Energy-saving control method and system for operation process of compressed air system

Abstract

The invention provides an energy-saving control method and system for the operation process of a compressed air system, and relates to the field of compressed air system control, and the method comprises the steps: obtaining the production activity data of the compressed air system in different historical periods and the corresponding compressed air demand, and constructing a time series data set; training a prediction model by using the time sequence data set to obtain a target prediction model; inputting the current production activity data into the target prediction model to obtain a demand prediction value of compressed air in a future preset time period; determining a control strategy corresponding to the minimum energy consumption of the compressed air system according to the demand predicted value and the operation energy consumption of each air compressor; and the working state of the air compressor is dynamically adjusted according to the control strategy. By implementing the method, the energy consumption of the compressed air system can be effectively reduced, and the energy efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of compressed air system control, and in particular to an energy-saving control method and system for a compressed air system operation process. Background Art

[0002] With the improvement of industrial automation, compressed air systems play an important role in various production activities. As an important power source, compressed air is widely used in many manufacturing processes, such as machine tool operation, product cleaning, refrigeration, etc. Due to its wide application in industrial production, energy consumption management of compressed air systems has become the key to improving energy efficiency and reducing costs.

[0003] In the related art, the operation control of the compressed air system mainly relies on fixed pressure settings and response mechanisms. The start and stop of the air compressor is usually controlled according to the preset upper and lower pressure limits of the air tank. When the pressure of the air tank is lower than the preset lower limit, the air compressor is started, and when the pressure of the air tank reaches the preset upper limit, the air compressor is shut down.

[0004] However, the control method of the compressed air system in the related art is not flexible and accurate enough in responding to the compressed air demand, and may result in higher energy consumption when the compressed air demand fluctuates. Summary of the invention

[0005] The present application provides an energy-saving control method and system for a compressed air system operation process, which are used to solve the problem of low energy efficiency in existing compressed air system control methods.

[0006] In a first aspect, the present application provides an energy-saving control method for a compressed air system operation process, which is applied to an intelligent control system. The method comprises: Obtain the production activity data of the compressed air system in different historical periods and the corresponding compressed air demand, and construct a time series data set; Using the time series data set to train a prediction model, a target prediction model is obtained, and the target prediction model is used to predict the demand for compressed air within a preset time period in the future; Input the current production activity data into the target prediction model to obtain the predicted value of the demand for compressed air within a preset time period in the future; Determine the control strategy corresponding to the minimum energy consumption of the compressed air system according to the predicted demand value and the operating energy consumption of each compressor; The working state of the air compressor is dynamically adjusted according to the control strategy, and the working state includes starting and stopping.

[0007] Through the above embodiments, the intelligent control system obtains the production activity data of the compressed air system in different historical periods and the corresponding compressed air demand, constructs a time series data set and trains the prediction model to obtain a target prediction model that can accurately predict future compressed air demand. According to the prediction results and the operating energy consumption of each air compressor, a control strategy with the lowest energy consumption is formulated, and the working state of the air compressor, such as starting and stopping, is dynamically adjusted, thereby effectively reducing the energy consumption of the compressed air system and improving energy efficiency.

[0008] In some embodiments, before the step of determining the control strategy corresponding to the minimum energy consumption of the compressed air system by using the demand forecast value and the operating energy consumption of each compressor, the method further includes: Obtaining historical operation data and corresponding historical energy consumption data of each air compressor to obtain a training data set, wherein the historical operation data includes output pressure and output flow; An energy consumption prediction model corresponding to each air compressor is trained according to the training data set to obtain a target energy consumption prediction model corresponding to each air compressor; The current operating data of each air compressor is input into the corresponding target energy consumption prediction model to obtain the operating energy consumption of each air compressor.

[0009] Through the above embodiment, the intelligent control system obtains the historical operation data and energy consumption data of each air compressor, trains an energy consumption prediction model, and predicts the specific energy consumption of each air compressor. In this way, the operation of each air compressor can be adjusted more finely to ensure that the energy consumption of each machine is optimized while meeting the compressed air demand, thereby further reducing the energy consumption of the entire compressed air system.

[0010] In some embodiments, after the step of inputting the current operating data of each air compressor into the corresponding target energy consumption prediction model to obtain the operating energy consumption of each air compressor, the method further includes: The operating data is adjusted to obtain the operating energy consumption prediction values ​​corresponding to different air compressors under different operating data; Based on the predicted value of the operating energy consumption, a curve of the operating energy consumption of different air compressors changing with the operating data is generated; Determine the operating data corresponding to each air compressor when the energy consumption of the compressed air system is minimum according to the demand forecast value and the change curve; Adjust each air compressor parameter based on the operating data.

[0011] Through the above embodiments, the intelligent control system not only predicts the energy consumption of each air compressor, but also generates an energy consumption change curve according to the change of operating data. According to the compressed air demand and energy consumption prediction, the specific operating parameters of each air compressor, such as output pressure and flow, are adjusted to further optimize energy consumption.

[0012] In some embodiments, before the step of inputting the current production activity data into the target prediction model to obtain the predicted value of the demand for compressed air within a future preset time period, the step further includes: Get the current electricity price charging rules in different time periods; The priority working time period of the air compressor within the future preset time period is determined according to the charging rule, and the future preset time period includes a priority working time period and a non-priority working time period.

[0013] Through the above embodiment, the intelligent control system determines the priority working time period of the air compressor by obtaining the current electricity price and its charging rules in different time periods. This method can intelligently adjust the operation of the air compressor according to the electricity price, avoid consuming a large amount of electricity during the period of high electricity price, thereby reducing operating costs and improving economic benefits.

[0014] In some embodiments, after the step of determining the priority working time period of the air compressor within the future preset time period according to the charging rule, the method further includes: During the priority working time period, the gas tank storage capacity is controlled to reach the upper limit value.

[0015] Through the above embodiments, the intelligent control system ensures that the storage capacity of the air storage tank reaches the upper limit value during the priority working time period with lower electricity prices. In this way, the air compressor can be fully operated during the period with low electricity prices, and the stored compressed air can be used during the period with high electricity prices, thus avoiding additional energy consumption and costs.

[0016] In some embodiments, before the step of determining the control strategy corresponding to the minimum energy consumption of the compressed air system according to the demand forecast value and the operating energy consumption of each compressor, the method further includes: Receive the set value of compressed air demand from the user; Use this set value instead of the demand forecast value.

[0017] Through the above embodiments, the intelligent control system provides a more direct and personalized control method by receiving the compressed air demand set by the user instead of the output of the prediction model. This enables the system to more accurately adjust the operation of the air compressor according to actual demand without relying entirely on the prediction model, thereby providing more precise control in specific situations.

[0018] In some embodiments, after the step of dynamically adjusting the working state of the air compressor according to the control strategy, the method further includes: Real-time detection of the amount of compressed air in the gas tank; If it is detected that the reserves are lower than the lower limit or higher than the upper limit, a reserves warning will be issued.

[0019] Through the above embodiments, the intelligent control system monitors the compressed air reserves in the gas tank in real time and issues an early warning when the reserves are lower than a lower limit or exceed an upper limit, thereby increasing the safety and reliability of the system.

[0020] In a second aspect, the present application provides an intelligent control system, the intelligent control system comprising: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the intelligent control system can implement a compressed air system operation process energy-saving control method provided in the above embodiment, which will not be repeated here.

[0021] In a third aspect, the present application provides a computer-readable storage medium, including instructions. When the instructions are executed on an intelligent control system, the intelligent control system can implement an energy-saving control method for a compressed air system operation process provided in the above-mentioned embodiment, which will not be repeated here.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on an intelligent control system, the intelligent control system can implement an energy-saving control method for the operation process of a compressed air system provided in the above-mentioned embodiment, which will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Use the prediction model trained by historical data to accurately predict future compressed air demand and energy consumption of each air compressor, and then formulate the optimal energy consumption control strategy. This method not only improves energy efficiency by dynamically adjusting the working state of the air compressor, but also significantly reduces the operating cost of the entire system.

[0024] 2. Taking into account the time-varying characteristics of electricity prices, energy consumption and costs are optimized, especially in areas where electricity prices fluctuate significantly. By intelligently choosing to operate the air compressor during periods of low electricity prices and using stored compressed air during periods of high electricity prices, not only energy costs are reduced, but also the flexibility and economy of energy utilization efficiency are improved.

[0025] 3. By real-time detection of the amount of compressed air in the gas tank and issuing an early warning when the threshold is reached, this solution effectively prevents potential safety risks caused by abnormal reserves. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of an energy-saving control method for a compressed air system operation process in an embodiment of the present application; Figure 2It is another flow chart of an energy-saving control method for a compressed air system operation process in an embodiment of the present application; Figure 3 It is a flow chart of the intelligent control system determining the priority working time period of the air compressor in the embodiment of the present application; Figure 4 It is a schematic diagram of the structure of a physical device of the intelligent control system in the embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of an energy-saving control method for the operation process of a compressed air system in an embodiment of the present application.

[0030] S101. Obtain production activity data of the compressed air system in different historical periods and the corresponding compressed air demand, and construct a time series data set.

[0031] Specifically, the intelligent control system obtains the operation data of the compressed air system in a preset historical period (such as 1 year) by exchanging data with multiple sensors, flow meters, pressure gauges and other equipment in the compressed air system. The operation data includes production activity data, such as the start and stop time of the equipment, the operating status of the production line, product output, etc., and also includes the compressed air demand data in the corresponding time period, such as the volume of compressed air consumed, pressure changes, etc.

[0032] The intelligent control system collects and records production activity data and the corresponding compressed air demand at a certain time granularity (such as every minute, every 10 minutes) to form a time series data set. For example, the production data and the corresponding compressed air consumption every 10 minutes in the past month can be obtained to form a time series data set containing 4320 data points (30 days * 24 hours * 6 times / hour).

[0033] S102: Use the time series data set to train a prediction model to obtain a target prediction model.

[0034] After obtaining the time series data set, the intelligent control system uses a machine learning algorithm to train a target prediction model that can predict the demand for compressed air within a preset time period in the future.

[0035] Specifically, the intelligent control system divides the time series data into training sets and test sets in chronological order, uses preset time series algorithms (such as long short-term memory networks, gated recurrent unit networks, etc.) to build a prediction model framework, and inputs the preprocessed training set data into the built prediction model to start training, which helps predict the model construction time, the correlation characteristics between production data and compressed air demand. After the training is completed, the performance of the prediction model is evaluated using the test set data, and the prediction model with the prediction accuracy that meets the expected value is selected as the final target prediction model.

[0036] S103, inputting the current production activity data into the target prediction model to obtain the predicted value of the demand for compressed air within a preset time period in the future.

[0037] The control system obtains the current production activity data from the monitoring equipment of the compressed air system to generate a time series data set, where the production activity data includes information such as equipment operation status and production tasks. This information can usually be collected from the PLC or sensors of the production equipment through communication methods such as industrial Ethernet and fieldbus. Then, the intelligent control system inputs the processed time series data into the target prediction model. Based on the input time series data, the model outputs the corresponding compressed air demand forecast value in the future preset time period (such as the next 10 minutes).

[0038] It should be noted that in order to cope with various changes in the production process, intelligent control systems usually continuously obtain real-time data and periodically (such as every minute) make rolling forecasts for demand in the future to ensure that the forecast results can be synchronized with actual production needs in a timely manner.

[0039] In addition, due to the complexity of the industrial environment, the production activity data may contain noise or anomalies. Before the data is input into the model, the intelligent control system will clean and pre-process the data to remove or correct abnormal data to avoid affecting the prediction effect of the model.

[0040] S104. Determine a control strategy corresponding to when the energy consumption of the compressed air system is minimized based on the demand forecast value and the operating energy consumption of each compressor.

[0041] After obtaining the forecast value of compressed air demand in the future, the intelligent control system formulates the operation strategy of the air compressor accordingly, in order to reduce the overall energy consumption of the system as much as possible while meeting the demand.

[0042] Specifically, the intelligent control system obtains the energy consumption characteristic data of each compressor. By analyzing the actual power consumption data of the compressor under different working conditions (such as different load rates, exhaust pressures, etc.), an energy efficiency model of the compressor can be established. The energy efficiency model can predict the energy consumption of the compressor under given operating parameters. Specifically, methods such as piecewise linear fitting and polynomial fitting can be used to obtain the functional relationship between the energy consumption of the compressor and its operating parameters. In practical applications, the intelligent control system can collect the operating data of the compressor in real time and dynamically correct the energy efficiency model to adapt to changes in the performance of the compressor.

[0043] Then, the intelligent control system matches and calculates the predicted value of compressed air demand with the compressor energy efficiency model, and selects a compressor combination solution so that the energy consumption of the entire compressed air system is minimized while meeting the demand.

[0044] S105. Dynamically adjust the working state of the air compressor according to the control strategy.

[0045] When the intelligent control system generates the optimized compressor operation strategy, the control strategy is parsed into a series of control instructions and sent to the corresponding compressor control unit through the communication protocol. These instructions include starting / stopping the compressor, setting the exhaust pressure, adjusting the load rate, etc., which are not limited here.

[0046] It should be noted that in the field of industrial automation, standard industrial communication protocols can be used for data exchange between intelligent control systems and field equipment, such as Modbus, Profibus, Profinet, etc. Air compressors are generally equipped with their own control units (such as PLCs) that support these communication protocols. The intelligent control system establishes a communication connection with the control unit of each compressor based on the selected protocol.

[0047] In the above embodiment, the intelligent control system obtains the production activity data of the compressed air system in different historical periods and the corresponding compressed air demand, constructs a time series data set and trains the prediction model to obtain a target prediction model that can accurately predict future compressed air demand. According to the prediction results and the operating energy consumption of each air compressor, a control strategy with the lowest energy consumption is formulated, and the working state of the air compressor, such as starting and stopping, is dynamically adjusted, thereby effectively reducing the energy consumption of the compressed air system and improving energy efficiency.

[0048] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of an energy-saving control method for the operation process of a compressed air system in an embodiment of the present application.

[0049] S201. Obtain historical operation data and corresponding historical energy consumption data of each air compressor to obtain a training data set.

[0050] The intelligent control system builds a training data set by collecting the operating data and energy consumption data of each air compressor over a period of time. These data usually include the operating parameters of the air compressor (such as pressure, flow, temperature, etc.) and the corresponding energy consumption values ​​(such as electrical energy consumption).

[0051] For example, a factory has three air compressors (A, B, C), each equipped with sensors and data acquisition systems. These devices can record the operating status and energy consumption of the air compressors in real time. In the past year, the intelligent control system has collected data every 10 minutes, forming a huge historical data set. This data set contains the performance of each air compressor under different working conditions and the corresponding energy consumption level.

[0052] S202. Train an energy consumption prediction model corresponding to each air compressor based on the training data set to obtain a target energy consumption prediction model corresponding to each air compressor.

[0053] Since the characteristics and operating conditions of each air compressor may be different, the intelligent control system trains an exclusive prediction model for each air compressor to more accurately capture the energy consumption characteristics of each air compressor and improve the prediction accuracy.

[0054] Taking the three air compressors (A, B, and C) mentioned above as an example, the intelligent control system groups the historical data set according to the air compressor to form three sub-data sets. Each sub-data set contains the historical operating data and historical energy consumption values ​​of an air compressor under different operating conditions. Then, feature engineering and model training are performed on each sub-data set to obtain three independent energy consumption prediction models (model A, model B, and model C). These models take the operating parameters of the air compressor as input and output the predicted energy consumption value.

[0055] Through the application of the above technologies, the intelligent control system can train a high-quality energy consumption prediction model for each air compressor. These models can predict the future energy consumption level of the air compressor based on its real-time operating status, providing a basis for subsequent optimization control.

[0056] S203: Input the current operating data of each air compressor into the corresponding target energy consumption prediction model to obtain the operating energy consumption of each air compressor.

[0057] After completing the training of the energy consumption prediction model, the intelligent control system inputs the current operating data of each air compressor (such as pressure, flow, temperature, etc.) into the corresponding prediction model to obtain the estimated energy consumption of the air compressor under the current working conditions. For example, take the previous three air compressors (A, B, C) as an example. Assume that at a certain moment, the operating pressure of air compressor A is 0.8Mpa and the flow rate is 10m³ / min; the operating pressure of air compressor B is 0.7Mpa and the flow rate is 8m³ / min; the operating pressure of air compressor C is 0.9Mpa and the flow rate is 12m³ / min. The intelligent control system inputs these parameters into model A, model B, and model C respectively, and obtains the energy consumption prediction values ​​of the three air compressors in the current state, for example, the energy consumption of air compressor A is 50kWh, the energy consumption of air compressor B is 40kWh, and the energy consumption of air compressor C is 60kWh.

[0058] Through the application of the above technologies, the intelligent control system can monitor the energy consumption level of each air compressor in real time, providing a basis for subsequent optimization control. At the same time, it can also help the system to detect abnormal situations in time (such as sudden increase in energy consumption) and take corresponding measures (such as adjusting operating parameters, arranging maintenance, etc.).

[0059] S204, adjusting the operating data to obtain the operating energy consumption prediction values ​​corresponding to different air compressors under different operating data.

[0060] After obtaining the predicted energy consumption value of each air compressor under the current operating conditions, the intelligent control system simulates different operating conditions by adjusting the operating data of the air compressor (such as pressure, flow, etc.), and uses the energy consumption prediction model to calculate the corresponding operating energy consumption prediction value, thereby obtaining a multi-dimensional mapping relationship between energy consumption and operating parameters.

[0061] Take three air compressors (A, B, C) as an example. The intelligent control system can appropriately adjust the pressure and flow of each air compressor near the current operating point to generate a series of new operating data combinations. For example, for air compressor A, the pressure can be changed within the range of 0.712m³ / min to form a pressure-flow grid. Then, the parameters of each grid point are input into model A to obtain the corresponding energy consumption prediction value. Repeat the same operation for air compressors B and C, and finally obtain the energy consumption distribution of the three air compressors under different operating conditions.

[0062] S205. Generate a curve of the change of the operating energy consumption of different air compressors with the operating data based on the predicted value of the operating energy consumption.

[0063] After obtaining the predicted energy consumption values ​​of the air compressor under different operating data, the intelligent control system generates a curve of the energy consumption of each air compressor with respect to the operating parameters (such as pressure and flow) by curve fitting the predicted energy consumption values, and intuitively shows the quantitative relationship between energy consumption and parameters. Let's take three air compressors (A, B, C) as an example. For air compressor A, the intelligent control system has obtained the predicted energy consumption values ​​under different pressure and flow combinations, forming a discrete set of data points. By performing surface fitting (such as polynomial fitting, spline fitting, etc.) on these data points, a continuous energy consumption function E_A=f(P_A, Q_A) can be obtained, where E_A represents the energy consumption of air compressor A, and P_A and Q_A represent its pressure and flow, respectively. Similarly, the data points of air compressors B and C are fitted to obtain functions E_B=f(P_B, Q_B) and E_C=f(P_C, Q_C).

[0064] Through the application of the above technologies, the intelligent control system can generate high-quality energy consumption curves for each air compressor. These curves not only reveal the energy efficiency characteristics of the air compressor under different working conditions, but also provide an important basis for subsequent optimization control. Based on the energy consumption curve, the energy consumption level of the air compressor at any operating point can be quickly calculated, the effects of different optimization measures can be evaluated, and finally the optimal operating parameters with the lowest energy consumption can be found.

[0065] S206: Use the set value of the compressed air demand by the user to replace the demand forecast value.

[0066] In actual production, the demand for compressed air is often set by users (such as downstream production lines) based on production plans and experience, and does not completely rely on the prediction of the intelligent control system. Therefore, when optimizing the operating parameters of the air compressor, if it is detected that there is a set value for the compressed air demand by the user, the set value is used to replace the demand predicted by the system to ensure that the supply of compressed air can meet the actual production needs.

[0067] S207. Determine the operating data corresponding to each air compressor when the energy consumption of the compressed air system is minimum based on the demand forecast value and the change curve, and then adjust the parameters of each air compressor.

[0068] After obtaining the compressed air demand (user set value or system predicted value) and the air compressor energy consumption change curve, the intelligent control system needs to comprehensively consider these two factors and determine the optimal operating parameters of each air compressor when the total energy consumption of the system is minimized while meeting the demand constraint.

[0069] Take three air compressors (A, B, C) as an example. Assuming that the current compressed air demand is 1200m³ / h, the goal of the intelligent control system is to find a set of operating parameters {(P_A, Q_A), (P_B, Q_B), (P_C, Q_C)} that minimizes the total energy consumption E_total of the system while satisfying the following constraints: Gas production constraint: Q_A + Q_B + Q_C = 1200m³ / h; Energy consumption function: E_A=f(P_A, Q_A), E_B=f(P_B, Q_B), E_C=f(P_C, Q_C); Parameter range: P_min ≤ P_A, P_B, P_C ≤ P_max; Q_min ≤ Q_A, Q_B, Q_C ≤ Q_max; Among them, E_total = E_A + E_B + E_C, which represents the total energy consumption of the compressed air system. P_min, P_max, Q_min, and Q_max represent the upper and lower limits of pressure and flow respectively. The corresponding operating parameters can be obtained by solving the minimum energy consumption function E_total under the above inequality constraints.

[0070] Through the application of the above technologies, the intelligent control system can quickly and accurately determine the optimal operating parameters under the current working conditions. These parameters not only ensure that the supply of compressed air meets production needs, but also maximize the energy efficiency of the system. According to the optimization results, the control system will send corresponding control instructions to each air compressor (such as adjusting the pressure valve, changing the frequency of the inverter, etc.) to achieve automatic adjustment of parameters.

[0071] The following is a flow chart of a process for determining the priority working time period of the air compressor by the intelligent control system in the embodiment of the present application. Figure 3 shown.

[0072] S301, obtaining charging rules of current electricity prices in different time periods.

[0073] The intelligent control system obtains the dynamic charging rules of the current electricity price in different time periods by accessing the API or database provided by the power company. These rules usually include electricity prices in different time periods such as peak hours, normal hours, and off-peak hours, as well as changes in electricity prices under special circumstances such as holidays and seasonal adjustments.

[0074] S302: Determine the priority working time period of the air compressor within a future preset time period according to the charging rules.

[0075] Based on the electricity price charging rules obtained in the previous step, the intelligent control system analyzes and calculates the expected electricity price level for each period in the future (such as 24 hours), and then determines the priority working time period of the air compressor.

[0076] In specific implementation, the intelligent control system uses 0.5-1 hour as the time granularity, and generates a time series containing the expected electricity price based on the current time and the preset time range. Then a price threshold is set (such as 0.6 yuan / kWh), and the time periods below the threshold are marked as the air compressor priority working period, and the time periods above the threshold are marked as non-priority periods. If the electricity prices of multiple consecutive time periods are lower than the threshold, they can be merged into a longer priority working period.

[0077] S303: Control the storage capacity of the gas storage tank to reach an upper limit value during the priority working time period.

[0078] After determining the priority working time period of the air compressor, the intelligent control system will control the air compressor to run at full load during this period to produce as much compressed air as possible, while controlling the storage volume in the air storage tank to be close to the upper limit value.

[0079] S304: Detect the storage amount of compressed air in the gas storage tank in real time.

[0080] To ensure the continuity and reliability of compressed air supply, the intelligent control system can detect the compressed air reserves in the gas tank in real time through online monitoring equipment such as pressure sensors and flow meters, providing accurate data support for subsequent scheduling control.

[0081] The frequency of data collection can be flexibly set according to actual needs, and can be collected every 1-10 minutes. The collected data mainly includes: pressure and temperature in the gas tank, instantaneous flow rate and cumulative flow rate of the inlet and outlet, etc. Through these parameters, key indicators such as the current gas tank storage capacity and remaining available time can be accurately calculated.

[0082] S305: If it is detected that the reserves are lower than the lower limit or higher than the upper limit, a reserves warning is issued.

[0083] The intelligent control system determines whether the current reserves are within a reasonable range based on the real-time data collected in the previous step. Generally speaking, the lower limit of reserves is set at 10-20% of the total capacity, and the upper limit is set at 90-95% of the total capacity. Once the range is exceeded, the system will issue an early warning, prompting the staff or the automatic control system to take countermeasures.

[0084] The intelligent control system of the embodiment of the present invention is applied to electronic equipment. Figure 4 A schematic diagram of the architecture of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0085] It should be noted that Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0086] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling related hardware through instructions (computer programs), and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein a plurality of instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided in the embodiment of the present invention.

[0087] Specifically, the storage medium and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more signal lines. The storage medium stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, random access storage medium (Random Access Memory, referred to as: RAM), read-only storage medium (Read Only Memory, referred to as: ROM), programmable read-only storage medium (Programmable Read-Only Memory, referred to as: PROM), erasable read-only storage medium (Erasable Programmable Read-Only Memory, referred to as: EPROM), electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, referred to as: EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the program after receiving the execution instruction.

[0088] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0089] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0090] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for energy-saving control of compressed air system operation process, applied to intelligent control system, characterized in that: The method comprises: Obtain the production activity data of the compressed air system in different historical periods and the corresponding compressed air demand, and construct a time series data set; Using the time series data set to train a prediction model to obtain a target prediction model, wherein the target prediction model is used to predict the demand for compressed air within a future preset time period; Inputting current production activity data into the target prediction model to obtain a predicted value of compressed air demand within a future preset time period; Determining a control strategy corresponding to when the energy consumption of the compressed air system is minimized according to the demand forecast value and the operating energy consumption of each compressor; The working state of the air compressor is dynamically adjusted according to the control strategy, and the working state includes starting and stopping.

2. The method according to claim 1, characterized in that Before the step of determining the control strategy corresponding to the minimum energy consumption of the compressed air system by using the demand forecast value and the operating energy consumption of each compressor, the method further includes: Obtaining historical operation data and corresponding historical energy consumption data of each air compressor to obtain a training data set, wherein the historical operation data includes output pressure and output flow; Training an energy consumption prediction model corresponding to each air compressor according to the training data set to obtain a target energy consumption prediction model corresponding to each air compressor; The current operating data of each air compressor is input into the corresponding target energy consumption prediction model to obtain the operating energy consumption of each air compressor.

3. The method according to claim 2, characterized in that After the step of inputting the current operating data of each air compressor into the corresponding target energy consumption prediction model to obtain the operating energy consumption of each air compressor, the method further includes: Adjusting the operating data to obtain operating energy consumption prediction values ​​corresponding to different air compressors under different operating data; Generate a curve of the change of the operating energy consumption of different air compressors with the operating data based on the operating energy consumption prediction value; Determining the operating data corresponding to each air compressor when the energy consumption of the compressed air system is minimum according to the demand forecast value and the change curve; Each air compressor parameter is adjusted according to the operating data.

4. The method according to claim 1, characterized in that: Before the step of inputting the current production activity data into the target prediction model to obtain the predicted value of the demand for compressed air within a future preset time period, the method further includes: Get the current electricity price charging rules in different time periods; The priority working time period of the air compressor within the future preset time period is determined according to the charging rule, and the future preset time period includes a priority working time period and a non-priority working time period.

5. The method according to claim 4, characterized in that After the step of determining the priority working time period of the air compressor within the future preset time period according to the charging rules, the method further includes: During the priority working time period, the gas storage tank storage capacity is controlled to reach an upper limit value.

6. The method according to claim 1, characterized in that Before the step of determining the control strategy corresponding to the minimum energy consumption of the compressed air system according to the demand forecast value and the operating energy consumption of each compressor, the method further includes: Receive the set value of compressed air demand from the user; The set value is used to replace the demand quantity forecast value.

7. The method according to claim 1, characterized in that After the step of dynamically adjusting the working state of the air compressor according to the control strategy, the method further includes: Real-time detection of the amount of compressed air in the gas tank; If it is detected that the reserves are lower than the lower limit of reserves or exceed the upper limit of reserves, a reserves warning is issued.

8. An intelligent control system, characterized in that: The intelligent control system includes: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the intelligent control system to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on an intelligent control system, the intelligent control system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on an intelligent control system, the intelligent control system is enabled to execute the method according to any one of claims 1 to 7.

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