A data monitoring method and system for digital energy air compression stations

Abstract

This invention relates to the field of industrial big data monitoring and mining technology, specifically to a data monitoring method and system for digital energy air compressor stations. The invention first constructs a hash ring and obtains the air flow sequence of each outlet pipe, then obtains the load change sequence of each air compressor, further analyzes the data distribution in the load change sequence, and determines the load scheduling necessity of each air compressor in the hash ring. Then, based on the air flow and change trend of each load outlet pipe of each air compressor in the hash ring, the scheduling suitability coefficient of each load outlet pipe is obtained. Finally, based on the load scheduling necessity and scheduling suitability coefficient, the hash ring is adjusted to perform scheduling planning for the digital energy air compressor station. This invention improves the monitoring and scheduling effect of digital energy air compressor stations by monitoring and analyzing the air compressor load and air demand in the station, and by using the data distribution concept of a hash ring to plan and schedule the station.

Description

Technical Field

[0001] This invention relates to the field of industrial big data monitoring and mining technology, specifically to a data monitoring method and system for digital energy air compressor stations. Background Technology

[0002] A digital energy air compressor station is an air compressor unit system that uses digital technology to control and optimize its operation. It is usually an important energy-consuming device in industrial production and is widely used in the industrial field. By monitoring and tracking the operating status and load pressure of the air compressor in real time, it can help operators to identify the causes of low energy efficiency in a timely manner, thereby optimizing operating strategies and scheduling, reducing unnecessary energy waste, and achieving energy conservation and consumption reduction.

[0003] Existing technologies typically control the start and stop of air compressors in air compressor stations based on the overall air consumption monitoring data of the gas-using workshop. While this can save energy and reduce consumption to some extent, some air compressors may be in a state of long-term high-intensity operation or long-term idleness. Long-term high-intensity operation will increase the wear and tear of air compressors, while idle air compressors cannot fully utilize their efficiency, thus increasing equipment wear and energy waste. Summary of the Invention

[0004] To address the technical problem of unsatisfactory monitoring and scheduling performance of digital energy air compressor stations in existing technologies, the present invention aims to provide a data monitoring method and system for digital energy air compressor stations. The specific technical solution adopted is as follows:

[0005] This invention proposes a data monitoring method for digital energy air compressor stations, the method comprising:

[0006] At the current monitoring time, a hash ring is constructed based on the air compressors operating in the digital energy air compressor station and the open air outlet pipes, and the air flow sequence of each air outlet pipe within the preset historical monitoring period is obtained;

[0007] Obtain all load outlet pipes corresponding to each air compressor in the hash ring, and obtain the load change sequence of each air compressor based on the air flow sequence of each load outlet pipe; obtain the load scheduling necessity of each air compressor in the hash ring based on the data distribution in the load change sequence of each air compressor in the hash ring.

[0008] Based on the air flow rate of each load outlet pipe of each air compressor in the hash ring, the proportion of the total air flow rate of all load outlet pipes, and the changing trend of the air flow rate sequence of each load outlet pipe, the scheduling suitability coefficient of each load outlet pipe is obtained.

[0009] The hash ring is adjusted based on the load scheduling necessity of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipe of each air compressor to perform scheduling planning for the digital energy air compressor station.

[0010] Furthermore, the method for obtaining the hash ring includes:

[0011] Based on the MD5 algorithm, the air compressor node code value is obtained by combining the number of the running air compressor and the number of the opened air outlet pipe. The air outlet pipe node code value is obtained by combining the number of the opened air outlet pipe. All the air compressor node code values ​​and all the air outlet pipe node code values ​​are mapped into a ring hash space, and the air compressors in the hash ring are used as physical nodes to obtain the hash ring.

[0012] Furthermore, the method for obtaining the load change sequence includes:

[0013] Based on the air flow data at the same time in the air flow sequence of all load outlet pipes corresponding to each air compressor in the hash ring, the load parameters of each air compressor at the corresponding time are obtained; the load parameters of each air compressor at each time within the preset historical monitoring period are sorted in chronological order to construct a load change sequence.

[0014] Furthermore, the method for obtaining the necessity of load scheduling includes:

[0015] In the load change sequence of all air compressors in the hash ring, based on the probability distribution of all load parameters, obtain all high load parameters and all low load parameters corresponding to the load change sequence of each air compressor.

[0016] Based on the total number and average of the high load parameters in the load change sequence corresponding to each air compressor, and combined with the total number of the low load parameters, the load scheduling necessity of the corresponding air compressor in the hash ring is obtained.

[0017] Furthermore, the method for obtaining the high load parameter and the low load parameter includes:

[0018] Obtain the mean μ and standard deviation σ of all load parameters, construct the probability distribution curve, and take all load parameters outside μ+σ as the original high load parameters, and take all load parameters outside μ-σ as the original low load parameters.

[0019] All original high load parameters in the load change sequence of each air compressor in the hash ring are taken as the high load parameters of the corresponding air compressor, and all original low load parameters in the load change sequence of each air compressor in the hash ring are taken as the low load parameters of the corresponding air compressor.

[0020] Further, the method for obtaining the load scheduling necessity of the corresponding air compressor in the hash ring based on the total number and average of the high load parameters in the load change sequence corresponding to each air compressor, combined with the total number of the low load parameters, includes:

[0021] When the total number of high load parameters is not zero, the ratio of the total number of high load parameters to the length of the load change sequence is used as the first scheduling parameter; the average of all high load parameters is used as the second scheduling parameter; the first scheduling parameter and the second scheduling parameter are combined to obtain the load scheduling necessity of the corresponding air compressor in the hash ring.

[0022] When the total number of high load parameters is zero and the total number of low load parameters is greater than or equal to a preset value, the load scheduling necessity of the corresponding air compressor in the hash ring is set to a preset negative value.

[0023] Furthermore, the method for obtaining the scheduling suitability coefficient includes:

[0024] Air compressors with a load scheduling necessity greater than a preset threshold are classified as high-intensity air compressors, and air compressors with a load scheduling necessity of a preset negative value are classified as low-intensity air compressors.

[0025] In the air flow sequence of each load outlet pipe of each high-strength air compressor, the ratio between the air flow at each moment and the total air flow of all load outlet pipes at the corresponding moment is accumulated, and the accumulated value is used as the outlet load parameter of the corresponding load outlet pipe.

[0026] The air flow sequence of each load outlet pipe of each of the high-strength air compressors is fitted with a straight line, and the slope of the fitted straight line is used as the outlet load trend parameter of the corresponding load outlet pipe.

[0027] By integrating the outlet load parameters and the outlet load trend parameters, the scheduling suitability coefficient of the corresponding load outlet pipeline is obtained.

[0028] Furthermore, the method for adjusting the hash ring includes:

[0029] The load outlet pipe with the highest scheduling suitability coefficient among all load outlet pipes of the high-strength air compressor, and all load outlet pipes of the low-strength air compressor, are all designated as outlet pipes to be scheduled; the non-low-strength air compressor with the lowest load scheduling necessity is designated as the matching node of the outlet pipe to be scheduled.

[0030] In the hash ring, all physical nodes corresponding to low-intensity air compressors are deleted, and virtual nodes are added between each scheduled air outlet pipeline and the corresponding matching air compressor in the clockwise direction; wherein the virtual node is the matching node, and in the clockwise direction, there are no air compressors or non-scheduled air outlet pipelines between the matching node and the corresponding scheduled air outlet pipeline.

[0031] Furthermore, the method for obtaining the gas flow rate sequence includes:

[0032] At the current moment, the preset historical monitoring period is divided into a preset number of unit monitoring sub-periods, and the total gas flow rate of each gas outlet pipe in each unit monitoring sub-period is obtained; the total gas flow rate is used as a sequence element and sorted in chronological order to construct the gas flow rate sequence of the corresponding gas outlet pipe in the preset historical monitoring period.

[0033] The present invention also proposes a data monitoring system for digital energy air compressor stations, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the data monitoring method for digital energy air compressor stations.

[0034] The present invention has the following beneficial effects:

[0035] This invention constructs a hash ring based on the operating air compressors and open outlet pipes in a digital energy air compressor station at the current monitoring time, and obtains the air flow sequence of each outlet pipe within a preset historical monitoring period to prepare for subsequent analysis of air compressor load for scheduling and allocation. It also obtains all load outlet pipes corresponding to each air compressor in the hash ring, and based on the air flow sequence of each load outlet pipe, obtains the load change sequence of each air compressor. Based on the data distribution in the load change sequence of each air compressor in the hash ring, it analyzes the load pressure of each air compressor, and thus obtains the load scheduling necessity for each air compressor in the hash ring. The invention emphasizes the importance of monitoring and analyzing the load and air demand of air compressors in digital energy air compressor stations. It further analyzes the air flow rate of each load outlet pipe of each air compressor within the hash ring, considering its proportion relative to the total air flow rate of all load outlet pipes, and the changing trend of the air flow rate sequence of each load outlet pipe. This yields a scheduling suitability coefficient for each load outlet pipe, facilitating the subsequent selection of load outlet pipes to be scheduled and diverted, thus alleviating the load pressure on the air compressors. Finally, based on the necessity of load scheduling for each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipe for each air compressor, the hash ring is adjusted to plan the scheduling of the digital energy air compressor station. This invention improves the monitoring and scheduling effectiveness of digital energy air compressor stations by monitoring and analyzing the air compressor load and air demand in the station, and by utilizing the data allocation concept of the hash ring to plan and schedule the station. Attached Figure Description

[0036] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart illustrating a data monitoring method for digital energy air compressor stations provided in one embodiment of the present invention;

[0038] Figure 2 A probability distribution curve of a load parameter provided in one embodiment of the present invention;

[0039] Figure 3 This is a comparison diagram of a hash ring before and after adjustment, provided as an embodiment of the present invention. Detailed Implementation

[0040] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a data monitoring method and system for digital energy air compressor stations proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] The following description, in conjunction with the accompanying drawings, details a specific scheme for a data monitoring method and system for digital energy air compressor stations provided by the present invention.

[0043] Please see Figure 1 The diagram illustrates a flowchart of a data monitoring method for digital energy air compressor stations according to an embodiment of the present invention, specifically including:

[0044] Step S1: At the current monitoring time, construct a hash ring based on the air compressors operating in the digital energy air compressor station and the open air outlet pipes, and obtain the air flow sequence of each air outlet pipe within a preset historical monitoring period.

[0045] To monitor the operation of digital energy air compressor stations and optimize scheduling, one embodiment of the present invention first constructs a hash ring based on the air compressors currently operating in the digital energy air compressor station and the air outlet pipes opened to deliver air to various demand workshops at the current monitoring time. The hash ring balances the distribution of air compressors and air outlet pipes, thereby ensuring the efficient operation of the digital energy air compressor station while meeting the compressed air supply requirements.

[0046] Considering that the purpose of constructing a hash ring is to achieve efficient data distribution to balance the load, the air compressor can be regarded as a node in the hash ring, and the air outlet pipe can be regarded as data to be allocated and stored in the node in the hash ring. Based on the scheduling and allocation idea of ​​the hash ring, the load of the air compressor can be balanced to avoid the air compressor being overloaded or idle, thereby improving the monitoring and scheduling effect of the digital energy air compressor station.

[0047] Based on this, in a preferred embodiment of the present invention, the method for obtaining the hash ring includes:

[0048] Based on the MD5 algorithm, the node code value of the air compressor is obtained by combining the number of the running air compressor and the node code value of the air outlet pipe is obtained by combining the number of the opened air outlet pipe. All the node code values ​​of the air compressor and the node code values ​​of all the air outlet pipe are mapped into a ring hash space, and the air compressors in the hash ring are used as physical nodes to obtain the hash ring.

[0049] It should be noted that each air compressor and each outlet pipe in the digital energy air compressor station has a unique number for management purposes. The MD5 algorithm and hash ring construction are existing technologies well-known to those skilled in the art, and only the construction process is briefly described here:

[0050] As an example, first, the number of each running air compressor is hashed using MD5 to obtain the corresponding 32-bit hexadecimal string. Then, the 32-bit hexadecimal string is converted into an integer, and the integer value is moduloed to obtain its mapping position in the hash ring. Similarly, the mapping position of each air outlet pipe in the hash ring can be obtained. By mapping all running air compressors and open air outlet pipes to their corresponding positions in the hash ring, the hash ring at the current moment is obtained.

[0051] Furthermore, considering that gas demand usually changes due to production plans and other reasons, the gas flow rate of each open gas outlet pipe will also change, which in turn will cause the actual load pressure of the air compressor to change continuously. Therefore, one embodiment of the present invention will further obtain the gas flow rate sequence of each gas outlet pipe within a preset historical monitoring period at the current monitoring time, and then combine it with the hash ring analysis to analyze the load pressure change of each air compressor, thereby adjusting the hash ring and optimizing the monitoring and scheduling of the digital energy air compressor station.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the gas flow rate sequence includes:

[0053] At the current moment, the preset historical monitoring period is divided into a preset number of unit monitoring sub-periods, and the total gas flow rate of each gas outlet pipe in each unit monitoring sub-period is obtained; the total gas flow rate is used as a sequence element and sorted in chronological order to construct the gas flow rate sequence of the corresponding gas outlet pipe in the preset historical monitoring period.

[0054] As an example, firstly, the preset historical monitoring period is set to 10 minutes of the current time, and the preset quantity is set to 10. For example, if the current time is 10:10, the 10-minute period between 10:00 and 10:10 is set as the preset historical monitoring period. Each minute within these 10 minutes is taken as a unit monitoring sub-period. The total gas flow rate delivered to the workshop by each gas outlet pipe in each unit monitoring sub-period is collected by the flow meter at the gas outlet pipe. Each total gas flow rate is taken as a sequence element and sorted according to the time sequence to construct a gas flow rate sequence.

[0055] In other embodiments, the implementer may also set preset historical monitoring periods and preset quantities according to actual needs.

[0056] Step S2: Obtain all load outlet pipes corresponding to each air compressor in the hash ring, and obtain the load change sequence of each air compressor based on the air flow sequence of each load outlet pipe; obtain the load scheduling necessity of each air compressor in the hash ring based on the data distribution in the load change sequence of each air compressor in the hash ring.

[0057] To analyze the load intensity or pressure of each air compressor, this embodiment of the invention first obtains all the load outlet pipes corresponding to each air compressor in the hash ring, and then obtains the load change sequence of the corresponding air compressor based on the air flow sequence of each load outlet pipe, so as to optimize the scheduling based on the load change of the air compressor.

[0058] It should be noted that the first air compressor in the clockwise direction of each air outlet pipe in the hash ring is taken as its matched air compressor, which means that the air demand of the air outlet pipe generates a load on the corresponding matched air compressor. The structure and analysis of the hash ring are well known technologies and will not be elaborated here.

[0059] Preferably, in one embodiment of the present invention, considering that the greater the air flow rate of the load outlet pipe corresponding to each air compressor in the hash ring, the greater the load intensity of the air compressor, the air compressor load can be evaluated based on the air flow rate of all load outlet pipes at the same time, and then the air compressor load change can be analyzed; based on this, the method for obtaining the load change sequence includes:

[0060] Based on the air flow data at the same time in the air flow sequence of all load outlet pipes corresponding to each air compressor in the hash ring, the load parameters of each air compressor at the corresponding time are obtained; the load parameters of each air compressor at each time within the preset historical monitoring period are sorted in chronological order to construct a load change sequence.

[0061] As an example, in the air flow sequence of all load outlet pipes corresponding to each air compressor in the hash ring, the sum of the air flow data at the same time is used as the load parameter of the air compressor at the corresponding time. Then, the load parameter at each time is used as a sequence element and sorted according to the time sequence to construct the load change sequence, where the load change sequence has the same sequence direction as each air flow sequence.

[0062] After obtaining the load change sequence of each air compressor in the hash ring, the load intensity of the air compressor in the preset historical monitoring period can be evaluated based on the data distribution in the load change sequence of each air compressor in the hash ring. This allows us to determine the necessity of load scheduling for each air compressor in the hash ring, preparing for subsequent optimization scheduling and load balancing.

[0063] Preferably, in one embodiment of the present invention, considering that probability distribution can help understand the distribution characteristics of data, thereby helping to assess the load tendency of the air compressor and determine whether it needs to be scheduled; therefore, the method for obtaining the necessity of load scheduling includes:

[0064] In the load change sequence of all air compressors in the hash ring, based on the probability distribution of all load parameters, obtain all high load parameters and all low load parameters in the corresponding load change sequence of each air compressor.

[0065] Based on the total number and average of high load parameters in the load change sequence corresponding to each air compressor, and combined with the total number of low load parameters, the necessity of load scheduling for the corresponding air compressor in the hash ring is obtained.

[0066] In a preferred embodiment of the present invention, considering that in a normal distribution, the distribution of data can be evaluated based on the mean and standard deviation of all load parameters, thereby identifying which load parameters exceed the normal range, i.e., helping to identify abnormally high or low load parameters, and thus assessing the load condition of the air compressor for optimized scheduling; based on this, the method for obtaining high and low load parameters includes:

[0067] Obtain the mean μ and standard deviation σ of all load parameters, construct the probability distribution curve, and take all load parameters outside μ+σ as the original high load parameters, and take all load parameters outside μ-σ as the original low load parameters.

[0068] Take all the original high load parameters in the load change sequence of each air compressor in the hash ring as the high load parameters of the corresponding air compressor, and take all the original low load parameters in the load change sequence of each air compressor in the hash ring as the low load parameters of the corresponding air compressor.

[0069] Please see Figure 2 It illustrates a probability distribution curve of load parameters provided in an embodiment of the present invention, indicating the distribution range of the original high load parameters and the original low load parameters. All load parameters distributed to the left of dashed line 1 represent the original low load parameters, and all load parameters distributed to the right of dashed line 2 represent the original high load parameters. It should be noted that... Figure 2 The probability distribution curve is normally distributed, but in specific implementations, there may be skewed distributions. However, the methods for obtaining the original low-load parameters and the original high-load parameters are the same for each probability distribution curve, and will not be listed one by one.

[0070] Considering that the more high-load parameters each air compressor has, and the higher the average level of these high-load parameters, the more likely the air compressor is to operate at high load for extended periods, thus increasing the necessity for load scheduling; and considering that the more low-load parameters there are, the lower the operating efficiency of the air compressor, and the lower the necessity to keep it running, the more its load outlet should be diverted to other air compressors to reduce energy consumption, thus increasing the necessity for load scheduling.

[0071] Based on this, in a preferred embodiment of the present invention, when the total number of high load parameters is not zero, the ratio of the total number of high load parameters to the length of the load change sequence is used as the first scheduling parameter; the average of all high load parameters is used as the second scheduling parameter; the first scheduling parameter and the second scheduling parameter are combined to obtain the load scheduling necessity of the corresponding air compressor in the hash ring.

[0072] When the total number of high load parameters is zero and the total number of low load parameters is greater than or equal to a preset value, the load scheduling necessity of the corresponding air compressor in the hash ring is set to a preset negative value.

[0073] As an example, when the total number of high-load parameters is not zero, the load scheduling necessity is calculated based on the formula for calculating load scheduling necessity. The formula for calculating load scheduling necessity is:

[0074] Where j is the sequence number of the air compressor in the hash ring; DB j The necessity of load scheduling for the j-th air compressor in the hash ring; lg j Let A be the total number of high load parameters in the load change sequence of the j-th air compressor in the hash ring; let A be the sequence length of the load change sequence of the j-th air compressor in the hash ring, which is also a preset number. This is the first scheduling parameter; is the mean of the high load parameters in the load change sequence of the j-th air compressor in the hash ring, and is also the second scheduling parameter; norm() is the linear normalization function.

[0075] In the formula for calculating the necessity of load scheduling, the first scheduling parameter and the second scheduling parameter are multiplied and combined to obtain the load scheduling necessity of the corresponding air compressor. This means that the more high load parameters an air compressor has and the higher its level, the more it should be scheduled. In other embodiments, the implementer may also combine the two through basic mathematical operations or related mapping methods such as addition or weighted summation, or may use other normalization methods, which will not be elaborated here.

[0076] When the total number of high load parameters is zero and the total number of low load parameters is greater than or equal to a preset value, the load scheduling necessity of the corresponding air compressor in the hash ring is set to a preset negative value. In this example, the preset value is set to a preset number of 10 and the preset negative value is -1. That is, when the load parameters in the load change sequence of the air compressor are all low load parameters, its load scheduling necessity is directly set to -1.

[0077] In other examples, implementers may also set other preset values, the range of which should be more than 80% of the total length of the load change sequence, to ensure the rationality of the assessment of the necessity of load scheduling; other preset negative values ​​may also be set to distinguish between high load and low load conditions, which will not be elaborated here.

[0078] At this point, the load scheduling necessity of each air compressor in the hash ring has been obtained, so as to evaluate the high load probability and low load probability of the air compressor and thus evaluate the scheduling.

[0079] Step S3: Based on the air flow rate of each load outlet pipe of each air compressor in the hash ring, and its proportion relative to the total air flow rate of all load outlet pipes, and combined with the changing trend of the air flow rate sequence of each load outlet pipe, obtain the scheduling suitability coefficient of each load outlet pipe.

[0080] Considering that when an air compressor operates at high intensity for a long time, its internal hardware ages faster than other air compressors, and multiple air compressors operate at similar intensity simultaneously, the overall aging process of the digital energy air compressor system can be reduced, as can the overall power consumption. Furthermore, considering that among all the load outlet pipes of the air compressor, the load outlet pipe with the larger and continuously increasing air demand has the greatest impact on the load pressure of the air compressor, when the air compressor is operating at high load, the load outlet pipe with the larger air demand can be redirected to other air compressors operating at lower loads, thus balancing the load pressure.

[0081] Therefore, in this embodiment of the invention, the scheduling suitability coefficient of each load outlet pipe is obtained based on the air flow information of each load outlet pipe of each air compressor in the hash ring, combined with the changing trend of the air flow sequence of each load outlet pipe. The scheduling suitability coefficient reflects the suitability of each load outlet pipe being scheduled to other air compressors to balance the load, and indirectly reflects the influence of the load outlet pipe on the load pressure of the air compressor, so as to arrange the scheduling and balance the load in the future.

[0082] Preferably, in one embodiment of the present invention, the method for obtaining the scheduling suitability coefficient includes:

[0083] Air compressors with a load scheduling necessity greater than a preset threshold are classified as high-intensity air compressors, and air compressors with a load scheduling necessity of a preset negative value are classified as low-intensity air compressors.

[0084] In the air flow sequence of each load outlet pipe of each high-strength air compressor, the ratio between the air flow at each moment and the total air flow of all load outlet pipes at the corresponding moment is accumulated, and the accumulated value is used as the outlet load parameter of the corresponding load outlet pipe.

[0085] The air flow sequence of each load outlet pipe of each high-strength air compressor is fitted with a straight line, and the slope of the fitted straight line is used as the outlet load trend parameter of the corresponding load outlet pipe.

[0086] By integrating the outlet load parameters and outlet load trend parameters, the scheduling suitability coefficient of the corresponding load outlet pipeline is obtained.

[0087] As an example, a preset threshold is set to 0.7. Air compressors with a load scheduling necessity greater than 0.7 are classified as high-intensity air compressors, and air compressors with a load scheduling necessity equal to -1 are classified as low-intensity air compressors. Further, the outlet load parameters of each load outlet pipe of each high-intensity air compressor are calculated. Specifically, the air flow rate at each moment in the air flow rate sequence of each load outlet pipe is used as the numerator, and the total outlet flow rate of all load outlet pipes of the high-intensity air compressor at the corresponding moment is used as the denominator. The ratio reflects its relative proportion, which indirectly reflects the influence of the load outlet pipe on the load pressure of the high-intensity air compressor.

[0088] Simultaneously, the air flow sequence of each load outlet pipe of the high-strength air compressor is linearly fitted using the least squares method. The slope of the fitted line is calculated based on a two-point formula. The slope reflects the trend of air flow change in the load outlet pipe. The larger the slope, the greater its influence on the load pressure of the high-strength air compressor, and the larger the outlet load trend parameter. Finally, the outlet load parameter and the outlet load trend parameter are multiplied and combined, and the product is linearly normalized to obtain the scheduling suitability coefficient of the corresponding load outlet pipe. In other examples, the implementer can also combine the two through basic mathematical operations or related mapping methods such as addition or weighted summation, or other normalization methods can be used, which will not be elaborated here.

[0089] It should be noted that the least squares method for line fitting and slope calculation are existing technologies. Implementers may also use other known technologies to assess the trend of change, or other normalization methods, which will not be elaborated here.

[0090] Step S4: Adjust the hash ring according to the load scheduling necessity of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipe of each air compressor to carry out scheduling planning for the digital energy air compressor station.

[0091] After obtaining the load scheduling necessity of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipe of each air compressor, the hash ring can be adjusted based on the current load situation to analyze and schedule the air compressors and load outlet pipes to balance the load.

[0092] Preferably, in one embodiment of the present invention, considering that in order to alleviate the load pressure of high-intensity air compressors, the load air outlet pipeline with the largest and continuously increasing air output demand should be dispatched to other air compressors operating at lower loads; and considering that the operating necessity of low-intensity air compressors is relatively low, their load air outlet pipelines can be dispatched to other air compressors, thereby shutting down the current low-intensity air compressor, ensuring air supply while reducing operating costs; based on this, the adjustment method of the hash ring includes:

[0093] The load outlet pipe with the highest scheduling suitability coefficient among all load outlet pipes of high-intensity air compressors, and all load outlet pipes of low-intensity air compressors, are all designated as outlet pipes to be scheduled; the non-low-intensity air compressors with the lowest load scheduling necessity are designated as matching nodes for the outlet pipes to be scheduled.

[0094] In the hash ring, delete all physical nodes corresponding to low-intensity air compressors, and add virtual nodes between each scheduled air outlet pipeline and the corresponding matching air compressor in the clockwise direction; where the virtual nodes are matching nodes, in the clockwise direction, there are no air compressors or non-scheduled air outlet pipelines between the matching nodes and the corresponding scheduled air outlet pipelines.

[0095] It should be noted that adjustments to the hash ring, such as adding or deleting nodes or adding virtual nodes, are existing techniques well-known to those skilled in the art. This section only provides a brief example of the adjustment process; please refer to [link to relevant documentation]. Figure 3 It shows a comparison diagram before and after hash ring adjustment according to an embodiment of the present invention; Figure 3 The left image shows the hash ring before adjustment, and the right image shows the hash ring after adjustment. In the hash ring, the small circles with numerical labels correspond to air outlet pipe nodes, and the small circles with letter labels correspond to air compressor nodes. The matching relationship between the air outlet pipes and air compressors is indicated by the dashed arrows, for example... Figure 3 In the left figure, air outlet pipe 3 is the load air outlet pipe of air compressor c. Similarly, air outlet pipes 2, 4, and 6 are the load air outlet pipes of air compressor b, and so on.

[0096] As an example, if air compressor b is currently determined to be a high-intensity air compressor, and its load outlet pipe 2 has the highest scheduling suitability coefficient, then load outlet pipe 2 is set as the outlet pipe to be scheduled. The non-low-intensity air compressor 3, which currently has the lowest scheduling necessity, is set as the matching node for the outlet pipe to be scheduled. That is, load outlet pipe 2 needs to be matched with air compressor 3. Since the hash ring usually matches the first physical node in the clockwise direction, and air compressor 3 is not the first physical node, a virtual node is added in the clockwise direction. Figure 3 In the right-hand diagram, the dashed letter labels resembling small circles also represent air compressor 3. The virtual node corresponding to air compressor 3 should be immediately adjacent to the load outlet pipe 2. Figure 3 At the location shown in the right figure, avoid misassigning the load outlet pipes 4 or 6, and instead assign the outlet pipe to be scheduled to the non-low-intensity air compressor 3, which has the least need for current load scheduling, thereby relieving the load pressure on the high-intensity air compressor b.

[0097] For example, if air compressor a is a low-intensity air compressor, its load outlet pipes 1 and 5 can be redirected to air compressor c, and the corresponding physical node of air compressor a can be deleted directly. It should be noted that this example does not... Figure 3 The comparison is shown in the middle.

[0098] In one embodiment of the present invention, by obtaining the adjusted hash ring, the digital energy air compressor station can be further scheduled and planned. Specifically, based on the physical nodes and virtual nodes in the adjusted hash ring, the corresponding numbered air compressors will be started and stopped, thereby ensuring the dynamic balance of the load of each air compressor in the digital energy air compressor station and improving the data monitoring effect of the digital energy air compressor station.

[0099] In one embodiment of the present invention, after planning and scheduling the digital energy air compressor station at the current moment, the hash ring can be reconstructed at the next future monitoring moment to analyze the load status of the activated air compressors and then perform normalized scheduling. It should be noted that the methods for constructing, analyzing, and adjusting the hash ring at each monitoring moment are consistent with the methods described in steps S1-S4, and will not be repeated here. However, it should be noted that when evaluating the load outlet pipe of each air compressor, each type of coded air compressor may correspond to multiple nodes in the hash ring, such as... Figure 3 In the adjusted hash ring, air compressor 3 corresponds to two nodes in the hash ring. When analyzing the load changes of the air compressor, these two nodes are still treated as a single physical node for analysis; this is a well-known technology familiar to those skilled in the art, and will not be elaborated here.

[0100] The present invention also proposes a data monitoring system for digital energy air compressor stations, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the data monitoring method for digital energy air compressor stations described in steps S1-S4.

[0101] In summary, this invention first constructs a hash ring and obtains the air flow sequence of each outlet pipe at the current monitoring time; then, based on the air flow sequence of each load outlet pipe, it obtains the load change sequence of each air compressor; further, based on the data distribution in the load change sequence of each air compressor in the hash ring, it obtains the load scheduling necessity of each air compressor in the hash ring; based on the air flow and the changing trend in the air flow sequence of each load outlet pipe of each air compressor in the hash ring, it obtains the scheduling suitability coefficient of each load outlet pipe; finally, based on the load scheduling necessity of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipe of each air compressor, it adjusts the hash ring to perform scheduling planning for the digital energy air compressor station. This invention improves the monitoring and scheduling effect of digital energy air compressor stations by monitoring and analyzing the air compressor load and air demand in digital energy air compressor stations, and by using the data distribution concept of hash rings to plan and schedule digital energy air compressor stations.

[0102] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A data monitoring method for digital energy air compressor stations, characterized in that, The method includes: At the current monitoring time, a hash ring is constructed based on the air compressors operating in the digital energy air compressor station and the open air outlet pipes, and the air flow sequence of each air outlet pipe within the preset historical monitoring period is obtained; Obtain all load outlet pipes corresponding to each air compressor in the hash ring, and obtain the load change sequence of each air compressor based on the air flow sequence of each load outlet pipe; obtain the load scheduling necessity of each air compressor in the hash ring based on the data distribution in the load change sequence of each air compressor in the hash ring. Based on the air flow rate of each load outlet pipe of each air compressor in the hash ring, the proportion of the total air flow rate of all load outlet pipes, and the changing trend of the air flow rate sequence of each load outlet pipe, the scheduling suitability coefficient of each load outlet pipe is obtained. The hash ring is adjusted based on the load scheduling necessity of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipe of each air compressor to perform scheduling planning for the digital energy air compressor station.

2. The data monitoring method for digital energy air compressor stations according to claim 1, characterized in that, The method for obtaining the hash ring includes: Based on the MD5 algorithm, the air compressor node code value is obtained by combining the number of the running air compressor and the number of the opened air outlet pipe. The air outlet pipe node code value is obtained by combining the number of the opened air outlet pipe. All the air compressor node code values ​​and all the air outlet pipe node code values ​​are mapped into a ring hash space, and the air compressors in the hash ring are used as physical nodes to obtain the hash ring.

3. The data monitoring method for digital energy air compressor stations according to claim 1, characterized in that, The method for obtaining the load change sequence includes: Based on the air flow data at the same time in the air flow sequence of all load outlet pipes corresponding to each air compressor in the hash ring, the load parameters of each air compressor at the corresponding time are obtained; the load parameters of each air compressor at each time within the preset historical monitoring period are sorted in chronological order to construct a load change sequence.

4. The data monitoring method for digital energy air compressor stations according to claim 1, characterized in that, The method for obtaining the necessity of load scheduling includes: In the load change sequence of all air compressors in the hash ring, based on the probability distribution of all load parameters, obtain all high load parameters and all low load parameters corresponding to the load change sequence of each air compressor. Based on the total number and average of the high load parameters in the load change sequence corresponding to each air compressor, and combined with the total number of the low load parameters, the load scheduling necessity of the corresponding air compressor in the hash ring is obtained.

5. A data monitoring method for digital energy air compressor stations according to claim 4, characterized in that, The methods for obtaining the high load parameters and the low load parameters include: Obtain the mean μ and standard deviation σ of all load parameters, construct the probability distribution curve, and take all load parameters outside μ+σ as the original high load parameters, and take all load parameters outside μ-σ as the original low load parameters. All original high load parameters in the load change sequence of each air compressor in the hash ring are taken as the high load parameters of the corresponding air compressor, and all original low load parameters in the load change sequence of each air compressor in the hash ring are taken as the low load parameters of the corresponding air compressor.

6. The data monitoring method for digital energy air compressor stations according to claim 4, characterized in that, The method for determining the load scheduling necessity of the corresponding air compressor in the hash ring based on the total number and average of the high load parameters in the load change sequence corresponding to each air compressor, combined with the total number of the low load parameters, includes: When the total number of high load parameters is not zero, the ratio of the total number of high load parameters to the length of the load change sequence is used as the first scheduling parameter; the average of all high load parameters is used as the second scheduling parameter; the first scheduling parameter and the second scheduling parameter are combined to obtain the load scheduling necessity of the corresponding air compressor in the hash ring. When the total number of high load parameters is zero and the total number of low load parameters is greater than or equal to a preset value, the load scheduling necessity of the corresponding air compressor in the hash ring is set to a preset negative value.

7. The data monitoring method for digital energy air compressor stations according to claim 1, characterized in that, The method for obtaining the scheduling suitability coefficient includes: Air compressors with a load scheduling necessity greater than a preset threshold are classified as high-intensity air compressors, and air compressors with a load scheduling necessity of a preset negative value are classified as low-intensity air compressors. In the air flow sequence of each load outlet pipe of each high-strength air compressor, the ratio between the air flow at each moment and the total air flow of all load outlet pipes at the corresponding moment is accumulated, and the accumulated value is used as the outlet load parameter of the corresponding load outlet pipe. The air flow sequence of each load outlet pipe of each of the high-strength air compressors is fitted with a straight line, and the slope of the fitted straight line is used as the outlet load trend parameter of the corresponding load outlet pipe. By integrating the outlet load parameters and the outlet load trend parameters, the scheduling suitability coefficient of the corresponding load outlet pipeline is obtained.

8. A data monitoring method for digital energy air compressor stations according to claim 7, characterized in that, The method for adjusting the hash ring includes: The load outlet pipe with the highest scheduling suitability coefficient among all load outlet pipes of the high-strength air compressor, and all load outlet pipes of the low-strength air compressor, are all designated as outlet pipes to be scheduled; the non-low-strength air compressor with the lowest load scheduling necessity is designated as the matching node of the outlet pipe to be scheduled. In the hash ring, all physical nodes corresponding to low-intensity air compressors are deleted, and virtual nodes are added between each scheduled air outlet pipeline and the corresponding matching air compressor in the clockwise direction; wherein the virtual node is the matching node, and in the clockwise direction, there are no air compressors or non-scheduled air outlet pipelines between the matching node and the corresponding scheduled air outlet pipeline.

9. A data monitoring method for digital energy air compressor stations according to claim 1, characterized in that, The method for obtaining the gas flow rate sequence includes: At the current moment, the preset historical monitoring period is divided into a preset number of unit monitoring sub-periods, and the total gas flow rate of each gas outlet pipe in each unit monitoring sub-period is obtained; the total gas flow rate is used as a sequence element and sorted in chronological order to construct the gas flow rate sequence of the corresponding gas outlet pipe in the preset historical monitoring period.

10. A data monitoring system for digital energy air compressor stations, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the data monitoring method for a digital energy air compressor station as described in any one of claims 1-9.

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