is as follows:

[0082] ;

[0083] a j , b j , c j —— The fitting parameters of the energy consumption characteristic curve of the jth air compressor.

[0084] Constraints are established during scheduling, and the constraints include load balance constraints, gas production constraints, start-stop times constraints of air compressors, and binary variable constraints.

[0085] Among them, the load balance constraint is:

[0086] ;

[0087] In the above formula, is the required gas consumption during period t.

[0088] There are differences in the gas production of air compressors under operating conditions. When the rotational speed is constant, when the gas production is lower than a certain value, the air compressor will surge. Therefore, the minimum gas production of the air compressor is the surge flow; the maximum flow is generally the rated flow of the air compressor, and its expression is as follows:

[0089] The gas production constraint is ;

[0090] In the above formula, is the minimum gas production of the j-th air compressor, that is, the surge flow; is the minimum gas production of the j-th air compressor, that is, the rated flow.

[0091] The start-stop times of the air compressor are restricted, and its expression is as follows:

[0092] ; M j is the maximum allowable number of shutdowns per day of the j-th air compressor.

[0093] Binary variable constraint:

[0094] represents that the air compressor is in the operating state, represents that the air compressor is in the shutdown state, and its expression is as follows:

[0095] .

[0096] Embodiment 2:

[0097] The present invention also provides a computer device, the device includes a memory and a processor, the memory is used to store computer instructions, and the processor executes the computer instructions to execute the optimization scheduling method of an air compressor system as described above.

[0098] Embodiment 3:

[0099] The present invention also provides a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the optimization scheduling method of an air compressor system as described above.

[0100] The present invention adopts an improved prediction algorithm. By calculating the similarity index, it selects the air compressor that best matches the load demand, and makes accurate predictions based on the operating mechanism and historical data of the air compressor, thereby improving the accuracy and reliability of the prediction algorithm.

[0101] Optimizing resource allocation: When selecting an air compressor, the present invention not only considers the parameters of the air compressor, but also comprehensively considers the operating status and load demand of the air compressor, thereby achieving a reasonable selection and scheduling of the air compressor, avoiding the situation of overcapacity or undercapacity of the air compressor, optimizing resource allocation, and improving production efficiency.

[0102] Reducing energy consumption: On the premise of ensuring the load usage requirements, the present invention minimizes the operating energy consumption of the air compressor unit. By optimizing the selection and scheduling of the air compressor, it realizes the efficient utilization of energy and reduces energy consumption.

[0103] Improving system energy efficiency: The mathematical model of the energy consumption of the air compressor system of the present invention takes into account the energy consumption characteristic curve and operating status of the air compressor. By optimizing the start-stop and operating status of the air compressor, it realizes the improvement of the energy efficiency of the air compressor system.

[0104] High flexibility: The scheduling method of the present invention can flexibly adjust the selection and scheduling strategy of the air compressor according to different load demands and environmental conditions, improving the adaptability and flexibility of the system. In summary, compared with the prior art, the present invention has higher prediction accuracy, better resource allocation, lower energy consumption, higher system energy efficiency and higher flexibility. It is an advanced air compressor control method with important practical value.

[0105] As used in the specification and claims, certain terms are used to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. The specification and claims do not use the difference in names as a way to distinguish components, but use the difference in functions of components as the criterion for distinction. As used throughout the specification and claims, "comprising" is an open-ended term and should be interpreted as "including but not limited to". "Substantially" means within an acceptable error range. Those skilled in the art can solve technical problems within a certain error range and basically achieve the technical effect.

[0106] It should be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a commodity or system comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such commodity or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the commodity or system comprising the element.

[0107] The foregoing description has shown and described several preferred embodiments of the present invention. However, as mentioned above, it should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be altered within the scope of the inventive concept herein through the above teachings or the skills or knowledge in the relevant field. Any alterations and changes made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. An optimization scheduling method for an air compressor system, characterized in that: The steps include: S100, obtain the air flow value of each air compressor in a certain operation cycle, and the data is X{x1, x2, x3…x n }, and obtain the parameter data Y related to the air flow n Where n=1, 2, 3…, the specific data of the parameter data is Y n {Y n1 , Y n2 , Y n3 …Y nn }, calculate the data X{x1, x2, x3…x n } and Y n {Y n1 , Y n2 , Y n3 …Y nn }, select parameter data with strong correlation; S200, the parameter data with strong correlation is predicted by the POA-LSTM prediction model to obtain the predicted air flow value of each air compressor, and the predicted power of each air compressor is calculated according to the predicted air flow value; S300, calculating a similarity index based on the predicted power and load power of each air compressor, and determining an air compressor that can meet the load usage requirements when the similarity index is positive; determining the number of air compressors that can meet the load usage requirements based on the similarity index is Z, and establishing a mathematical model of energy consumption of the air compressor system, and its objective function is: ； In the above formula, the operating energy consumption of the EC air compressor unit; is a binary variable, It represents the operating status of the j-th air compressor in the time period; is the predicted air flow value of the j-th air compressor in period t; the operating energy consumption of the j-th air compressor in period t; ω is the operating energy consumption of the j-th air compressor from shutdown to startup; is the air compressor running time; Z is the air compressor that can meet the load usage requirements; The relationship between the predicted air flow value of the j-th air compressor in period t is as follows: ; a j 、b j 、c j ——The fitting parameters of the energy consumption characteristic curve of the jth air compressor; Constraints are established during scheduling, including load balance constraints, gas production constraints, pipeline pressure constraints, air compressor start and stop times constraints, and binary variable constraints.

2. The optimization scheduling method of an air compressor system according to claim 1, characterized in that: The parameter data Y related to the air flow in step S100 n Including compressed air leakage, exhaust pressure, air compressor operating current, air tank pressure, and air compressor exhaust temperature.

3. The optimization scheduling method of an air compressor system according to claim 1, characterized in that: Air flow value X and air flow related parameter data Y n The correlation coefficient calculation formula is: ； in The value range of is [0, 1.0], the range of strong correlation is [0.9-1.0], the range of moderate correlation is [0.6-0.9), the range of medium correlation is [0.4-0.6), and the range of no correlation is [0 -0.4).

4. The optimization scheduling method of an air compressor system according to claim 1, characterized in that: After obtaining the predicted air flow value, the predicted power of each air compressor is calculated according to the formula: P = k×Q×s / 60; where P represents the output power of the air compressor in kilowatts; k is a constant with a value of 1.471; Q represents the air flow in cubic meters per minute; and s is the speed of the air compressor in revolutions per minute.

5. The method for optimizing and scheduling an air compressor system according to claim 1, characterized in that: The similarity index between the predicted power and load power calculation of each air compressor is calculated as follows: Determine the operation period T, and the predicted power of each air compressor in the operation period is recorded as P N {P N1 , P N2 …, P Ni }, where P Ni is the predicted power of the air compressor in period i, and the load power in one operating cycle is P L {P L1 , P L2 …, P Li }, then the similarity index between the two is R(P N , P L ) is calculated as follows: ； ； ； ； In the above formula, is the covariance between the predicted power and load power of each air compressor; and is the variance of the predicted power and load power of each air compressor, It is the average value of the predicted power of the air compressor in one operating cycle; It is the average value of load power in one operating cycle.

6. A computer device, characterized in that: The device includes a memory and a processor, the memory is used to store computer instructions, and the processor executes the computer instructions to execute the optimization scheduling method of an air compressor system described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the optimization scheduling method for an air compressor system as described in any one of claims 1-5.

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