Air compressor energy consumption optimization system and method based on big data analysis

Abstract

The invention discloses an air compressor energy consumption optimization system and method based on big data analysis. The system comprises a data acquisition module, a data processing module and an air compressor adjusting module. Relates to the technical field of energy conservation of air compressors, and solves the technical problem of higher energy consumption in the operation process of the air compressor in the prior art. According to the method, the operation condition of each air compressor is analyzed on the basis of the air consumption demand curve and the multiple operation curves, and the operation characteristic data corresponding to each air compressor are obtained; constructing an exhaust priority sequence based on the operation characteristic data; and starting a plurality of air compressors based on the air consumption predicted value and the exhaust priority sequence. According to the method, the gas consumption demand curve and the linear fitting operation curve are constructed, the operation characteristics of all the air compressors are analyzed, the exhaust priority sequence is constructed, and the gas consumption prediction model is combined, so that the gas consumption demand in the future is accurately estimated. And the optimal air compressor combination is intelligently selected to start based on the predicted value and the priority sequence, so that the operation energy consumption of the air compressors can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of air compressor energy saving, and specifically relates to an air compressor energy consumption optimization system and method based on big data analysis. Background Art

[0002] In modern industrial production, air compressors are key power source equipment, and their energy consumption accounts for an important part of the company's energy consumption. Traditional air compressor management systems usually lack in-depth analysis of operating data, resulting in low equipment efficiency, excessive energy consumption, and increased maintenance costs. The air compressor itself has the problem of high energy consumption. If it operates inefficiently for a long time, resource waste is difficult to avoid. Therefore, it is necessary to optimize the energy efficiency of the air compressor in a timely manner to reduce the occurrence of resource waste.

[0003] The prior art collects the operating information of the air compressor, analyzes the operating information to obtain the energy efficiency characteristic data of the air compressor, and adjusts the operating frequency of the air compressor based on the energy efficiency characteristic data, thereby reducing the energy consumption of the air compressor during operation. However, the technical solutions of the prior art are difficult to optimize the operating status of several air compressors in the air compressor group in a targeted manner, resulting in high energy consumption of the air compressor during operation; in addition, the solutions of the prior art are difficult to estimate the gas demand in advance based on the historical gas consumption, and adjust the operating status of the air compressor based on the gas demand, resulting in insufficient and inaccurate optimization of the energy consumption of the air compressor.

[0004] The present invention proposes an air compressor energy consumption optimization system and method based on big data analysis to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an air compressor energy consumption optimization system and method based on big data analysis, which is used to solve the problem that the technical solutions of the prior art are difficult to perform targeted optimization of the operating states of several air compressors in an air compressor unit, resulting in high energy consumption of the air compressor during operation; in addition, the solutions of the prior art are difficult to estimate the gas demand in advance based on the historical gas consumption, and adjust the operating state of the air compressor according to the gas demand, resulting in the technical problem that the energy consumption optimization of the air compressor is not timely and accurate enough.

[0006] To achieve the above-mentioned object, a first aspect of the present invention provides an air compressor energy consumption optimization system based on big data analysis, comprising: a data processing module, and a data acquisition module and an air compressor adjustment module connected thereto;

[0007] The data acquisition module is used to collect the historical gas consumption data of the park in several consecutive cycles; obtain the operating data of each air compressor in the air compressor group in several consecutive cycles; wherein the air compressor group includes several air compressors; the operating data includes exhaust volume and energy consumption;

[0008] The data processing module: draws a gas demand curve based on historical gas consumption data; performs linear fitting on the operating data of each air compressor to obtain a number of operating curves; analyzes the operating status of each air compressor based on the gas demand curve and the several operating curves to obtain the operating characteristic data corresponding to each air compressor; constructs an exhaust priority sequence based on the operating characteristic data; wherein the operating curve includes an exhaust volume curve and an energy consumption curve; and,

[0009] Input historical gas consumption data into the park gas consumption prediction model to obtain the park's gas consumption prediction value; wherein the park gas consumption prediction model is built based on an artificial intelligence model;

[0010] The air compressor adjustment module enables a number of air compressors based on the predicted air consumption value and the exhaust priority sequence.

[0011] Preferably, drawing a gas consumption demand curve based on historical gas consumption data includes:

[0012] The historical gas consumption data of the park in several consecutive periods are extracted; time is used as the independent variable and the historical gas consumption data is used as the dependent variable, and the gas consumption demand curve is drawn through linear fitting.

[0013] Preferably, the linear fitting of the operating data of each air compressor comprises:

[0014] Extract the exhaust volume and energy consumption from the operating data of each air compressor in several consecutive cycles; use linear fitting to draw the exhaust volume curve of each air compressor with time as the independent variable and exhaust volume as the dependent variable; use linear fitting to draw the energy consumption curve of each air compressor with time as the independent variable and energy consumption as the dependent variable.

[0015] Preferably, the analysis of the operating status of each air compressor based on the gas demand curve and the plurality of operating curves includes:

[0016] Extract the gas demand curves in several consecutive cycles and the operation curves corresponding to several air compressors; mark the start time of several consecutive cycles as t1, mark the end time of several consecutive cycles as t2, mark the gas demand curve as g(t), mark the exhaust volume curves corresponding to several air compressors as fi(t), and mark the energy consumption curves corresponding to several air compressors as hi(t); wherein t is time, and t1≦t≦t2; i=1, 2, ..., n, where n is the total number of air compressors;

[0017] Extract the maximum exhaust volume from the exhaust volume curve corresponding to each air compressor, and mark the maximum exhaust volume as the maximum exhaust volume;

[0018] Calculate the difference between the gas demand curve and the exhaust volume curve corresponding to each air compressor to obtain the exhaust volume difference corresponding to each air compressor;

[0019] Calculate the ratio between the exhaust volume curve fi(t) of each air compressor and the corresponding energy consumption curve hi(t), and mark the corresponding ratio as the energy efficiency ratio;

[0020] The maximum exhaust volume, exhaust volume difference and energy efficiency ratio are integrated into the operation characteristic data.

[0021] It should be noted that the exhaust volume difference refers to the difference between the cumulative exhaust volume of each air compressor and the cumulative gas consumption of the park during the collection period;

[0022] Preferably, the difference between the calculated gas demand curve and the exhaust volume curve corresponding to each air compressor includes:

[0023] Extract the gas demand curve g(t) and the exhaust volume curve fi(t) corresponding to each air compressor; through the formula Calculate the exhaust volume difference PQCi corresponding to air compressor i.

[0024] Preferably, the step of constructing an exhaust priority sequence based on the operating characteristic data includes:

[0025] Extract the maximum exhaust volume, exhaust volume difference and energy efficiency ratio value from the operation characteristic data of each air compressor; determine whether the energy efficiency ratio value of each air compressor is greater than the preset energy efficiency threshold; if yes, mark the energy efficiency label of the corresponding air compressor as 1; if no, mark the energy efficiency label of the corresponding air compressor as 0;

[0026] The numbers corresponding to the air compressors with energy efficiency label values ​​of 1 are added to the first priority sequence, and the numbers corresponding to the air compressors with energy efficiency label values ​​of 0 are added to the second priority sequence;

[0027] Calculate the corresponding energy-saving priority coefficient based on the operating characteristic data of each air compressor;

[0028] The first priority sequence and the second priority sequence are sorted respectively in descending order according to the energy-saving priority coefficient;

[0029] The first priority sequence and the second priority sequence are integrated into an exhaust priority sequence.

[0030] Preferably, the calculation of the corresponding energy-saving priority coefficient based on the operating characteristic data of each air compressor includes:

[0031] Extract the maximum exhaust volume, exhaust volume difference and energy efficiency ratio from the operating characteristic data of each air compressor; Calculate the energy-saving priority coefficient JYXi of air compressor i; where t2-t1 is the duration of several consecutive cycles, ZPLi is the maximum exhaust volume of air compressor i, PQCi is the exhaust volume difference of air compressor i, NXSi is the energy efficiency ratio of air compressor i; a, b, and c are all influence coefficients greater than 0.

[0032] Preferably, the park gas consumption prediction model is constructed based on an artificial intelligence model, including:

[0033] The historical gas consumption data of the park in several consecutive periods are extracted and integrated into several groups of training data and test data; the artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test results; finally, a park gas consumption prediction model is obtained, which inputs the historical gas consumption data of the most recent several consecutive periods and outputs the predicted value of gas consumption in the prediction period; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0034] Preferably, the enabling of several air compressors based on the predicted gas consumption value and the exhaust priority sequence includes:

[0035] T1: Extract gas consumption forecast value, first priority sequence, second priority sequence;

[0036] T2: Calculate the sum of the maximum exhaust volumes of several air compressors in the first priority sequence and mark it as the first air usage threshold;

[0037] T3: Calculate the sum of the maximum exhaust volumes of several air compressors in the second priority sequence and mark it as the second air usage threshold;

[0038] T4: mark the sum of the first gas usage threshold and the second gas usage threshold as the third gas usage threshold;

[0039] T5: Determine whether the predicted gas consumption value is greater than the third gas consumption threshold; if yes, generate a gas shortage prompt message; if no, jump to T6;

[0040] T6: Determine whether the predicted gas consumption value is less than the first gas consumption threshold;

[0041] If yes, then select several air compressors from the first priority sequence in turn, set the operating states of the selected air compressors to on, and set the operating states of the air compressors in the second priority sequence to off;

[0042] If not, the operating states of the air compressors in the first priority sequence and the second priority sequence are all set to on; wherein the operating state of the air compressor includes on and off.

[0043] A second aspect of the present invention provides an air compressor energy consumption optimization method based on big data analysis, comprising:

[0044] S1: Collect historical gas consumption data of the park where the air compressor unit is located; obtain the operating data of each air compressor in the air compressor unit;

[0045] S2: Draw a gas demand curve based on historical gas consumption data;

[0046] S3: performing linear fitting on the operating data of each air compressor to obtain several operating curves;

[0047] S4: Analyze the operating conditions of each air compressor based on the gas demand curve and several operating curves to obtain operating characteristic data corresponding to each air compressor; and construct an exhaust priority sequence based on the operating characteristic data;

[0048] S5: inputting historical gas consumption data into the park gas consumption prediction model to obtain the park's gas consumption prediction value;

[0049] S6: Enable several air compressors based on the predicted air consumption and exhaust priority sequence.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. Aiming at the problem of optimizing the energy consumption of air compressors, the present invention collects the historical gas consumption data of the park and the operating data of each air compressor to construct a gas demand curve and a linear fitting operating curve. Based on this, the operating characteristics of each air compressor are analyzed and an exhaust priority sequence is constructed. Combined with the gas consumption prediction model, an accurate prediction of future gas demand is achieved. Based on the predicted value and the priority sequence, the optimal air compressor combination is intelligently selected for startup, which effectively reduces energy consumption, reduces energy waste, improves system efficiency, and extends the service life of equipment, reduces operating costs, and significantly improves the economic benefits and energy efficiency of the enterprise.

[0052] 2. The present invention obtains the maximum exhaust volume, exhaust volume difference and energy efficiency ratio by comprehensively analyzing the gas demand curve of the park, the exhaust volume curve and the energy consumption curve of each air compressor, so that the operating characteristic data obtained by analysis can accurately reflect the actual operating conditions of each air compressor, which is convenient for subsequent targeted adjustment and optimization of the air compressor unit according to the operating characteristic data of each air compressor, thereby helping to reduce the energy consumption of the air compressor unit during operation.

[0053] 3. The present invention calculates the first gas usage threshold and the second gas usage threshold corresponding to the first priority sequence and the second priority sequence, and marks the sum of the first gas usage threshold and the second gas usage threshold as the third gas usage threshold; by judging the relationship between the predicted gas usage value and the first gas usage threshold, the second gas usage threshold, and the third gas usage threshold, targeted adjustments are made to several air compressors in the air compressor group, thereby achieving the goal of reducing the number of air compressors turned on as much as possible while ensuring the gas demand, which is beneficial to reducing the energy consumption of the air compressor during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 It is an overall flow chart of the air compressor energy consumption optimization method based on big data analysis of the present invention;

[0056] Figure 2 It is a schematic diagram of the principle of the air compressor energy consumption optimization system based on big data analysis of the present invention;

[0057] Figure 3 This is a flow chart of enabling the air compressor based on the predicted gas consumption value and the exhaust priority sequence in the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1-Figure 3 , the first aspect of the present invention provides an air compressor energy consumption optimization system based on big data analysis, including: a data processing module, and a data acquisition module and an air compressor adjustment module connected thereto;

[0060] Data collection module: used to collect the historical gas consumption data of the park in several consecutive cycles; obtain the operating data of each air compressor in the air compressor group in several consecutive cycles; wherein the air compressor group includes several air compressors; the operating data includes exhaust volume and energy consumption;

[0061] Data processing module: draw a gas demand curve based on historical gas consumption data; perform linear fitting on the operating data of each air compressor to obtain several operating curves; analyze the operating status of each air compressor based on the gas demand curve and several operating curves to obtain the operating characteristic data corresponding to each air compressor; construct an exhaust priority sequence based on the operating characteristic data; wherein the operating curve includes an exhaust volume curve and an energy consumption curve; and,

[0062] Input historical gas consumption data into the park gas consumption prediction model to obtain the park's gas consumption prediction value; wherein the park gas consumption prediction model is built based on an artificial intelligence model;

[0063] Air compressor regulation module: Enables several air compressors based on the predicted air consumption and exhaust priority sequence.

[0064] In this embodiment, drawing a gas demand curve based on historical gas consumption data includes:

[0065] The historical gas consumption data of the park in several consecutive periods are extracted; time is used as the independent variable and the historical gas consumption data is used as the dependent variable, and the gas consumption demand curve is drawn through linear fitting.

[0066] In this embodiment, linear fitting is performed on the operating data of each air compressor, including:

[0067] Extract the exhaust volume and energy consumption from the operating data of each air compressor in several consecutive cycles; use linear fitting to draw the exhaust volume curve of each air compressor with time as the independent variable and exhaust volume as the dependent variable; use linear fitting to draw the energy consumption curve of each air compressor with time as the independent variable and energy consumption as the dependent variable.

[0068] The present invention extracts the exhaust volume and energy consumption from the operating data of each air compressor in several continuous cycles, and linearly fits the exhaust volume and energy consumption respectively to obtain an exhaust volume curve and an energy consumption curve, which is convenient for subsequent analysis based on the exhaust volume curve and the energy consumption curve, thereby making the operating characteristic data of each air compressor obtained by analysis more accurate.

[0069] In this embodiment, the operating status of each air compressor is analyzed based on the gas demand curve and several operating curves, including:

[0070] Extract the gas demand curves in several consecutive cycles and the operation curves corresponding to several air compressors; mark the start time of several consecutive cycles as t1, mark the end time of several consecutive cycles as t2, mark the gas demand curve as g(t), mark the exhaust volume curves corresponding to several air compressors as fi(t), and mark the energy consumption curves corresponding to several air compressors as hi(t); wherein t is time, and t1≦t≦t2; i=1, 2, ..., n, where n is the total number of air compressors;

[0071] Extract the maximum exhaust volume from the exhaust volume curve corresponding to each air compressor, and mark the maximum exhaust volume as the maximum exhaust volume;

[0072] Calculate the difference between the gas demand curve and the exhaust volume curve corresponding to each air compressor to obtain the exhaust volume difference corresponding to each air compressor;

[0073] Calculate the ratio between the exhaust volume curve fi(t) of each air compressor and the corresponding energy consumption curve hi(t), and mark the corresponding ratio as the energy efficiency ratio;

[0074] The maximum exhaust volume, exhaust volume difference and energy efficiency ratio are integrated into the operation characteristic data.

[0075] It should be noted that the exhaust volume difference refers to the difference between the cumulative exhaust volume of each air compressor and the cumulative gas consumption of the park during the collection period.

[0076] The present invention obtains the maximum exhaust volume, exhaust volume difference and energy efficiency ratio by comprehensively analyzing the gas consumption demand curve of the park, the exhaust volume curve and the energy consumption curve of each air compressor, so that the operating characteristic data obtained by analysis can accurately reflect the actual operating conditions of each air compressor, which is convenient for subsequent targeted adjustment and optimization of the air compressor unit according to the operating characteristic data of each air compressor, thereby helping to reduce the energy consumption of the air compressor unit during operation.

[0077] In this embodiment, calculating the difference between the gas demand curve and the exhaust volume curve corresponding to each air compressor includes:

[0078] Extract the gas demand curve g(t) and the exhaust volume curve fi(t) corresponding to each air compressor; through the formula Calculate the exhaust volume difference PQCi corresponding to air compressor i.

[0079] For example, the cumulative exhaust volume of the air compressor 1 in several consecutive hours is set The cumulative value of gas consumption in the park within several consecutive hours The exhaust volume difference of air compressor 1 is calculated by the formula PQC1=4500.

[0080] In this embodiment, an exhaust priority sequence is constructed based on the operating characteristic data, including:

[0081] Extract the maximum exhaust volume, exhaust volume difference and energy efficiency ratio value from the operation characteristic data of each air compressor; determine whether the energy efficiency ratio value of each air compressor is greater than the preset energy efficiency threshold; if yes, mark the energy efficiency label of the corresponding air compressor as 1; if no, mark the energy efficiency label of the corresponding air compressor as 0;

[0082] The numbers corresponding to the air compressors with energy efficiency label values ​​of 1 are added to the first priority sequence, and the numbers corresponding to the air compressors with energy efficiency label values ​​of 0 are added to the second priority sequence;

[0083] Calculate the corresponding energy-saving priority coefficient based on the operating characteristic data of each air compressor;

[0084] The first priority sequence and the second priority sequence are sorted respectively in descending order according to the energy-saving priority coefficient;

[0085] The first priority sequence and the second priority sequence are integrated into an exhaust priority sequence.

[0086] Exemplarily, the air compressor in this embodiment is a fixed-frequency screw air compressor, and the maximum exhaust volume, exhaust volume difference and energy efficiency ratio of several air compressors are shown in the following table:

[0087]

[0088]

[0089] Set the energy efficiency threshold to 10m 3 / kwh, since the energy efficiency ratios of air compressors 1, 2, and 6 are greater than the preset energy efficiency threshold, the energy efficiency labels of the corresponding air compressors are set to 1; since the energy efficiency ratios of air compressors 3, 4, and 5 are less than the preset energy efficiency threshold, the energy efficiency labels of the corresponding air compressors are set to 0; the numbers corresponding to the air compressors with energy efficiency label values ​​of 1 are added to the first priority sequence to obtain the first priority sequence {1, 2, 6}; the numbers corresponding to the air compressors with energy efficiency label values ​​of 0 are added to the second priority sequence to obtain the second priority sequence {3, 4, 5}; the first priority sequence and the second priority sequence are sorted in descending order according to the energy-saving priority coefficients; the first priority sequence and the second priority sequence are integrated into the exhaust priority sequence.

[0090] In this embodiment, the corresponding energy-saving priority coefficient is calculated based on the operating characteristic data of each air compressor, including:

[0091] Extract the maximum exhaust volume, exhaust volume difference and energy efficiency ratio from the operating characteristic data of each air compressor; Calculate the energy-saving priority coefficient JYXi of air compressor i; where t2-t1 is the duration of several consecutive cycles, ZPLi is the maximum exhaust volume of air compressor i, PQCi is the exhaust volume difference of air compressor i, and NXSi is the energy efficiency ratio of air compressor i; a, b, and c are all influence coefficients greater than 0, and the specific values ​​of the influence coefficients a, b, and c are set by relevant experts based on experience.

[0092] Exemplarily, the influence coefficients a=3, b=10, c=1.5 are set; the duration of several continuous cycles is t2-t1=60min, the maximum exhaust volume ZPL1 of air compressor 1 is 20, the exhaust volume difference PQC1 of air compressor 1 is 4500, and the energy efficiency ratio NXS1 of air compressor 1 is 13.8; the energy-saving priority coefficient JYX1≈39.11 of air compressor 1 is calculated by the formula.

[0093] It is worth noting that the values ​​of the impact coefficients a and b are related to the peak gas consumption of the park. When the peak gas consumption of the park is larger, the values ​​of the impact coefficients a and b are set to be larger; the value of the impact coefficient c is related to the electricity cost of the park. When the electricity cost of the park is larger, the value of the impact coefficient c is set to be larger.

[0094] It should be noted that the larger the maximum exhaust volume, the smaller the exhaust volume difference and the smaller the energy efficiency ratio, the larger the calculated energy-saving priority coefficient. The larger the energy-saving priority coefficient is, the lower the comprehensive energy consumption of the corresponding air compressor during actual operation and the higher the operating efficiency.

[0095] In this embodiment, the park gas consumption prediction model is constructed based on an artificial intelligence model, including:

[0096] The historical gas consumption data of the park in several consecutive periods are extracted and integrated into several groups of original data, 80% of the original data are used as training data and 20% as test data; the artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test results; finally, a park gas consumption prediction model is obtained, which inputs the historical gas consumption data of the most recent several consecutive periods and outputs the predicted value of the gas consumption in the prediction period; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0097] The present invention extracts historical gas consumption data of the park in several consecutive periods, and uses the historical gas consumption data to train an artificial intelligence model. After the training is completed, a park gas consumption prediction model is obtained. By inputting the historical gas consumption data of several consecutive periods into the park gas consumption prediction model, the gas consumption prediction value of the prediction period is obtained; the air compressor adjustment module can obtain the changes in gas consumption in advance and adjust the operating status of each air compressor in the air compressor group in time, which is beneficial to reduce the energy consumption of the air compressor during operation.

[0098] In this embodiment, several air compressors are activated based on the predicted gas consumption value and the exhaust priority sequence, including:

[0099] T1: Extract gas consumption forecast value, first priority sequence, second priority sequence;

[0100] T2: Calculate the sum of the maximum exhaust volumes of several air compressors in the first priority sequence and mark it as the first air usage threshold;

[0101] T3: Calculate the sum of the maximum exhaust volumes of several air compressors in the second priority sequence and mark it as the second air usage threshold;

[0102] T4: mark the sum of the first gas usage threshold and the second gas usage threshold as the third gas usage threshold;

[0103] T5: Determine whether the predicted gas consumption value is greater than the third gas consumption threshold; if yes, generate a gas shortage prompt message; if no, jump to T6;

[0104] T6: Determine whether the predicted gas consumption value is less than the first gas consumption threshold;

[0105] If yes, then select several air compressors from the first priority sequence in turn, set the operating states of the selected air compressors to on, and set the operating states of the air compressors in the second priority sequence to off;

[0106] If not, the operating states of the air compressors in the first priority sequence and the second priority sequence are all set to on; wherein the operating state of the air compressor includes on and off.

[0107] For example, the gas consumption forecast value is set to 70m 3 / min, the first gas consumption threshold is 92m 3 / min, the second gas consumption threshold is 94m 3 / min, the third gas consumption threshold is 186m 3 / min; since the predicted gas consumption value is less than the first gas consumption threshold, several air compressors are selected from the first priority sequence in turn, the operating states of the selected air compressors are set to on, and the operating states of the air compressors in the second priority sequence are set to off.

[0108] The present invention calculates the first gas usage threshold and the second gas usage threshold corresponding to the first priority sequence and the second priority sequence, and marks the sum of the first gas usage threshold and the second gas usage threshold as the third gas usage threshold; by judging the relationship between the predicted gas usage value and the first gas usage threshold, the second gas usage threshold, and the third gas usage threshold, targeted adjustments are made to a number of air compressors in the air compressor group, thereby achieving the goal of reducing the number of air compressors turned on as much as possible while ensuring the gas demand, which is beneficial to reducing the energy consumption of the air compressor during operation.

[0109] A second aspect of the present invention provides an air compressor energy consumption optimization method based on big data analysis, comprising:

[0110] S1: Collect historical gas consumption data of the park where the air compressor unit is located; obtain the operating data of each air compressor in the air compressor unit;

[0111] S2: Draw a gas demand curve based on historical gas consumption data;

[0112] S3: performing linear fitting on the operating data of each air compressor to obtain several operating curves;

[0113] S4: Analyze the operating conditions of each air compressor based on the gas demand curve and several operating curves to obtain operating characteristic data corresponding to each air compressor; and construct an exhaust priority sequence based on the operating characteristic data;

[0114] S5: inputting historical gas consumption data into the park gas consumption prediction model to obtain the park's gas consumption prediction value;

[0115] S6: Enable several air compressors based on the predicted air consumption and exhaust priority sequence.

[0116] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0117] Working principle of the present invention:

[0118] The present invention collects historical gas consumption data of the park where the air compressor unit is located; obtains the operating data of each air compressor in the air compressor unit; draws a gas consumption demand curve based on the historical gas consumption data; performs linear fitting on the operating data of each air compressor to obtain a plurality of operating curves; analyzes the operating status of each air compressor based on the gas consumption demand curve and the plurality of operating curves to obtain the operating characteristic data corresponding to each air compressor; constructs an exhaust priority sequence based on the operating characteristic data; inputs the historical gas consumption data into a park gas consumption prediction model to obtain a gas consumption prediction value of the park; and activates a plurality of air compressors based on the gas consumption prediction value and the exhaust priority sequence.

[0119] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. Air compressor energy consumption optimization system based on big data analysis, including: The data processing module, and the data acquisition module and air compressor adjustment module connected thereto are characterized in that: The data acquisition module is used to collect the historical gas consumption data of the park in several consecutive cycles; obtain the operation data of each air compressor in the air compressor group in several consecutive cycles; wherein the air compressor group includes several air compressors; The data processing module: draws a gas demand curve based on historical gas consumption data; performs linear fitting on the operating data of each air compressor to obtain a number of operating curves; analyzes the operating status of each air compressor based on the gas demand curve and the several operating curves to obtain the operating characteristic data corresponding to each air compressor; constructs an exhaust priority sequence based on the operating characteristic data; and, Input historical gas consumption data into the park gas consumption prediction model to obtain the park's gas consumption prediction value; wherein the park gas consumption prediction model is built based on an artificial intelligence model; The air compressor adjustment module enables a number of air compressors based on the predicted air consumption value and the exhaust priority sequence.

2. The air compressor energy consumption optimization system based on big data analysis according to claim 1 is characterized in that: The drawing of a gas demand curve based on historical gas consumption data includes: The historical gas consumption data of the park in several consecutive periods are extracted; time is used as the independent variable and the historical gas consumption data is used as the dependent variable, and the gas consumption demand curve is drawn through linear fitting.

3. The air compressor energy consumption optimization system based on big data analysis according to claim 1 is characterized in that: The linear fitting of the operating data of each air compressor comprises: Extract the exhaust volume and energy consumption from the operating data of each air compressor in several consecutive cycles; use time as the independent variable and exhaust volume as the dependent variable to draw the exhaust volume curve of each air compressor through linear fitting; use time as the independent variable and energy consumption as the dependent variable to draw the energy consumption curve of each air compressor through linear fitting; mark the exhaust volume curve and energy consumption curve as operating curves.

4. The air compressor energy consumption optimization system based on big data analysis according to claim 1 is characterized in that: The analysis of the operating conditions of each air compressor based on the gas demand curve and the plurality of operating curves includes: Extract the gas demand curves in several consecutive cycles and the operation curves corresponding to several air compressors; mark the start time of several consecutive cycles as t1, mark the end time of several consecutive cycles as t2, mark the gas demand curve as g(t), mark the exhaust volume curves corresponding to several air compressors as fi(t), and mark the energy consumption curves corresponding to several air compressors as hi(t); wherein t is time, and t1≦t≦t2; i=1, 2, ..., n, where n is the total number of air compressors; Extract the maximum exhaust volume from the exhaust volume curve corresponding to each air compressor, and mark the maximum exhaust volume as the maximum exhaust volume; Calculate the difference between the gas demand curve and the exhaust volume curve corresponding to each air compressor, and mark the corresponding difference as the exhaust volume difference; Calculate the ratio between the exhaust volume curve fi(t) of each air compressor and the corresponding energy consumption curve hi(t), and mark the corresponding ratio as the energy efficiency ratio; The maximum exhaust volume, exhaust volume difference and energy efficiency ratio are integrated into the operation characteristic data.

5. The air compressor energy consumption optimization system based on big data analysis according to claim 4 is characterized in that: The difference between the calculated gas demand curve and the exhaust volume curve corresponding to each air compressor includes: Extract the gas demand curve g(t) and the exhaust volume curve fi(t) corresponding to each air compressor; through the formula Calculate the exhaust volume difference PQCi corresponding to air compressor i.

6. The air compressor energy consumption optimization system based on big data analysis according to claim 4 is characterized in that: The step of constructing an exhaust priority sequence based on the operating characteristic data includes: Extract the maximum exhaust volume, exhaust volume difference and energy efficiency ratio value from the operation characteristic data of each air compressor; determine whether the energy efficiency ratio value of each air compressor is greater than the preset energy efficiency threshold; if yes, mark the energy efficiency label of the corresponding air compressor as 1; if no, mark the energy efficiency label of the corresponding air compressor as 0; The numbers corresponding to the air compressors with energy efficiency label values ​​of 1 are added to the first priority sequence, and the numbers corresponding to the air compressors with energy efficiency label values ​​of 0 are added to the second priority sequence; Calculate the corresponding energy-saving priority coefficient based on the operating characteristic data of each air compressor; The first priority sequence and the second priority sequence are sorted respectively in descending order according to the energy-saving priority coefficient; The first priority sequence and the second priority sequence are integrated into an exhaust priority sequence.

7. The air compressor energy consumption optimization system based on big data analysis according to claim 6 is characterized in that: The calculation of the corresponding energy-saving priority coefficient based on the operating characteristic data of each air compressor includes: Extract the maximum exhaust volume, exhaust volume difference and energy efficiency ratio from the operating characteristic data of each air compressor; Calculate the energy-saving priority coefficient JYXi of air compressor i; where t2-t1 is the duration of several consecutive cycles, ZPLi is the maximum exhaust volume of air compressor i, PQCi is the exhaust volume difference of air compressor i, NXSi is the energy efficiency ratio of air compressor i; a, b, and c are all influence coefficients greater than 0.

8. The air compressor energy consumption optimization system based on big data analysis according to claim 1 is characterized in that: The park gas consumption prediction model is constructed based on an artificial intelligence model and includes: The historical gas consumption data of the park in several consecutive periods are extracted and integrated into several groups of training data and test data; the artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test results; finally, a park gas consumption prediction model is obtained, which inputs the historical gas consumption data of the most recent several consecutive periods and outputs the predicted value of gas consumption in the prediction period; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

9. The air compressor energy consumption optimization system based on big data analysis according to claim 1 is characterized in that: The method of enabling several air compressors based on the predicted gas consumption value and the exhaust priority sequence includes: T1: Extract gas consumption forecast value, first priority sequence, second priority sequence; T2: Calculate the sum of the maximum exhaust volumes of several air compressors in the first priority sequence and mark it as the first air usage threshold; T3: Calculate the sum of the maximum exhaust volumes of several air compressors in the second priority sequence and mark it as the second air usage threshold; T4: mark the sum of the first gas usage threshold and the second gas usage threshold as the third gas usage threshold; T5: Determine whether the predicted gas consumption value is greater than the third gas consumption threshold; if yes, generate a gas shortage prompt message; if no, jump to T6; T6: Determine whether the predicted gas consumption value is less than the first gas consumption threshold; If yes, then select several air compressors from the first priority sequence in turn, set the operating states of the selected air compressors to on, and set the operating states of the air compressors in the second priority sequence to off; If not, the operating states of the air compressors in the first priority sequence and the second priority sequence are all set to on; wherein the operating state of the air compressor includes on and off.

10. An air compressor energy consumption optimization method based on big data analysis, based on the operation of an air compressor energy consumption optimization system based on big data analysis according to any one of claims 1 to 9, characterized in that: include: S1: Collect historical gas consumption data of the park where the air compressor unit is located; obtain the operating data of each air compressor in the air compressor unit; S2: Draw a gas demand curve based on historical gas consumption data; S3: performing linear fitting on the operating data of each air compressor to obtain several operating curves; S4: Analyze the operating conditions of each air compressor based on the gas demand curve and several operating curves to obtain operating characteristic data corresponding to each air compressor; and construct an exhaust priority sequence based on the operating characteristic data; S5: inputting historical gas consumption data into the park gas consumption prediction model to obtain the park's gas consumption prediction value; S6: Enable several air compressors based on the predicted air consumption and exhaust priority sequence.

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