Intelligent centralized control method, system and storage medium for air compressor unit

Abstract

The present invention discloses an intelligent centralized control method, system and storage medium for an air compressor unit, which establishes a first correlation model for characterizing the correlation between the air pressure fluctuations of each air supply node, and a second correlation model for characterizing the correlation between the air flow temperature in a specific section of the air supply network and the air pressure of each air supply node, and configures a corresponding fluctuation model and weight value for each air supply node, so that the system can predict the air pressure demand of each air supply node in a set time period in the future based on the current air pressure data and temperature data of each air supply node, and then adjust the air pressure of the air supply node by changing the conduction relationship of the air supply node and the local temperature of the air supply network, so as to respond to the gas demand at the air supply node in advance, realize precise control of the air pressure at the end of the air supply network, and reduce the air pressure fluctuation at the end of the air supply network.

Description

Technical Field

[0001] The present invention relates to the technical field of control of air compressor units in air compressor stations, and more specifically, to an intelligent centralized control method, system and storage medium for air compressor units. Background Art

[0002] In automated production lines such as automotive manufacturing, pneumatic systems can effectively improve production efficiency. Air compressors, serving as the air source, are the core equipment of the entire pneumatic system. During pneumatic system operation, air is first compressed by the compressor, then dried, filtered, and pressure-regulated before being supplied via the air supply network to various pneumatic terminals, such as cylinders, air motors, and air grippers. To reduce pressure fluctuations at each pneumatic terminal and ensure production accuracy and efficiency, multiple air compressors are typically deployed in a single air compression station to achieve continuous, stable air supply. At least one air storage tank is also installed in the air supply network, such as at the compressor outlet, to buffer pressure fluctuations within the network.

[0003] In order to ensure the gas supply quality of the gas supply network and reduce the fluctuation amplitude of the air pressure in the pipeline network, a pressure sensor is usually installed in the gas tank. When the pressure value in the gas tank is detected to be lower than the first set value, the air compressor is started. When the air pressure value in the gas tank is higher than the second set value, the air compressor is disconnected or the power of the air compressor is adjusted, so that the air pressure in the gas tank is finally maintained within the set range.

[0004] However, in actual applications, the inventors discovered that existing air compressor control methods are insufficient to meet the needs of high-precision pneumatic systems. For example, in ultra-precision air flotation platforms, even small pressure fluctuations at the end of the air supply network can cause the platform to vibrate or even scrape against the base. The main reason for these unstable and easily fluctuating air pressure at the pneumatic terminal is that the air supply system responds too slowly to changes in terminal air pressure.

[0005] Analysis has confirmed that the main reasons for the long response time of the pneumatic system include:

[0006] (1) Transmission delay of control signal: When the pressure sensor in the gas tank detects the pressure change, the pressure change at the pneumatic terminal at the end of the gas supply network has already been very large. At this time, starting the air compressor through the controller to increase the pressure obviously cannot quickly solve the problem of insufficient air pressure at the end of the gas supply network;

[0007] (2) Air flow delay in the gas supply network: Since air is highly compressible, the pressure change generated in the gas tank needs a certain delay before it can be transmitted to the end of the network through the pressure regulating valve. Especially when multiple pneumatic terminals at the end of the gas supply network are connected in parallel, if one of the pneumatic terminals is in a deflated state, the pneumatic output force of the other pneumatic terminals will be affected, and it will obviously take a certain amount of time to make up for this pressure loss.

[0008] In summary, how to shorten the air supply response time in the pneumatic system is the key to improving the air supply quality of the pneumatic system. Summary of the Invention

[0009] In order to solve the problem of unstable gas supply in air compressor stations during actual use, which leads to fluctuations in air pressure at the end of the gas supply network, the first purpose of the present invention is to propose an intelligent centralized control method for air compressor units. Based on the air pressure fluctuation data at the end of the gas supply network, a targeted air supply model is generated to control the air compressor units in the air compressor station to respond to gas demand in advance. At the same time, through the linkage control of the gas supply pipelines between the pipelines and the adjustment of the gas state, precise control of the air pressure at the end of the gas supply network is achieved, and the air pressure fluctuation at the end of the gas supply network is reduced. In order to realize the above-mentioned intelligent centralized control method for air compressor units, the second purpose of this application is to propose an intelligent centralized control system for air compressor units. Finally, it is proposed to protect a computer-readable storage medium loaded with a program module for realizing the intelligent centralized control method for air compressor units. The specific scheme is as follows:

[0010] An intelligent centralized control method for an air compressor unit, based on the air compressor unit and the connected air supply network, includes:

[0011] Establishing and storing a first correlation model for characterizing the correlation relationship between the air pressure fluctuations of each air supply node;

[0012] Establishing and storing a second correlation model for characterizing the correlation between the air flow temperature in a specific section of the gas supply network and the air pressure at each gas supply node;

[0013] Obtain and generate a fluctuation model for predicting the pressure fluctuation trend of each gas supply node based on the historical pressure and temperature data of each gas supply node, and associate each fluctuation model with the ID of each gas supply node for storage;

[0014] Based on the historical air pressure and temperature data of each gas supply node, the reliability of each fluctuation model is verified and a weight value is assigned to each fluctuation model.

[0015] Obtain the air pressure data, temperature data of each air supply node at the current moment and the conduction status between each air supply node;

[0016] Based on the fluctuation model and weight value of each gas supply node, combined with the first correlation model and the second correlation model, the expected gas pressure value of each gas supply node in the next period is calculated to generate the corresponding gas pressure demand;

[0017] According to the air pressure requirement, adjusting the air pressure of each air supply node and / or the conduction state between each air supply node;

[0018] The gas supply node includes a bifurcation point where at least two gas supply branches are formed from the same gas supply main line;

[0019] Adjusting the gas pressure of each gas supply node includes at least one of adjusting the gas pressure of a gas storage tank connected to the gas supply node, adjusting the gas pressure of an associated gas supply node, and adjusting the local temperature of the gas supply network.

[0020] Through the above technical solution, the gas demand of each gas supply node in the future set time period can be predicted more accurately, and the gas pressure of the gas storage tank or the related gas supply node can be adjusted in advance, or the connectivity between the gas supply nodes can be changed to compensate for the response delay caused by signal transmission, air compressor start and stop, etc., and reduce the pressure fluctuation at the target gas supply node. At the same time, when adjusting the gas pressure of the gas supply node, the short-term impact of the gas temperature in the gas supply network on the gas pressure in the network is fully considered, and the pressure change caused by the temperature change of the local network is used to compensate for the response time required for the pressure to be transmitted along the gas supply network, thereby further shortening the gas supply response time at the target gas supply node, reducing the pressure fluctuation at the target gas supply node, achieving stable gas supply at the end of the gas supply network, and improving the gas supply quality.

[0021] Furthermore, the configuration method of each gas supply node includes:

[0022] The gas supply network forms a tree-shaped gas supply structure from the gas storage tank toward the pneumatic terminal, and the gas supply nodes are divided into homologous nodes and heterologous nodes according to whether the gas storage tanks connected to the gas supply nodes are the same;

[0023] Estimate the gas demand period of each pneumatic terminal at the end of the gas supply network based on the production line process, group pneumatic terminals with similar gas demand periods into one group, and then form multiple period groups. Based on the location layout of each pneumatic terminal, group each pneumatic terminal located in the same area into one group, and then form multiple location groups.

[0024] Select pneumatic terminals in the same location group and in different time groups and connect them to share the same air supply node;

[0025] Establishing controllable conductive connections between a number of homologous nodes, and between a number of homologous nodes and heterologous nodes in a gas supply network and setting trigger conditions, and when the trigger conditions are met, conducting the plurality of homologous nodes, or the homologous nodes and heterologous nodes;

[0026] The pressure fluctuation correlation relationship between each gas supply node in the first correlation model includes the correlation relationship between the same-source nodes and the correlation relationship between the same-source nodes and the different-source nodes in the conductive state;

[0027] The first correlation model includes the relationship between the air pressure fluctuation amplitude and the fluctuation response delay between the air supply nodes.

[0028] The above technical solution can evenly distribute each pneumatic terminal to different gas supply nodes based on their location distribution and gas demand time, avoiding concentrated gas demand during the same period, reducing air pressure fluctuations at the gas supply nodes, and ensuring gas supply quality. By establishing a controllable connection relationship between homologous and heterologous nodes, when the air pressure of multiple gas supply nodes connected to the same gas tank fluctuates significantly, these pressure fluctuations can be buffered by connecting to other gas tanks, thereby improving the safety redundancy of the entire pneumatic system.

[0029] Furthermore, a fluctuation model is generated to predict the pressure fluctuation trend of each gas supply node, including:

[0030] Obtain the gas demand of each pneumatic terminal connected to the gas supply node in each time period and integrate them to generate a theoretical gas demand model;

[0031] Obtain historical air pressure and temperature data of the gas supply node, combine it with the theoretical gas usage model, and generate a correction function based on a neural network algorithm to correct the theoretical gas usage model to generate the fluctuation model;

[0032] The parameters of the correction function include time data, temperature data, and data on the conduction status between the current gas supply node and other gas supply nodes.

[0033] Through the above technical solution, a theoretical gas consumption model can be accurately generated based on the gas demand information of the pneumatic terminals connected to each gas supply node, and then the influence of external factors such as temperature and other gas supply nodes can be incorporated into it, so that the generated fluctuation model can more accurately predict the air pressure fluctuation trend of the corresponding gas supply node.

[0034] Furthermore, according to the gas pressure requirement, the conduction state between each gas supply node is adjusted, including:

[0035] Based on the fluctuation model corresponding to the current gas supply node, obtain the gas pressure fluctuation value P1 of the gas supply node in the next period;

[0036] Obtaining the connectivity status between the current gas supply node and the associated gas supply node, calculating the pressure fluctuation value P2 of the current gas supply node caused by the connected associated gas supply node in the next time period based on the first association model, and generating the time delay t1 required for the pressure fluctuation value P2 to act on the current gas supply node;

[0037] According to the air pressure fluctuation value P1 and the air pressure fluctuation value P2, the theoretical air pressure value P3 of the current air supply node at each moment in the next period is obtained;

[0038] Get the current air pressure value P0 of the gas supply node, and subtract it from the theoretical air pressure value P3 to obtain the air pressure value Pt that needs to be compensated;

[0039] Obtaining the air pressure values of other air supply nodes associated with the current air supply node, and adjusting the conduction state between the current air supply node and the associated air supply nodes according to the air pressure value Pt and the first association model;

[0040] The step of adjusting the conduction state between the current gas supply node and the associated gas supply node includes:

[0041] Obtain the target gas supply node ID that needs to be turned on and the time delay t2 required for the gas pressure to act on the current gas supply node after the target gas supply node is turned on, based on the first correlation model and the required gas pressure value Pt;

[0042] Based on the time delay t2 and the time delay t1, the conduction time between the current gas supply node and the target gas supply node is determined, and based on the fluctuation model corresponding to the current gas supply node, the conduction duration between the current gas supply node and the target gas supply node is calculated and determined.

[0043] Through the above technical solution, the fluctuation model corresponding to each gas supply node and the first correlation model can be used to predict the air pressure value that needs to be compensated for the current gas supply node in the next time period, and then the corresponding target gas supply node can be matched through the first correlation model, and the current gas supply node and the target gas supply node can be connected at the appropriate time. In this way, the associated gas supply nodes in the gas supply network can be used to achieve balanced distribution of gas pressure in the network and reduce air pressure fluctuations at the gas supply nodes.

[0044] Furthermore, the method of adjusting the local temperature of the gas supply network to adjust the gas pressure of the gas supply node includes:

[0045] A heating device for heating the air flow in the gas supply network is arranged upstream of each gas supply node;

[0046] Based on the correlation between the air flow temperature in a specific section of the air supply network and the air pressure at each air supply node stored in the second correlation model, directly generate the required adjustment temperature T and the corresponding heating power and heating duration; and / or

[0047] Based on the pipeline parameters and gas property parameters at the gas supply node, a corresponding heating calculation formula is configured for each gas supply node and stored in association with each gas supply node ID;

[0048] Obtain the air pressure value Pt that needs to be compensated for at the air supply node, and calculate the corresponding heating power and heating time according to the heating calculation formula.

[0049] Through the above technical solution, a heating device is directly configured at a position adjacent to the upstream of the gas supply node, and the heating parameters required to compensate for the air pressure value Pt are directly obtained through the second association model, which is convenient and efficient. When the second association model cannot quickly find the corresponding heating parameters under specific circumstances, the heating parameters required by the heating device are quickly calculated through the heating calculation formula corresponding to the gas supply node, thereby responding to the air pressure demand at the gas supply node in a short time and reducing the air pressure fluctuation at the gas supply node.

[0050] Furthermore, the reliability of each volatility model is verified and weighted accordingly, including:

[0051] Import the historical air pressure and temperature data of each gas supply node into the corresponding fluctuation model in units of time periods to obtain the expected air pressure value generated by the fluctuation model;

[0052] Comparing the expected air pressure value with the actual air pressure value in the historical air pressure data to generate air pressure deviation values for each time period, and integrating the air pressure deviation values based on the time period to form a deviation curve;

[0053] Count the amplitudes of the deviation curves of each gas supply node, obtain the maximum amplitude and divide it into multiple marked intervals with equal spans;

[0054] Associating multiple labeled intervals with specific assignments;

[0055] The mark interval is used to calibrate the amplitude of each time period in each deviation curve and assign a value to each time period to form a weight value of the fluctuation model.

[0056] Through the above technical solution, a reliability weight value that fluctuates over time can be assigned to the fluctuation model corresponding to each gas supply node. This makes the prediction results more accurate when estimating the current gas supply node and calculating the impact of associated gas supply nodes on the current gas supply node, which is beneficial to improving the accuracy of air pressure control and reducing air pressure fluctuations at the gas supply node.

[0057] Furthermore, based on the historical air pressure data and temperature data of each air supply node, a fluctuation model for predicting the air pressure fluctuation trend of each air supply node is generated, which also includes:

[0058] Collect and store the air pressure and temperature data of each gas supply node in a data storage unit, and establish a data connection between the data storage unit and the cloud server;

[0059] Set the update time of each wave model;

[0060] Detect whether the current time meets the set conditions. If so, send the data stored in the data storage unit and the current fluctuation model to the cloud server, generate a new correction function and correct the current fluctuation model;

[0061] The revised fluctuation model is issued and stored in association with the gas supply node ID.

[0062] Through the above technical solution, the cloud-based artificial intelligence large model can be used to regularly correct the fluctuation model of each gas supply node, making the prediction of the air pressure fluctuation trend at the gas supply node more accurate.

[0063] In order to implement the above-mentioned intelligent centralized control method for air compressor units, the present application also proposes an intelligent centralized control system for air compressor units, including a pipe network component and a control component;

[0064] The pipe network assembly includes: at least two air compressors and a gas supply network connected thereto, wherein each gas branch point in the gas supply network forms a gas supply node, and a conducting pipe and a switch valve are arranged between specific gas supply nodes in the gas supply network;

[0065] Each of the gas supply nodes is provided with a temperature sensor for detecting the temperature of the air flow and a pressure sensor for detecting the pressure at the gas supply node; a heating device for heating the air flow in the gas supply network is provided at the upstream position of each of the gas supply nodes;

[0066] The control component includes:

[0067] A data acquisition unit is configured to be connected to the temperature sensor, the pressure sensor and the switch valve signals, and is used to obtain real-time air pressure data, temperature data at each air supply node and conduction status data between each air supply node;

[0068] a data storage unit configured to be data-connected to the data acquisition unit and the cloud server, and used to store historical air pressure data, temperature data, and conduction status data of each gas supply node, a first correlation model for characterizing the correlation between air pressure fluctuations at each gas supply node, a second correlation model for characterizing the correlation between the air flow temperature in a specific section of the gas supply network and the air pressure at each gas supply node, conduction status data between each gas supply node in the gas supply network, and a fluctuation model corresponding to each gas supply node and its weight value;

[0069] a data processing unit configured to be data-connected to the data acquisition unit and the data storage unit, and configured to obtain the air pressure data and temperature data of each air supply node at the current moment, and the conduction status between each air supply node; and calculate the expected air pressure value of each air supply node in the next time period based on the fluctuation model and weight value of each air supply node, in combination with the first correlation model and the second correlation model, and generate the corresponding air pressure demand;

[0070] A control output unit is configured to be data-connected to the data processing unit and to be control-connected to the air compressor and each of the heating devices and the switch valve, and to adjust the air pressure of each air supply node and / or the conduction state between each air supply node according to the air pressure demand;

[0071] Among them, adjusting the air pressure of each gas supply node includes: adjusting the air pressure of the gas tank connected to the gas supply node by adjusting the operating status of the air compressor, adjusting the air pressure of the gas supply node associated with the current gas supply node, and adjusting the local temperature of the gas supply network.

[0072] Furthermore, the control component further includes:

[0073] a fluctuation model weight configuration unit configured to import historical air pressure data and temperature data of each gas supply node into the corresponding fluctuation model in units of time periods, verify the reliability of each fluctuation model, assign a weight value to each fluctuation model, and output the weight value to the data processing unit;

[0074] The fluctuation model update unit is configured to collect and store the air pressure data and temperature data of each gas supply node in the data storage unit based on the set update time node, and send the data stored in the data storage unit and the current fluctuation model to the cloud server, generate a new correction function and correct the current fluctuation model, and send the corrected fluctuation model to the data storage unit, and store it in association with the gas supply node ID.

[0075] A computer-readable storage medium for intelligent centralized control of an air compressor unit, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intelligent centralized control method of an air compressor unit as described above are implemented.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] (1) By collecting the historical fluctuation data of each gas supply node, a fluctuation model is established for each gas supply node, and a weight value representing the reliability is configured for each fluctuation model. In this way, the pressure fluctuation trend of each gas supply node in the future set period can be predicted. At the same time, by establishing the correlation between the pressure fluctuations of each gas supply node, the pressure change of a single gas supply node can be predicted more accurately, so that the control system can make control adjustments in advance, shorten the response time of the entire pneumatic system, reduce the pressure fluctuation at the gas supply node, and improve the gas supply quality;

[0078] (2) By regulating the temperature of the local gas supply network, the air pressure at a local location in the gas supply network can be accurately adjusted, compensating for the time delay in the transmission of air pressure from the gas storage tank / air compressor outlet to the gas supply node, shortening the gas demand response time at the end of the gas supply network, and reducing the air pressure fluctuation at the gas supply node and pneumatic terminal;

[0079] (3) By connecting the gas supply nodes belonging to different air compressors / gas tanks in the gas supply network at the appropriate time, the failure of the entire pneumatic system due to the failure of a single gas tank / air compressor can be avoided, and the safety redundancy of the entire pneumatic system can be improved. At the same time, the connection relationship between heterogeneous nodes can be used to balance the air pressure in the entire gas supply network and reduce the air pressure fluctuation in the gas supply network. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is an overall schematic diagram of the method of the present invention;

[0081] Figure 2 Schematic diagram of the method for generating a wave model;

[0082] Figure 3 Schematic diagram of the method for assigning weights to the volatility model. DETAILED DESCRIPTION

[0083] The present application will be further described in detail below with reference to the embodiments and drawings, but the implementation methods of the present application are not limited thereto.

[0084] To make the description of the embodiments of the present application clearer, before further describing the intelligent centralized control method for air compressor units, the following definitions are given of the relevant feature names involved in the embodiments of the present application, and a brief description of the relevant structural configuration of the gas supply network is given:

[0085] The gas supply network involved in this application adopts a common tree-shaped gas supply structure, that is, the gas supply network extends in a tree-shaped layout from the gas storage tank toward the pneumatic terminal. The specific configuration order is: air compressor, gas storage tank, filter, dryer, gas supply pipeline, and gas port connected to each pneumatic terminal.

[0086] In the implementation manner of the present application, the air supply node is defined as a bifurcation point where at least two air supply branches are formed by diverting from the same air supply main line. For example, if the air outlet of the dryer is connected to multiple air supply branches through an air supply main pipe, it can be regarded as an air supply node here. The simplest air supply node is a "Y"-shaped node.

[0087] At the same time, in a gas supply network with multiple gas storage tanks, each gas supply node is divided into a homologous node and a heterologous node according to whether the gas storage tanks to which each gas supply node is ultimately connected are the same.

[0088] Based on the above definitions and descriptions, in order to more clearly illustrate and implement the intelligent centralized control method of the air compressor unit described in this application, in the embodiment of this application, the configuration method of each of the air supply nodes is as follows:

[0089] Based on the production line process, the gas demand periods of each pneumatic terminal at the end of the gas supply network are estimated. Pneumatic terminals with similar gas demand periods are grouped together to form multiple time period groups. Based on the location layout of each pneumatic terminal, pneumatic terminals in the same area are grouped together to form multiple location groups. Pneumatic terminals in the same location group but in different time period groups are then selected and connected to share the same gas supply node.

[0090] The above technical solution can evenly distribute each pneumatic terminal to different gas supply nodes according to its location distribution and gas demand time, avoid concentrated gas consumption in the same period, reduce air pressure fluctuations at the gas supply nodes, and ensure gas supply quality.

[0091] To enhance the safety redundancy of the pneumatic system and, in special circumstances, utilize heterogeneous nodes to buffer pressure fluctuations at adjacent gas supply nodes, configuring the gas supply nodes also involves establishing controllable connections between multiple homogeneous nodes in the gas supply network, as well as between multiple homogeneous nodes and heterogeneous nodes, and setting trigger conditions. When the trigger conditions are met, these homogeneous nodes, or homogeneous nodes and heterogeneous nodes, are connected. This allows for significant pressure fluctuations at multiple gas supply nodes connected to the same gas tank to be mitigated by connecting them to other gas tanks.

[0092] The intelligent centralized control method of an air compressor unit described in the embodiment of the present application is based on the air compressor unit and the gas supply network connected thereto, such as Figure 1 As shown, it mainly includes the following steps:

[0093] S100, establishing and storing a first correlation model for characterizing the correlation relationship between gas pressure fluctuations at each gas supply node;

[0094] S200, establishing and storing a second correlation model for characterizing the correlation between the air flow temperature in a specific section of the gas supply network and the air pressure at each gas supply node;

[0095] S300, acquiring and generating a fluctuation model for predicting the pressure fluctuation trend of each gas supply node based on historical pressure and temperature data of each gas supply node, and storing each fluctuation model in association with the ID of each gas supply node;

[0096] S400, based on the historical air pressure data and temperature data of each gas supply node, verify the reliability of each fluctuation model and assign a weight value to each fluctuation model accordingly;

[0097] At step S500, the air pressure and temperature data of each air supply node at the current moment, as well as the conduction status between each air supply node, are obtained. Based on the fluctuation model and weight value of each air supply node, combined with the first and second correlation models, the expected air pressure value of each air supply node in the next time period is calculated to generate the corresponding air pressure demand.

[0098] S600: Adjusting the air pressure at each gas supply node and / or the connectivity between each gas supply node based on the air pressure requirement. In step S600, adjusting the air pressure at each gas supply node includes at least one of adjusting the air pressure of a gas storage tank connected to the gas supply node, adjusting the air pressure at an associated gas supply node, and adjusting the local temperature of the gas supply network.

[0099] In step S100, the pressure fluctuation correlation between the gas supply nodes in the first correlation model includes the correlation between the same-source nodes and the correlation between the same-source nodes and the different-source nodes in the conductive state. The above correlation includes the pressure fluctuation amplitude relationship and the fluctuation response delay relationship between the gas supply nodes.

[0100] As detailed above, since nodes of the same source are interconnected, when the pressure at one supply node drops, it will inevitably cause pressure fluctuations at adjacent or even more distant supply nodes. Typically, the patterns of such correlation fluctuations at these supply nodes can be captured in a closed gas supply network. Methods for obtaining such correlations include, but are not limited to: obtaining them through theoretical deduction based on the physical connection structure of the gas supply network; obtaining them through fitting algorithms by collecting pressure data from each supply node during system testing; or obtaining them through big data training using a neural network model.

[0101] The correlation in the first correlation model mentioned above includes the amplitude relationship and the fluctuation response delay. For example, when considering only the correlation between two gas supply nodes A and B, when the air pressure at gas supply node A fluctuates, the air pressure at the adjacent gas supply node B also fluctuates. The correlation between the two can be defined as: the amplitude of the fluctuation at gas supply node B following that of gas supply node A, and the response time of gas supply node B.

[0102] In step S200, a second correlation model is established and stored to characterize the relationship between the airflow temperature in a specific section of the gas supply network and the air pressure at each gas supply node. This second correlation model refers to the temperature in a specific section of the gas supply network, such as a predetermined length of pipe upstream of a gas supply node, or the temperature at a specific gas supply node. Within a closed space, the temperature and pressure of the gas are linearly related. Therefore, this second correlation model can be a directly stored temperature-pressure data table or a calculation formula model for calculating the air pressure based on the current gas temperature.

[0103] In the embodiment of the present application, in order to facilitate rapid acquisition of the impact results of the associated gas supply nodes, the above-mentioned first association model and the second association model are preferably stored in the form of a two-dimensional data table.

[0104] In the above step S300, a fluctuation model is generated to predict the pressure fluctuation trend of each gas supply node, such as Figure 2 Shown, including:

[0105] S310, obtain the gas demand of each pneumatic terminal connected to the gas supply node in each time period, and integrate them to generate a theoretical gas demand model. In detail, the above gas demand can be obtained based on the type of pneumatic terminal and the steps of the production process. For example, if the pneumatic terminal is a cylinder, the amount of gas consumed by the cylinder for each movement is fixed. On the automated production line, the operating time of each pneumatic terminal is often fixed. Therefore, the gas demand of each gas supply node in a certain time period can be calculated. In practice, the gas demand of the gas supply node in each time period can also be obtained based on the air pressure data statistics during the pneumatic system test.

[0106] The theoretical gas usage model obtained through theoretical calculations or test data is also affected by various factors in actual production practice, such as the ambient temperature of the production line, temporary changes in the conductivity status between gas supply nodes, etc. In order to more accurately predict the pressure fluctuation trend of each gas supply node in the next period, the generation of the fluctuation model described in this application also includes the following steps:

[0107] S320: Obtain historical air pressure and temperature data for the gas supply node, combine it with the theoretical gas usage model, and generate a correction function based on a neural network algorithm to correct the theoretical gas usage model to generate the fluctuation model. The correction function's parameters include time data, temperature data, and data on the connectivity between the current gas supply node and other gas supply nodes.

[0108] It should be pointed out that the generated fluctuation model is actually a fluctuation curve with time and temperature as the parameters, which reflects the changes in the air pressure value of the gas supply node under different time and temperature conditions.

[0109] The above-mentioned fluctuation model generation scheme combines theoretical models with practical applications, incorporating external influences such as temperature and other gas supply nodes, so that the generated fluctuation model can more accurately predict the pressure fluctuation trend of the corresponding gas supply node.

[0110] In order to facilitate the processing and prediction of air pressure data of each gas supply node, in the implementation mode of the present application, the above-mentioned fluctuation model is associated with the gas supply node ID information and stored. In the working state, the corresponding fluctuation model can be quickly retrieved by simply inputting the ID of the gas supply node.

[0111] In order to ensure that each gas supply node can accurately supply gas and shorten the response time of each gas supply node, an optimized fluctuation model is generated based on the historical air pressure and temperature data of each gas supply node to predict the pressure fluctuation trend of each gas supply node, which also includes the waveform automatic update optimization step:

[0112] S331, collect and store the air pressure data and temperature data of each gas supply node in the data storage unit, establish a data connection between the above data storage unit and the cloud server. In actual application, the data at each gas supply node is summarized in the factory or workshop server, and then uploaded to the cloud through the network.

[0113] S332, setting the update time of each wave model.

[0114] S333, detecting whether the current time meets the set conditions. If so, the data stored in the data storage unit and the current fluctuation model are sent to the cloud server, a new correction function is generated, and the current fluctuation model is corrected.

[0115] S334: Send the revised fluctuation model and associate it with the gas supply node ID for storage.

[0116] The above technical solution can use the cloud-based artificial intelligence large model provided by the data service company to regularly correct the fluctuation model of each gas supply node, making the prediction of the air pressure fluctuation trend at the gas supply node more accurate, while also reducing the computing power configuration at the production site.

[0117] In practice, since there may be connections between the various gas supply nodes, when the air pressure at one of the gas supply nodes fluctuates, the fluctuation will be transmitted to the other gas supply nodes. In order to reduce the impact of highly fluctuating gas supply nodes on the entire gas supply network, the reliability of each gas supply node will also be verified in the implementation of this application.

[0118] In step S400, the reliability of each fluctuation model is verified and a weight value is assigned to each fluctuation model. Figure 3 As shown, further comprising:

[0119] S410 , importing historical air pressure data and temperature data of each air supply node into a corresponding fluctuation model in units of time periods to obtain an expected air pressure value generated by the fluctuation model.

[0120] S420 , comparing the expected air pressure value with the actual air pressure value in the historical air pressure data to generate air pressure deviation values for each time period, and integrating the air pressure deviation values based on the time period to form a deviation curve.

[0121] S430 calculates the amplitude of the deviation curve for each gas supply node, obtains the maximum amplitude, and divides it into multiple labeled intervals of equal span. In practical applications, to eliminate interference, the maximum and minimum amplitude data can be removed. Labeling intervals divide the amplitude into multiple numerical intervals. Amplitudes falling within the same numerical interval are labeled with the same value, i.e., assigned a specific value.

[0122] S440 , associating multiple tag intervals with specific assignments.

[0123] S450: calibrate the amplitude of each time period in each deviation curve using the marking interval and assign a value to each time period to form a weight value of the fluctuation model.

[0124] The above step S400 can assign a reliability weight value that fluctuates over time to the fluctuation model corresponding to each gas supply node. For example, if there are three associated gas supply nodes, A, B, and C, according to the first associated model, when the air pressure of the two gas supply nodes A and B decreases by 0.1 MPa respectively, the air pressure of the gas supply node C decreases by 0.08 MPa and 0.06 MPa respectively. However, since the reliability weight value of the fluctuation model of the gas supply node A is 1 and the reliability weight value of the fluctuation model of the gas supply node B is 0.8, when the air pressure of the two gas supply nodes A and B decreases by 0.1 MPa at the same time, the air pressure of the gas supply node C decreases by 0.08*1+0.06*0.8=0.128 MPa. It can be seen from the above estimation process that when estimating the current gas supply node and calculating the impact of the associated gas supply nodes on the current gas supply node, introducing the reliability weight value can make the prediction result more accurate, which is beneficial to improving the accuracy of air pressure control and reducing the air pressure fluctuation at the gas supply node.

[0125] In step S600, the conduction state between each gas supply node is adjusted according to the gas pressure requirement, further comprising:

[0126] S610: Based on the fluctuation model corresponding to the current gas supply node, obtain the air pressure fluctuation value P1 of the gas supply node for the next time period. When the next time period lasts for a short time period, the air pressure fluctuation value P1 can be considered a fixed value. When the next time period lasts for a long time period, the air pressure fluctuation value P1 can be considered a fluctuation curve with the time period as the horizontal axis and the fluctuation amplitude as the vertical axis.

[0127] S611: Obtain the connectivity status between the current gas supply node and the associated gas supply node, calculate the pressure fluctuation value P2 of the current gas supply node caused by the connected associated gas supply node in the next time period based on the first correlation model, and simultaneously generate the time delay t1 required for the pressure fluctuation value P2 to act on the current gas supply node;

[0128] S612: Obtain theoretical air pressure values P3 at each moment of the current air supply node in the next period based on the air pressure fluctuation values P1 and P2. Similarly, the theoretical air pressure value P3 can be a fixed value or a fluctuation curve.

[0129] S613, obtaining the current air pressure value P0 of the air supply node, and subtracting it from the theoretical air pressure value P3 to obtain the air pressure value Pt that needs to be compensated;

[0130] At step S614, the air pressure values of other air supply nodes associated with the current air supply node are obtained. Based on the air pressure value Pt and the first correlation model, the conduction state between the current air supply node and the associated air supply nodes is adjusted. In practical applications, a match or calculation based on the first correlation model is sufficient to achieve a conduction state combination corresponding to the air pressure value Pt.

[0131] As detailed in step S650 above, in one embodiment, adjusting the conduction state between the current gas supply node and the associated gas supply node includes:

[0132] S6141, obtaining the ID of the target gas supply node to be connected and the time delay t2 required for the gas pressure of the target gas supply node to act on the current gas supply node after the gas pressure of the target gas supply node is connected, based on the first correlation model and the required gas pressure value Pt;

[0133] S6142, based on the time delay t2 and the time delay t1, determine the conduction time between the current gas supply node and the target gas supply node, and calculate and determine the conduction duration between the current gas supply node and the target gas supply node based on the fluctuation model corresponding to the current gas supply node.

[0134] The above technical solution can predict the air pressure value that needs to be compensated for the current air supply node in the next time period through the fluctuation model corresponding to each air supply node and the first correlation model, and then obtain the corresponding target air supply node through matching through the first correlation model, and connect the current air supply node and the target air supply node at the appropriate time. In this way, the associated air supply nodes in the gas supply network can be used to achieve balanced distribution of air pressure in the network and reduce air pressure fluctuations at the gas supply nodes.

[0135] In step S600, the method for adjusting the local temperature of the gas supply network to adjust the gas pressure of the gas supply node specifically includes:

[0136] S620: A heating device, such as a heating wire or a heating plate, is configured upstream of each gas supply node for heating the gas flow in the gas supply network.

[0137] S6211, directly generating the required regulated temperature and the corresponding heating power and heating duration based on the correlation between the air flow temperature in a specific section of the gas supply network and the air pressure at each gas supply node stored in the second correlation model; and / or

[0138] S6212: Based on the pipeline parameters and gas property parameters at the gas supply node, a corresponding heating calculation formula is configured for each gas supply node and stored in association with each gas supply node ID.

[0139] S622: Obtain the gas pressure value Pt that needs to be compensated for the gas supply node, and calculate the corresponding heating power and heating time according to the gas heating calculation formula.

[0140] The above technical solution places a heater directly upstream of the gas supply node, allowing the heating parameters required to compensate for the air pressure value Pt to be directly determined using the second correlation model. This is a convenient and efficient approach. In specific situations where the second correlation model cannot quickly locate the corresponding heating parameters, the heating calculation formula corresponding to the gas supply node is used to quickly calculate the required heating parameters for the heater. This allows for a quick response to the gas pressure demand at the gas supply node, reducing pressure fluctuations at the gas supply node.

[0141] In order to implement the above-mentioned intelligent centralized control method of the air compressor unit, the present application also proposes an intelligent centralized control system for the air compressor unit, which mainly includes a pipe network component and a control component.

[0142] The pipeline network assembly includes at least two air compressors and a connected gas supply network. Each gas flow distribution point in the gas supply network forms a gas supply node. Conductive pipelines and on / off valves are configured between specific gas supply nodes in the gas supply network. The on / off valves are configured as solenoid valves electrically connected to the control assembly for easy on / off control.

[0143] Each of the gas supply nodes is equipped with a temperature sensor for detecting the temperature of the air flow and a pressure sensor for detecting the pressure at the gas supply node. In a specific embodiment, an air flow sensor can be used to reversely calculate the air pressure of the gas supply node based on the air flow data detected. A heating device for heating the air flow in the gas supply network is equipped upstream of each of the gas supply nodes. The heating device is preferably configured as an electric heater arranged side by side in the gas supply pipeline along the direction of the air flow, and the control end of the electric heater is connected to the control component. In actual application, the installation position of the electric heater is located upstream of the gas supply node, and the distance between the electric heater and the gas supply node is determined by the air flow velocity in the gas supply pipeline and the heating power of the electric heater, so as to ensure that the heated gas can reach the gas supply node within the set time period.

[0144] In the implementation manner of the present application, the control component mainly includes a data acquisition unit, a data storage unit, a data processing unit, and a control output unit.

[0145] The data acquisition unit is configured to be connected to the temperature sensor, the pressure sensor and the signals of each switch valve, and is used to obtain real-time air pressure data, temperature data at each air supply node and the conduction status between each air supply node.

[0146] The data storage unit is configured to be data-connected to the data acquisition unit and the cloud server, and can be implemented by a data storage device and a network communication module arranged inside a factory or workshop.

[0147] The data storage unit is primarily used to store historical air pressure and temperature data for each gas supply node, a first correlation model used to characterize the correlation between air pressure fluctuations at each gas supply node, a second correlation model used to characterize the correlation between the airflow temperature in a specific section of the gas supply network and the air pressure at each gas supply node, conduction status data between each gas supply node in the gas supply network, and the corresponding fluctuation model and weight value for each gas supply node. Preferably, all of the above data is synchronized to cloud storage in real time via the network.

[0148] The data processing unit is configured to be data-connected to the data acquisition unit and the data storage unit, and is used to obtain the current air pressure and temperature data for each air supply node, as well as the conductivity status between each air supply node. Based on the fluctuation model and weight values of each air supply node, combined with the first and second correlation models, the data processing unit calculates and / or searches for the expected air pressure value for each air supply node in the next time period, thereby generating the corresponding air pressure demand for each air supply node. In practical applications, this data processing unit can be implemented by a PC deployed at the production site.

[0149] The control output unit is configured to be data-connected to the data processing unit and to be control-connected to the air compressor and each of the heating devices and the switch valves. According to the air pressure demand, combined with the first association model and the second association model, a corresponding control instruction signal is generated and sent to the air compressor, each switch valve and the heating device to adjust the air pressure of each air supply node and / or the conduction state between each air supply node. Wherein, adjusting the air pressure of each air supply node includes: adjusting the air pressure of the air tank connected to the air supply node by adjusting the operating state of the air compressor, adjusting the air pressure of the air supply node associated with the current air supply node, and adjusting the local temperature of the air supply network. In actual applications, the above-mentioned control output unit can be configured as a PLC control module connected to the host computer.

[0150] In order to update the fluctuation model and improve the accuracy of each fluctuation model prediction, the control component also includes a fluctuation model weight configuration unit and a fluctuation model update unit.

[0151] The fluctuation model weight configuration unit is configured to import the historical air pressure data and temperature data of each gas supply node into the corresponding fluctuation model in units of time periods, verify the reliability of each fluctuation model, assign a weight value to each fluctuation model, and output it to the data processing unit. The specific implementation method is as described in the previous method and will not be repeated here.

[0152] The fluctuation model update unit is configured to collect and store the air pressure data and temperature data of each gas supply node in the data storage unit based on the set update time node, and send the data stored in the data storage unit and the current fluctuation model to the cloud server, generate a new correction function and correct the current fluctuation model, and send the corrected fluctuation model to the data storage unit, and store it in association with the gas supply node ID.

[0153] Finally, to facilitate the promotion and use of the intelligent centralized control method for air compressor units described in the embodiments of this application, we also provide for the protection of a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the intelligent centralized control method for air compressor units described above. Such computer-readable storage media include, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof.

[0154] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent centralized control method for an air compressor unit, based on an air compressor unit and its connected air supply network, characterized in that: include: Establishing and storing a first correlation model for characterizing the correlation relationship between the air pressure fluctuations of each air supply node; Establishing and storing a second correlation model for characterizing the correlation between the air flow temperature in a specific section of the gas supply network and the air pressure at each gas supply node; Obtain and generate a fluctuation model for predicting the pressure fluctuation trend of each gas supply node based on the historical pressure and temperature data of each gas supply node, and associate each fluctuation model with the ID of each gas supply node for storage; Based on the historical air pressure and temperature data of each gas supply node, the reliability of each fluctuation model is verified and a weight value is assigned to each fluctuation model. Obtain the air pressure data, temperature data of each air supply node at the current moment and the conduction status between each air supply node; Based on the fluctuation model and weight value of each gas supply node, combined with the first correlation model and the second correlation model, the expected gas pressure value of each gas supply node in the next period is calculated to generate the corresponding gas pressure demand; According to the air pressure requirement, adjusting the air pressure of each air supply node and / or the conduction state between each air supply node; The gas supply node includes a bifurcation point where at least two gas supply branches are formed from the same gas supply main line; Adjusting the gas pressure of each gas supply node includes at least one of adjusting the gas pressure of a gas storage tank connected to the gas supply node, adjusting the gas pressure of an associated gas supply node, and adjusting the local temperature of the gas supply network; According to the gas pressure requirements, adjust the conduction status between each gas supply node, including: Based on the fluctuation model corresponding to the current gas supply node, obtain the gas pressure fluctuation value P1 of the gas supply node in the next period; Obtaining the connectivity status between the current gas supply node and the associated gas supply node, calculating the pressure fluctuation value P2 of the current gas supply node caused by the connected associated gas supply node in the next time period based on the first association model, and generating the time delay t1 required for the pressure fluctuation value P2 to act on the current gas supply node; According to the air pressure fluctuation value P1 and the air pressure fluctuation value P2, the theoretical air pressure value P3 of the current air supply node at each moment in the next period is obtained; Get the current air pressure value P0 of the gas supply node, and subtract it from the theoretical air pressure value P3 to obtain the air pressure value Pt that needs to be compensated; Obtaining the air pressure values of other air supply nodes associated with the current air supply node, and adjusting the conduction state between the current air supply node and the associated air supply nodes according to the air pressure value Pt and the first association model; The step of adjusting the conduction state between the current gas supply node and the associated gas supply node includes: Obtain the target gas supply node ID that needs to be turned on and the time delay t2 required for the gas pressure to act on the current gas supply node after the target gas supply node is turned on, based on the first correlation model and the required gas pressure value Pt; Based on the time delay t2 and the time delay t1, determining the conduction time between the current gas supply node and the target gas supply node, and calculating and determining the conduction duration between the current gas supply node and the target gas supply node based on the fluctuation model corresponding to the current gas supply node; Methods for regulating the local temperature of the gas supply network to adjust the gas pressure at the gas supply node include: A heating device for heating the air flow in the gas supply network is arranged upstream of each gas supply node; Based on the correlation between the air flow temperature in a specific section of the air supply network and the air pressure at each air supply node stored in the second correlation model, directly generate the required adjustment temperature T and the corresponding heating power and heating duration; and / or Based on the pipeline parameters and gas property parameters at the gas supply node, a corresponding heating calculation formula is configured for each gas supply node and stored in association with each gas supply node ID; Obtain the air pressure value Pt that needs to be compensated for at the air supply node, and calculate the corresponding heating power and heating time according to the heating calculation formula; Verify the reliability of each volatility model and assign weights to each volatility model, including: Import the historical air pressure and temperature data of each gas supply node into the corresponding fluctuation model in units of time periods to obtain the expected air pressure value generated by the fluctuation model; Comparing the expected air pressure value with the actual air pressure value in the historical air pressure data to generate air pressure deviation values for each time period, and integrating the air pressure deviation values based on the time period to form a deviation curve; Count the amplitudes of the deviation curves of each gas supply node, obtain the maximum amplitude and divide it into multiple marked intervals with equal spans; Associating multiple labeled intervals with specific assignments; The mark interval is used to calibrate the amplitude of each time period in each deviation curve and assign a value to each time period to form a weight value of the fluctuation model.

2. The intelligent centralized control method for air compressor units according to claim 1, characterized in that: The configuration method of each gas supply node includes: The gas supply network forms a tree-shaped gas supply structure from the gas storage tank toward the pneumatic terminal, and the gas supply nodes are divided into homologous nodes and heterologous nodes according to whether the gas storage tanks connected to the gas supply nodes are the same; Estimate the gas demand period of each pneumatic terminal at the end of the gas supply network based on the production line process, group pneumatic terminals with similar gas demand periods into one group, and then form multiple period groups. Based on the location layout of each pneumatic terminal, group each pneumatic terminal located in the same area into one group, and then form multiple location groups. Select pneumatic terminals in the same location group and in different time groups and connect them to share the same air supply node; Establishing controllable conductive connections between a number of homologous nodes, and between a number of homologous nodes and heterologous nodes in a gas supply network and setting trigger conditions, and when the trigger conditions are met, conducting the plurality of homologous nodes, or the homologous nodes and heterologous nodes; The pressure fluctuation correlation relationship between each gas supply node in the first correlation model includes the correlation relationship between the same-source nodes and the correlation relationship between the same-source nodes and the different-source nodes in the conductive state; The first correlation model includes the relationship between the air pressure fluctuation amplitude and the fluctuation response delay between the air supply nodes.

3. The intelligent centralized control method for air compressor units according to claim 2, characterized in that: Generate a fluctuation model to predict the pressure fluctuation trend of each gas supply node, including: Obtain the gas demand of each pneumatic terminal connected to the gas supply node in each time period and integrate them to generate a theoretical gas demand model; Obtain historical air pressure and temperature data of the gas supply node, combine it with the theoretical gas usage model, and generate a correction function based on a neural network algorithm to correct the theoretical gas usage model to generate the fluctuation model; The parameters of the correction function include time data, temperature data, and data on the conduction status between the current gas supply node and other gas supply nodes.

4. The intelligent centralized control method for air compressor units according to claim 1, characterized in that: Based on the historical air pressure and temperature data of each air supply node, a fluctuation model is generated to predict the air pressure fluctuation trend of each air supply node, which also includes: Collect and store the air pressure and temperature data of each gas supply node in a data storage unit, and establish a data connection between the data storage unit and the cloud server; Set the update time of each wave model; Detect whether the current time meets the set conditions. If so, send the data stored in the data storage unit and the current fluctuation model to the cloud server, generate a new correction function and correct the current fluctuation model; The revised fluctuation model is issued and stored in association with the gas supply node ID.

5. An intelligent centralized control system for air compressor units, characterized in that: Used to implement the intelligent centralized control method of an air compressor unit according to any one of claims 1 to 4, comprising a pipe network component and a control component; The pipe network assembly includes: at least two air compressors and a gas supply network connected thereto, wherein each gas branch point in the gas supply network forms a gas supply node, and a conducting pipe and a switch valve are arranged between specific gas supply nodes in the gas supply network; Each of the gas supply nodes is provided with a temperature sensor for detecting the temperature of the air flow and a pressure sensor for detecting the pressure at the gas supply node; a heating device for heating the air flow in the gas supply network is provided at the upstream position of each of the gas supply nodes; The control component includes: A data acquisition unit is configured to be connected to the temperature sensor, the pressure sensor and the switch valve signals, and is used to obtain real-time air pressure data, temperature data at each air supply node and conduction status data between each air supply node; a data storage unit configured to be data-connected to the data acquisition unit and the cloud server, and used to store historical air pressure data, temperature data, and conduction status data of each gas supply node, a first correlation model for characterizing the correlation between air pressure fluctuations at each gas supply node, a second correlation model for characterizing the correlation between the air flow temperature in a specific section of the gas supply network and the air pressure at each gas supply node, conduction status data between each gas supply node in the gas supply network, and a fluctuation model corresponding to each gas supply node and its weight value; a data processing unit configured to be data-connected to the data acquisition unit and the data storage unit, and configured to obtain the air pressure data and temperature data of each air supply node at the current moment, and the conduction status between each air supply node; and calculate the expected air pressure value of each air supply node in the next time period based on the fluctuation model and weight value of each air supply node, in combination with the first correlation model and the second correlation model, and generate the corresponding air pressure demand; A control output unit is configured to be data-connected to the data processing unit and to be control-connected to the air compressor and each of the heating devices and the switch valve, and to adjust the air pressure of each air supply node and / or the conduction state between each air supply node according to the air pressure demand; Wherein, regulating the gas pressure of each gas supply node includes: regulating the gas pressure of the gas storage tank connected to the gas supply node by adjusting the operating state of the air compressor, regulating the gas pressure of the gas supply node associated with the current gas supply node, and regulating the local temperature of the gas supply network; The control component also includes: a fluctuation model weight configuration unit configured to import historical air pressure data and temperature data of each gas supply node into the corresponding fluctuation model in units of time periods, verify the reliability of each fluctuation model, assign a weight value to each fluctuation model, and output the weight value to the data processing unit; The fluctuation model update unit is configured to collect and store the air pressure data and temperature data of each gas supply node in the data storage unit based on the set update time node, and send the data stored in the data storage unit and the current fluctuation model to the cloud server, generate a new correction function and correct the current fluctuation model, and send the corrected fluctuation model to the data storage unit, and store it in association with the gas supply node ID.

6. A computer-readable storage medium for intelligent centralized control of an air compressor unit, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent centralized control method for an air compressor unit as described in any one of claims 1 to 4.

Images