Air compressor operation and maintenance scheduling method and system

Abstract

The invention relates to the technical field of air compressors, and particularly discloses an air compressor operation and maintenance scheduling method and system.The method comprises the steps that historical air consumption data of air consumption equipment is obtained, and an air consumption change period is divided according to the historical air consumption data of the air consumption equipment; determining typical working conditions of the gas consumption equipment according to the gas consumption change period, and determining load predicted values of the air compressors corresponding to the gas consumption equipment according to the typical working conditions of the gas consumption equipment; real-time load data of the air compressor are obtained, and the load prediction value is corrected according to the real-time load data of the air compressor; and determining the load grade of each air compressor according to the corrected load predicted value, and scheduling the air compressors according to the load grade of each air compressor. According to the method, the optimal air compressor scheduling strategy is formulated by predicting the load data of the air compressors, so that the control precision of the air compressors can be effectively improved, the operation energy consumption can be dynamically reduced, and the optimal operation and maintenance effect can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of air compressors, and more specifically, to an air compressor operation and maintenance scheduling method and system. Background Art

[0002] As a product that consumes power energy, air compressors are widely used in the industrial field and are an important energy source for industrial products. Intelligent operation and maintenance of air compressors is also an important part of modern air compressor system management. However, in the actual application scenarios of air compressors, the reasonable operation and maintenance of air compressors is often very difficult due to problems such as the wide distribution of air compressors, uneven levels of management personnel, and different usage methods. Unreasonable operation and maintenance will cause problems such as increased power consumption of air compressor operation and maintenance and weakened function of air compressors, which will ultimately result in cost waste of air compressor system management. Using the intelligent operation and maintenance scheduling optimization strategy of air compressors, the air compressor system management can be unified in a short time, which can greatly reduce management costs and optimize management effects.

[0003] However, the use scenarios of air compressors in the prior art are relatively complex. For some complex industrial scenarios, multiple air compressors need to be used in coordination to meet the gas demand. If the multiple air compressors are not scheduled reasonably under complex and changeable working conditions, it will cause increased energy consumption of the air compressor or excessive wear of the equipment. Summary of the invention

[0004] The present invention provides an air compressor operation and maintenance scheduling method and system, which are used to solve the problems of insufficient control accuracy and high energy consumption of air compressor operation and maintenance scheduling in the prior art. The method comprises:

[0005] Obtain historical gas consumption data of gas-consuming equipment, and divide the gas consumption change period according to the historical gas consumption data of gas-consuming equipment;

[0006] Determine the typical working conditions of the gas-consuming equipment according to the gas consumption change cycle, and determine the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical working conditions of the gas-consuming equipment;

[0007] Obtain the real-time load data of the air compressor, and correct the load prediction value according to the real-time load data of the air compressor;

[0008] The load level of each air compressor is determined according to the corrected load prediction value, and the air compressor is dispatched according to the load level of each air compressor.

[0009] Furthermore, dividing the gas consumption change period according to the historical gas consumption data of the gas-consuming equipment includes:

[0010] Draw a gas consumption change curve based on the historical gas consumption of the gas-consuming equipment, and filter out the valley points and peak points in the gas consumption change curve;

[0011] Two adjacent valley value points are divided into a gas consumption change cycle, and the gas consumption change curve is divided according to the gas consumption change cycle to obtain a number of sub-change curves;

[0012] A preset minimum cycle threshold is obtained, and a gas consumption change cycle that is less than the preset minimum cycle threshold is merged into a previous gas consumption change cycle to obtain a final gas consumption change cycle.

[0013] Furthermore, the method of determining the typical working condition of the gas-consuming equipment according to the gas consumption variation cycle includes:

[0014] Calculate the average of the absolute values ​​of the slopes of the two valley points adjacent to the maximum peak point on the sub-change curve within the gas consumption change period to obtain the fluctuation parameter of the gas consumption change period;

[0015] Normalize the fluctuation parameter, multiply the normalized fluctuation parameter by the maximum peak point to obtain the curve characteristic value;

[0016] Clustering each gas consumption change period based on the curve characteristic value, and determining the curve characteristic value closest to the cluster center according to the clustering result;

[0017] The gas consumption variation period corresponding to the curve characteristic value closest to the cluster center is determined as the typical variation period, and the typical variation period of each cluster partition is obtained;

[0018] The typical operating conditions of each gas consumption change period are determined according to the sub-change curves within the typical change period of each cluster partition, and the typical operating conditions of each gas consumption change period are merged to obtain the typical operating conditions of the gas-consuming equipment.

[0019] Furthermore, clustering each gas consumption change period based on the curve characteristic value includes:

[0020] A curve characteristic data set is established according to the curve characteristic values ​​of the sub-change curves within the gas consumption change period, and k initial clustering centers of the curve characteristic data set are randomly selected;

[0021] Calculate the Euclidean distance from the curve characteristic value in the curve characteristic data set to the initial cluster center, and divide each curve characteristic value into corresponding partitions according to the Euclidean distance from the curve characteristic value in the curve characteristic data set to the initial cluster center;

[0022] Calculate the average value of the curve characteristic value in each partition, and re-determine the cluster center according to the average value of the curve characteristic value in each partition;

[0023] Repeat the above steps until the cluster center no longer changes or the number of iterations reaches the preset iteration threshold, and obtain k final cluster centers. According to the clustering results, determine the partition of the gas consumption change period by the characteristic values ​​of each curve.

[0024] Furthermore, the load prediction value of the air compressor corresponding to each gas-consuming equipment is determined according to the typical working conditions of the gas-consuming equipment, including:

[0025] Obtain the historical operation data of the air compressor, and determine the historical operating conditions of the gas-consuming equipment and the corresponding air compressor load based on the historical operation data of the air compressor;

[0026] A training sample set is established according to the historical operating conditions of the gas-consuming equipment and the corresponding air compressor load, an air compressor load prediction model is established according to the training sample set, and the air compressor load prediction model is trained to obtain a trained air compressor load prediction model;

[0027] The typical working conditions of the gas-consuming equipment are input into the trained air compressor load prediction model to obtain the air compressor load prediction value.

[0028] Furthermore, the load prediction value is corrected according to the real-time load data of the air compressor, including:

[0029] Draw a real-time load change curve according to the real-time load data of the air compressor, calculate the correlation coefficient of the real-time load change curve, and obtain the real-time correlation coefficient;

[0030] Draw a predicted load change curve according to the load prediction value corresponding to the real-time load of the air compressor, calculate the correlation coefficient of the predicted load change curve, and obtain the predicted correlation coefficient;

[0031] The sum of the real-time correlation coefficient and the predicted correlation coefficient is calculated to obtain the predicted deviation value, and the predicted value of the air compressor load is corrected according to the predicted deviation value.

[0032] Further, the correcting of the predicted value of the air compressor load according to the predicted deviation value includes:

[0033] The load prediction value of the air compressor is corrected according to the load prediction value correction formula, and the load prediction value correction formula is specifically:

[0034]

[0035] Among them, E is the corrected load forecast value, E α is the initial load prediction value, R α is the preset standard deviation value, R is the predicted deviation value, and Q is the preset range coefficient.

[0036] Further, the determining the load level of each air compressor according to the corrected load prediction value includes:

[0037] Calculate the difference between the corrected load prediction value and the preset standard load to obtain the air compressor load difference;

[0038] Determine whether the air compressor load difference is greater than a first preset threshold value, and if the air compressor load difference is greater than the first preset threshold value, set the preset first level as the load level of the air compressor;

[0039] If the air compressor load difference is less than or equal to the first preset threshold, determining whether the air compressor load difference is greater than the second preset threshold;

[0040] If the air compressor load difference is greater than a second preset threshold, the preset second level is set as the load level of the air compressor;

[0041] If the air compressor load difference is less than or equal to the second preset threshold, the preset third level is set as the load level of the air compressor.

[0042] Furthermore, the scheduling of the air compressors according to the load level of each air compressor includes:

[0043] When the load level of the air compressor is the first preset level, an additional air compressor is turned on;

[0044] When the load level of the air compressor is the second preset level, the air compressor is not dispatched;

[0045] When the load level of the air compressor reaches the third preset level, one air compressor is shut down.

[0046] In order to achieve the above object, the present invention also provides an air compressor operation and maintenance scheduling system, comprising:

[0047] An acquisition module is used to acquire historical gas consumption data of gas-consuming equipment and divide the gas consumption change period according to the historical gas consumption data of gas-consuming equipment;

[0048] A prediction module is used to determine the typical working conditions of gas-consuming equipment according to the gas consumption change cycle, and determine the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical working conditions of the gas-consuming equipment;

[0049] A correction module is used to obtain real-time load data of the air compressor and correct the load prediction value according to the real-time load data of the air compressor;

[0050] The scheduling module is used to determine the load level of each air compressor according to the corrected load prediction value, and schedule the air compressor according to the load level of each air compressor.

[0051] The beneficial effects of the present invention are:

[0052] By applying the above technical scheme, the present invention can accurately determine the typical operating conditions of each cycle by dividing the gas consumption change cycle of the electrical equipment, thereby obtaining the typical operating conditions of each gas-consuming equipment, predicting the load of the air compressor through the typical operating conditions and scheduling the air compressor based on the load prediction value, thereby formulating the optimal air compressor scheduling strategy, which can effectively improve the control accuracy of the air compressor, dynamically reduce the operating energy consumption, and achieve the best operation and maintenance effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 The overall flow chart of an air compressor operation and maintenance scheduling method proposed in an embodiment of the present invention is shown;

[0055] Figure 2 A structural schematic diagram of an air compressor operation and maintenance scheduling system proposed in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] The present application embodiment provides an air compressor operation and maintenance scheduling method, such as Figure 1 As shown, the method includes:

[0058] S101, obtaining historical gas consumption data of gas-consuming equipment, and dividing the gas consumption change period according to the historical gas consumption data of the gas-consuming equipment.

[0059] In some embodiments of the present application, the gas consumption change cycle is divided according to the historical gas consumption data of the gas-consuming equipment, including: drawing a gas consumption change curve according to the historical gas consumption of the gas-consuming equipment, and screening out the valley points and peak points in the gas consumption change curve; dividing two adjacent valley points into a gas consumption change cycle, and dividing the gas consumption change curve according to the gas consumption change cycle to obtain a number of sub-change curves; obtaining a preset minimum cycle threshold, and merging the gas consumption change cycle that is less than the preset minimum cycle threshold into the previous gas consumption change cycle to obtain the final gas consumption change cycle.

[0060] In this embodiment, two adjacent valley points and one peak point in the gas consumption change curve are taken as a gas consumption change cycle to segment the gas consumption change curve. At the same time, the gas consumption change cycle that is less than the preset minimum cycle threshold is merged with the previous gas consumption change cycle in the gas consumption change curve to achieve the segmentation of the gas consumption change curve, which is convenient for the calculation of subsequent typical working conditions.

[0061] S102, determining typical operating conditions of gas-consuming equipment according to a gas consumption variation cycle, and determining load prediction values ​​of air compressors corresponding to the gas-consuming equipment according to the typical operating conditions of the gas-consuming equipment.

[0062] In some embodiments of the present application, the method of determining the typical operating condition of the gas-consuming equipment based on the gas consumption change cycle includes: calculating the average value of the absolute values ​​of the slopes of two valley points adjacent to the maximum peak point on the sub-change curve within the gas consumption change cycle to obtain the fluctuation parameter of the gas consumption change cycle; normalizing the fluctuation parameter, multiplying the normalized fluctuation parameter with the maximum peak point to obtain the curve characteristic value; clustering each gas consumption change cycle based on the curve characteristic value, and determining the curve characteristic value closest to the cluster center according to the clustering result; determining the gas consumption change cycle corresponding to the curve characteristic value closest to the cluster center as the typical change cycle, and obtaining the typical change cycle of each cluster partition; determining the typical operating condition of each gas consumption change cycle according to the sub-change curve within the typical change cycle of each cluster partition, and merging the typical operating conditions of each gas consumption change cycle to obtain the typical operating condition of the gas-consuming equipment.

[0063] In this embodiment, the fluctuation parameter of the gas consumption change cycle is obtained by calculating the average value of the absolute value of the maximum slope of the sub-change curve, the maximum peak point is corrected by the fluctuation parameter, the curve characteristics of the sub-change curve are extracted, and each gas consumption change cycle is clustered based on the curve characteristic value to obtain a typical change cycle. The gas consumption change cycle in the same partition is replaced by the typical change cycle, and the replaced gas consumption change cycles are merged to obtain the typical operating condition of the gas-consuming equipment.

[0064] In some embodiments of the present application, the clustering of each gas consumption change period based on the curve characteristic value includes: establishing a curve characteristic data set according to the curve characteristic values ​​of the sub-change curves within the gas consumption change period, and randomly selecting k initial clustering centers of the curve characteristic data set; calculating the Euclidean distance from the curve characteristic value in the curve characteristic data set to the initial clustering center, and dividing each curve characteristic value into corresponding partitions according to the Euclidean distance from the curve characteristic value in the curve characteristic data set to the initial clustering center; calculating the average value of the curve characteristic value in each partition, and re-determining the clustering center according to the average value of the curve characteristic value in each partition; repeatedly iterating the above steps until the clustering center no longer changes or the number of iterations reaches a preset iteration threshold, obtaining k final clustering centers, and determining the partition of the gas consumption change period for each curve characteristic value according to the clustering result.

[0065] In this embodiment, the k value is set according to the number of gas consumption change cycles. In this embodiment, the k value is set to 8, and each gas consumption change cycle is partitioned based on the k-means clustering algorithm to obtain the typical working conditions of each partition.

[0066] In some embodiments of the present application, the method of determining the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical operating condition of the gas-consuming equipment includes: obtaining historical operating data of the air compressor, and determining the historical operating conditions of the gas-consuming equipment and the corresponding air compressor load according to the historical operating data of the air compressor; establishing a training sample set according to the historical operating conditions of the gas-consuming equipment and the corresponding air compressor load, establishing an air compressor load prediction model according to the training sample set and training the air compressor load prediction model to obtain a trained air compressor load prediction model; inputting the typical operating conditions of the gas-consuming equipment into the trained air compressor load prediction model to obtain the air compressor load prediction value.

[0067] S103, acquiring real-time load data of the air compressor, and correcting the load prediction value according to the real-time load data of the air compressor.

[0068] In some embodiments of the present application, the load prediction value is corrected according to the real-time load data of the air compressor, including: drawing a real-time load change curve according to the real-time load data of the air compressor, calculating the correlation coefficient of the real-time load change curve, and obtaining the real-time correlation coefficient; drawing a predicted load change curve according to the load prediction value corresponding to the real-time load of the air compressor, calculating the correlation coefficient of the predicted load change curve, and obtaining the predicted correlation coefficient; calculating the sum of the real-time correlation coefficient and the predicted correlation coefficient to obtain the predicted deviation value, and correcting the air compressor load prediction value according to the predicted deviation value.

[0069] In this embodiment, the correlation coefficient of the real-time load change curve and the predicted load change curve is calculated based on the Pearson correlation coefficient algorithm, and the air compressor load prediction value is corrected by the sum of the real-time correlation coefficient and the predicted correlation coefficient. The larger the sum of the real-time correlation coefficient and the predicted correlation coefficient, the higher the correlation between the two curves, and the smaller the correction value of the air compressor load prediction value.

[0070] In some embodiments of the present application, the correction of the air compressor load prediction value according to the prediction deviation value includes: correcting the air compressor load prediction value according to a load prediction value correction formula, and the load prediction value correction formula is specifically:

[0071]

[0072] Among them, E is the corrected load forecast value, E α is the initial load prediction value, R α is the preset standard deviation value, R is the predicted deviation value, and Q is the preset range coefficient.

[0073] S104, determining the load level of each air compressor according to the corrected load prediction value, and scheduling the air compressor according to the load level of each air compressor.

[0074] In some embodiments of the present application, determining the load level of each air compressor based on the corrected load prediction value includes: calculating the difference between the corrected load prediction value and the preset standard load to obtain the air compressor load difference; judging whether the air compressor load difference is greater than a first preset threshold value, if the air compressor load difference is greater than the first preset threshold value, setting the preset first level as the load level of the air compressor; if the air compressor load difference is less than or equal to the first preset threshold value, judging whether the air compressor load difference is greater than the second preset threshold value; if the air compressor load difference is greater than the second preset threshold value, setting the preset second level as the load level of the air compressor; if the air compressor load difference is less than or equal to the second preset threshold value, setting the preset third level as the load level of the air compressor.

[0075] In some embodiments of the present application, the scheduling of air compressors according to the load level of each air compressor includes: when the load level of the air compressor is a first preset level, adding an air compressor; when the load level of the air compressor is a second preset level, not scheduling the air compressor; when the load level of the air compressor is a third preset level, shutting down an air compressor.

[0076] Based on the same technical concept, such as Figure 2 As shown, the present invention also provides an air compressor operation and maintenance scheduling system, comprising:

[0077] An acquisition module is used to acquire historical gas consumption data of gas-consuming equipment, and divide the gas consumption change cycle according to the historical gas consumption data of the gas-consuming equipment; a prediction module is used to determine the typical working conditions of the gas-consuming equipment according to the gas consumption change cycle, and determine the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical working conditions of the gas-consuming equipment; a correction module is used to acquire real-time load data of the air compressor, and correct the load prediction value according to the real-time load data of the air compressor; a scheduling module is used to determine the load level of each air compressor according to the corrected load prediction value, and schedule the air compressor according to the load level of each air compressor.

[0078] By applying the above technical scheme, the present invention obtains the historical gas consumption data of the gas-consuming equipment, divides the gas consumption change cycle according to the historical gas consumption data of the gas-consuming equipment; determines the typical working condition of the gas-consuming equipment according to the gas consumption change cycle, and determines the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical working condition of the gas-consuming equipment; obtains the real-time load data of the air compressor, and corrects the load prediction value according to the real-time load data of the air compressor; determines the load level of each air compressor according to the corrected load prediction value, and dispatches the air compressor according to the load level of each air compressor. The present invention predicts the load data of the air compressor, thereby formulating the optimal air compressor scheduling strategy, which can effectively improve the control accuracy of the air compressor, dynamically reduce the operating energy consumption, and achieve the best operation and maintenance effect.

[0079] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An air compressor operation and maintenance scheduling method, characterized in that: The method comprises: Obtain historical gas consumption data of gas-consuming equipment, and divide the gas consumption change period according to the historical gas consumption data of gas-consuming equipment; Determine the typical working conditions of the gas-consuming equipment according to the gas consumption change cycle, and determine the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical working conditions of the gas-consuming equipment; Obtain the real-time load data of the air compressor, and correct the load prediction value according to the real-time load data of the air compressor; The load level of each air compressor is determined according to the corrected load prediction value, and the air compressor is dispatched according to the load level of each air compressor.

2. The air compressor operation and maintenance scheduling method according to claim 1, characterized in that: The dividing of the gas consumption change period according to the historical gas consumption data of the gas-consuming equipment includes: Draw a gas consumption change curve based on the historical gas consumption of the gas-consuming equipment, and filter out the valley points and peak points in the gas consumption change curve; Divide two adjacent valley value points into a gas consumption change cycle, and divide the gas consumption change curve according to the gas consumption change cycle to obtain a number of sub-change curves; A preset minimum cycle threshold is obtained, and a gas consumption change cycle that is less than the preset minimum cycle threshold is merged into a previous gas consumption change cycle to obtain a final gas consumption change cycle.

3. The air compressor operation and maintenance scheduling method according to claim 2 is characterized in that: Determining the typical working condition of the gas-consuming equipment according to the gas consumption variation cycle includes: Calculate the average of the absolute values ​​of the slopes of the two valley points adjacent to the maximum peak point on the sub-change curve within the gas consumption change period to obtain the fluctuation parameter of the gas consumption change period; Normalize the fluctuation parameter, multiply the normalized fluctuation parameter by the maximum peak point to obtain the curve characteristic value; Clustering each gas consumption change period based on the curve characteristic value, and determining the curve characteristic value closest to the cluster center according to the clustering result; The gas consumption variation period corresponding to the curve characteristic value closest to the cluster center is determined as the typical variation period, and the typical variation period of each cluster partition is obtained; The typical operating conditions of each gas consumption change period are determined according to the sub-change curves within the typical change period of each cluster partition, and the typical operating conditions of each gas consumption change period are merged to obtain the typical operating conditions of the gas-consuming equipment.

4. The air compressor operation and maintenance scheduling method according to claim 3 is characterized in that: The clustering of each gas consumption change period based on the curve characteristic value includes: A curve characteristic data set is established according to the curve characteristic values ​​of the sub-change curves within the gas consumption change period, and k initial clustering centers of the curve characteristic data set are randomly selected; Calculate the Euclidean distance from the curve characteristic value in the curve characteristic data set to the initial cluster center, and divide each curve characteristic value into corresponding partitions according to the Euclidean distance from the curve characteristic value in the curve characteristic data set to the initial cluster center; Calculate the average value of the curve characteristic value in each partition, and re-determine the cluster center according to the average value of the curve characteristic value in each partition; Repeat the above steps until the cluster center no longer changes or the number of iterations reaches the preset iteration threshold, and obtain k final cluster centers. According to the clustering results, determine the partition of the gas consumption change period by the characteristic values ​​of each curve.

5. The air compressor operation and maintenance scheduling method according to claim 3 is characterized in that: Determining the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical working conditions of the gas-consuming equipment includes: Obtain the historical operation data of the air compressor, and determine the historical operating conditions of the gas-consuming equipment and the corresponding air compressor load based on the historical operation data of the air compressor; A training sample set is established according to the historical operating conditions of the gas-consuming equipment and the corresponding air compressor load, an air compressor load prediction model is established according to the training sample set, and the air compressor load prediction model is trained to obtain a trained air compressor load prediction model; The typical working conditions of the gas-consuming equipment are input into the trained air compressor load prediction model to obtain the air compressor load prediction value.

6. The air compressor operation and maintenance scheduling method according to claim 5, characterized in that: The method of correcting the load prediction value according to the real-time load data of the air compressor includes: Draw a real-time load change curve according to the real-time load data of the air compressor, calculate the correlation coefficient of the real-time load change curve, and obtain the real-time correlation coefficient; Draw a predicted load change curve according to the load prediction value corresponding to the real-time load of the air compressor, calculate the correlation coefficient of the predicted load change curve, and obtain the predicted correlation coefficient; The sum of the real-time correlation coefficient and the predicted correlation coefficient is calculated to obtain the predicted deviation value, and the predicted value of the air compressor load is corrected according to the predicted deviation value.

7. The air compressor operation and maintenance scheduling method according to claim 6, characterized in that: The method of correcting the predicted value of the air compressor load according to the predicted deviation value includes: The load prediction value of the air compressor is corrected according to the load prediction value correction formula, and the load prediction value correction formula is specifically: Among them, E is the corrected load forecast value, E α is the initial load prediction value, R α is the preset standard deviation value, R is the predicted deviation value, and Q is the preset range coefficient.

8. The air compressor operation and maintenance scheduling method according to claim 6, characterized in that: Determining the load level of each air compressor according to the corrected load prediction value includes: Calculate the difference between the corrected load prediction value and the preset standard load to obtain the air compressor load difference; Determine whether the air compressor load difference is greater than a first preset threshold value, and if the air compressor load difference is greater than the first preset threshold value, set the preset first level as the load level of the air compressor; If the air compressor load difference is less than or equal to the first preset threshold, determining whether the air compressor load difference is greater than the second preset threshold; If the air compressor load difference is greater than a second preset threshold, the preset second level is set as the load level of the air compressor; If the air compressor load difference is less than or equal to the second preset threshold, the preset third level is set as the load level of the air compressor.

9. The air compressor operation and maintenance scheduling method according to claim 8, characterized in that: The step of scheduling the air compressors according to the load level of each air compressor includes: When the load level of the air compressor is the first preset level, an additional air compressor is turned on; When the load level of the air compressor is the second preset level, the air compressor is not dispatched; When the load level of the air compressor reaches the third preset level, one air compressor is shut down.

10. An air compressor operation and maintenance scheduling system, characterized in that: include: An acquisition module is used to acquire historical gas consumption data of gas-consuming equipment and divide the gas consumption change period according to the historical gas consumption data of gas-consuming equipment; A prediction module is used to determine the typical working conditions of gas-consuming equipment according to the gas consumption change cycle, and determine the load prediction value of the air compressor corresponding to each gas-consuming equipment according to the typical working conditions of the gas-consuming equipment; A correction module is used to obtain real-time load data of the air compressor and correct the load prediction value according to the real-time load data of the air compressor; The scheduling module is used to determine the load level of each air compressor according to the corrected load prediction value, and schedule the air compressor according to the load level of each air compressor.

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