Data monitoring method and system for digital energy air compression station

Abstract

The invention relates to the technical field of industrial big data monitoring and mining, in particular to a data monitoring method and system for a digital energy air compression station. The method comprises the following steps: firstly, constructing a Hash ring, acquiring an air flow sequence of each air outlet pipeline, further acquiring a load change sequence of each air compressor, and further acquiring the load scheduling necessity of each air compressor in the Hash ring according to a data distribution condition in the load change sequence; then, according to the air flow and the change trend of each load air outlet pipeline of each air compressor in the Hash ring, the scheduling appropriate coefficient of each load air outlet pipeline is obtained; and finally, based on the load scheduling necessity and the scheduling suitability coefficient, adjusting the Hash ring so as to perform scheduling planning on the digital energy air compression station. The digital energy air compression station is planned and dispatched by monitoring and analyzing the load condition and the gas consumption requirement of the air compressor in the digital energy air compression station and by means of the data distribution idea of the Hash ring, and the monitoring and dispatching effect on the digital energy air compression station is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial big data monitoring and mining, and in particular to a data monitoring method and system for a digital energy air compressor station. Background Art

[0002] A digital energy air compressor station is an air compressor system that uses digital technology to control and optimize operation. It is usually an important energy-consuming equipment in industrial production and is widely used in the industrial field. By real-time monitoring and tracking of key parameters such as the operating status and load pressure of the air compressor, it can help operators promptly discover the causes of low energy efficiency, and then optimize operating strategies and scheduling, reduce unnecessary energy waste, and achieve energy saving and consumption reduction.

[0003] Existing technologies usually control the start and stop of air compressors in air compression stations based on the overall gas consumption monitoring data of the gas-using workshops. Although this can save energy and reduce consumption to a certain 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 of the air compressors, while idle air compressors cannot fully exert their efficiency, but instead increase equipment loss and energy waste. Summary of the invention

[0004] In order to solve the technical problem that the monitoring and dispatching effect of the digital energy air compressor station in the prior art is not good, the purpose of the present invention is to provide a data monitoring method and system for the digital energy air compressor station. The technical solution adopted is as follows:

[0005] The present invention proposes a data monitoring method for a digital energy air compressor station, the method comprising:

[0006] At the current monitoring moment, a hash ring is constructed based on the running air compressors and open outlet pipes in the digital energy air compressor station, and the air flow sequence of each outlet pipe in 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 according to the air flow sequence of each load outlet pipe; obtain the necessity of load scheduling of each air compressor in the hash ring according to the data distribution in the load change sequence of each air compressor in the hash ring;

[0008] According to the air flow rate of each load outlet pipeline of each air compressor in the hash ring, the proportion of the total air flow rate of all load outlet pipelines, and the change trend in the air flow rate sequence of each load outlet pipeline, the scheduling suitability coefficient of each load outlet pipeline is obtained;

[0009] According to the load scheduling necessity of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipeline of each air compressor, the hash ring is adjusted 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 in combination with the number of the running air compressor, and the outlet pipe node code value is obtained in combination with the number of the opened outlet pipe; all the air compressor node code values ​​and all the outlet pipe node code values ​​are mapped into a circular hash space, and the air compressor in the hash ring is used as a physical node to obtain a hash ring.

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

[0013] According to 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 in 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, according to the probability distribution of all load parameters, obtain all high load parameters and all low load parameters in the load change sequence corresponding to each air compressor;

[0016] According to the total number and average value 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, the necessity of load scheduling 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 a probability distribution curve, take all load parameters distributed outside μ+σ as the original high load parameters, and take all load parameters distributed 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 used as 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 used as low load parameters of the corresponding air compressor.

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

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

[0022] When the total number of the high load parameters is zero and the total number of the 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] The air compressor whose load scheduling necessity is greater than a preset threshold is regarded as a high-intensity air compressor, and the air compressor whose load scheduling necessity is a preset negative value is regarded as a low-intensity air compressor;

[0025] In the air flow sequence of each load outlet pipe of each high-intensity 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] Performing a straight line fitting on the air flow sequence of each load outlet pipe of each high-intensity air compressor, and taking the slope of the fitting straight line as the outlet load trend parameter of the corresponding load outlet pipe;

[0027] The outlet load parameter and the outlet load trend parameter are integrated to obtain a scheduling suitability coefficient of the corresponding load outlet pipeline.

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

[0029] The load outlet pipeline with the largest scheduling suitability coefficient among all the load outlet pipelines of the high-intensity air compressor and all the load outlet pipelines of the low-intensity air compressor are used as the outlet pipelines to be scheduled; the non-low-intensity air compressor with the least load scheduling necessity is used as the matching node of the outlet pipeline to be scheduled;

[0030] In the hash ring, all physical nodes corresponding to low-intensity air compressors are deleted, and a virtual node is added between each of the outlet pipelines to be scheduled 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-outlet pipelines to be scheduled between the matching node and the corresponding outlet pipeline to be scheduled.

[0031] Furthermore, the method for acquiring the airflow 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 air flow of each outlet pipe in each unit monitoring sub-period is obtained; the total air flow is used as a sequence element and sorted in chronological order to construct an air flow sequence of the corresponding outlet pipe in the preset historical monitoring period.

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

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

[0035] The present invention constructs a hash ring based on the running air compressors and opened air outlet pipes in the digital energy air compressor station at the current monitoring moment, and obtains the air flow sequence of each air outlet pipe in a preset historical monitoring period, so as to prepare for the subsequent analysis of the air compressor load situation for scheduling and allocation; obtains all the load air outlet pipes corresponding to each air compressor in the hash ring, and obtains the load change sequence of each air compressor according to the air flow sequence of each load air outlet pipe; analyzes the load pressure of each air compressor according to the data distribution in the load change sequence of each air compressor in the hash ring, and then obtains the load scheduling necessary for each air compressor in the hash ring. The necessity of load scheduling of each air compressor in the hash ring is prepared for subsequent optimization scheduling and load balancing; further, according to 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, combined with the change trend in the air flow rate sequence of each load outlet pipe, the scheduling suitability coefficient of each load outlet pipe is obtained, so as to facilitate the subsequent screening of the load outlet pipes to be scheduled and diverted, and share the load pressure of the air compressor; finally, according to the necessity of load scheduling 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 is adjusted to schedule and plan the digital energy air compressor station. The present invention monitors and analyzes the load conditions and gas demand of the air compressors in the digital energy air compressor station, and uses the data allocation idea of ​​the hash ring to plan and schedule the digital energy air compressor station, thereby improving the monitoring and scheduling effect of the digital energy air compressor station. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flow chart of a data monitoring method for a digital energy air compressor station provided by an embodiment of the present invention;

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

[0039] Figure 3 A comparison diagram before and after a hash ring adjustment is provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the data monitoring method and system for a digital energy air compressor station proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0041] Unless defined otherwise, 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 belongs.

[0042] The following is a detailed description of a data monitoring method and system for a digital energy air compressor station provided by the present invention in conjunction with the accompanying drawings.

[0043] See also Figure 1 , which shows a flow chart of a data monitoring method for a digital energy air compressor station provided by an embodiment of the present invention, specifically comprising:

[0044] Step S1, at the current monitoring moment, a hash ring is constructed based on the air compressors running in the digital energy air compressor station and the opened air outlet pipes, and the air flow sequence of each air outlet pipe in a preset historical monitoring period is obtained.

[0045] In order to monitor the operation of the digital energy air compressor station to optimize the scheduling, an embodiment of the present invention will first build a hash ring based on the running air compressors in the digital energy air compressor station and the outlet pipes opened to transport air to each demand workshop at the current monitoring moment. The air compressors and outlet pipes are balanced and distributed through the hash ring, so as to balance the load of the air compressor while meeting the compressed air supply, thereby ensuring the efficient operation of the digital energy air compressor station.

[0046] Considering that the purpose of constructing the 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 outlet pipeline can be regarded as the data to be allocated and stored in the node in the hash ring. The air compressor load can be balanced based on the scheduling and allocation idea of ​​the hash ring 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 air compressor node code value is obtained in combination with the number of the running air compressor, and the outlet pipe node code value is obtained in combination with the number of the opened outlet pipe; all air compressor node code values ​​and all outlet pipe node code values ​​are mapped to the circular hash space, and the air compressor in the hash ring is used as a physical node to obtain a hash ring.

[0049] It should be noted that each air compressor and each outlet pipe in the digital energy air compressor station corresponds to a unique number for management. The construction of the MD5 algorithm and the hash ring is already an existing technology well known to those skilled in the art. Here we only briefly describe its construction process:

[0050] As an example, first perform MD5 hashing on the serial number of each running air compressor to obtain the corresponding 32-bit hexadecimal string, then convert the 32-bit hexadecimal string into an integer, take the modulus of the integer value, and obtain its mapping position in the hash ring; similarly, the mapping position of each outlet pipe in the hash ring can be obtained; all running air compressors and open outlet pipes are mapped to the corresponding positions in the hash ring to obtain the hash ring at the current moment.

[0051] Taking into account that the gas demand usually changes according to production plans and other reasons, the gas flow rate of each open outlet pipe will also change, which will cause the actual load pressure of the air compressor to constantly change; therefore, an embodiment of the present invention will further obtain the gas flow sequence of each outlet pipe within the preset historical monitoring period at the current monitoring moment, and then analyze the load pressure changes of each air compressor in combination with the hash ring, and then adjust the hash ring to optimize the monitoring and scheduling of the digital energy air compressor station.

[0052] Preferably, in one embodiment of the present invention, the method for acquiring the airflow 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 of each outlet pipe in each unit monitoring sub-period is obtained; the total gas flow is used as a sequence element and sorted in chronological order to construct the gas flow sequence of the corresponding outlet pipe in the preset historical monitoring period.

[0054] As an example, first set the preset historical monitoring period to the historical 10 minutes of the current time, and set the preset number to 10. For example, if the current time is 10:10, set the 10-minute period between 10:00 and 10:10 as the preset historical monitoring period, and take each minute within these 10 minutes as a unit monitoring sub-period. The total gas flow of the gas delivered to the workshop by each gas outlet pipe in each unit monitoring sub-period is obtained through the flow meter at the gas outlet pipe. Each total gas flow is used as a sequence element and is sorted in chronological order to construct a gas flow sequence.

[0055] In other embodiments, the implementer may also set the preset historical monitoring period and preset quantity 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 necessity of load scheduling for 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] In order to analyze the load intensity or pressure of each air compressor, an embodiment of the present invention will first obtain all load outlet pipes corresponding to each air compressor in the hash ring, and further obtain the load change sequence of the corresponding air compressor according to the air flow sequence of each load outlet pipe, so as to optimize the scheduling based on the load change of the air compressor later.

[0058] It should be noted that the first air compressor in the clockwise direction of each outlet pipe in the hash ring is taken as its matching air compressor, that is, it is considered that the gas demand of the outlet pipe generates a load for the corresponding matching air compressor; the composition and analysis of the hash ring are well-known technologies and will not be repeated 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 load of the air compressor can be evaluated based on the air flow rate of all load outlet pipes at the same time, and then the load change of the air compressor can be analyzed; based on this, the method for obtaining the load change sequence includes:

[0060] According to 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 in 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 taken as the load parameter of the air compressor at the corresponding time, and then the load parameter at each time is taken as the sequence element, and sorted based on the time sequence to construct a 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 during 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, thereby obtaining the necessity of load scheduling for each air compressor in the hash ring and preparing for subsequent optimized 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 evaluate 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, according to the probability distribution of all load parameters, all high load parameters and all low load parameters in the load change sequence corresponding to each air compressor are obtained;

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

[0066] Among them, 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, and then which load parameters are beyond the normal range can be identified, that is, it can help identify abnormal high load parameters or low load parameters, so as to evaluate the load condition of the air compressor to optimize scheduling; based on this, the method for obtaining high load parameters and low load parameters includes:

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

[0068] All original high load parameters in the load change sequence of each air compressor in the hash ring are used as 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 used as low load parameters of the corresponding air compressor.

[0069] See also Figure 2 , which shows a probability distribution curve of a load parameter provided by an embodiment of the present invention, wherein the distribution range of the original high load parameter and the original low load parameter is indicated, wherein all the load parameters distributed on the left side of the dotted line 1 are the original low load parameters, and all the load parameters distributed on the right side of the dotted line 2 are the original high load parameters; it should be noted that, Figure 2 The probability distribution curve is a normal distribution, and there may be a skewed distribution in the specific implementation. However, based on each probability distribution curve, the method of obtaining the original low load parameters and the original high load parameters is consistent, and examples are not given one by one.

[0070] Considering that when the number of high-load parameters corresponding to each air compressor is greater and the average level of high-load parameters is higher, it means that the current air compressor is more likely to be prone to long-term high-load operation, and the necessity of load scheduling is higher; considering that when the number of low-load parameters is greater, it means that the working efficiency of the current air compressor is lower, the necessity of maintaining the startup state is lower, and the load outlet pipeline should be diverted to other air compressors to reduce energy consumption, and the necessity of load scheduling is higher;

[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 mean of all high-load parameters is used as the second scheduling parameter; the first scheduling parameter and the second scheduling parameter are integrated to obtain the necessity of load scheduling 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 a calculation formula for load scheduling necessity, and the calculation formula for load scheduling necessity is:

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

[0075] In the calculation formula for the necessity of load scheduling, the first scheduling parameter and the second scheduling parameter are specifically multiplied and combined to obtain the necessity of load scheduling of the corresponding air compressor, so that the more high load parameters the air compressor has and the higher the level is, the more it should be scheduled; in other embodiments, the implementer may also combine the two through basic mathematical operations such as addition or weighted summation or related mapping methods, and may also use other normalization methods, which are not repeated 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 the preset value, the load scheduling necessity of the corresponding air compressor in the hash ring is set to a preset negative value; wherein, since the preset value is set to the preset number 10 in this example, 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, the load scheduling necessity is directly set to -1;

[0077] In other examples, the implementer may also set other preset values, and the value range 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 conditions and low load conditions, which will not be elaborated here.

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

[0079] Step S3, according to the air flow of each load outlet pipe of each air compressor in the hash ring, the proportion of the total air flow of all load outlet pipes, and the changing trend in the air flow sequence of each load outlet pipe, obtain the scheduling suitability coefficient of each load outlet pipe.

[0080] Considering that when an air compressor is in high-intensity operation for a long time, the aging speed of its internal hardware is faster than that of other air compressors, and multiple air compressors are working at the same intensity at the same time, the overall aging process of the digital energy air compressor system can be reduced, and the overall power consumption can also be reduced; considering that among all the load outlet pipes of the air compressor, the load outlet pipes with large and continuously increasing air outlet demand have the greatest impact on the load pressure of the air compressor, when the air compressor is in a high-load operation state, the load outlet pipes with large air outlet demand are dispatched to the other air compressors in a lower load operation state, so that the load pressure can be balanced;

[0081] Therefore, the embodiment of the present invention will obtain the scheduling suitability coefficient of each load outlet pipeline based on the air flow information of each load outlet pipeline of each air compressor in the hash ring and the changing trend in the air flow sequence of each load outlet pipeline; the scheduling suitability coefficient reflects the suitability of each load outlet pipeline to be scheduled to other air compressors to balance the load, and indirectly reflects the impact of the load outlet pipeline on the load pressure of the air compressor, so as to arrange the subsequent scheduling and balance the load.

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

[0083] The air compressors whose load scheduling necessity is greater than a preset threshold are regarded as high-intensity air compressors, and the air compressors whose load scheduling necessity is a preset negative value are regarded as low-intensity air compressors;

[0084] In the air flow sequence of each load outlet pipe of each high-intensity 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] Perform linear fitting on the air flow sequence of each load outlet pipe of each high-intensity air compressor, and use the slope of the fitted straight line as the outlet load trend parameter of the corresponding load outlet pipe;

[0086] The outlet load parameters and outlet load trend parameters are integrated to obtain the dispatch suitability coefficient of the outlet pipeline corresponding to the load.

[0087] As an example, the preset threshold is set to 0.7, and the air compressors with a load scheduling necessity greater than 0.7 are regarded as high-intensity air compressors, and the air compressors with a load scheduling necessity equal to -1 are regarded as low-intensity air compressors; the outlet load parameters of each load outlet pipe of each high-intensity air compressor are further calculated, and specifically the air flow rate at each moment in the air flow sequence of each load outlet pipe is used as the numerator, and the total air 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, and indirectly reflects the influence of the load outlet pipe on the load pressure of the high-intensity air compressor;

[0088] At the same time, the air flow sequence of each load outlet pipe of the high-intensity air compressor is linearly fitted by the least squares method, and the slope of the fitting line is calculated based on the two-point formula. The slope reflects the change trend of the air flow of the load outlet pipe. The larger the slope is positive, the greater the impact on the load pressure of the high-intensity 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 such as addition or weighted summation or related mapping methods, and can also use other normalization methods, which are not repeated here.

[0089] It should be noted that the least square method for straight line fitting and slope calculation are both existing technologies. Implementers may also use other well-known technical means to evaluate the trend of changes, or other normalization means, which will not be elaborated here.

[0090] Step S4, according to the necessity of load scheduling of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipeline of each air compressor, adjust the hash ring to perform scheduling planning for the digital energy air compressor station.

[0091] After obtaining the necessity of load scheduling for 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 compressor and the load outlet pipe to balance the load.

[0092] Preferably, in one embodiment of the present invention, in order to relieve the load pressure of the high-intensity air compressor, the load outlet pipeline with the largest and continuously increasing demand for air outlet should be dispatched to the other air compressors in the lower load operation state; and considering that the operation necessity of the low-intensity air compressor is relatively low, its load outlet pipeline can be dispatched to the other air compressors, and then the current low-intensity air compressor can be shut down to ensure the air outlet supply while reducing the operating cost; based on this, the adjustment method of the hash ring includes:

[0093] The load outlet pipeline with the largest scheduling suitability coefficient among all the load outlet pipelines of the high-intensity air compressor and all the load outlet pipelines of the low-intensity air compressor are used as the outlet pipelines to be scheduled; the non-low-intensity air compressor with the least load scheduling necessity is used as the matching node of the outlet pipeline to be scheduled;

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

[0095] It should be noted that the adjustment of the hash ring, such as adding or deleting nodes or adding virtual nodes, is already a well-known prior art to those skilled in the art, and the adjustment process is briefly described here by way of example; please refer to Figure 3 , which shows a comparison diagram before and after a hash ring adjustment provided by an embodiment of the present invention; Figure 3 The left picture in the middle is the hash ring before adjustment, and the right picture is the hash ring after adjustment. The small circles with numbers in the hash ring correspond to the nodes of the outlet pipe, and the small circles with letters correspond to the nodes of the air compressor. The matching relationship between the outlet pipe and the air compressor is indicated by the dotted arrow. For example, Figure 3 In the left figure, the outlet pipe 3 is the load outlet pipe of the air compressor c. Similarly, the outlet pipes 2, 4, and 6 are the load outlet pipes of the 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 largest scheduling suitability coefficient, then the load outlet pipe 2 is set as the outlet pipe to be scheduled, and the non-low-intensity air compressor 3 with the least current load scheduling necessity is used as the matching node of the outlet pipe to be scheduled, that is, the 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 its clockwise direction, that is, Figure 3 The dotted letter-labeled small circle in the right figure is also the air compressor 3, where the virtual node corresponding to the air compressor 3 should be close to the load outlet pipe 2, such as Figure 3 At the position in the right figure, avoid misallocation of the load outlet pipe 4 or 6, and then allocate the outlet pipe to be scheduled to the non-low-intensity air compressor 3 with the least necessity for current load scheduling, thereby alleviating the load pressure of the high-intensity air compressor b;

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

[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, the air compressors with corresponding numbers will be started and stopped based on the air compressors corresponding to the physical nodes and virtual nodes in the adjusted hash ring, thereby ensuring the dynamic balance of the load of each air compressor in the digital energy air compressor station, thereby 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 rebuilt at the next future monitoring moment to analyze the load of the turned-on air compressor and perform normalized scheduling. It should be noted that the construction, analysis and adjustment methods of the hash ring at each monitoring moment are consistent with the methods described in steps S1-S4 and are not repeated here. However, it should be noted that when evaluating the load outlet pipeline of each air compressor, each coded air compressor may correspond to multiple nodes in the hash ring, such as Figure 3 In the adjusted hash ring, the air compressor 3 corresponds to two nodes in the hash ring. When analyzing the load change of the air compressor, these two nodes are still regarded as one physical node for analysis; this is a well-known technology well known to those skilled in the art and will not be described in detail here.

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

[0101] In summary, the present invention first constructs a hash ring and obtains the air flow sequence of each outlet pipe at the current monitoring moment; then, according to the air flow sequence of each load outlet pipe, obtains the load change sequence of each air compressor, and further obtains the load scheduling necessity of each air compressor in the hash ring according to the data distribution in the load change sequence of each air compressor in the hash ring; according to the air flow of each load outlet pipe of each air compressor in the hash ring and the change trend in the air flow sequence, obtain the scheduling suitability coefficient of each load outlet pipe; finally, 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, adjust the hash ring to schedule the digital energy air compressor station. The present invention monitors and analyzes the load conditions and gas demand of the air compressors in the digital energy air compressor station, and plans and schedules the digital energy air compressor station with the help of the data distribution idea of ​​the hash ring, thereby improving the monitoring and scheduling effect of the digital energy air compressor station.

[0102] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. 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, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A data monitoring method for a digital energy air compressor station, characterized in that: The method comprises: At the current monitoring moment, a hash ring is constructed based on the running air compressors and open outlet pipes in the digital energy air compressor station, and the air flow sequence of each outlet pipe in 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 according to the air flow sequence of each load outlet pipe; obtain the necessity of load scheduling of each air compressor in the hash ring according to the data distribution in the load change sequence of each air compressor in the hash ring; According to the air flow rate of each load outlet pipeline of each air compressor in the hash ring, the proportion of the total air flow rate of all load outlet pipelines, and the change trend in the air flow rate sequence of each load outlet pipeline, the scheduling suitability coefficient of each load outlet pipeline is obtained; According to the load scheduling necessity of each air compressor in the hash ring and the scheduling suitability coefficient of each load outlet pipeline of each air compressor, the hash ring is adjusted to perform scheduling planning for the digital energy air compressor station.

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

3. According to claim 1, a data monitoring method for a digital energy air compressor station is characterized in that: The method for acquiring the load change sequence includes: According to 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 in the preset historical monitoring period are sorted in chronological order to construct a load change sequence.

4. According to claim 1, a data monitoring method for a digital energy air compressor station is 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, according to the probability distribution of all load parameters, obtain all high load parameters and all low load parameters in the load change sequence corresponding to each air compressor; According to the total number and average value 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, the necessity of load scheduling of the corresponding air compressor in the hash ring is obtained.

5. A data monitoring method for a digital energy air compressor station according to claim 4, characterized in that: The method for obtaining the high load parameter and the low load parameter includes: Obtain the mean μ and standard deviation σ of all load parameters, construct a probability distribution curve, take all load parameters distributed outside μ+σ as the original high load parameters, and take all load parameters distributed 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 used as 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 used as low load parameters of the corresponding air compressor.

6. A data monitoring method for a digital energy air compressor station according to claim 4, characterized in that: The method for obtaining the necessity of load scheduling for the corresponding air compressor in the hash ring according to the total number and average value of the high load parameters in the load change sequence corresponding to each air compressor, combined with the total number of low load parameters, includes: When the total number of the high load parameters is not zero, the ratio of the total number of the high load parameters to the length of the load change sequence is used as the first scheduling parameter; the average of all the high load parameters is used as the second scheduling parameter; the first scheduling parameter and the second scheduling parameter are integrated to obtain the necessity of load scheduling of the corresponding air compressor in the hash ring; When the total number of the high load parameters is zero and the total number of the 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. A data monitoring method for a digital energy air compressor station according to claim 1, characterized in that: The method for obtaining the scheduling suitability coefficient includes: The air compressor whose load scheduling necessity is greater than a preset threshold is regarded as a high-intensity air compressor, and the air compressor whose load scheduling necessity is a preset negative value is regarded as a low-intensity air compressor; In the air flow sequence of each load outlet pipe of each high-intensity 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; Performing a straight line fitting on the air flow sequence of each load outlet pipe of each high-intensity air compressor, and taking the slope of the fitting straight line as the outlet load trend parameter of the corresponding load outlet pipe; The outlet load parameter and the outlet load trend parameter are integrated to obtain a scheduling suitability coefficient of the corresponding load outlet pipeline.

8. A data monitoring method for a digital energy air compressor station according to claim 7, characterized in that: The method for adjusting the hash ring includes: The load outlet pipeline with the largest scheduling suitability coefficient among all the load outlet pipelines of the high-intensity air compressor and all the load outlet pipelines of the low-intensity air compressor are used as the outlet pipelines to be scheduled; the non-low-intensity air compressor with the least load scheduling necessity is used as the matching node of the outlet pipeline to be scheduled; In the hash ring, all physical nodes corresponding to low-intensity air compressors are deleted, and a virtual node is added between each of the outlet pipelines to be scheduled 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-outlet pipelines to be scheduled between the matching node and the corresponding outlet pipeline to be scheduled.

9. A data monitoring method for a digital energy air compressor station according to claim 1, characterized in that: The method for obtaining the airflow 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 air flow of each outlet pipe in each unit monitoring sub-period is obtained; the total air flow is used as a sequence element and sorted in chronological order to construct an air flow sequence of the corresponding outlet pipe in the preset historical monitoring period.

10. A data monitoring system for digital energy air compressor stations, characterized in that: It includes 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 a data monitoring method for a digital energy air compressor station as described in any one of claims 1 to 9.

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