Air compressor mother pipe pressure regulation and control method, system, equipment and medium

Abstract

The invention provides an air compressor mother pipe pressure regulation and control method, system and device and a medium. The method comprises the steps that air compressor mother pipe pressure data and tail end pressure data of each branch pipe serve as input variables, tail end flow data of each branch pipe serve as output variables, a data set is predicted based on an incomplete measurement scene, and a first prediction model is established; obtaining current prediction data of the output variable based on a first prediction model; taking the current flow prediction data of the tail end of each branch pipe output by the first prediction model as an input variable, taking the pressure data of the main pipe of the air compressor as an output variable, and establishing a second prediction model based on the prediction data set in the incomplete measurement scene; on the basis of the second prediction model, current pressure prediction data of the air compressor mother pipe are obtained; and the current pressure of the air compressor mother pipe is regulated and controlled by taking the current pressure prediction data, output by the second prediction model, of the air compressor mother pipe as the current regulation and control target value, so that the reliability of mother pipe pressure control is effectively improved, and the energy cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of air compressor control, and in particular to an air compressor main pipe pressure control method, system, equipment and medium. Background Art

[0002] The air compressor system usually has multiple terminals (branch pipe terminals) during the air supply process. The current air compressor air supply management scenario is generally set to install flow meters at all terminals. However, in the actual air supply process, due to factors such as high cost or complex installation, it is impossible to configure flow meters at all terminals at the same time. In this case, it is impossible to obtain flow data from all terminals at the same time, and the monitoring and guidance of the air compressor air supply process cannot reach the expected level.

[0003] Moreover, the current air compressor system generally lacks sufficient control capabilities in the design of the main pipe pressure value. It can only simply set a sufficiently high main pipe pressure threshold (default value) based on the empirical value, or determine the main pipe pressure threshold based on the actual various operating parameters of the air compressor. The factors affecting the main pipe pressure control at the end of all branch pipes have not been considered, resulting in low reliability of the main pipe pressure control, causing energy waste and high cost problems.

[0004] In view of this problem, the present invention provides an air compressor main pipe pressure control method, system, equipment and medium to solve the above problem. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention innovatively proposes a method, system, equipment and medium for regulating the main pipe pressure of an air compressor, which effectively solves the problem of low reliability of main pipe pressure control and high energy cost caused by the prior art, effectively improves the reliability of main pipe pressure control and reduces energy cost.

[0006] A first aspect of the present invention provides a method for regulating the pressure of an air compressor main pipe, comprising:

[0007] Obtain the main pipe pressure data, main pipe flow data, each branch pipe end pressure data, each branch pipe end flow data, and establish a prediction data set under the incomplete measurement scenario;

[0008] Taking the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables and the flow data of each branch pipe end as output variables, a first prediction model is established based on the prediction data set in the incomplete measurement scenario; the real-time data of the input variables is input into the first prediction model to obtain the current prediction data of the output variables;

[0009] Taking the current flow prediction data of each branch pipe end output by the first prediction model as input variables and the air compressor main pipe pressure data as output variables, a second prediction model is established based on the prediction data set in the incomplete measurement scenario; the current flow prediction data of each branch pipe end output by the first prediction model is input into the second prediction model to obtain the current pressure prediction data of the air compressor main pipe;

[0010] The current pressure prediction data of the air compressor main pipe output by the second prediction model is used as the current control target value, and the current pressure of the air compressor main pipe is regulated according to the difference between the current monitored pressure of the air compressor main pipe and the current pressure prediction data of the air compressor main pipe.

[0011] Optionally, obtaining the air compressor main pipe pressure data, main pipe flow data, each branch pipe end pressure data, each branch pipe end flow data, and establishing a prediction data set in an incomplete measurement scenario specifically includes:

[0012] Install a pressure gauge and flow meter at the main pipe, install a pressure gauge at the end of all branch pipes, and install a flow meter at only one branch pipe end at a time;

[0013] Maintain the normal operation of all air compressors, obtain the pressure count value and flow count value at the main pipe, the pressure count value at the end of each branch pipe, maintain the pressure count value and flow count value at the main pipe, the pressure count value at the end of each branch pipe remains unchanged, and collect the flow count value at the end of the branch pipe where the flow meter is currently installed;

[0014] Each time, only one of the pressure count value at the main pipe and the pressure count value at the end of each branch pipe is changed; the flow count value at the end of the branch pipe where the flow meter is currently installed is collected again until a preset number of sample count values ​​at the end of the branch pipe where the flow meter is currently installed are collected;

[0015] The flow meter is installed at the end of the next branch pipe, and the flow count value of the next branch pipe end with a preset number of samples is collected until the flow count values ​​of the preset number of samples are collected at the ends of all branch pipes, thereby obtaining a predicted data set of the data to be trained in an incomplete scenario.

[0016] Furthermore, for the flow count values ​​at the end of the branch pipe where no flow meter is currently installed in the data to be trained in the incomplete scenario, a Boolean mask is set to mark the null value data or missing value data that are not considered for subsequent training calculations.

[0017] Optionally, for the data to be trained in an incomplete scenario, when the flow count value at the end of the branch pipe where the flow meter is currently installed is not less than the flow count value at the main pipe, the current pressure count value and flow count value at the main pipe are maintained, the current pressure count value at the end of each branch pipe remains unchanged, and the flow count value at the end of the branch pipe where the flow meter is currently installed is recollected.

[0018] Furthermore, taking the current flow prediction data of each branch pipe end output by the first prediction model as the input variable and the main pipe pressure data of the air compressor as the output variable, based on the prediction data set in the incomplete measurement scenario, before establishing the second prediction model, it also includes:

[0019] Determine whether the current flow prediction data of each branch pipe end obtained by inputting the real-time data of the input variables into the first prediction model is less than the flow count value at the current main pipe. If the current flow prediction data of a branch pipe end is not less than the monitoring value of the flow meter at the current main pipe, then re-input the real-time data of the input variables into the first prediction model to predict the current flow of the branch pipe end, or, after correcting the structural parameters of the first prediction model, predict the current flow of the branch pipe end until the current flow prediction data of all branch pipe ends are less than the flow count value at the current main pipe.

[0020] Optionally, the models, configurations, manufacturers, and usage conditions of all air compressors are the same. Optionally, both the first prediction model and the second prediction model are prediction models based on neural networks.

[0021] A second aspect of the present invention provides an air compressor main pipe pressure control system, comprising:

[0022] The acquisition module acquires the main pipe pressure data, main pipe flow data, pressure data at the end of each branch pipe, and flow data at the end of each branch pipe of the air compressor, and establishes a prediction data set under an incomplete measurement scenario;

[0023] The first establishment module uses the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables, and the flow data of each branch pipe end as output variables, and establishes a first prediction model based on the prediction data set in the incomplete measurement scenario; inputs the real-time data of the input variables into the first prediction model to obtain the current prediction data of the output variables;

[0024] The second establishment module uses the current flow prediction data of each branch pipe end output by the first prediction model as input variables and the air compressor main pipe pressure data as output variables to establish a second prediction model based on the prediction data set in the incomplete measurement scenario; the current flow prediction data of each branch pipe end output by the first prediction model is input into the second prediction model to obtain the current pressure prediction data of the air compressor main pipe;

[0025] The control module uses the current pressure prediction data of the air compressor main pipe output by the second prediction model as the current control target value, and controls the current pressure of the air compressor main pipe according to the difference between the current monitored pressure of the air compressor main pipe and the current pressure prediction data of the air compressor main pipe.

[0026] The third aspect of the present invention provides an electronic device, comprising: a memory for storing a computer program; a processor for implementing the steps of an air compressor main pipe pressure control method as described in the first aspect of the present invention when executing the computer program.

[0027] The fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of an air compressor main pipe pressure control method as described in the first aspect of the present invention are implemented.

[0028] The technical solution adopted by the present invention includes the following technical effects:

[0029] 1. The technical solution of the present invention uses the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables, and the flow data of each branch pipe end as output variables, and establishes a first prediction model based on the prediction data set in the incomplete measurement scenario; based on the first prediction model, the current prediction data of the output variable is obtained; using the current flow prediction data of each branch pipe end output by the first prediction model as input variables, and the main pipe pressure data of the air compressor as output variables, based on the prediction data set in the incomplete measurement scenario, a second prediction model is established; based on the second prediction model, the current pressure prediction data of the main pipe of the air compressor is obtained; using the current pressure prediction data of the main pipe of the air compressor output by the second prediction model as the current control target value, the current pressure of the main pipe of the air compressor is adjusted. For regulation, not only can comprehensive monitoring be achieved without installing flow meters at each end, thereby reducing the overall cost of the system; but also the current pressure prediction data of the air compressor main pipe is obtained based on the flow prediction data at the end of each branch pipe, and the current pressure prediction data of the air compressor main pipe is used as the current regulation target value (current main pipe pressure threshold); compared with determining the main pipe pressure threshold according to various operating parameters of the air compressor, this solution involves fewer types of data and is more convenient to implement, and can realize the effective combination of branch pipe end flow monitoring and main pipe pressure control; effectively solve the problem of low reliability of main pipe pressure control caused by existing technology and high energy cost, effectively improve the reliability of main pipe pressure control and reduce energy cost.

[0030] 2. In the technical solution of the present invention, a pressure gauge and a flow meter are installed at the main pipe, and a pressure gauge is installed at the end of all branch pipes. At the same time, a flow meter is installed at the end of only one branch pipe; the flow meter is installed at the end of each branch pipe in turn, and a preset number of sample flow count values ​​at the end of the branch pipe are collected until a preset number of sample flow count values ​​are collected at the ends of all branch pipes, thereby obtaining a predicted data set of the data to be trained in an incomplete scenario, thereby reducing the monitoring cost of the flow at the end of the branch pipe.

[0031] 3. In the technical solution of the present invention, for the data to be trained in an incomplete scenario, when the flow count value at the end of the branch pipe where the flow meter is currently installed is not less than the flow count value at the main pipe, the current pressure count value and flow count value at the main pipe are maintained, the current pressure count value at the end of each branch pipe remains unchanged, and the flow count value at the end of the branch pipe where the flow meter is currently installed is recollected, thereby ensuring the accuracy of the predicted data set in the incomplete scenario, thereby ensuring the accuracy of the data monitoring of the branch pipe end flow.

[0032] 4. In the technical solution of the present invention, it is judged whether the real-time data of the input variables are input into the first prediction model to obtain the current flow prediction data of each branch pipe end, which is not less than the flow count value at the current main pipe. If the current flow prediction data of a branch pipe end is not less than the monitoring value of the flow meter at the current main pipe, the real-time data of the input variables are re-input into the first prediction model to predict the current flow of the branch pipe end, or the structural parameters of the first prediction model are corrected to predict the current flow of the branch pipe end, until the current flow prediction data of all branch pipe ends are not less than the flow count value at the current main pipe, thereby ensuring the accuracy of the prediction data set in the incomplete scenario, thereby ensuring the accuracy of the data monitoring of the branch pipe end flow.

[0033] 5. The model, configuration, manufacturer and usage of all air compressors in the technical solution of the present invention are the same, which further ensures the accuracy of the prediction data set in non-complete scenarios, thereby further ensuring the accuracy of data monitoring of branch pipe end flow and main pipe pressure control.

[0034] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A schematic diagram of a process of a method in Embodiment 1 of the present invention;

[0037] Figure 2 Schematic diagram of the process of step S1 in the method of embodiment 1 of the present invention;

[0038] Figure 3 It is a simplified schematic diagram of the air compressor system in the method of Example 1 of the scheme of the present invention;

[0039] Figure 4 Another schematic diagram of the process of the method of Embodiment 1 in the scheme of the present invention;

[0040] Figure 5 This is a schematic diagram of the structure of the system in Embodiment 2 of the present invention;

[0041] Figure 6 This is a schematic diagram of the structure of the device of Example 3 in the solution of the present invention. DETAILED DESCRIPTION

[0042] In order to clearly illustrate the technical features of the present solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the accompanying drawings are not necessarily drawn to scale. The present invention omits the description of known components and processing techniques and processes to avoid unnecessary limitations on the present invention.

[0043] Embodiment 1

[0044] like Figure 1 As shown, the present invention provides a method for regulating the pressure of an air compressor main pipe, comprising:

[0045] S1, obtain the main pipe pressure data, main pipe flow data, each branch pipe end pressure data, each branch pipe end flow data, and establish a prediction data set under the incomplete measurement scenario;

[0046] S3, using the air compressor main pipe pressure data and each branch pipe end pressure data as input variables, and each branch pipe end flow data as output variables, and based on the prediction data set in the incomplete measurement scenario, establish a first prediction model; input the real-time data of the input variables into the first prediction model to obtain the current prediction data of the output variables;

[0047] S5, using the current flow prediction data of each branch pipe end output by the first prediction model as input variables, and the air compressor main pipe pressure data as output variables, and establishing a second prediction model based on the prediction data set in the incomplete measurement scenario; inputting the current flow prediction data of each branch pipe end output by the first prediction model into the second prediction model to obtain the current pressure prediction data of the air compressor main pipe;

[0048] S7, taking the current pressure prediction data of the air compressor main pipe output by the second prediction model as the current control target value, and regulating the current pressure of the air compressor main pipe according to the difference between the current monitored pressure of the air compressor main pipe and the current pressure prediction data of the air compressor main pipe.

[0049] Among them, Figure 2 As shown, in step S1, the main pipe pressure data, main pipe flow data, each branch pipe end pressure data, each branch pipe end flow data of the air compressor are obtained, and the prediction data set under the incomplete measurement scenario is established, which specifically includes:

[0050] S11, install a pressure gauge and flow meter at the main pipe, install a pressure gauge at the end of all branch pipes, and install a flow meter at only one branch pipe end at a time;

[0051] like Figure 3 The figure shows a simplified schematic diagram of an air compressor system. Air compressor 1, air compressor 2, air compressor 3, and air compressor 4 are four air compressors without any difference (all air compressors have the same model, configuration, manufacturer, and usage). The main pipe is the output end where the four air compressors are connected together. The main pipe is always equipped with a pressure gauge P0 and a flow meter F0. Terminal 1, terminal 2, and terminal 3 are the three output ends (branch ends) of the main pipe. There is no effect of sorting. All terminals are equipped with pressure gauges such as P1, P2, and P3, but only one terminal is equipped with a flow meter at the same time. If it is installed at terminal 1, then F1 (flow meter value at terminal 1) has flow data, and F2 (flow meter value at terminal 2) and F3 (flow meter value at terminal 3) are empty. The same applies to installing at terminal 2 or terminal 3.

[0052] S12, maintaining the normal operation of all air compressors, obtaining the pressure count value and flow count value at the main pipe, the pressure count value at the end of each branch pipe, maintaining the pressure count value and flow count value at the main pipe, the pressure count value at the end of each branch pipe unchanged, and collecting the flow count value at the end of the branch pipe where the flow meter is currently installed;

[0053] After installing the flow meter at terminal 1, maintain the normal operation of the four air compressors. By observing the pressure count values ​​of the main pipe and terminals 1, 2, and 3, maintain the pressure count value P0 at the main pipe, the pressure count value P1 at terminal 1, the pressure count value P2 at terminal 2, and the pressure count value P3 at terminal 3 unchanged, and collect the flow count value F1 at terminal 1 at this time.

[0054] S13, each time only changing any one of the pressure count value at the main pipe and the pressure count value at the end of each branch pipe; collecting the flow count value at the end of the branch pipe where the flow meter is currently installed again, until a preset number of sample count values ​​at the end of the branch pipe where the flow meter is currently installed are collected;

[0055] Change the values ​​of the main pipe and terminal pressure (when changing the terminal pressure of one of the branch pipes, the main pipe pressure is changed by default, because in practice, changes in the main pipe pressure often lead to changes in the pressures of each terminal; when the main pipe pressure is changed, the terminal pressure of the branch pipe will also change accordingly), collect the value F1 of the terminal 1 flowmeter again, and repeat the operation until a sufficient number of samples of F1 values ​​are collected.

[0056] S14, add a flow meter to the next branch pipe end, collect a preset number of sample flow count values ​​at the next branch pipe end, until all branch pipe ends have collected the preset number of sample flow count values, and obtain a predicted data set of the data to be trained in an incomplete scenario.

[0057] Then install the flow meter to terminal 2, repeat the operation of terminal 1, and the same process for terminal 3, and obtain the incomplete scenario training data shown in Table 1.

[0058] Table 1: Training data table for incomplete scenarios

[0059]

[0060] Among them, for the flow count value at the end of the branch pipe where the flow meter is not currently installed in the data to be trained in the incomplete scenario, a Boolean mask is set to mark the null value data or missing value data that is not considered for subsequent training calculations.

[0061] When the main pipe pressure changes, the main pipe flow data will also change. Generally, there is no way to directly adjust the main pipe flow. When predicting the terminal flow through pressure data, the main pipe flow is an important criterion and limit value (the terminal flow must not be greater than or equal to the main pipe flow). For the training data in the non-complete scenario, when the flow count value at the end of the branch pipe with the flow meter installed is not less than the flow count value at the main pipe, maintain the current pressure count value and flow count value at the main pipe, and keep the current pressure count value at each branch pipe unchanged. Re-collect the flow count value at the end of the branch pipe with the flow meter installed.

[0062] Wherein, in step S3, the training set data in the obtained prediction data set under incomplete scenarios (prediction data set under incomplete scenarios) is standardized to unify the dimensions.

[0063] In order to make the data easier to process in the first prediction model, the first prediction model standardizes all the data, which means that all the data are converted into a unified format. By setting their mean to zero and variance to one, the training speed of the first prediction model can be accelerated and the accuracy of the first prediction model can be improved. In addition, standardization can eliminate the dimensional differences of the data, making the training of the first prediction model more stable and efficient, ensuring that the data at different ends have a consistent scale, thereby improving the learning efficiency and prediction ability of the first prediction model.

[0064] Set a custom loss function (for example, it can be an MSE function, i.e., a mean square error function), and set a Boolean mask for the null values ​​of x in the incomplete scene training data to mark which data are null values ​​that are not considered for subsequent calculations.

[0065] Because it is impossible to install flow meters at all terminals at the same time, there are missing values ​​in the data. Traditional processing methods may ignore these data or replace them with certain fixed values, but these methods will affect the precision and accuracy of the model. By customizing the loss function, these missing data points are ignored when training the first prediction model, ensuring that the first prediction model only learns valid data, thereby improving the precision and robustness of the first prediction model.

[0066] Define the model building function, initialize the first prediction model in order, set the neural network input layer, hidden layer, output layer, set the activation function (for example, to the ReLU function), add the dropout layer, compile the model, select the optimizer, and set the loss function to the custom loss function set above.

[0067] Build the first prediction model, the input shape is the number of columns of the standardized input data, define the early stop callback function to prevent stuck, train the first prediction model, and set the parameters.

[0068] Output the predicted training data, denormalize the predicted value and true value of the training data, calculate the mean square error and determination coefficient to evaluate the first prediction model, and obtain a traffic identification model (first prediction model) in an incomplete scenario that approximates the complete data training effect.

[0069] like Figure 4 As shown, the embodiment of the present invention further provides a method for regulating the pressure of an air compressor main pipe, that is, before step S5, it also includes:

[0070] S4, judging whether the current flow prediction data of each branch pipe end obtained by inputting the real-time data of the input variables into the first prediction model is less than the flow count value at the current main pipe. If the judgment result is yes, executing step S5; if the judgment result is no, executing step S3, that is, re-inputting the real-time data of the input variables into the first prediction model to predict the current flow of the branch pipe end, or correcting the structural parameters of the first prediction model to predict the current flow of the branch pipe end, until the current flow prediction data of all branch pipe ends are less than the flow count value at the current main pipe.

[0071] In step S5, a model (second prediction model) for predicting the main pipe pressure based on the flow rate is established. The specific process may be:

[0072] The predicted complete flow data set (the flow prediction data at each branch end) obtained by step S3 is standardized, and the flow prediction data at each branch end is used as the input to train a model that can predict the main pipe pressure in real time through the input flow data.

[0073] In order to make the data easier to process in the second prediction model, the second prediction model standardizes all the data, which means that all the data are converted into a unified format. By setting their mean to zero and variance to one, the training speed of the second prediction model can be accelerated and the accuracy of the second prediction model can be improved. In addition, standardization can eliminate the dimensional differences of the data, making the training of the second prediction model more stable and efficient, ensuring that the data at different ends have a consistent scale, thereby improving the learning efficiency and prediction ability of the second prediction model.

[0074] Set a custom loss function (for example, it can be an MSE function, i.e., a mean square error function), and set a Boolean mask for the null values ​​of x in the incomplete scene training data to mark which data are null values ​​that are not considered for subsequent calculations.

[0075] Because it is impossible to install flow meters at all terminals at the same time, there are missing values ​​in the data. Traditional processing methods may ignore these data or replace them with certain fixed values, but these methods will affect the precision and accuracy of the model. By customizing the loss function, these missing data points are ignored when training the second prediction model, ensuring that the second prediction model only learns valid data, thereby improving the accuracy and robustness of the second prediction model.

[0076] Define the model building function, initialize the sequential second prediction model, set the neural network input layer, hidden layer, output layer, set the activation function (for example, the ReLU function), and set the loss function to the custom loss function set above.

[0077] Build the second prediction model, the input shape is the number of columns of the standardized input data, define the early stop callback function to prevent stuck, train the second prediction model, and set the parameters.

[0078] Output the predicted training data, denormalize the predicted values ​​and true values ​​of the training data, calculate the mean square error and determination coefficient to evaluate the second prediction model, adjust the parameters to improve the model accuracy, and obtain the flow prediction main pipe pressure model (second prediction model) in an incomplete scenario that approximates the complete data training effect.

[0079] It should be noted that both the first prediction model and the second prediction model are prediction models based on neural networks. The neural network may be a BP neural network or other types of neural networks, and the present invention does not limit this.

[0080] Among them, in step S7, the current pressure prediction data of the air compressor main pipe output by the second prediction model is used as the current control target value, and the current pressure of the air compressor main pipe is regulated according to the difference between the current monitored pressure of the air compressor main pipe and the current pressure prediction data of the air compressor main pipe. For the pressures of terminal 1, terminal 2, and terminal 3, there are generally (same or different) process constraints for the lower limit of pressure. When the terminal pressure during production is higher than the lower limit requirement, the energy saving purpose can be achieved by reducing the corresponding terminal pressure to be equal to it.

[0081] For example, the measured current pressure of the main pipe is 0.539531 kPa (the current monitoring pressure of the main pipe, all subsequent pressure units are kPa by default), the maximum threshold of the main pipe pressure is set to 0.53 (the process constraint requirement of the main pipe pressure), the terminal pressure 1 is 0.510998 (the current monitoring pressure of the branch pipe terminal 1), the terminal pressure 2 is 0.530519 (the current monitoring pressure of the branch pipe terminal 2), the terminal pressure is 0.526239 (the current monitoring pressure of the branch pipe terminal 1), and the terminal flow 1 is 16.89187 M 3 / s (current flow prediction data of branch pipe end 1, all subsequent flow units default to M 3 / s), terminal flow 2 is 7.751181 (current flow prediction data of branch terminal 2), and terminal flow 3 is 9.413598 (current flow prediction data of branch terminal 3). By inputting the predicted data of the flow at the end of each branch pipe into the second prediction model, it is concluded that normal production can be achieved when the main pipe pressure is 0.528876 (current pressure prediction data of the main pipe). The main pipe pressure prediction data at this time is used as the control target value, with an optimization space of 0.010665 (0.539531 kPa-0.528876), and it is sent to the air compression system for execution. At the same time, the trigger mechanism for the air compression system to regulate the current pressure of the air compressor main pipe can be real-time, or according to changes in operating conditions, or it can be a set trigger cycle. That is, according to changes in operating conditions, the air compression system can monitor and measure the terminal flow prediction data F1, F2, and F3. When the operating conditions change suddenly, such as the increase in the terminal flow prediction data reaches a preset change threshold, the current main pipe pressure prediction data is predicted to ensure or restart the optimization calculation and control process; or, that is, when the preset trigger cycle is reached, the air compression system can monitor and measure the terminal flow prediction data F1, F2, and F3, and predict the current main pipe pressure prediction data to ensure or restart the optimization calculation and control process to achieve real-time optimization iteration.

[0082] In practical applications, due to cost or other factors, it is impossible to install flow meters at all terminals at the same time. According to this solution, the problem of not being able to measure the flow at all terminals at the same time can be solved, and a data basis can be provided for subsequent main pipe pressure data prediction and regulation.

[0083] Through the prediction of the first prediction model, it is no longer necessary to install flow meters at each end at the same time, which not only saves costs, but also reduces the complexity of installation and maintenance, and realizes comprehensive prediction and monitoring of the air compressor terminal flow while reducing equipment investment, thereby not only providing a cost-effective flow monitoring solution, reducing the use of physical equipment, reducing the overall cost of the system, improving economic benefits, and achieving energy conservation and consumption reduction; but also, it can also perform real-time regulation of the mother pipe pressure based on the air compressor terminal flow prediction data.

[0084] Using deep learning technology, a black box model (the first prediction model and the second prediction model) was constructed. The black box model can understand and learn the relationship between the various parts of the system. Even in the absence of comprehensive measurement data, the flow conditions of the entire system can be inferred based on the existing data. The terminal flow identification model and flow prediction mother pipe pressure with similar training effects as the complete data training set can be trained through an incomplete data training set.

[0085] The technical solution of the present invention uses the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables, and the flow data of each branch pipe end as output variables, and establishes a first prediction model based on the prediction data set in the incomplete measurement scenario; based on the first prediction model, the current prediction data of the output variable is obtained; using the current flow prediction data of each branch pipe end output by the first prediction model as input variables, and the main pipe pressure data of the air compressor as output variables, based on the prediction data set in the incomplete measurement scenario, a second prediction model is established; based on the second prediction model, the current pressure prediction data of the main pipe of the air compressor is obtained; using the current pressure prediction data of the main pipe of the air compressor output by the second prediction model as the current control target value, the current pressure of the main pipe of the air compressor is controlled. The flow rate of each branch pipe can be regulated and controlled, which not only eliminates the need to install flow meters at each end to achieve comprehensive monitoring, thereby reducing the overall cost of the system; the current pressure prediction data of the air compressor main pipe is obtained based on the flow prediction data at the end of each branch pipe, and the current pressure prediction data of the air compressor main pipe is used as the current regulation target value (current main pipe pressure threshold); compared with determining the current main pipe pressure threshold according to the operating parameters of the air compressor, this solution involves fewer types of data and is more convenient to implement, and can achieve an effective combination of branch pipe end flow monitoring and main pipe pressure control; it effectively solves the problem of low reliability of main pipe pressure control and high energy cost caused by the existing technology, effectively improves the reliability of main pipe pressure control, and reduces energy cost.

[0086] In the technical solution of the present invention, a pressure gauge and a flow meter are installed at the main pipe, and a pressure gauge is installed at the end of all branch pipes, and a flow meter is installed at only one end of the branch pipe at the same time; the flow meter is installed at each end of the branch pipe in turn, and a preset number of sample flow count values ​​at the end of the branch pipe are collected until a preset number of sample flow count values ​​are collected at the ends of all branch pipes, so as to obtain a predicted data set of the data to be trained in an incomplete scenario, thereby reducing the monitoring cost of the flow at the end of the branch pipe.

[0087] In the technical solution of the present invention, in the training data under the incomplete scenario, when the flow count value of the branch pipe end with the flow meter installed is not less than the flow count value at the main pipe, the current pressure count value and flow count value at the main pipe are maintained, the current pressure count value at each branch pipe end remains unchanged, and the flow count value at the branch pipe end with the flow meter installed is re-collected, so as to ensure the accuracy of the prediction data set under the incomplete scenario, thereby ensuring the accuracy of the data monitoring of the branch pipe end flow. In the technical solution of the present invention, it is judged that the real-time data of the input variable is input into the first prediction model to obtain the current flow prediction data of each branch pipe end, whether it is not less than the flow count value at the current main pipe. If the current flow prediction data of a branch pipe end is not less than the monitoring value of the flow meter at the current main pipe, the real-time data of the input variable is input into the first prediction model again to predict the current flow of the branch pipe end, or the structural parameters of the first prediction model are corrected to predict the current flow of the branch pipe end, until the current flow prediction data of all branch pipe ends are not less than the flow count value at the current main pipe, thereby ensuring the accuracy of the prediction data set under the incomplete scenario, thereby ensuring the accuracy of the data monitoring of the branch pipe end flow.

[0088] The model, configuration, manufacturer and usage of all air compressors in the technical solution of the present invention are the same, which further ensures the accuracy of the prediction data set in the incomplete scenario, thereby further ensuring the accuracy of the data monitoring of the branch pipe end flow and the main pipe pressure control.

[0089] Embodiment 2

[0090] like Figure 5 As shown, the technical solution of the present invention also provides an air compressor main pipe pressure control system, comprising:

[0091] The acquisition module 101 acquires the main pipe pressure data, main pipe flow data, each branch pipe end pressure data, each branch pipe end flow data of the air compressor, and establishes a prediction data set in an incomplete measurement scenario;

[0092] The first establishment module 102 uses the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables, and the flow data of each branch pipe end as output variables, and establishes a first prediction model based on the prediction data set in the incomplete measurement scenario; inputs the real-time data of the input variables into the first prediction model to obtain the current prediction data of the output variables;

[0093] The second establishment module 103 uses the current flow prediction data of each branch pipe end output by the first prediction model as an input variable and the air compressor main pipe pressure data as an output variable to establish a second prediction model based on the prediction data set in the incomplete measurement scenario; the current flow prediction data of each branch pipe end output by the first prediction model is input into the second prediction model to obtain the current pressure prediction data of the air compressor main pipe;

[0094] The control module 104 uses the current pressure prediction data of the air compressor main pipe output by the second prediction model as the current control target value, and controls the current pressure of the air compressor main pipe according to the difference between the current monitored pressure of the air compressor main pipe and the current pressure prediction data of the air compressor main pipe.

[0095] It should be noted that the implementation process of the acquisition module 101, the first establishment module 102, the second establishment module 103, and the control module 104 in this embodiment corresponds to the method steps in the first embodiment, and the present invention will not be repeated here.

[0096] The technical solution of the present invention uses the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables, and the flow data of each branch pipe end as output variables, and establishes a first prediction model based on the prediction data set in the incomplete measurement scenario; based on the first prediction model, the current prediction data of the output variable is obtained; using the current flow prediction data of each branch pipe end output by the first prediction model as input variables, and the main pipe pressure data of the air compressor as output variables, and based on the prediction data set in the incomplete measurement scenario, a second prediction model is established; based on the second prediction model, the current pressure prediction data of the main pipe of the air compressor is obtained; using the current pressure prediction data of the main pipe of the air compressor output by the second prediction model as the current control target value, the current pressure of the main pipe of the air compressor is controlled Not only can comprehensive monitoring be achieved without installing a flow meter at each end, thereby reducing the overall cost of the system; but also the current pressure prediction data of the air compressor main pipe is obtained based on the flow prediction data at the end of each branch pipe, and the current pressure prediction data of the air compressor main pipe is used as the current control target value (current main pipe pressure threshold); compared with determining the current main pipe pressure threshold according to various operating parameters of the air compressor, this solution involves fewer types of data and is more convenient to implement, and can realize the effective combination of branch pipe end flow monitoring and main pipe pressure control; it effectively solves the problem of low reliability of main pipe pressure control caused by the existing technology, resulting in energy waste and high cost problems, effectively improves the reliability of main pipe pressure control, and reduces energy costs.

[0097] In the technical solution of the present invention, a pressure gauge and a flow meter are installed at the main pipe, and a pressure gauge is installed at the end of all branch pipes, and a flow meter is installed at only one end of the branch pipe at the same time; the flow meter is installed at each end of the branch pipe in turn, and a preset number of sample flow count values ​​at the end of the branch pipe are collected until a preset number of sample flow count values ​​are collected at the ends of all branch pipes, so as to obtain a predicted data set of the data to be trained in an incomplete scenario, thereby reducing the monitoring cost of the flow at the end of the branch pipe.

[0098] In the technical solution of the present invention, in the training data under the incomplete scenario, when the flow count value of the branch pipe end with the flow meter installed is not less than the flow count value at the main pipe, the current pressure count value and flow count value at the main pipe are maintained, the current pressure count value at each branch pipe end remains unchanged, and the flow count value at the branch pipe end with the flow meter installed is re-collected, so as to ensure the accuracy of the prediction data set under the incomplete scenario, thereby ensuring the accuracy of the data monitoring of the branch pipe end flow. In the technical solution of the present invention, it is judged that the real-time data of the input variable is input into the first prediction model to obtain the current flow prediction data of each branch pipe end, whether it is not less than the flow count value at the current main pipe. If the current flow prediction data of a branch pipe end is not less than the monitoring value of the flow meter at the current main pipe, the real-time data of the input variable is input into the first prediction model again to predict the current flow of the branch pipe end, or the structural parameters of the first prediction model are corrected to predict the current flow of the branch pipe end, until the current flow prediction data of all branch pipe ends are not less than the flow count value at the current main pipe, thereby ensuring the accuracy of the prediction data set under the incomplete scenario, thereby ensuring the accuracy of the data monitoring of the branch pipe end flow.

[0099] The model, configuration, manufacturer and usage of all air compressors in the technical solution of the present invention are the same, which further ensures the accuracy of the prediction data set in the incomplete scenario, thereby further ensuring the accuracy of the data monitoring of the branch pipe end flow.

[0100] Embodiment 3

[0101] like Figure 6 As shown, the technical solution of the present invention also provides an electronic device, which is characterized in that it includes: a memory 201 for storing a computer program; a processor 202 for implementing the steps of an air compressor main pipe pressure control method as in Example 1 when executing the computer program.

[0102] The memory 201 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of these data include: any computer program for operating on the electronic device. It can be understood that the memory 201 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a ferromagnetic random access memory, a flash memory, a magnetic surface memory, an optical disc, or a read-only optical disc (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache.By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), direct memory bus random access memory (DRRAM). The memory 201 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memory.

[0103] The method disclosed in the above embodiment of the present application can be applied to the processor 202, or implemented by the processor 202. The processor 202 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 202 or the instruction in the form of software. The above processor 202 may be a general-purpose processor, a DSP (Digital Signal Processing, that is, a chip capable of implementing digital signal processing technology), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 202 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 201, and the processor 202 reads the program in the memory 201 and completes the steps of the above method in combination with its hardware. When the processor 202 executes the program, the corresponding processes in the various methods of the embodiments of the present application are implemented, which will not be described here for the sake of brevity.

[0104] Embodiment 4

[0105] The technical solution of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of an air compressor main pipe pressure control method as in Example 1 are implemented.

[0106] For example, it includes a memory 201 storing a computer program, and the computer program can be executed by a processor 202 to complete the steps of the aforementioned method. The computer readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, FlashMemory, magnetic surface storage, optical disk, or CD-ROM.

[0107] It can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiment are executed; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks. Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. And the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.

[0108] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for regulating the pressure of an air compressor main pipe, characterized in that: include: Obtain the main pipe pressure data, main pipe flow data, each branch pipe end pressure data, each branch pipe end flow data, and establish a prediction data set under the incomplete measurement scenario; Taking the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables and the flow data of each branch pipe end as output variables, a first prediction model is established based on the prediction data set in the incomplete measurement scenario; the real-time data of the input variables is input into the first prediction model to obtain the current prediction data of the output variables; Taking the current flow prediction data of each branch pipe end output by the first prediction model as input variables and the air compressor main pipe pressure data as output variables, a second prediction model is established based on the prediction data set in the incomplete measurement scenario; the current flow prediction data of each branch pipe end output by the first prediction model is input into the second prediction model to obtain the current pressure prediction data of the air compressor main pipe; The current pressure prediction data of the air compressor main pipe output by the second prediction model is used as the current control target value, and the current pressure of the air compressor main pipe is regulated according to the difference between the current monitored pressure of the air compressor main pipe and the current pressure prediction data of the air compressor main pipe.

2. The method for controlling the pressure of an air compressor main pipe according to claim 1, characterized in that: Obtain the compressor main pipe pressure data, main pipe flow data, each branch pipe end pressure data, each branch pipe end flow data, and establish a prediction data set under the incomplete measurement scenario, including: Install a pressure gauge and flow meter at the main pipe, install a pressure gauge at the end of all branch pipes, and install a flow meter at only one branch pipe end at a time; Maintain the normal operation of all air compressors, obtain the pressure count value and flow count value at the main pipe, the pressure count value at the end of each branch pipe, maintain the pressure count value and flow count value at the main pipe, the pressure count value at the end of each branch pipe remains unchanged, and collect the flow count value at the end of the branch pipe where the flow meter is currently installed; Each time, only one of the pressure count value at the main pipe and the pressure count value at the end of each branch pipe is changed; the flow count value at the end of the branch pipe where the flow meter is currently installed is collected again until a preset number of sample count values ​​at the end of the branch pipe where the flow meter is currently installed are collected; The flow meter is installed at the end of the next branch pipe, and the flow count value of the next branch pipe end with a preset number of samples is collected until the flow count values ​​of the preset number of samples are collected at the ends of all branch pipes, thereby obtaining a predicted data set of the data to be trained in an incomplete scenario.

3. The method for controlling the pressure of an air compressor main pipe according to claim 2, characterized in that: For the flow count values ​​at the end of the branch pipe where no flow meter is currently installed in the data to be trained in the incomplete scenario, a Boolean mask is set to mark the null value data or missing value data that is not considered for subsequent training calculations.

4. The method for controlling the pressure of an air compressor main pipe according to claim 2, characterized in that: For the data to be trained in the incomplete scenario, when the flow count value at the end of the branch pipe where the flow meter is currently installed is not less than the flow count value at the main pipe, the current pressure count value and flow count value at the main pipe are maintained, the current pressure count value at the end of each branch pipe remains unchanged, and the flow count value at the end of the branch pipe where the flow meter is currently installed is collected again.

5. The method for controlling the pressure of the main pipe of an air compressor according to claim 4, characterized in that: Taking the current flow prediction data of each branch pipe end output by the first prediction model as the input variable and the main pipe pressure data of the air compressor as the output variable, based on the prediction data set in the incomplete measurement scenario, before establishing the second prediction model, it also includes: Determine whether the current flow prediction data of each branch pipe end obtained by inputting the real-time data of the input variables into the first prediction model is less than the flow count value at the current main pipe. If the current flow prediction data of a branch pipe end is not less than the monitoring value of the flow meter at the current main pipe, then re-input the real-time data of the input variables into the first prediction model to predict the current flow of the branch pipe end, or, after correcting the structural parameters of the first prediction model, predict the current flow of the branch pipe end until the current flow prediction data of all branch pipe ends are less than the flow count value at the current main pipe.

6. A method for controlling the pressure of an air compressor main pipe according to any one of claims 1 to 5, characterized in that: The models, configurations, manufacturers and usage of all air compressors are the same.

7. A method for controlling the pressure of an air compressor main pipe according to any one of claims 1 to 5, characterized in that: Both the first prediction model and the second prediction model are prediction models based on neural networks.

8. An air compressor main pipe pressure control system, characterized in that: include: The acquisition module acquires the main pipe pressure data, main pipe flow data, pressure data at the end of each branch pipe, and flow data at the end of each branch pipe of the air compressor, and establishes a prediction data set under an incomplete measurement scenario; The first establishment module uses the pressure data of the main pipe of the air compressor and the pressure data of each branch pipe end as input variables, and the flow data of each branch pipe end as output variables, and establishes a first prediction model based on the prediction data set in the incomplete measurement scenario; inputs the real-time data of the input variables into the first prediction model to obtain the current prediction data of the output variables; The second establishment module uses the current flow prediction data of each branch pipe end output by the first prediction model as input variables and the air compressor main pipe pressure data as output variables to establish a second prediction model based on the prediction data set in the incomplete measurement scenario; the current flow prediction data of each branch pipe end output by the first prediction model is input into the second prediction model to obtain the current pressure prediction data of the air compressor main pipe; The control module uses the current pressure prediction data of the air compressor main pipe output by the second prediction model as the current control target value, and controls the current pressure of the air compressor main pipe according to the difference between the current monitored pressure of the air compressor main pipe and the current pressure prediction data of the air compressor main pipe.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, used to implement the steps of an air compressor main pipe pressure control method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the air compressor main pipe pressure control method according to any one of claims 1 to 7 are implemented.

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