Air compressor optimization operation method, equipment and system based on edge-cloud collaborative control

Abstract

The present application relates to the field of compressed air technology, and in particular to an air compressor optimization operation method, device and system based on edge-cloud collaborative control, wherein the method includes: obtaining the operating data and system unit energy consumption of each air compressor in the compressed air system; optimizing the system pressure band according to the operating data and system unit energy consumption to obtain the optimal system pressure band of the compressed air system, and determining the optimal start and stop sequence of each air compressor according to the real-time specific power; sending the optimal system pressure band and the optimal start and stop sequence to the edge controller of the compressed air system, and controlling one or more air compressors to start or stop according to the optimal start sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band. Thus, the problem that the operation of multiple air compressors in the compressed air system is usually controlled by a fixed pressure band and the start and stop sequence of the air compressor in the related art is solved, which has poor adjustability and leads to poor energy saving of the entire air compression station.

Description

Technical Field

[0001] The present application relates to the field of compressed air technology, and in particular to an air compressor optimization operation method, equipment and system based on edge-cloud collaborative control. Background Art

[0002] Compressed air, recognized as the fourth largest energy source, boasts advantages such as easy availability, safety, reliability, and high specific power density. It is widely used across various industries for power transmission, purge, instrument air, and process air. Air compressors, among other things, are energy conversion devices for compressed air, converting electrical energy into compressed air energy. Air compressors consume 8%-10% of total electricity consumption, and for some companies, this can account for over 50% of their total electricity consumption.

[0003] Therefore, air compressor energy saving has always been an area of ​​focus for energy conservation and consumption reduction. For situations where multiple air compressors are running at the same time, how to optimize the operation of the air compressors is one of the key issues in solving the high energy consumption of air compressors. Summary of the Invention

[0004] The present application provides an air compressor optimization operation method, equipment and system based on edge-cloud collaborative control to solve the problem that the related technology usually adopts a fixed pressure band and the start and stop sequence of the air compressor to control the operation of multiple air compressors in the compressed air system, which has poor adjustability and leads to poor energy saving of the entire air compression station.

[0005] The first aspect of the present application provides an air compressor optimization operation method based on edge-cloud collaborative control. The method is applied to a cloud server or an edge intelligent gateway. If the method is applied to the edge intelligent gateway, the cloud server is used to upgrade the algorithm for the operation of the edge intelligent gateway, including the following steps: obtaining the operation data and system unit energy consumption of each air compressor in the compressed air system; optimizing the system pressure band according to the operation data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system, calculating the real-time specific power of each air compressor according to the operation data, and determining the optimal start and stop sequence of each air compressor according to the real-time specific power; sending the optimal system pressure band and the optimal start and stop sequence to the edge side controller of the compressed air system, wherein the edge side controller determines the number of air compressors to be started or stopped in the compressed air system according to the optimal system pressure band, and controls one or more of the air compressors to start or stop according to the optimal start sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band.

[0006] Optionally, the operating data includes the start and stop frequency of the air compressor, and the system pressure band is optimized according to the operating data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system, including: obtaining the first air compressor start and stop frequency and the first system unit energy consumption of the compressed air system in the previous cycle, and the second air compressor start and stop frequency and the second system unit energy consumption of the compressed air system in the current cycle; if the second air compressor start and stop frequency is less than the frequency threshold, and the second system unit energy consumption is less than the first system unit energy consumption, then reducing the current system pressure band to the optimal system pressure band according to a first preset amplitude; if the second air compressor start and stop frequency is less than the frequency threshold, and the second system unit energy consumption is greater than the first system unit energy consumption, or the second air compressor start and stop frequency is greater than the frequency threshold, then increasing the current system pressure band to the optimal system pressure band according to a second preset amplitude.

[0007] Optionally, before obtaining the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle, it also includes: identifying whether the current cycle is the first cycle; if the current cycle is the first cycle, obtaining the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the second cycle, otherwise obtaining the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle.

[0008] Optionally, the operating data includes the operating status of each air compressor, the flow matrix and the power of each air compressor when loaded, and the real-time specific power of each air compressor is calculated based on the operating data, including: calculating the flow of each air compressor when loaded based on the operating status of each air compressor and the flow matrix, and calculating the real-time specific power of each air compressor based on the flow of each air compressor when loaded and the power of each air compressor when loaded.

[0009] Optionally, determining the optimal start-stop sequence of each air compressor based on the real-time specific power includes: sorting the air compressors in descending order according to the real-time specific power to obtain the optimal start-stop sequence, wherein the efficiency of the air compressor with a high specific power is greater than that of the air compressor with a low specific power, the air compressor with the highest ratio is started at startup, and the air compressor with the lowest specific power is stopped at shutdown.

[0010] The second aspect of the present application provides an air compressor optimization operation method based on edge-cloud collaborative control. The method is applied to an edge-side controller, and the cloud server upgrades the algorithm running the edge-side controller, including the following steps: obtaining the operating data and system unit energy consumption of each air compressor in the compressed air system; optimizing the system pressure band according to the operating data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system, calculating the real-time specific power of each air compressor according to the operating data, and determining the optimal start and stop sequence of each air compressor according to the real-time specific power; determining the number of air compressors to be started or stopped in the compressed air system according to the optimal system pressure band, and controlling one or more of the air compressors to start or stop according to the optimal start sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band.

[0011] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the air compressor optimization operation method based on edge-cloud collaborative control as described in the above embodiment.

[0012] Optionally, the electronic device is a cloud server, an edge intelligent gateway or an edge-side controller.

[0013] The fourth aspect of the present application provides a compressed air system, comprising: multiple air compressors; a collection device for collecting operating data and system unit energy consumption of each air compressor; an edge-side controller for determining the number of air compressors to be started or stopped in the compressed air system based on the optimal system pressure band, and controlling one or more of the air compressors to start or stop according to the optimal start-up sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band, wherein the electronic equipment described in the above embodiment is used to optimize the pressure band and optimize the air compressor for the operating data and system unit energy consumption to obtain the optimal system pressure band and the optimal start-up sequence.

[0014] The fifth embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement the air compressor optimization operation method based on edge-cloud collaborative control as described in the above embodiment.

[0015] Therefore, this application has at least the following beneficial effects:

[0016] This application adopts edge-cloud collaborative technology, with the edge-side controller as the actuator, issuing data collection-level instructions for the operation of the air compressor system, and the cloud (cloud platform) performing data analysis, algorithm operation, and collaborative control with the edge-side controller. By combining edge control with cloud algorithms, the pressure band optimization of the air compressor system and the optimization of equipment operation in scenarios where multiple air compressors are operating are realized, and the advantages of high stability and real-time performance of edge-side control are fully utilized in combination with the powerful data processing capabilities and intelligent algorithms of the cloud to achieve the optimal solution for the optimized operation of multiple air compressors. The cloud algorithm can realize the dynamic calculation of the pressure bandwidth based on big data analysis, and dynamically allocate the priority start-stop sequence according to the efficiency of each air compressor calculated by big data, so as to achieve the optimized operation of multiple air compressors, thereby achieving the purpose of energy saving for the entire air compressor station, reducing industry production costs, reducing carbon emissions, and creating economic and social benefits. As a result, the technical problems in the related technology that it is impossible to achieve the simultaneous optimized operation of multiple air compressors, resulting in poor energy saving of the entire air compressor station, are solved.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 A flowchart of an air compressor optimization operation method based on edge-cloud collaborative control provided according to an embodiment of the present application;

[0020] Figure 2 A logic flow chart for optimizing the pressure band according to an embodiment of the present application;

[0021] Figure 3 A logical flow chart of air compressor optimization according to an embodiment of the present application;

[0022] Figure 4 A flowchart of an air compressor optimization operation method based on edge-cloud collaborative control provided according to an embodiment of the present application;

[0023] Figure 5 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application;

[0024] Figure 6 Schematic diagram of a compressed air system according to an embodiment of the present application;

[0025] Figure 7 This is a diagram of the edge-cloud collaboration technology architecture provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0027] In the operating scenario of multiple air compressors, traditional control methods fall into two categories: one is to set cascading pressure bands, creating a certain overlap between the loading and unloading pressures of each air compressor, allowing the compressors to load and unload based on pressure feedback. This control method results in a wide pressure band for the entire air compressor system and prolonged unloading periods. The unloading state consumes approximately 40% of the energy consumed when fully loaded, and this energy is not converted into compressed air energy, resulting in wasted energy. Therefore, the disadvantages of cascading pressure bands are obvious: excessively wide pressure bands can lead to prolonged unloading periods, resulting in wasteful operation. The other approach is a group control system operating only on the edge side. This system utilizes a single PLC controller, based on single-point pressure feedback from the main pipe, to maintain system pressure within a set fixed pressure band and sequentially start and stop multiple air compressors. This control method allows for a fixed pressure band within a narrow range, typically around 0.5-1 bar, which can somewhat address the issue of excessively wide stacked pressure bands. It also allows the compressor to be controlled according to a pre-set start / stop sequence, optimizing operation. However, because the edge-side PLC utilizes logic sequence control, the pressure band settings and compressor start / stop sequence are all pre-set fixed values ​​and cannot be adjusted based on changes in operating conditions or compressor performance, making it a suboptimal solution.

[0028] The following describes the air compressor optimization operation method, equipment and system based on edge-cloud collaborative control of the embodiment of the present application with reference to the accompanying drawings. In view of the fact that the air compressor mentioned in the above background technology is the main equipment for producing compressed air, but the air compressor has high energy consumption, it is necessary to find a method for optimizing the operation of the air compressor. The present application provides an operation control method for a compressed air system. In this method, by combining edge-side control with cloud-side algorithm, the pressure band optimization of the air compressor system and the optimization control of equipment operation in the operation scenario of multiple air compressors are realized. The cloud-side algorithm can realize dynamic adjustment of the pressure bandwidth based on big data analysis, and dynamically allocate the priority start-stop sequence based on the efficiency of each air compressor calculated by big data, so as to realize the optimized operation of multiple air compressors, thereby achieving the purpose of energy saving of the entire air compressor station. Thus, the problem of the related technology generally using a fixed pressure band and the start-stop sequence of the air compressor to control the operation of multiple air compressors in the compressed air system is solved, which has poor adjustability and leads to poor energy saving of the entire air compressor station.

[0029] It should be noted that the air compressor optimization operation method based on edge-cloud collaborative control described in the following embodiment can be applied to a cloud server or an edge intelligent gateway. When this method is applied to an edge intelligent gateway, the cloud server upgrades the algorithm running on the edge intelligent gateway.

[0030] Specifically, Figure 1 A flow chart of an air compressor optimization operation method based on edge-cloud collaborative control provided in an embodiment of the present application.

[0031] like Figure 1 As shown, the air compressor optimization operation method based on edge-cloud collaborative control includes the following steps:

[0032] In step S101, the operating data and system unit energy consumption of each air compressor in the compressed air system are obtained.

[0033] The operating data includes the air compressor start-stop frequency F, the operating status of each air compressor, the flow matrix, and the power of each air compressor when loaded.

[0034] It should be noted that operating data and system unit energy consumption can be obtained based on cloud IoT data and other methods.

[0035] In step S102, the system pressure band is optimized based on the operating data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system. The real-time specific power of each air compressor is calculated based on the operating data, and the optimal start and stop sequence of each air compressor is determined based on the real-time specific power.

[0036] It is understandable that the embodiments of the present application can be specifically divided into two parts: (1) pressure band optimization (obtaining the optimal system pressure band); (2) air compressor optimization (obtaining the optimal start and stop sequence of each air compressor).

[0037] Specifically, the embodiment of the present application can optimize the system pressure band according to the operating data and system units to obtain the optimal system pressure band, and can calculate the real-time specific power of each air compressor according to the operating data to determine the optimal start and stop sequence of each air compressor. This changes the problem that the setting of the pressure band and the start and stop sequence of the air compressor in the traditional control method are all pre-set fixed values ​​and cannot be adjusted according to changes in working conditions and changes in compressor performance, and solves the problem of dynamic adjustment of the system pressure band and dynamic adjustment of the start and stop sequence of the air compressor.

[0038] In the embodiment of the present application, the system pressure band is optimized according to the operation data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system, including: obtaining the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle, and the start-stop frequency of the second air compressor and the second system unit energy consumption of the compressed air system in the current cycle; if the start-stop frequency of the second air compressor is less than the frequency threshold and the second system unit energy consumption is less than the first system unit energy consumption, then reduce the current system pressure band to the optimal system pressure band by the first preset amplitude; if the start-stop frequency of the second air compressor is less than the frequency threshold and the second system unit energy consumption is greater than the first system unit energy consumption, or the start-stop frequency of the second air compressor is greater than the frequency threshold, then increase the current system pressure band to the optimal system pressure band by the second preset amplitude.

[0039] Among them, the width W of the system pressure band is taken as the difference between the starting pressure and the stopping pressure on the pre-set edge side controller; the frequency threshold is represented by Ft; the amplitude is represented by B, and both the first preset amplitude and the second preset amplitude can be set according to specific situations and are not limited thereto.

[0040] It can be understood that the specific process of optimizing the system pressure band and obtaining the optimal system pressure band in the embodiment of the present application is as follows: (1) Obtain the start-stop frequency F1 of the first air compressor and the first system unit energy consumption P1 of the compressed air system in the previous cycle, and the start-stop frequency F2 of the second air compressor and the second system unit energy consumption P2 in the current cycle; (2) If F2 < Ft and P2 < P1, then adjust the broadband width of the pressure in the narrow direction, and the adjustment amplitude is B; (3) If F2 < Ft and P2 > P1 or F2 > Ft, then adjust the broadband width of the pressure in the wide direction, and the adjustment amplitude is B; (4) According to the above steps, calculate cyclically until the system pressure band is adjusted to the optimal system pressure band.

[0041] In the embodiment of the present application, before obtaining the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle, it further includes: identifying whether the current cycle is the first cycle; if the current cycle is the first cycle, then obtain the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the second cycle, otherwise obtain the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle.

[0042] It can be understood that in the embodiment of the present application, before obtaining the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle, it is necessary to identify whether the current cycle is the first cycle. If so, obtain the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the second cycle. If not, obtain the start-stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle.

[0043] In summary, the specific execution logic for optimizing the pressure belt is as follows: Figure 2 as shown:

[0044] 1. Obtain the project ID of the platform.

[0045] Each project needs to establish a project ID, a system ID, and a device ID in the cloud.

[0046] 2. Initialize parameters.

[0047] Set the initialization input parameters required for the cloud algorithm.

[0048] ⑴ Pressure belt width: W (take the difference between the starting pressure and the stopping pressure on the pre-set edge side controller).

[0049] ⑵ Correction coefficient: K

[0050] ⑶ Air compressor start-stop frequency threshold: Ft

[0051] 3. According to the IoT data information in the cloud, count the air compressor start-stop frequency F and the system unit energy consumption P within the statistical period.

[0052] 4. According to information such as input parameters and IoT data, the algorithm runs in the cloud, and the system unit energy consumption P1 in the first cycle is counted;

[0053] 5. Count the air compressor start-stop frequency F2 and the system unit energy consumption P2 in the second cycle.

[0054] If F2 < Ft and P2 < P1, adjust the pressure belt width in the narrow direction, with an adjustable adjustment amplitude of B;

[0055] If F2 < Ft and P2 > P1, adjust the pressure belt width in the wide direction, with an adjustable adjustment amplitude of B;

[0056] If F2 > Ft, adjust the pressure belt width in the wide direction, with an adjustable adjustment amplitude of B;

[0057] 6. Count the air compressor start-stop frequency F3 and the system unit energy consumption P3 in the third cycle.

[0058] If F3 < Ft and P3 < P2, adjust the pressure belt width in the narrow direction, with an adjustable adjustment amplitude of B;

[0059] If F3 < Ft and P3 > P2, adjust the pressure belt width in the wide direction, with an adjustable adjustment amplitude of B;

[0060] If F3 > Ft, adjust the pressure belt width in the wide direction, with an adjustable adjustment amplitude of B.

[0061] 7. Calculate in a loop according to the above program.

[0062] In an embodiment of the present application, the real-time specific power of each air compressor is calculated based on the operating data, including: calculating the flow rate of each air compressor when loaded based on the operating status of each air compressor and the flow matrix, and calculating the real-time specific power of each air compressor based on the flow rate of each air compressor when loaded and the power of each air compressor when loaded.

[0063] The flow rate calculation method of each air compressor when loaded is as follows: solving by least square method or adding flow-power ratio constraint to obtain the flow rate of each air compressor when loaded.

[0064] It can be understood that the embodiment of the present application can calculate the flow rate of each air compressor when loaded based on the operating status and flow matrix of each air compressor, and then calculate the real-time specific power of each air compressor based on the flow rate of each air compressor when loaded and the power of each air compressor when loaded, so as to subsequently determine the optimal start and stop sequence of each air compressor based on the real-time specific power.

[0065] In an embodiment of the present application, the optimal start-stop sequence of each air compressor is determined based on the real-time specific power, including: sorting the air compressors in order from high to low according to the real-time specific power to obtain the optimal start-stop sequence, wherein the efficiency of the air compressor with a high specific power is greater than that of the air compressor with a low specific power, the air compressor with the highest ratio is started at startup, and the air compressor with the lowest specific power is stopped at shutdown.

[0066] It can be understood that the real-time specific power obtained based on the above embodiment is used to sort the air compressor efficiencies from high to low, with higher specific power having higher efficiency and lower specific power having higher efficiency. The air compressor with the highest specific power is started at startup, and the air compressor with the lowest specific power is stopped at shutdown.

[0067] In summary, the optimal start and stop sequence of each air compressor is determined based on the real-time specific power as follows:

[0068] (1) Read the operating status, flow matrix, and loading power of each air compressor;

[0069] (2) Solve the problem using the least squares method or add a flow-power ratio constraint to obtain the flow rate of each air compressor when loaded;

[0070] (3) Using the flow rate of each air compressor when loaded and the power of each air compressor when loaded at the corresponding time point calculated in the previous step, calculate the real-time specific power of the air compressor;

[0071] (4) Based on historical data, the air compressor efficiency is sorted from high to low based on specific power. High specific power has low efficiency, while low specific power has high efficiency. This solves the problem that the edge-side controller can only start and stop the air compressor in a fixed order.

[0072] The specific power of each air compressor is calculated based on its operating data. When the air compressor needs to be started, the high-efficiency air compressor is prioritized; when the air compressor needs to be shut down, the low-efficiency air compressor is prioritized. This allows for dynamic adjustment of the start-stop sequence to optimize operation. The algorithm-calculated sorting results are sent to the edge controller through the SAAS (Software-as-a-Service) application, enabling dynamic edge-cloud collaborative adjustment of the compressor start-stop sequence.

[0073] In summary, the specific execution logic of air compressor optimization is as follows: Figure 3 As shown:

[0074] 1. Get the project ID of the platform

[0075] Each project needs to establish a project ID, system ID, and device ID in the cloud.

[0076] 2. Initialization parameters

[0077] Determine the statistical period.

[0078] 3. Based on the IoT data information in the cloud, read the following data: the operating status of each air compressor, the flow matrix, and the power of each air compressor when loaded.

[0079] 4. Use the least squares method to solve or add the flow-power ratio constraint to obtain the flow rate of each air compressor when loaded.

[0080] 5. Using the flow rate of each air compressor when loaded and the power when loaded at the corresponding time point calculated in the fourth step, calculate the real-time specific power of the air compressor.

[0081] 6. Based on historical data, sort the air compressor efficiency from high to low according to power ratio. The higher the power ratio, the lower the efficiency, and the lower the power ratio, the higher the efficiency.

[0082] In step S103, the optimal system pressure band and the optimal start-stop sequence are sent to the edge-side controller of the compressed air system, wherein the edge-side controller determines the number of air compressors to be started or stopped in the compressed air system according to the optimal system pressure band, and controls one or more air compressors in each air compressor to start or stop according to the optimal start sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band.

[0083] Among them, the device that controls the start or stop of each air compressor is the edge-side controller.

[0084] It can be understood that the embodiment of the present application can send the optimal system pressure band and the optimal start-stop sequence obtained in the above steps to the edge side controller of the compressed air system. The edge side controller can determine the number of air compressors to be started or stopped in the compressed air system based on the optimal system pressure band, and control one or more air compressors in each air compressor to start or stop according to the optimal start sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band, thereby realizing the optimization of the air compressor system pressure band and the optimization control of equipment operation in the scenario where multiple air compressors are operating.

[0085] According to the operation control method of the compressed air system proposed in the embodiment of the present application, a cloud + edge intelligent gateway + edge side controller method is adopted. The edge side controller is responsible for real-time control (logical control) of equipment such as air compressors and collects data from the equipment. The intelligent gateway runs the intelligent algorithm and optimizes the algorithm results with the edge gateway in real time to achieve dynamic adjustment of control on the edge side. At the same time, the intelligent gateway uploads the data collected by the edge side controller to the cloud through communication. The cloud server is mainly responsible for big data analysis and algorithm training. When the algorithm is optimized and upgraded, the algorithm on the intelligent gateway will be remotely OTA (Over The Air) through the 4G / 5G network. Air, over-the-air download technology) upgrade, thereby realizing the optimization control function of edge-cloud collaboration; it can realize the optimization of the pressure band of the air compression system and the optimization of equipment operation in the operation scenario of multiple air compressors, and realize the optimal solution for the optimized operation of multiple air compressors. It can realize the dynamic calculation of the pressure bandwidth and the dynamic allocation of the priority start and stop sequence according to the efficiency of each air compressor, and realize the optimized operation of multiple air compressors. It can dynamically adjust the system pressure band and the optimal start and stop of the air compressor to achieve the purpose of energy saving of the entire air compression station, reduce industry production costs, reduce carbon emissions, and create economic and social benefits.

[0086] Secondly, referring to the accompanying drawings, a method for optimizing the operation of an air compressor based on edge-cloud collaborative control proposed according to an embodiment of the present application is described, wherein the method is applied to an edge-side controller, and the cloud server upgrades the algorithm running on the edge-side controller.

[0087] Figure 4 This is a flowchart of an air compressor optimization operation method based on edge-cloud collaborative control in an embodiment of the present application.

[0088] like Figure 4 As shown, the air compressor optimization operation method based on edge-cloud collaborative control includes the following steps:

[0089] In step S201, the operating data and system unit energy consumption of each air compressor in the compressed air system are obtained.

[0090] The operating data and the system unit energy consumption have been described in the above embodiments and will not be repeated here.

[0091] In step S202, the system pressure band is optimized based on the operating data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system. The real-time specific power of each air compressor is calculated based on the operating data, and the optimal start and stop sequence of each air compressor is determined based on the real-time specific power.

[0092] The calculation method of the optimal system pressure range of the compressed air system and the optimal start-stop sequence of the air compressor has been described in the above embodiment and will not be repeated here.

[0093] It is understandable that the system pressure band can be optimized according to the operating data and system units to obtain the optimal system pressure band, and the real-time specific power of each air compressor can be calculated according to the operating data to determine the optimal start and stop sequence of each air compressor. This changes the problem that the setting of the pressure band and the start and stop sequence of the air compressor in the traditional control method are all pre-set fixed values ​​and cannot be adjusted according to changes in working conditions and changes in compressor performance. It solves the problem of dynamic adjustment of the system pressure band and dynamic adjustment of the start and stop sequence of the air compressor.

[0094] In step S203, the number of air compressors to be started or stopped in the compressed air system is determined according to the optimal system pressure band, and one or more air compressors in each air compressor are controlled to start or stop according to the optimal start sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band.

[0095] It can be understood that the edge-side controller of the embodiment of the present application can determine the number of air compressors to be started or stopped in the compressed air system based on the optimal system pressure band, and control one or more air compressors to start or stop according to the optimal start-up sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band.

[0096] It should be noted that the above explanation of the embodiment of the operation control method of the compressed air system is also applicable to the operation control device of the compressed air system of this embodiment, and will not be repeated here.

[0097] According to the air compressor optimization operation method based on edge-cloud collaborative control proposed in the embodiment of the present application, the cloud + edge side controller (running intelligent algorithm) method is adopted, the intelligent algorithm is run on the edge side server, and the edge server is used to perform real-time control of the air compressor system. The cloud no longer participates in real-time control, but only performs algorithm OTA upgrades, thereby realizing edge-cloud collaborative control and optimizing the operation of the air compressor system. Specifically, the algorithm is run on the edge server, and the edge server performs real-time control of air compressors and other equipment according to the algorithm output results; the edge server uploads the collected data to the cloud through the gateway, and the cloud performs big data analysis and algorithm training. When the algorithm is optimized, the algorithm in the edge side server is OTA upgraded.

[0098] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0099] Memory 501 , processor 502 , and computer programs stored in the memory 501 and executable on the processor 502 .

[0100] When the processor 502 executes the program, the operation control method of the compressed air system provided in the above embodiment is implemented.

[0101] Furthermore, the electronic device further includes:

[0102] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0103] The memory 501 is used to store computer programs that can be run on the processor 502 .

[0104] The memory 501 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0105] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0106] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0107] The processor 502 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0108] In an embodiment of the present application, the electronic device is a cloud server, an edge intelligent gateway or an edge-side controller.

[0109] Figure 6 The present invention provides a block diagram of a compressed air system according to an embodiment of the present application.

[0110] like Figure 6 As shown, the compressed air system 20 includes: multiple air compressors 21, collection equipment 22 and edge-side controller 23.

[0111] Among them, the collection device 22 is used to collect the operating data and system unit energy consumption of each air compressor; the edge side controller 23 is used to determine the number of air compressors to be started or stopped in the compressed air system according to the optimal system pressure band, and control one or more air compressors in each air compressor to start or stop according to the optimal start-up sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band, wherein the operating data and system unit energy consumption are optimized by using electronic equipment such as the above embodiment to optimize the pressure band and optimize the air compressor to obtain the optimal system pressure band and the optimal start-up sequence.

[0112] The edge-side controller uses a logic control program to maintain the compressed air system within a certain pressure range based on single-point pressure feedback from the main pipe, and determines the start and stop of the air compressor based on the pressure. The main logic is as follows:

[0113] 1. Edge-side controller automatically starts the air compressor logic

[0114] The air compressor needs to meet the following conditions:

[0115] (1) The air compressor is in shutdown state

[0116] (2) The air compressor has no faults

[0117] (3) The air compressor downtime is greater than the operation interval (modifiable)

[0118] (4) Set the air compressor to automatic mode (manual and automatic modes)

[0119] The edge-side controller monitors the pressure of the gas supply main pipe in real time. When the real-time pressure of the gas supply main pipe is lower than the starting pressure (which can be modified), the air compressor is started immediately (the one with higher starting authority is selected). After a delay, if the real-time pressure of the main pipe is still lower than the starting pressure, the next air compressor is started, and so on.

[0120] 2. Edge-side controller automatically stops the air compressor logic

[0121] The air compressor must meet the following conditions:

[0122] (1) The current operation time of the air compressor is greater than the operation interval time (can be modified)

[0123] (2) The air compressor has no faults

[0124] (3) The air compressor is in operation

[0125] (4) Set the air compressor to automatic mode (manual and automatic modes)

[0126] When the number of unloaded air compressors equals one, the main pipe pressure is monitored in real time. If the main pipe pressure is higher than the shutdown protection pressure (modifiable), a delay of a certain time (shutdown delay (modifiable)) is set. When the time is up, the unloaded air compressor is immediately stopped. If the main pipe pressure is still higher than the shutdown pressure, the next air compressor is stopped, and so on.

[0127] It should be noted that the cloud uses the data uploaded by the Internet of Things to analyze, calculate and train algorithms for data such as the main pipe gas supply pressure, main pipe flow, air compressor operating status, air compressor fault status, air compressor operating time, and air compressor operating power (or current). Based on the pressure band optimization algorithm and the air compressor optimization algorithm, the starting pressure, shutdown pressure and start and stop sequence of the edge-side controller are dynamically adjusted to adapt to working conditions.

[0128] In summary, the embodiments of the present application can also use a variety of architectures to run the smart algorithm, as follows:

[0129] (1) The compressed air system provided in the embodiment of the present application can adopt a cloud + edge server (running smart algorithms) architecture, run smart algorithms on the edge server, and use the edge server to perform real-time control of the air compression system. The cloud no longer participates in real-time control, but only performs algorithm OTA upgrades.

[0130] This method also enables collaborative cloud-edge control, optimizing the operation of air compressor systems. Specifically, the algorithm runs on an edge server, which then performs real-time control of equipment like the air compressor based on the algorithm's output. The edge server then uploads the collected data to the cloud via a gateway, where it performs big data analysis and algorithm training. When the algorithm is optimized, an over-the-air (OTA) upgrade is performed on the edge server.

[0131] (2) The embodiment of the present application adopts the architecture of cloud + edge intelligent gateway + edge side controller.

[0132] In this method, the edge-side controller is responsible for real-time control of equipment such as air compressors, collecting data from the equipment, and controlling air compressors and other equipment in real time through built-in programs; the intelligent gateway runs the intelligent algorithm, optimizes the algorithm results with the edge gateway in real time, and realizes dynamic adjustment of control on the edge side. At the same time, the intelligent gateway uploads the data collected by the edge-side controller to the cloud through communication; the cloud is mainly responsible for big data analysis and algorithm training, and provides strategic recommendations for system pressure band optimization and air compressor optimization, which are then sent to the edge-side controller through SAAS applications. When the algorithm is optimized and upgraded, the algorithm on the intelligent gateway will be remotely OTA upgraded through the 4G / 5G network, thereby realizing collaborative control between the cloud and edge sides, giving full play to the real-time and stability of the edge side and the advantages of cloud big data processing and intelligent algorithms, and realizing dynamic optimization operation of the compressed air system.

[0133] In summary, the edge-cloud collaborative technology architecture provided by the embodiment of this application is as follows Figure 7 As shown in the figure, the edge controller acts as an actuator, issuing data collection-level instructions for the air compressor system's operation. The cloud performs data analysis, runs algorithms, and collaborates with the edge controller for control. Air compressors, electricity meters, flow meters, pressure sensors, and other devices transmit their data to the cloud through a communication gateway. The cloud calculates and develops optimization strategies based on the IoT data and intelligent algorithms, according to a set period. These strategies are then distributed to the edge controller via a SaaS product (PC or mobile device) via an intelligent gateway. The edge controller then performs real-time control based on the optimized parameters, optimizing the system's pressure range and the compressor's start-up and shutdown sequences.

[0134] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned air compressor optimization operation method based on edge-cloud collaborative control.

[0135] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0136] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0137] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0138] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0139] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0140] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An air compressor optimization operation method based on edge-cloud collaborative control, characterized in that: The method is applied to an edge intelligent gateway, wherein a cloud server is used to upgrade an algorithm running on the edge intelligent gateway, wherein the method comprises the following steps: Obtain the operating data of each air compressor in the compressed air system and the system unit energy consumption; Optimizing the system pressure band according to the operating data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system, calculating the real-time specific power of each air compressor according to the operating data, and determining the optimal start-stop sequence of each air compressor according to the real-time specific power; The optimal system pressure band and the optimal start-stop sequence are sent to the edge-side controller of the compressed air system, wherein the edge-side controller determines the number of air compressors to be started or stopped in the compressed air system according to the optimal system pressure band, and controls one or more of the air compressors to start or stop according to the optimal start-stop sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band.

2. The method according to claim 1, characterized in that The operating data includes the start and stop frequency of the air compressor, and the system pressure band is optimized according to the operating data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system, including: Obtaining the start / stop frequency of the first air compressor and the unit energy consumption of the first system of the compressed air system in the previous cycle, and the start / stop frequency of the second air compressor and the unit energy consumption of the second system of the compressed air system in the current cycle; If the start / stop frequency of the second air compressor is less than the frequency threshold, and the second system unit energy consumption is less than the first system unit energy consumption, then reducing the current system pressure band to the optimal system pressure band according to a first preset range; If the start / stop frequency of the second air compressor is less than the frequency threshold, and the second system unit energy consumption is greater than the first system unit energy consumption, or the start / stop frequency of the second air compressor is greater than the frequency threshold, the current system pressure band is increased to the optimal system pressure band according to a second preset amplitude.

3. The method according to claim 2, characterized in that Before obtaining the start / stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle, the method further includes: Identify whether the current cycle is the first cycle; If the current cycle is the first cycle, the start / stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the second cycle are obtained; otherwise, the start / stop frequency of the first air compressor and the first system unit energy consumption of the compressed air system in the previous cycle are obtained.

4. The method according to claim 1, wherein The operating data includes the operating status of each air compressor, a flow matrix, and the power of each air compressor when loaded. The real-time specific power of each air compressor is calculated based on the operating data, including: The flow rate of each air compressor when loaded is calculated according to the operating status of each air compressor and the flow matrix, and the real-time specific power of each air compressor is calculated according to the flow rate of each air compressor when loaded and the power of each air compressor when loaded.

5. The method according to claim 1, wherein Determining the optimal start and stop sequence of each air compressor according to the real-time specific power includes: The air compressors are sorted in descending order according to the real-time specific power to obtain the optimal start-stop sequence, wherein the efficiency of the air compressor with a high specific power is greater than that of the air compressor with a low specific power, the air compressor with the highest specific power is started at startup, and the air compressor with the lowest specific power is stopped at shutdown.

6. A method for optimizing the operation of an air compressor based on edge-cloud collaborative control, characterized in that: The method is applied to an edge-side controller, and a cloud server upgrades the algorithm running on the edge-side controller, wherein the method includes the following steps: Obtain the operating data of each air compressor in the compressed air system and the system unit energy consumption; Optimizing the system pressure band according to the operating data and the system unit energy consumption to obtain the optimal system pressure band of the compressed air system, calculating the real-time specific power of each air compressor according to the operating data, and determining the optimal start-stop sequence of each air compressor according to the real-time specific power; The number of air compressors to be started or stopped in the compressed air system is determined based on the optimal system pressure band, and one or more of the air compressors are controlled to start or stop according to the optimal start-stop sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the air compressor optimization operation method based on edge-cloud collaborative control as described in any one of claims 1 to 6.

8. The electronic device according to claim 7, wherein: The electronic device is a cloud server, an edge intelligent gateway or an edge side controller.

9. A compressed air system, characterized in that: include: Multiple air compressors; Collection equipment, used to collect the operating data of each air compressor and the system unit energy consumption; An edge-side controller is used to determine the number of air compressors to be started or stopped in the compressed air system based on the optimal system pressure band, and to control the start or stop of one or more of the air compressors according to the optimal start-stop sequence, so that the system pressure band of the compressed air system is adjusted to the optimal system pressure band, wherein the electronic device according to any one of claims 7 or 8 is used to optimize the pressure band and optimize the air compressors for the operating data and the system unit energy consumption to obtain the optimal system pressure band and the optimal start-stop sequence.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the air compressor optimization operation method based on edge-cloud collaborative control as described in any one of claims 1-6.

Images