Air compressor energy-saving control method and system based on big data analysis

Abstract

The invention discloses an air compressor energy-saving control method and system based on big data analysis, and relates to the technical field of data analysis, the system comprises a delay prediction module, an air consumption early warning module and a hierarchical scheduling module; the delay prediction module is used for monitoring the loading time of each air compressor in real time and making an air compressor energy-saving control decision in real time according to the delay duration of the air compressor; the air consumption early warning module is used for predicting whether the air compressor at the corresponding position meets the air consumption peak value requirement or not in advance according to the air consumption peak values required by different positions; and the hierarchical scheduling module is used for real-time distribution of air consumption peak values of the air compressors at the corresponding positions by real-time judgment of pipe network pressure fluctuation abnormity during joint control of the multiple air compressors in combination with hierarchical scheduling. According to the air compressor energy-saving control method and system based on big data analysis, the situation that the loading time of the air compressor is prolonged without reasons is avoided, whether the air compressor at the corresponding position meets the air consumption peak value requirement or not is predicted in advance, and the problem of frequent start and stop caused by oscillation of the air compressor is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an air compressor energy-saving control method and system based on big data analysis. Background Art

[0002] Air compressor energy-saving control based on big data analysis is an intelligent solution that uses the Internet of Things, data collection, machine learning and intelligent algorithms to monitor, analyze and optimize air compressor operating data in real time to reduce energy consumption and improve efficiency. Its core is to use big data technology to explore the operating laws of air compressors, dynamically adjust control strategies, and achieve energy-saving goals.

[0003] At present, there are some shortcomings in the energy-saving control of air compressors based on big data analysis: 1. The loading time of the air compressor cannot be accurately predicted, and there may be a risk of unwarranted extension of the loading time of the air compressor, reducing the accuracy of the energy-saving control of the air compressor; 2. When multiple air compressors are controlled in conjunction, the gas consumption peaks required at different locations cannot be monitored in real time, and it is impossible to predict whether the air compressors at the corresponding locations meet the gas consumption peak demand, reducing the stability of the use of the air compressor; 3. When multiple air compressors are controlled in conjunction, the gas consumption peaks cannot be allocated in real time, and the stability of the execution of the coordinated strategy cannot be guaranteed, resulting in fluctuations in the pipeline network pressure and easy to cause frequent start and stop problems.

[0004] Therefore, an air compressor energy-saving control method and system based on big data analysis are proposed to solve the above problems. Summary of the invention

[0005] The main purpose of the present invention is to provide an air compressor energy-saving control method and system based on big data analysis to solve the problems raised in the above background.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: an air compressor energy-saving control method and system based on big data analysis, including a delay prediction module, a gas consumption warning module and a hierarchical scheduling module; The delay prediction module is used to monitor the loading time of each air compressor in real time, and predict the delay duration of the air compressor loading in real time according to the standard loading time, and judge whether the loading time of the air compressor deviates in real time in combination with the air compressor loading time, and make energy-saving control decisions for the air compressor in real time according to the delay duration of the air compressor; The gas consumption warning module is used to control multiple air compressors and monitor the gas consumption peaks required by air compressors at different locations in real time, and predict in advance whether the air compressors at the corresponding locations meet the gas consumption peak demand based on the gas consumption peaks required at different locations, and monitor the gas consumption peaks required at different locations in real time; The hierarchical scheduling module is used to receive the abnormal gas outlet results of multiple air compressors during joint control in real time, judge the abnormal pipeline pressure fluctuation during joint control of multiple air compressors in real time, and allocate the gas consumption peak of the air compressor at the corresponding position in real time in combination with the hierarchical scheduling to ensure the stability of the execution of the collaborative strategy.

[0007] The delay prediction module includes a region setting unit, a loading time monitoring unit, a delay duration prediction unit and an energy-saving control decision unit; The area setting unit is used to set the air compressor area range, and set multiple air compressors within the area range, and arrange the multiple air compressors in order from small to large using Arabic numerals.

[0008] The loading time monitoring unit is used to calculate the loading time of the air compressor in real time, and the calculation formula is as follows: ; in, Indicates Loading time of air compressor, Indicates the volume of the gas tank. Indicates the rated working pressure, Indicates the minimum starting pressure, Indicates the compressor exhaust volume, It represents the efficiency coefficient, which is usually set between 0.6 and 0.9, taking leakage and heat loss into account.

[0009] The delay duration prediction unit is used to set the standard loading time of the air compressor, and predict the delay duration of the air compressor loading in real time according to the standard loading time. The prediction method is as follows: According to the loading time parameters of the corresponding air compressor at the corresponding position at the current moment, and the environmental parameters of the corresponding air compressor at the corresponding position at each moment are collected in real time through the temperature sensor, the loading time parameters and environmental parameters of the corresponding air compressor at the corresponding position at each moment are combined to predict the loading time delay duration of different air compressors at different positions in real time. The calculation formula is as follows: ; ; ; in, The time is the moment when the loading time suddenly changes under the execution state of the environmental parameters of the corresponding air compressor at each moment. is the execution time period of the corresponding position under the loading state of the air compressor, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, is the set delay duration percentage, and They respectively represent the loading time delay duration of the corresponding air compressor at the corresponding position under different environmental conditions; Set the loading time delay threshold, calculate the difference between the loading time delay and the loading time delay threshold, and subtract the former from the latter. If the difference is less than or equal to 0.02 or greater than or equal to -0.02, it means that the loading time delay of the corresponding air compressor at the corresponding position is normal. If the difference is greater than 0.02 or less than -0.02, it means that the loading time delay of the corresponding air compressor at the corresponding position is abnormal. The energy-saving control decision unit is used to set the energy-saving control decision according to the loading time delay duration of the corresponding air compressor at the corresponding position. The energy-saving control decision is combined with the reasons that affect the loading time delay duration, which include equipment aging, high temperature of the environment, load fluctuation, pipeline leakage and filter element blockage. The energy-saving control decision is set according to the decision tree. The decision tree of the energy-saving control decision is generated as follows: A[delay detection: t_delay>threshold?]-->|Yes|B{cause diagnosis}; B -->|Equipment aging|C1[load reduction operation + planned maintenance]; B -->|Ambient high temperature|C2[Enable auxiliary cooling system]; B -->|Load fluctuation|C3[adjust the timing of gas-using equipment + expand the capacity of gas storage tank]; B -->|Pipeline leakage|C4[Stop for leak detection + seal repair]; B -->|Filter element blocked|C5[Replace filter element + differential pressure monitoring]; A -->|No|D[Maintain current operating parameters].

[0010] The gas consumption warning module includes a gas consumption peak monitoring unit, a gas consumption peak prediction unit, a demand abnormality judgment unit and a corresponding position alarm unit; The gas consumption peak monitoring unit is used to collect the gas consumption demand of the corresponding position in real time through a data acquisition instrument and set a standard gas consumption value; The gas consumption peak prediction unit is used to calculate the predicted gas consumption peak Q under the dynamic change of the corresponding position in real time by combining temperature change, production plan load and the number of gas-consuming equipment running at the same time. predicted-peak , the calculation formula is as follows: Q predicted-peak =Qbase +α⋅ΔT+β⋅P prod +γ⋅N machines ; Among them, Q base represents the basic gas consumption, ΔT represents the temperature change, P prod Represents the production plan load, N machines represents the number of gas-consuming equipment running simultaneously, and α, β and γ represent regression coefficients.

[0011] The demand abnormality judgment unit is used to calculate the difference between the predicted gas consumption peak value and the standard gas consumption value. If the difference is less than or equal to 0.03 or greater than or equal to -0.03, it means that the corresponding air compressor at the corresponding position meets the gas consumption peak demand. If the difference is greater than 0.03 or less than -0.03, it means that the corresponding air compressor at the corresponding position does not meet the gas consumption peak demand. The corresponding position alarm unit is used to report to the system to issue an early warning reminder when the corresponding air compressor at the corresponding position does not meet the peak gas demand, and to locate the abnormal air compressor position in real time through the locator to obtain the abnormal gas outlet of the air compressor at the corresponding position in time.

[0012] The hierarchical scheduling module includes a data receiving unit, a pipe network pressure fluctuation monitoring unit, a hierarchical scheduling allocation unit and a decision execution adjustment unit; The data receiving unit is used to receive the abnormal air outlet results of multiple air compressors during joint control in real time through a data receiver, and to define the abnormal air compressor in real time through the sequence number.

[0013] The pipeline network pressure fluctuation monitoring unit is used to monitor the air compressor in real time through the data monitor, and calculate the pipeline network pressure fluctuation value when the gas equipment is suddenly started and stopped in real time. , the calculation formula is as follows: ; in, represents the elastic modulus of air, Indicates the flow rate change, represents the total volume of the pipe network, Indicates the change time, sets the pressure fluctuation threshold of the pipeline network. If the absolute value of is greater than or equal to 0.2, it means that the pressure fluctuation of the pipeline network is abnormal. If the absolute value of is less than 0.2, it means that the pressure fluctuation in the pipeline network is normal.

[0014] The step-by-step dispatching and allocation unit is used to calculate the priority allocation parameters of the air compressor step-by-step dispatching gas peak in real time. , the hierarchical scheduling adopts progressive hierarchical scheduling optimization, and the calculation formula is as follows: ; in, represents the priority allocation parameter of the i-th air compressor, Indicates the rated exhaust volume, that is, the maximum air supply under the standard. represents the input power, Indicates the current operating efficiency. Indicates the number of starts on the day. Indicates the current operating temperature. Indicates the maximum allowable operating temperature. represents the weight coefficient; The decision execution adjustment unit is used to adjust the energy-saving control decision in real time according to the priority allocation parameters, and adopt progressive hierarchical scheduling to optimize the energy-saving control decision parameters.

[0015] The air compressor energy-saving control method based on big data analysis includes the following steps: Step 1: Enter the delay prediction module, monitor the air compressor loading time in real time, and predict the delay duration of the air compressor loading in real time, and make air compressor energy-saving control decisions in real time according to the delay duration of the air compressor; Step 2: Enter the gas consumption warning module, monitor the gas consumption peaks required at different locations in real time through the joint control of multiple air compressors, and predict in advance whether the air compressors at the corresponding locations can meet the gas consumption peak demand based on the gas consumption peaks required at different locations, and promptly determine the gas outlet abnormalities of the air compressors at the corresponding locations; Step 3: Enter the hierarchical scheduling module, receive the abnormal gas outlet results of multiple air compressors in real time, combine the pipeline network pressure fluctuations, and use hierarchical scheduling to allocate the gas consumption peak of the air compressor at the corresponding position in real time to ensure the stability of the collaborative strategy execution.

[0016] The present invention has the following beneficial effects: 1. In the present invention, by setting a delay prediction module, during the air compressor energy-saving control operation based on big data analysis, the delay duration of the air compressor loading is predicted in real time, so that when performing big data analysis, a delay warning reminder can be given in real time in combination with the air compressor loading time, and it can be judged in real time whether there is a deviation in the loading time of the air compressor, so that the air compressor loading time can be accurately predicted, the air compressor loading time can be avoided from being extended unnecessarily, and the accuracy of the air compressor energy-saving control can be improved.

[0017] 2. In the present invention, by setting up a gas consumption warning module, during the energy-saving control operation of the air compressor based on big data analysis, the gas consumption peak required at different positions can be used to predict in advance whether the air compressor at the corresponding position can meet the gas consumption peak demand, and the gas outlet abnormality of the air compressor at the corresponding position can be judged in time. When the system is controlling multiple air compressors, it can monitor in real time whether the gas consumption peak demand required at different positions is abnormal, and predict in advance whether the air compressor at the corresponding position can meet the gas consumption peak demand, thereby increasing the stability of the use of the air compressor.

[0018] 3. In the present invention, by setting up a hierarchical scheduling module, during the energy-saving control operation of the air compressor based on big data analysis, the abnormal fluctuation of the pipeline pressure during the joint control of multiple air compressors is judged in real time, and the gas consumption peak of the air compressor at the corresponding position is allocated in real time in combination with the hierarchical scheduling. When the system is performing joint control of multiple air compressors, the gas consumption peak of each air compressor can be evenly distributed, the pipeline pressure fluctuation occurring when multiple air compressors are controlled together is reduced, the frequent start and stop problem of air compressor causing shock is prevented, and the safety and reliability of the energy-saving control of the air compressor is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the system architecture of the air compressor energy-saving control system based on big data analysis of the present invention; Figure 2 It is a schematic diagram of the architecture of the delay prediction module of the air compressor energy-saving control system based on big data analysis of the present invention; Figure 3 It is a schematic diagram of the architecture of the gas consumption warning module of the air compressor energy-saving control system based on big data analysis of the present invention; Figure 4 It is a schematic diagram of the architecture of a hierarchical scheduling module of an air compressor energy-saving control system based on big data analysis of the present invention; Figure 5 It is a flow chart of the air compressor energy-saving control method based on big data analysis of the present invention. DETAILED DESCRIPTION

[0020] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0021] Example 1, please refer to Figure 1-Figure 2 As shown: An air compressor energy-saving control method and system based on big data analysis, including a delay prediction module, a gas consumption warning module and a staged scheduling module; The delay prediction module is used to monitor the loading time of each air compressor in real time, and predict the delay duration of the air compressor loading in real time according to the standard loading time, and judge whether the loading time of the air compressor deviates in real time in combination with the air compressor loading time, and make energy-saving control decisions for the air compressor in real time according to the delay duration of the air compressor; The gas consumption warning module is used to control multiple air compressors and monitor the gas consumption peaks required by air compressors at different locations in real time, and predict in advance whether the air compressors at the corresponding locations meet the gas consumption peak demand based on the gas consumption peaks required at different locations, and monitor the gas consumption peaks required at different locations in real time; The hierarchical scheduling module is used to receive the abnormal gas outlet results of multiple air compressors during joint control in real time, judge the abnormal pipeline pressure fluctuation during joint control of multiple air compressors in real time, and allocate the gas consumption peak of the air compressor at the corresponding position in real time in combination with the hierarchical scheduling to ensure the stability of the execution of the collaborative strategy.

[0022] The delay prediction module includes a region setting unit, a loading time monitoring unit, a delay duration prediction unit, and an energy-saving control decision unit; The area setting unit is used to set the air compressor area range, and set multiple air compressors within the area range, and arrange the multiple air compressors in order from small to large using Arabic numerals.

[0023] The loading time monitoring unit is used to calculate the loading time of the air compressor in real time, and the calculation formula is as follows: ; in, Indicates Loading time of air compressor, Indicates the volume of the gas tank. Indicates the rated working pressure, Indicates the minimum starting pressure, Indicates the compressor exhaust volume, It represents the efficiency coefficient, which is usually set between 0.6 and 0.9, taking leakage and heat loss into account.

[0024] The delay duration prediction unit is used to set the standard loading time of the air compressor, and predict the delay duration of the air compressor loading in real time according to the standard loading time. The prediction method is as follows: According to the loading time parameters of the corresponding air compressor at the corresponding position at the current moment, and the environmental parameters of the corresponding air compressor at the corresponding position at each moment are collected in real time through the temperature sensor, the loading time parameters and environmental parameters of the corresponding air compressor at the corresponding position at each moment are combined to predict the loading time delay duration of different air compressors at different positions in real time. The calculation formula is as follows: ; ; ; in, The time is the moment when the loading time suddenly changes under the execution state of the environmental parameters of the corresponding air compressor at each moment. is the execution time period of the corresponding position under the loading state of the air compressor, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, is the set delay duration percentage, and They respectively represent the loading time delay duration of the corresponding air compressor at the corresponding position under different environmental conditions; Set the loading time delay threshold, calculate the difference between the loading time delay and the loading time delay threshold, and subtract the former from the latter. If the difference is less than or equal to 0.02 or greater than or equal to -0.02, it means that the loading time delay of the corresponding air compressor at the corresponding position is normal. If the difference is greater than 0.02 or less than -0.02, it means that the loading time delay of the corresponding air compressor at the corresponding position is abnormal. The energy-saving control decision unit is used to set the energy-saving control decision according to the loading time delay duration of the corresponding air compressor at the corresponding position. The energy-saving control decision is combined with the reasons that affect the loading time delay duration, which include equipment aging, high temperature of the environment, load fluctuation, pipeline leakage and filter element blockage. The energy-saving control decision is set according to the decision tree. The decision tree of the energy-saving control decision is generated as follows: A[delay detection: t_delay>threshold?]-->|Yes|B{cause diagnosis}; B -->|Equipment aging|C1[load reduction operation + planned maintenance]; B -->|Ambient high temperature|C2[Enable auxiliary cooling system]; B -->|Load fluctuation|C3[adjust the timing of gas-using equipment + expand the capacity of gas storage tank]; B -->|Pipeline leakage|C4[Stop for leak detection + seal repair]; B -->|Filter element blocked|C5[Replace filter element + differential pressure monitoring]; A -->|No|D[Maintain current operating parameters]. By predicting the delay duration of air compressor loading in real time, it is possible to conduct delay warning reminders in combination with the air compressor loading time during big data analysis, and judge in real time whether there is any deviation in the air compressor loading time, so that the air compressor loading time can be accurately predicted, avoiding unnecessary extension of the air compressor loading time, and improving the accuracy of air compressor energy-saving control.

[0025] Example 2, please refer to Figure 3 As shown: Based on the first embodiment, the gas consumption warning module includes a gas consumption peak monitoring unit, a gas consumption peak prediction unit, a demand abnormality judgment unit and a corresponding position alarm unit; The gas consumption peak monitoring unit is used to collect the gas consumption demand of the corresponding position in real time through a data acquisition instrument and set a standard gas consumption value; The gas consumption peak prediction unit is used to calculate the predicted gas consumption peak Q under the dynamic change of the corresponding position in real time by combining temperature change, production plan load and the number of gas-consuming equipment running at the same time. predicted-peak , the calculation formula is as follows: Q predicted-peak =Q base +α⋅ΔT+β⋅P prod +γ⋅N machines ; Among them, Q base represents the basic gas consumption, ΔT represents the temperature change, P prod Represents the production plan load, N machines represents the number of gas-consuming equipment running simultaneously, and α, β and γ represent regression coefficients.

[0026] The demand abnormality judgment unit is used to calculate the difference between the predicted gas consumption peak value and the standard gas consumption value. If the difference is less than or equal to 0.03 or greater than or equal to -0.03, it means that the corresponding air compressor at the corresponding position meets the gas consumption peak demand. If the difference is greater than 0.03 or less than -0.03, it means that the corresponding air compressor at the corresponding position does not meet the gas consumption peak demand. The corresponding position alarm unit is used to report to the system to issue an early warning reminder when the corresponding air compressor at the corresponding position does not meet the peak gas demand, and to locate the abnormal air compressor position in real time through the locator, so as to promptly obtain the abnormal gas outlet of the air compressor at the corresponding position, and predict in advance whether the air compressor at the corresponding position meets the peak gas demand through the peak gas demand required at different positions, and promptly judge the abnormal gas outlet of the air compressor at the corresponding position, so that the system can monitor in real time whether the peak gas demand required at different positions is abnormal when controlling multiple air compressors, and predict in advance whether the air compressor at the corresponding position meets the peak gas demand.

[0027] Example 3, please refer to Figure 4 As shown: Based on the first embodiment, the hierarchical scheduling module includes a data receiving unit, a pipe network pressure fluctuation monitoring unit, a hierarchical scheduling allocation unit and a decision execution adjustment unit; The data receiving unit is used to receive the abnormal air outlet results of multiple air compressors during joint control in real time through a data receiver, and to define the abnormal air compressor in real time through the sequence number.

[0028] The pipeline network pressure fluctuation monitoring unit is used to monitor the air compressor in real time through the data monitor, and calculate the pipeline network pressure fluctuation value when the gas equipment is suddenly started and stopped in real time. , the calculation formula is as follows: ; in, represents the elastic modulus of air, Indicates the flow rate change, represents the total volume of the pipe network, Indicates the change time, sets the pressure fluctuation threshold of the pipeline network. If the absolute value of is greater than or equal to 0.2, it means that the pressure fluctuation of the pipeline network is abnormal. If the absolute value of is less than 0.2, it means that the pressure fluctuation in the pipeline network is normal.

[0029] The step-by-step dispatching allocation unit is used to calculate the priority allocation parameter parameters of the air compressor step-by-step dispatching gas peak in real time. , the hierarchical scheduling adopts progressive hierarchical scheduling optimization, and the calculation formula is as follows: ; in, represents the priority allocation parameter of the i-th air compressor, Indicates the rated exhaust volume, that is, the maximum air supply under the standard. represents the input power, Indicates the current operating efficiency. Indicates the number of starts on the day. Indicates the current operating temperature. Indicates the maximum allowable operating temperature. represents the weight coefficient; The decision execution adjustment unit is used to adjust the energy-saving control decision in real time according to the priority allocation parameters, and adopts progressive hierarchical scheduling to optimize the energy-saving control decision parameters. The progressive hierarchical scheduling optimization is to calculate the difference between the actual air output of each air compressor and the actual demand gas peak. If the actual air output is less than the actual demand gas peak, the air output of multiple air compressors is scheduled for the air compressor at the current position. If the air output of the air compressor at the corresponding position is greater than 30% of the actual demand gas peak, the first level of air output scheduling is performed. The first level is represented by the advanced scheduling amount. If the air output of the air compressor at the corresponding position is greater than 6 of the actual demand gas peak, the air output of the air compressor at the current position is greater than 6 of the actual demand gas peak. 0%, the second level of gas output scheduling is carried out, and the second level is expressed as the intermediate scheduling volume. If the gas output of the air compressor at the corresponding position is greater than 80% of the actual demand gas peak, the third level of gas output scheduling is carried out, and the third level is expressed as the light scheduling volume. The first level is less than the second level and less than the third level. The gas peak of the air compressor at the corresponding position is allocated in real time in combination with the staged scheduling, so that when the system is controlling multiple air compressors, the gas peak of each air compressor can be evenly distributed, the pipeline pressure fluctuation caused by the joint control of multiple air compressors can be reduced, the frequent start and stop of the air compressor caused by shock can be prevented, and the safety and reliability of the energy-saving control of the air compressor can be improved.

[0030] In the present invention, an air compressor energy-saving control method and system based on big data analysis, when the system is in operation, first enters the delay prediction module, monitors the air compressor loading time in real time, and predicts the delay duration when the air compressor is loaded in real time, and makes an air compressor energy-saving control decision in real time according to the delay duration of the air compressor. When the air compressor energy-saving control operation based on big data analysis is performed, the loading time of each air compressor is monitored in real time, and the delay duration when the air compressor is loaded is predicted in real time according to the standard loading time, so that when performing big data analysis, a delay warning reminder can be given in real time in combination with the air compressor loading time, and the loading time of the air compressor can be judged in real time. Whether there is a deviation in the time, and make an energy-saving control decision for the air compressor in real time according to the delay time of the air compressor, so that the loading time of the air compressor can be accurately predicted, avoiding the unnecessary extension of the loading time of the air compressor, and improving the accuracy of the energy-saving control of the air compressor; enter the delay prediction module, through real-time monitoring of the air compressor loading time, and real-time prediction of the delay time when the air compressor is loaded, make an energy-saving control decision for the air compressor in real time according to the delay time of the air compressor; through the joint control of multiple air compressors, the gas consumption peak required at different locations is monitored in real time, and according to the gas consumption peak required at different locations, it is predicted in advance whether the air compressor at the corresponding location meets the gas consumption peak demand, and timely Determine the abnormality of the air outlet of the air compressor at the corresponding position, through the joint control of multiple air compressors, and monitor the gas peak required by the air compressors at different positions in real time, and predict in advance whether the air compressor at the corresponding position meets the gas peak demand based on the gas peak required at different positions, and timely determine the abnormality of the air outlet of the air compressor at the corresponding position, so that when the system is for the joint control of multiple air compressors, it can monitor in real time whether the gas peak demand required at different positions is abnormal, and predict in advance whether the air compressor at the corresponding position meets the gas peak demand, thereby increasing the stability of the use of the air compressor; by receiving the abnormal gas outlet results of multiple air compressor joint control in real time, combined with the pipe network pressure Fluctuations, combined with hierarchical scheduling, real-time allocation of the gas consumption peak of the air compressor at the corresponding position, to ensure the stability of the execution of the collaborative strategy, by real-time receiving the abnormal gas outlet results when multiple air compressors are controlled together, real-time judgment of the abnormal pipeline pressure fluctuation when multiple air compressors are controlled together, and real-time allocation of the gas consumption peak of the air compressor at the corresponding position in combination with hierarchical scheduling, to ensure the stability of the execution of the collaborative strategy, so that when the system is controlling multiple air compressors together, it can evenly allocate the gas consumption peak of each air compressor, reduce the pipeline pressure fluctuation when multiple air compressors are controlled together, prevent the frequent start and stop of air compressors causing shock, and improve the safety and reliability of air compressor energy-saving control.

[0031] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The air compressor energy-saving control system based on big data analysis is characterized by: The system includes a delay prediction module, a gas consumption warning module and a staged scheduling module; The delay prediction module is used to monitor the loading time of each air compressor in real time, and predict the delay duration of the air compressor loading in real time according to the standard loading time, and judge whether the loading time of the air compressor deviates in real time in combination with the air compressor loading time, and make energy-saving control decisions for the air compressor in real time according to the delay duration of the air compressor; The gas consumption warning module is used to control multiple air compressors and monitor the gas consumption peaks required by air compressors at different locations in real time, and predict in advance whether the air compressors at the corresponding locations meet the gas consumption peak demand based on the gas consumption peaks required at different locations, and monitor the gas consumption peaks required at different locations in real time; The hierarchical scheduling module is used to receive the abnormal gas outlet results of multiple air compressors during joint control in real time, judge the abnormal pipeline pressure fluctuation during joint control of multiple air compressors in real time, and allocate the gas consumption peak of the air compressor at the corresponding position in real time in combination with the hierarchical scheduling to ensure the stability of the execution of the collaborative strategy.

2. The system according to claim 1, characterized in that: The delay prediction module includes a region setting unit, a loading time monitoring unit, a delay duration prediction unit and an energy-saving control decision unit; The area setting unit is used to set the air compressor area range, and set multiple air compressors within the area range, and arrange the multiple air compressors in order from small to large using Arabic numerals.

3. The system according to claim 2, characterized in that: The loading time monitoring unit is used to calculate the loading time of the air compressor in real time, and the calculation formula is as follows: ;in, Indicates Loading time of air compressor, Indicates the volume of the gas tank. Indicates the rated working pressure, Indicates the minimum starting pressure, Indicates the compressor exhaust volume, It represents the efficiency coefficient, which is usually set between 0.6 and 0.9, taking leakage and heat loss into account.

4. The system according to claim 3, characterized in that: The delay duration prediction unit is used to set the standard loading time of the air compressor, and predict the delay duration of the air compressor loading in real time according to the standard loading time. The prediction method is as follows: According to the loading time parameters of the corresponding air compressor at the corresponding position at the current moment, and the environmental parameters of the corresponding air compressor at the corresponding position at each moment are collected in real time through the temperature sensor, the loading time parameters and environmental parameters of the corresponding air compressor at the corresponding position at each moment are combined to predict the loading time delay duration of different air compressors at different positions in real time. The calculation formula is as follows: ； ； ； in, The time is the moment when the loading time suddenly changes under the execution state of the environmental parameters of the corresponding air compressor at each moment. is the execution time period of the corresponding position under the loading state of the air compressor, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, The environmental parameters of the corresponding air compressor at the corresponding position are executed The number of loads at a time, is the set delay duration percentage, and They respectively represent the loading time delay duration of the corresponding air compressor at the corresponding position under different environmental conditions; Set the loading time delay threshold, calculate the difference between the loading time delay and the loading time delay threshold, and subtract the former from the latter. If the difference is less than or equal to 0.02 or greater than or equal to -0.02, it means that the loading time delay of the corresponding air compressor at the corresponding position is normal. If the difference is greater than 0.02 or less than -0.02, it means that the loading time delay of the corresponding air compressor at the corresponding position is abnormal. The energy-saving control decision unit is used to set the energy-saving control decision according to the loading time delay duration of the air compressor corresponding to the corresponding position. The energy-saving control decision is combined with the reasons that affect the loading time delay duration, including equipment aging, high temperature of the environment, load fluctuation, pipeline leakage and filter element blockage. The energy-saving control decision is set according to the decision tree.

5. The system according to claim 1, characterized in that: The gas consumption warning module includes a gas consumption peak monitoring unit, a gas consumption peak prediction unit, a demand abnormality judgment unit and a corresponding position alarm unit; The gas consumption peak monitoring unit is used to collect the gas consumption demand of the corresponding position in real time through a data acquisition instrument and set a standard gas consumption value; The gas consumption peak prediction unit is used to calculate the predicted gas consumption peak Q under the dynamic change of the corresponding position in real time by combining temperature change, production plan load and the number of gas-consuming equipment running at the same time. predicted-peak , the calculation formula is as follows: Q predicted-peak =Q base +α⋅ΔT+β⋅P prod +γ⋅N machines ; Among them, Q base represents the basic gas consumption, ΔT represents the temperature change, P prod Represents the production plan load, N machines represents the number of gas-consuming equipment running simultaneously, and α, β and γ represent regression coefficients.

6. The system according to claim 5, characterized in that: The demand abnormality judgment unit is used to calculate the difference between the predicted gas consumption peak value and the standard gas consumption value. If the difference is less than or equal to 0.03 or greater than or equal to -0.03, it means that the corresponding air compressor at the corresponding position meets the gas consumption peak demand. If the difference is greater than 0.03 or less than -0.03, it means that the corresponding air compressor at the corresponding position does not meet the gas consumption peak demand. The corresponding position alarm unit is used to report to the system to issue an early warning reminder when the corresponding air compressor at the corresponding position does not meet the peak gas demand, and to locate the abnormal air compressor position in real time through the locator to obtain the abnormal gas outlet of the air compressor at the corresponding position in time.

7. The system according to claim 1, characterized in that: The hierarchical scheduling module includes a data receiving unit, a pipe network pressure fluctuation monitoring unit, a hierarchical scheduling allocation unit and a decision execution adjustment unit; The data receiving unit is used to receive the abnormal air outlet results of multiple air compressors in real time through a data receiver during joint control, and to define the abnormal air compressor in real time through the sequence number.

8. The system according to claim 7, characterized in that: The pipeline network pressure fluctuation monitoring unit is used to monitor the air compressor in real time through the data monitor, and calculate the pipeline network pressure fluctuation value when the gas equipment is suddenly started and stopped in real time. , the calculation formula is as follows: ； in, represents the elastic modulus of air, Indicates the flow rate change, represents the total volume of the pipe network, Indicates the change time, sets the pressure fluctuation threshold of the pipeline network. If the absolute value of is greater than or equal to 0.2, it means that the pressure fluctuation of the pipeline network is abnormal. If the absolute value of is less than 0.2, it means that the pressure fluctuation in the pipeline network is normal.

9. The system according to claim 8, characterized in that: The step-by-step dispatching and allocation unit is used to calculate the priority allocation parameters of the air compressor step-by-step dispatching gas peak in real time. , the hierarchical scheduling adopts progressive hierarchical scheduling optimization, and the calculation formula is as follows: ； in, represents the priority allocation parameter of the i-th air compressor, Indicates the rated exhaust volume, that is, the maximum air supply under the standard. represents the input power, Indicates the current operating efficiency. Indicates the number of starts on the day. Indicates the current operating temperature. Indicates the maximum allowable operating temperature. represents the weight coefficient; The decision execution adjustment unit is used to adjust the energy-saving control decision in real time according to the priority allocation parameters, and adopt progressive hierarchical scheduling to optimize the energy-saving control decision parameters.

10. An air compressor energy-saving control method based on big data analysis, referring to the air compressor energy-saving control system based on big data analysis according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Enter the delay prediction module, monitor the air compressor loading time in real time, and predict the delay duration of the air compressor loading in real time, and make air compressor energy-saving control decisions in real time according to the delay duration of the air compressor; Step 2: Enter the gas consumption warning module, monitor the gas consumption peaks required at different locations in real time through the joint control of multiple air compressors, and predict in advance whether the air compressors at the corresponding locations can meet the gas consumption peak demand based on the gas consumption peaks required at different locations, and promptly determine the gas outlet abnormalities of the air compressors at the corresponding locations; Step 3: Enter the hierarchical scheduling module, receive the abnormal gas outlet results of multiple air compressors in real time, combine the pipeline network pressure fluctuations, and use hierarchical scheduling to allocate the gas consumption peak of the air compressor at the corresponding position in real time to ensure the stability of the collaborative strategy execution.

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