Intelligent air compressor control system and method based on deep learning and optimization algorithm

Abstract

The embodiment of the present specification provides an intelligent air compressor control system and method based on deep learning and optimization algorithm, the system comprises: a pressure sensing device configured to obtain pressure feature data; a power monitoring device configured to obtain air compressor power consumption data; a temperature sensing device configured to obtain air compressor temperature data; a processor configured to periodically determine the operating parameters of the air compressor based on the adjustment period. In one adjustment period, the processor is configured to: for an air compressor, based on the pressure feature data, the air compressor power consumption data and the air compressor temperature data, determine the operating parameters of the air compressor through the air compression model.

Description

Technical Field

[0001] This specification relates to the field of intelligent control, and in particular to an intelligent air compressor control system and method based on deep learning and optimization algorithms. Background Technology

[0002] In modern industry, air compressors, as air source equipment, are widely used in various stages of air-using production lines, including pneumatic tools and mechanical equipment. With increasingly stringent requirements for energy conservation and emission reduction, the energy consumption of air compressors has become a major concern.

[0003] Traditional air compressor control methods, such as proportional-integral-differential (PID) controllers, are widely used in air compressor control due to their simple structure and ease of implementation. However, PID controllers cannot adapt to complex nonlinear operating conditions and dynamic changes, and cannot achieve optimal control of air compressor energy consumption.

[0004] Therefore, it is desirable to provide an intelligent air compressor control system and method based on deep learning and optimization algorithms, which can achieve flexible control of the air compressor under various operating conditions, thereby reducing energy consumption and improving production efficiency. Summary of the Invention

[0005] This specification provides one or more embodiments of an intelligent air compressor control system based on deep learning and optimization algorithms. The system comprises a pressure sensing device, a power monitoring device, a temperature sensing device, and a processor. The pressure sensing device is connected to an air supply pipeline, which is connected to at least one of an air compressor, an air-consuming device, and an air-consuming production line. The pressure sensing device is configured to acquire pressure characteristic data, including air supply pressure data and / or air consumption pressure data. The power monitoring device is configured to acquire air compressor power consumption data. The temperature sensing device is mounted on the air compressor and configured to acquire air compressor temperature data. The processor is configured to periodically determine the air compressor's operating parameters based on an adjustment cycle. Within one adjustment cycle, the processor is configured to: for an air compressor, determine the air compressor's operating parameters based on the pressure characteristic data, air compressor power consumption data, and air compressor temperature data, using an air compressor model.

[0006] One embodiment of this specification provides an intelligent air compressor control method based on deep learning and optimization algorithms. The method is executed by a processor and includes: acquiring pressure characteristic data, including supply air pressure data and / or consumption air pressure data, through a pressure sensing device; acquiring air compressor power consumption data through a power monitoring device; acquiring air compressor temperature data through a temperature sensing device; and periodically determining the air compressor's operating parameters based on an air compressor model, according to the pressure characteristic data, air compressor power consumption data, and air compressor temperature data, based on an adjustment cycle.

[0007] This specification provides one or more embodiments of an intelligent air compressor control device based on deep learning and optimization algorithms. The device includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; and the at least one processor is used to execute at least a portion of the computer instructions to implement the method described above.

[0008] This specification provides one or more embodiments of a computer-readable storage medium, characterized in that the storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the method described above.

[0009] Beneficial Effects: The intelligent air compressor control system and method based on deep learning and optimization algorithms of this invention determines the operating parameters of the air compressor through an air compressor model. It can solve for the optimal strategy using reinforcement learning methods, ensuring stable air supply while reducing the energy consumption of the air compressor, guaranteeing that the air supply pressure and volume meet production needs, and improving product quality. By periodically determining the operating parameters of the air compressor based on the adjustment cycle, the control strategy can be adjusted in real time according to the current operating conditions, achieving stable control of the air compressor under various operating conditions. Attached Figure Description

[0010] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0011] Figure 1 This is an exemplary block diagram of an intelligent air compressor control system based on deep learning and optimization algorithms, as shown in some embodiments of this specification.

[0012] Figure 2 This is an exemplary flowchart of an intelligent air compressor control method based on deep learning and optimization algorithms, as shown in some embodiments of this specification.

[0013] Figure 3 These are exemplary schematic diagrams of an air compressor model shown according to some embodiments of this specification;

[0014] Figure 4 This is an exemplary schematic diagram illustrating the determination of operating parameters of an air compressor and / or cooling parameters of a cooling device according to some embodiments of this specification;

[0015] Figure 5 This is an exemplary schematic diagram illustrating the generation of alarm information according to some embodiments of this specification. Detailed Implementation

[0016] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0017] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0018] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0019] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0020] Figure 1 This is an exemplary block diagram of an intelligent air compressor control system based on deep learning and optimization algorithms, as shown in some embodiments of this specification.

[0021] In some embodiments, such as Figure 1 As shown, the intelligent air compressor control system 100 based on deep learning and optimization algorithms includes a pressure sensing device 110, a power monitoring device 120, a temperature sensing device 130, and a processor 140.

[0022] Pressure sensing device 110 refers to a device that can sense pressure signals and convert them into electrical signals. Pressure sensing device 110 includes analog pressure sensors, digital pressure sensors, etc.

[0023] In some embodiments, the pressure sensing device 110 is configured to acquire pressure characteristic data.

[0024] In some embodiments, the pressure sensing device 110 is connected to an air supply line, which is connected to at least one of an air compressor, an air-using device, and an air-using production line.

[0025] A gas supply pipeline is a pathway for gas transportation. It includes gas pipes, control valves, filters, etc. The input end of the gas supply pipeline is the air compressor, and the output end is the gas-consuming equipment or gas-consuming production line.

[0026] An air compressor is a device used to compress gas. Examples of air compressors include positive displacement compressors, reciprocating compressors, and rotary compressors.

[0027] In some embodiments, the air compressor draws in low-pressure air from the inlet and discharges high-pressure air from the outlet. Air in the supply pipeline flows from the high-pressure area to the low-pressure area under pressure. In other words, the high-pressure air discharged by the air compressor flows to the air-consuming equipment or production line through the supply pipeline.

[0028] In some embodiments, the air supply line is mechanically connected to the air compressor. For example, a threaded connection, a flange connection, etc.

[0029] Pneumatic equipment refers to equipment that requires the use of high-pressure gas. Examples include pneumatic picks, pneumatic rock drills, and riveting machines.

[0030] A gas-operated production line refers to a production line that requires the use of high-pressure gases. For example, in the food or pharmaceutical industries, high-pressure gases are needed to stir slurries, and in the chemical industry, certain gases are pressurized by air compressors to facilitate synthesis and polymerization.

[0031] In some embodiments, a plurality of pressure sensing devices 110 may be disposed at various locations in the gas supply line. For example, a pressure sensing device 110 disposed at the input end of the gas supply line is configured to acquire gas supply pressure data, and a pressure sensing device 110 disposed at the output end of the gas supply line is configured to acquire gas consumption pressure data.

[0032] The power monitoring device 120 is used to monitor the power consumption of electrical equipment. The power monitoring device 120 includes electronic energy meters, multi-function energy meters, etc.

[0033] In some embodiments, the power monitoring device 120 is located in the current loop of the air compressor and is configured to acquire power consumption data of the air compressor.

[0034] Temperature sensing device 130 refers to a device that can sense temperature signals and convert them into electrical signals. Temperature sensing device 130 includes resistance sensors, thermocouple sensors, etc.

[0035] In some embodiments, a temperature sensing device 130 is disposed on an air compressor and configured to acquire air compressor temperature data.

[0036] Processor 140 can process data and / or information related to the intelligent air compressor control system 100 based on deep learning and optimization algorithms. Processor 140 can execute program instructions based on this data, information, and / or processing results to perform one or more functions described in this application.

[0037] In some embodiments, processor 140 may include one or more sub-processing devices (e.g., single-core processing devices or multi-core multi-chip processing devices). By way of example only, processor may include one or any combination of a central processing unit (CPU), a digital signal processor (DSP), an embedded processor, etc.

[0038] In some embodiments, the processor 140 is configured to periodically determine the operating parameters of the air compressor based on an adjustment cycle. Within one adjustment cycle, the processor is configured to: for an air compressor, determine the operating parameters of the air compressor based on pressure characteristic data, air compressor power consumption data, and air compressor temperature data, using an air compressor model.

[0039] In some embodiments, such as Figure 1 As shown, processor 140 includes edge processor 141, remote device 142, multiple service databases 143, and control center 144.

[0040] Edge processor 141 refers to a processor located closer to the location where data is generated. In some embodiments, edge processor 141 is located in an air consumption production line and is configured to: for an air compressor, determine the operating parameters of the air compressor and / or the cooling parameters of the cooling equipment based on pressure characteristic data, air compressor power consumption data, air compressor temperature data, and intake air temperature, using an air compressor model; and send an update request to the control center in response to the air compressor operating parameters and / or the cooling parameters of the cooling equipment meeting update conditions within a preset time period.

[0041] Remote device 142 refers to a device located at a distance from the data generation location. In some embodiments, remote device 142 is located at a distance that communicates with the gas production line and is configured to periodically train the compressed air model based on an update cycle.

[0042] Service database 143 is used to store data and / or information related to the intelligent air compressor control system 100 based on deep learning and optimization algorithms. In some embodiments, multiple service databases are located in edge processor 141 and / or remote device 142 and are configured to store at least one of pressure characteristic data, air compressor power consumption data, and air compressor temperature data.

[0043] In some embodiments, data may be stored in multiple service databases 143 according to preset rules. For example, each service database 143 is configured to store storage pressure characteristic data, air compressor power consumption data, and air compressor temperature data generated by the most recent gas-consuming production line. Alternatively, each service database 143 may be configured to store storage pressure characteristic data, air compressor power consumption data, and air compressor temperature data generated by multiple gas-consuming production lines.

[0044] Control center 144 refers to a device used for data transmission and instruction execution. In some embodiments, control center 144 is configured to periodically transmit model parameters trained by a remote device to an edge processor based on an update cycle.

[0045] In some embodiments, such as Figure 1 As shown, the intelligent air compressor control system 100 based on deep learning and optimization algorithms also includes a cooling device 150, an environmental monitoring device 160, a sound monitoring device 170, and an image monitoring device 180.

[0046] Cooling equipment 150 is a device used to achieve the function of cooling. For example, cooling equipment 150 includes air-cooled coolers, water-cooled coolers, and double-cooled coolers.

[0047] In some embodiments, a cooling device 150 is disposed on an air compressor and configured to reduce the temperature of the air compressor.

[0048] The environmental monitoring device 160 is a device used to monitor the working environment of an air compressor.

[0049] In some embodiments, the environmental monitoring device 160 is configured to acquire the intake air temperature. In this case, the environmental monitoring device 160 may be a temperature sensor located at the air inlet of the air compressor.

[0050] The sound monitoring device 170 is a device used to collect sound data. For example, the sound monitoring device 170 is an audio acquisition device, etc.

[0051] In some embodiments, the sound monitoring device 170 is disposed on the gas production line and configured to collect sound data on the gas production line.

[0052] Image monitoring device 180 is a device used to acquire image data. For example, image monitoring device 180 includes a camera, video camera, etc.

[0053] In some embodiments, the image monitoring device 180 is disposed on the gas production line and configured to collect image data on the gas production line.

[0054] Some embodiments in this specification collect various parameters during the operation of the air compressor using multiple monitoring devices, and adjust the operating parameters of the air compressor based on these parameters. This allows for the integration of data from different sensors, enabling efficient utilization and in-depth analysis of information.

[0055] It should be understood that Figure 1 The system and its modules shown can be implemented in various ways.

[0056] It should be noted that the above description of the intelligent air compressor control system 100 and its modules based on deep learning and optimization algorithms is for convenience only and should not limit this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. In some embodiments, Figure 1 The pressure sensing device 110, power monitoring device 120, temperature sensing device 130, and processor 140 disclosed herein can be different modules within a single system, or a single module can perform the functions of two or more of the aforementioned modules. For example, the modules can share a single storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0057] Figure 2 This is an exemplary flowchart illustrating an intelligent air compressor control method based on deep learning and optimization algorithms, according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by a processor (e.g., processor 140).

[0058] Step 210: Obtain pressure characteristic data through a pressure sensing device.

[0059] Pressure characteristic data refers to data related to the pressure at various locations within the gas supply line. Pressure characteristic data includes supply pressure data and / or consumption pressure data.

[0060] Air supply pressure data refers to the pressure at the connection between the air compressor and the air supply pipeline. In some embodiments, the processor can determine the pressure sensing data collected by the pressure sensing device closest to the air compressor in the air supply pipeline as the air supply pressure data corresponding to the air compressor.

[0061] Gas pressure data refers to the pressure at the connection point between the gas-consuming equipment or gas-consuming production line and the gas supply pipeline. In some embodiments, the processor can determine the pressure sensing data collected by the pressure sensing device closest to the gas-consuming equipment or gas-consuming production line in the gas supply pipeline as the gas pressure data corresponding to the gas-consuming equipment or gas-consuming production line.

[0062] For more information on gas supply pipelines, air compressors, pressure sensing equipment, gas-using equipment, and gas-using production lines, please see [link to relevant information]. Figure 1 And related explanations.

[0063] Step 220: Obtain the power consumption data of the air compressor through the power monitoring device.

[0064] Air compressor power consumption data refers to data related to the electrical energy consumed by an air compressor. For example, air compressor power consumption data includes the amount of electricity consumed by the air compressor.

[0065] In some embodiments, the processor can collect the power consumption of the air compressor through a power monitoring device and determine the power consumption of the air compressor as the power consumption data of the air compressor.

[0066] For more information on power monitoring equipment, please see [link / reference]. Figure 1 And related explanations.

[0067] Step 230: Obtain air compressor temperature data through temperature sensing equipment.

[0068] Air compressor temperature data refers to the temperature of the air compressor. In some embodiments, the processor can acquire the air compressor temperature through a temperature sensing device installed on the air compressor, thereby obtaining the air compressor temperature data.

[0069] For more information on temperature sensing devices, please see [link / reference]. Figure 1 And related explanations.

[0070] Step 240: Based on the adjustment cycle, the operating parameters of the air compressor are periodically determined using the air compressor model, according to pressure characteristic data, air compressor power consumption data, and air compressor temperature data.

[0071] The adjustment cycle refers to the cycle in which the processor adjusts the operating parameters of the air compressor. In some embodiments, the adjustment cycle is a system preset value.

[0072] In some embodiments, the processor determines the adjustment cycle corresponding to the air compressor of the air-consuming production line based on the production plan of the air-consuming production line.

[0073] Production planning for a gas-using production line refers to the plan for producing products on that production line. Production planning for a gas-using production line must include at least the projected output of the products. The higher the projected output, the shorter the corresponding adjustment cycle. Production planning for a gas-using production line can be determined based on user input.

[0074] Operating parameters refer to parameters related to the operation of an air compressor. For example, operating parameters include valve opening degree, air compressor motor power, compressor start and stop, etc.

[0075] In some embodiments, within an adjustment cycle, for an air compressor, the processor determines the operating parameters of the air compressor based on pressure characteristic data, air compressor power consumption data, and air compressor temperature data, using an air compressor model.

[0076] An air compressor model is a model used to determine the operating parameters of an air compressor. In some embodiments, the air compressor model is a reinforcement learning (RL) model.

[0077] Figure 3 This is an exemplary schematic diagram of an air compressor model according to some embodiments of this specification. In some embodiments, such as Figure 3 As shown, the input of the compressed air model 320 includes operating status information 310, and the output of the compressed air model 320 includes optimal parameters 330. The compressed air model 320 includes an operating module 321 and an optimal action determination module 322.

[0078] Operating status information 310 refers to information related to the operating status of the air compressor. Operating status information 310 includes the pressure characteristic sequence, power consumption sequence, and temperature sequence corresponding to the air compressor. Specifically, the pressure characteristic sequence is a sequence obtained by arranging pressure characteristic data from multiple time points in chronological order; the power consumption sequence is a sequence obtained by arranging air compressor power consumption data from multiple time points in chronological order; and the temperature sequence is a sequence obtained by arranging air compressor temperature data from multiple time points in chronological order.

[0079] In some embodiments, before constructing the working state information 310, the processor can clean and normalize the collected data to improve data quality and usability, thereby improving the efficiency of model processing. Data cleaning includes, but is not limited to, one or more of data deduplication, missing value handling, and outlier handling. Normalization methods include, but are not limited to, min-max normalization or Z-score normalization.

[0080] The optimal parameter 330 is the preferred operating parameter for the air compressor in the next adjustment cycle.

[0081] In some embodiments, when determining the optimal parameter 330 based on the air compressor model 320, the operating state information 310 can be input into the air compressor model 320. Within the model, the operating state information 310 is input into the operating module 321, and the operating module 321 outputs a set of optional actions. The operating state information 310 and the set of optional actions are input into the optimal action determination module 322, and the optimal action determination module 322 outputs the optimal optional action 323. The operating parameters of the air compressor corresponding to the optimal optional action 323 output by the optimal action determination module 322 are determined as the optimal parameter 330 and used as the output of the air compressor model 320.

[0082] The working module 321 can determine the set of optional actions of the air compressor based on the working status information of the current adjustment cycle.

[0083] The set of optional actions refers to the set of actions that an air compressor can perform in a certain operating state. Actions that an air compressor can perform include adjusting valve opening and adjusting motor power. The set of optional actions for an air compressor can differ under different operating states. For example, when the valve opening is at its maximum, the set of optional actions for the air compressor does not include increasing the valve opening.

[0084] The optimal action determination module 322 can be used to determine the reward value of each optional action in the set of optional actions based on the working status information. The reward value can be used to evaluate the impact of adjusting the operating parameters of the air compressor according to the optional actions on product production.

[0085] In some embodiments, the optimal action determination module 322 in the trained air compressor model 320 stores the reward values ​​corresponding to the air compressor performing each optional action under various working state information. The reward value corresponding to performing an optional action under a certain working state information can be determined based on a set formula.

[0086] For example, the reward value is related to the energy efficiency coefficient and motor power after the air compressor performs the corresponding action, and the optimal action determination module 322 can determine the reward value based on the first excitation function.

[0087] For example, the first excitation function can be represented by the following formula (1).

[0088] G1=e×p (1)

[0089] Where e is the energy efficiency coefficient after the air compressor performs the action, p is the motor power after the air compressor performs the action, and G is the reward value corresponding to the performed action.

[0090] The coefficient of performance (COP) of an air compressor reflects its energy efficiency. A higher COP indicates lower energy consumption (e.g., lower power consumption). In some embodiments, the COP of an air compressor is positively correlated with the air supply pressure and negatively correlated with its power consumption.

[0091] In the practical application of the air compressor model, after the optimal action determination module 322 obtains the working status information and the set of optional actions, it can determine the reward value corresponding to each optional action under the working status information.

[0092] The optimal action determination module 322 can determine the action with the highest reward value as the optimal action 323 and output it.

[0093] In some embodiments, the optimal action determination module 322 can be a machine learning model, which can be implemented in various ways, such as a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), etc.

[0094] In some embodiments, the air compressor model can be trained using reinforcement learning methods, such as Deep Q-Learning Network (DQN) or Double Deep Q-Learning Network (DDQN). The first training sample can be the historical operating state information of the sample air compressor, and the first label is the optimal action corresponding to the sample air compressor under the historical operating state information. The first training sample can be obtained based on historical data. The first label can be obtained through reinforcement learning methods; for example, the first label can be the optimal action with the highest reward value determined based on formula (1).

[0095] As an example only, the processor can construct a training dataset based on the historical operating state information of the sample air compressor at the first historical time point and the operating parameters at the second historical time point after performing the corresponding actions. Based on the training dataset and the actual operating state of the sample air compressor at the second historical time point, the initial air compressor model can determine the reward value corresponding to each action performed by the air compressor under various operating state information based on excitation functions such as formula (1). The processor can further update the parameters of the working module and the optimal action determination module of the initial air compressor model based on the reward value and the loss function until the training stopping condition is met. The training stopping condition may include meeting the number of iterations.

[0096] In some embodiments, before constructing the training dataset, the processor can clean and normalize the collected historical data to improve the quality and availability of the data, thereby improving the model training effect.

[0097] In some embodiments, such as Figure 1As shown, the training of the air compressor model is performed by a remote device. After the initial training of the air compressor model is completed, the edge processor determines the operating parameters of the air compressor based on pressure characteristic data, air compressor power consumption data, and air compressor temperature data.

[0098] During the use of the compressed air model, the remote device periodically trains the compressed air model based on the update cycle, and the control center periodically transmits the trained model parameters from the remote device to the edge processor based on the update cycle.

[0099] In some embodiments, the update cycle is the system default value, for example, the update cycle is one week.

[0100] In some embodiments, the update cycle is related to the amount of production data from the gas-consuming production line. The larger the amount of production data, the shorter the update cycle.

[0101] Production data refers to data related to the production of products. Production data includes pressure characteristic data, air compressor power consumption data, and air compressor temperature data.

[0102] In some embodiments, the processor can determine the amount of production data based on the memory usage of the service database. The more memory the service database uses, the larger the amount of production data.

[0103] The larger the volume of production data, the greater the production volume of the gas-consuming production line and the more variable the conditions of the gas-consuming production line. In order to meet the accuracy requirements of the production process, it is necessary to shorten the model update cycle to improve the model's accuracy.

[0104] For more information on remote devices, edge processors, service databases, and control centers, see [link to relevant documentation]. Figure 1 And related explanations.

[0105] In some embodiments, in response to the air compressor's operating parameters and / or the cooling equipment's cooling parameters meeting a first update condition within a preset time period, the edge processor sends a first update request to the control center. The preset time period can be a system default value.

[0106] The first update condition is used to determine whether the control center has transmitted the trained model parameters to the edge processor.

[0107] In some embodiments, the first update condition is that the fluctuation of the air compressor's operating parameters exceeds a first fluctuation threshold within a preset time period, or the fluctuation of the cooling equipment's cooling parameters exceeds a second fluctuation threshold. The first and second fluctuation thresholds can be set based on experience.

[0108] For more information on the cooling parameters of cooling equipment, please refer to [link / reference]. Figure 4 And related explanations.

[0109] In some embodiments, the processor calculates the coefficient of variation (COP) of each operating parameter of the air compressor within a preset time period, and determines the mean of the COPs of multiple operating parameters as the fluctuation value of the air compressor's operating parameter within the preset time period. Specifically, the processor can acquire multiple values ​​of an operating parameter within the preset time period, determine the standard deviation and mean of the operating parameter's values ​​within the preset time period, and then use the ratio of the standard deviation to the mean of the operating parameter as the COP. The processor can determine the fluctuation value of the cooling parameters of the cooling equipment in the same way.

[0110] In some embodiments, the first fluctuation threshold and the second fluctuation threshold are related to the update cycle and the type of product produced by the gas-using production line. The shorter the update cycle, the lower the first fluctuation threshold and the second fluctuation threshold; the higher the quality requirements of the products produced by the gas-using production line, the lower the first fluctuation threshold and the second fluctuation threshold.

[0111] When the first update condition is met, the product quality fluctuates significantly, which may be due to the model parameters no longer being applicable. By sending the first update request, the model parameters can be proactively updated to improve product quality.

[0112] The first update request refers to a request for the control center to transfer the trained model parameters to the edge processor. In response to receiving the first update request, the control center transfers the trained model parameters to the edge processor.

[0113] Remote devices have long response times and require significant computing resources, while edge processors have short response times and fewer computing resources. Since model training takes a long time and requires substantial computing resources, choosing remote devices for model training and edge processors for model application satisfies both the rapid response requirements of the gas production line and the accuracy requirements of model training. By integrating data from different sensors, efficient information utilization and in-depth analysis can be achieved.

[0114] In some embodiments, within an adjustment cycle, the processor can simultaneously determine the operating parameters of multiple air compressors based on pressure characteristic data, power consumption data, and temperature data of multiple air compressors, using the air compressor model 320. At this time, the operating status information 310 includes a sequence combination of the operating status information of the multiple air compressors, and the optimal parameters 330 includes a sequence combination of the optimal operating parameters of the multiple air compressors.

[0115] For example, the working status information 310 can be represented as {[P1,E1,T1],[P2,E2,T2],…,[P n E n ,T n ]}. Among them, P n Let E be the pressure characteristic sequence of the nth air compressor. nLet T be the power consumption sequence of the nth air compressor. n Let be the temperature sequence of the nth air compressor. The optimal parameter 330 can be represented as [A1, A2, ..., A...]. n ], where A n This represents the optimal operating parameters for the nth air compressor.

[0116] In some embodiments, within an adjustment cycle, for an air compressor, the processor determines the operating parameters of the air compressor and / or the cooling parameters of the cooling equipment based on pressure characteristic data, air compressor power consumption data, air compressor temperature data, and intake air temperature, using an air compressor model. See more details. Figure 4 And related explanations.

[0117] Some embodiments in this specification determine the operating parameters of the air compressor through an air compressor model. The optimal strategy can be solved using reinforcement learning methods, ensuring stable air supply while reducing the air compressor's energy consumption, guaranteeing that the air supply pressure and volume meet production needs, and improving product quality. By periodically determining the air compressor's operating parameters based on an adjustment cycle, the control strategy can be adjusted in real time based on the current operating conditions, achieving stable control of the air compressor under various operating conditions.

[0118] It should be noted that the above description of process 200 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, steps 210-230 can be performed simultaneously.

[0119] Figure 4 This is an exemplary schematic diagram illustrating the determination of the operating parameters of an air compressor and / or the cooling parameters of a cooling device according to some embodiments of this specification.

[0120] In some embodiments, such as Figure 4 As shown, within one adjustment cycle, for an air compressor, the processor determines the operating parameters 421 of the air compressor and / or the cooling parameters 422 of the cooling equipment through the air compressor model 320 based on the pressure characteristic data 411, the air compressor power consumption data 412, the air compressor temperature data 413, and the intake air temperature 414.

[0121] For more information on air compressors and cooling equipment, please see [link / reference]. Figure 1 Related instructions. For more information on adjustment cycles, pressure characteristic data, air compressor power consumption data, air compressor temperature data, air compressor models, and air compressor operating parameters, please refer to [link to relevant documentation]. Figure 2 Related explanations.

[0122] The intake air temperature 414 refers to the temperature of the air drawn into the air compressor. In some embodiments, the processor obtains the intake air temperature 414 through an environmental monitoring device. For more information on the environmental monitoring device, see [link to relevant documentation]. Figure 1 And related explanations.

[0123] Cooling parameters of a cooling device refer to parameters related to the cooling process performed by the device. These parameters must include at least the device's power.

[0124] In some embodiments, such as Figure 4 As shown, while determining the operating parameters 421 of the air compressor, the air compressor model 320 can also be used to determine the cooling parameters 422 of the cooling equipment. At this time, the input of the air compressor model 320 (i.e., Figure 3 The operating status information 310 also includes the intake air temperature 414 and the output of the air pressure model 320 (i.e., Figure 3 The optimal parameter 330 also includes the preferred cooling parameters for the cooling equipment in the next adjustment cycle.

[0125] In some embodiments, when the air compressor model 320 is used to simultaneously determine the operating parameters 421 of the air compressor and the cooling parameters 422 of the cooling equipment, the optimal action determination module 322 can determine the reward value of the optional action based on the second excitation function. In some embodiments, the second excitation function is related to the operating parameters of the air compressor, the cooling parameters of the cooling equipment, the energy efficiency coefficient of the air compressor, and the energy efficiency coefficient of the cooling equipment. For example, the second excitation function can be represented by the following formula (2).

[0126] G2=α×e×p+β×ec×pc (2)

[0127] Where e is the energy efficiency coefficient of the air compressor after performing the optional action, p is the motor power of the air compressor after performing the optional action, ec is the energy efficiency coefficient of the cooling equipment after performing the optional action, pc is the power of the cooling equipment after performing the optional action, G2 is the score (i.e., the reward value of the optional action) corresponding to the air compressor and cooling equipment performing the optional action, and α and β are the weights. For more information on the energy efficiency coefficient of the air compressor, please refer to [link to relevant documentation]. Figure 3 And related explanations.

[0128] In some embodiments, α and β can be set based on actual needs. The higher the required efficiency of the air compressor, the larger α / β; the higher the safety requirements of the air compressor, the smaller α / β.

[0129] In some embodiments of this specification, a score corresponding to each combination of candidate operating parameters and candidate cooling parameters is calculated through a second excitation function, and the operating parameters of the air compressor and the cooling parameters of the cooling equipment are determined based on the score. This can find the optimal solution among multiple combinations of candidate operating parameters and candidate cooling parameters, thereby ensuring the stable operation of the air compressor, improving the safety of the air compressor, and reducing the energy consumption of the air compressor and cooling equipment.

[0130] For more information on the application and training of compressed air model 320, please refer to the relevant instructions in step 240.

[0131] In some embodiments, the input of the compressed air model 320 (i.e. Figure 3 The operating status information (310) also includes air supply anomaly data. Air supply anomaly data refers to data related to abnormal air supply to the air compressor. For more information on air supply anomaly data, please refer to [link to relevant documentation]. Figure 4 And related explanations.

[0132] Some embodiments in this specification determine the operating parameters of the air compressor and the cooling parameters of the cooling equipment based on abnormal air supply data. The resulting operating and cooling parameters are safer and more reliable, and can reduce the risk of air compressor failure.

[0133] In some embodiments, within an adjustment cycle, the processor can simultaneously determine the operating parameters of multiple air compressors and the cooling parameters of multiple cooling devices based on the pressure characteristic data, power consumption data, temperature data, and intake air temperature of multiple air compressors, using the air compressor model 320. At this time, the operating status information 310 includes a sequence combination of the operating status information of multiple air compressors, and the optimal parameters 330 includes a sequence combination of the optimal operating parameters of multiple air compressors and the optimal cooling parameters of multiple cooling devices.

[0134] For example, the working status information 310 can be represented as {[P1,E1,T1,t1],[P2,E2,T2,t2],…,[P n E n ,T n ,t n ]}. Among them, P n Let E be the pressure characteristic sequence of the nth air compressor. n Let T be the power consumption sequence of the nth air compressor. n Let t be the temperature sequence of the nth air compressor. n Let be the intake air temperature of the nth air compressor. The optimal parameter 330 can be expressed as [(A1,A2,…,A…]. n (B1,B2,…,B) m )], where A n B represents the optimal operating parameters for the nth air compressor. mThis represents the optimal cooling parameters corresponding to the m-th cooling device.

[0135] In some embodiments of this specification, taking into account the impact of ambient temperature on product production, the operating parameters of the air compressor and the cooling parameters of the cooling equipment are determined through an air compressor model. This ensures that the air supply of the air compressor and the ambient temperature meet production requirements and improve product quality.

[0136] Figure 5 This is an exemplary schematic diagram illustrating the generation of alarm information according to some embodiments of this specification.

[0137] In some embodiments, such as Figure 5 As shown, the processor determines abnormal air supply data 530 based on pressure characteristic data 411 and air compressor power consumption data 412; in response to the abnormal air supply data 530 meeting the warning conditions, it generates alarm information and sends it to the alarm component.

[0138] For more information on pressure characteristic data and air compressor power consumption data, please refer to [link / reference]. Figure 2 Related explanations.

[0139] Air supply anomaly data 530 refers to data related to abnormal air supply from the air compressor. For example, air supply anomaly data 530 includes the location of the anomaly and the air compressor temperature at the time of the anomaly. Air supply anomalies include, but are not limited to, the difference between the air supply pressure data and the air consumption pressure data exceeding a preset threshold, and the fluctuation value of the air supply volume at multiple time points exceeding a preset fluctuation threshold.

[0140] In some embodiments, the processor constructs a reference vector based on the pressure characteristic data and air compressor power consumption data during gas supply anomalies in historical data, and determines the abnormal location and air compressor temperature at the time of the anomaly as the labels corresponding to the reference vector, and constructs a reference vector library based on multiple reference vectors and labels.

[0141] The processor constructs a target vector based on current pressure characteristic data and air compressor power consumption data. It then matches this target vector against a reference vector library to obtain the reference vector with the highest similarity. The label corresponding to this reference vector is then identified as the current abnormal air supply data. The similarity can be determined based on vector distance, which includes, but is not limited to, cosine distance.

[0142] In some embodiments, when constructing the reference vector library, the processor can also perform clustering optimization on the reference vectors. For example, the reference vectors recorded in the reference vector library are the reference vectors corresponding to the cluster centers of each cluster, to obtain more concise and representative reference vectors. Clustering methods include, but are not limited to, the K-Means clustering algorithm and the DBSCAN clustering algorithm.

[0143] In some embodiments, for an air compressor, the processor determines air supply anomaly data 530 based on pressure characteristic data 411, air compressor power consumption data 412, sound data 511 and image data 512, through anomaly model 520.

[0144] Anomaly model 520 is a model used to determine gas supply anomaly data 530. In some embodiments, anomaly model 520 is a machine learning model, such as a deep neural network (DNN) model.

[0145] The inputs to the anomaly model 520 include pressure characteristic data 411, air compressor power consumption data 412, sound data 511, and image data 512. The output of the anomaly model 520 is air supply anomaly data 530.

[0146] Sound data 511 refers to data related to sound on the gas production line. In some embodiments, the processor collects sound data 511 via a sound monitoring device.

[0147] Image data 512 refers to data related to images of the gas production line. In some embodiments, the processor acquires image data 512 via an image monitoring device.

[0148] For more information on gas-powered production lines, please see [link / reference]. Figure 2 For more information on sound monitoring devices and image monitoring devices, please refer to the relevant instructions and explanations. Figure 1 And related explanations.

[0149] In some embodiments, the processor trains an anomaly model 520 based on multiple second training samples with second labels. For example, the processor can input multiple second training samples into an initial anomaly model, construct a loss function based on the output and second labels of the initial anomaly model, iteratively update the parameters of the initial anomaly model based on the loss function, and terminate the iteration when the iteration completion condition is met, thus obtaining the trained anomaly model. The iterative update method includes, but is not limited to, gradient descent, and the iteration completion condition can be the convergence of the loss function or the reaching of a threshold number of iterations.

[0150] The second training sample includes sample pressure characteristic data, sample air compressor power consumption data, sample sound data, and sample image data. The second label is the actual air supply anomaly data of the sample air compressor corresponding to the second training sample. The second training sample and the second label can be constructed based on historical data.

[0151] In some embodiments, the remote device 142 is configured to periodically train an anomaly model based on an update cycle. The control center 144 is configured to periodically transmit the model parameters of the anomaly model trained by the remote device 142 to the edge processor 141 based on the update cycle. The edge processor 141 is configured to: determine gas supply anomaly data based on data in the service database 143 and the anomaly model; and send a second update request to the control center 144 in response to gas supply anomaly data within a preset time period meeting a second update condition.

[0152] For more information on remote devices, edge processors, service databases, and control centers, see [link to relevant documentation]. Figure 1 And related explanations. For more information on the update cycle, please see [link / reference]. Figure 3 And related explanations.

[0153] The second update condition is used to determine whether the control center has transmitted the model parameters of the trained abnormal model to the edge processor.

[0154] In some embodiments, the second update condition is that the difference between the number of alarms and the actual number of faults within a preset time period is greater than a preset quantity threshold. More information about alarms is provided below.

[0155] In some embodiments, after receiving an alarm message, maintenance personnel will inspect the air compressor to determine whether the air compressor has malfunctioned, and thus obtain the actual number of malfunctions.

[0156] In some embodiments, the preset quantity threshold can be determined based on the amount of resources (human and material resources) available for handling the fault. The larger the amount of resources, the larger the preset quantity threshold.

[0157] The second update request refers to a request from the control center to transfer the model parameters of the trained anomaly model to the edge processor. In response to receiving the second update request, the control center transfers the model parameters of the trained anomaly model to the edge processor.

[0158] Some embodiments in this specification demonstrate a mode in which anomaly models are trained on remote devices and calculated by edge processors, enabling rapid and accurate assessment of the condition of air compressors and effectively ensuring the safety of the production process.

[0159] Some embodiments in this specification use anomaly models to evaluate the working status of air compressors based on data from multiple modalities such as images and audio. This allows for a more comprehensive analysis of the air compressor's status and makes it easier to detect anomalies.

[0160] Warning conditions are used to determine whether an air compressor has malfunctioned. In some embodiments, warning conditions include the fulfillment of any one of the following: the abnormal location is a preset risk location, the air compressor temperature exceeds a temperature threshold, etc. The temperature threshold can be set based on experience.

[0161] Preset risk locations refer to key locations that users pre-set based on the importance of each stage in the production process. For example, the preset location might be the air compressor outlet.

[0162] Alarm information refers to information related to the alarm triggered by the alarm component. For example, alarm information includes the faulty air compressor, the location of the fault, and the severity. In some embodiments, the processor identifies the abnormal location as the fault location and determines the severity based on the air compressor temperature; the higher the air compressor temperature, the greater the severity.

[0163] In some embodiments, after receiving an alarm message, the alarm component alerts the user. The alarm may take the form of one or more of the following: text prompt, sound prompt, voice prompt, vibration, flashing light, etc.

[0164] Some embodiments in this specification monitor the product manufacturing process in real time. When abnormal data occurs, an alarm is triggered by an alarm component installed in the gas production line. This allows maintenance personnel to be promptly alerted to repairs when equipment malfunctions, ensuring safe production of the product.

[0165] This specification provides one or more embodiments of an intelligent air compressor control device based on deep learning and optimization algorithms. The device includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; and the at least one processor is used to execute at least a portion of the computer instructions to implement the method described above.

[0166] This specification provides one or more embodiments of a computer-readable storage medium, characterized in that the storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the method described above.

[0167] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0168] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0169] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0170] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0171] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0172] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0173] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A smart air compressor control system based on deep learning and optimization algorithms, characterized in that, This includes pressure sensing devices, power monitoring devices, temperature sensing devices, cooling devices, environmental monitoring devices, and processors; The pressure sensing device is connected to the air supply line, which is connected to at least one of an air compressor, an air-consuming device, and an air-consuming production line. The pressure sensing device is configured to acquire pressure characteristic data, which includes air supply pressure data and / or air consumption pressure data. The power monitoring device is configured to acquire power consumption data of the air compressor. The temperature sensing device is installed on the air compressor and is configured to acquire air compressor temperature data. The cooling device is configured to reduce the temperature of the air compressor; The environmental monitoring device is configured to acquire the intake air temperature, which is the air temperature of the air drawn in by the air compressor; The processor is configured to periodically determine the operating parameters of the air compressor based on an adjustment cycle; During one of the adjustment cycles, the processor is configured to: For a given air compressor, based on the pressure characteristic data, the air compressor power consumption data, the intake air temperature, the air supply anomaly data, and the air compressor temperature data, the operating parameters of the air compressor and the cooling parameters of the cooling equipment are determined through an air compressor model. The air supply anomaly data includes the location of the anomaly and the air compressor temperature at the time of the anomaly. The air supply anomaly includes the difference between the air supply pressure data and the air consumption pressure data exceeding a preset threshold, and the fluctuation value of the air supply volume at multiple time points exceeding a preset fluctuation threshold. The air compressor model is a reinforcement learning model. The air compressor model includes a working module and an optimal action determination module. The optimal action determination module is used to determine the reward value of each optional action in the set of optional actions based on the working status information. The working status information includes the pressure characteristic sequence, power consumption sequence, and temperature sequence corresponding to the air compressor. The optional actions include adjusting the valve opening and adjusting the motor power. The reward value is used to evaluate the impact of adjusting the operating parameters of the air compressor according to the optional actions on product production. The reward value is determined based on a second excitation function, which is related to the operating parameters of the air compressor, the cooling parameters of the cooling equipment, the energy efficiency coefficient of the air compressor, and the energy efficiency coefficient of the cooling equipment. The second excitation function is expressed by the following formula: in, The coefficient of performance (COP) of the air compressor after performing the optional action. The motor power after the air compressor performs the optional action. The energy efficiency ratio of the cooling equipment after performing the optional action. The power output after the optional action is performed on the cooling device. The reward value corresponding to the execution of the optional action by the air compressor and the cooling equipment. , As weight.

2. The system as described in claim 1, characterized in that, The processor includes an edge processor, a remote device, multiple service databases, and a control center; The plurality of service databases are located in the edge processor and / or the remote device, and the plurality of service databases are configured to store at least one of the pressure characteristic data, the air compressor power consumption data, and the air compressor temperature data; The remote device is located at a remote end that communicates with the gas production line and is configured to periodically train the air compression model based on an update cycle; the control center is configured to periodically transmit the model parameters trained by the remote device to the edge processor based on the update cycle. The update cycle is negatively correlated with the amount of production data of the gas-using production line; the production data includes the pressure characteristic data, the air compressor power consumption data, and the air compressor temperature data; The edge processor is located in the gas production line and is configured as follows: For one of the air compressors, based on the pressure characteristic data, the power consumption data of the air compressor, the temperature data of the air compressor, and the intake air temperature, the operating parameters of the air compressor and the cooling parameters of the cooling equipment are determined through the air compressor model. In response to the fact that the operating parameters of the air compressor and / or the cooling parameters of the cooling equipment meet the update conditions within a preset time period, an update request is sent to the control center.

3. The system as described in claim 1, characterized in that, The system also includes an alarm component, which is installed in the gas production line and configured to trigger an alarm based on alarm information. The processor is further configured to: Based on the pressure characteristic data and the air compressor power consumption data, the abnormal air supply data is determined; In response to the gas supply anomaly data meeting the early warning conditions, an alarm message is generated and sent to the alarm component.

4. The system as described in claim 3, characterized in that, The system also includes a sound monitoring device and an image monitoring device, which are installed in the gas production line. The sound monitoring device is configured to acquire sound data, and the image monitoring device is configured to acquire image data. The processor is further configured to: For one of the air compressors, based on the pressure characteristic data, the air compressor power consumption data, the sound data, and the image data, the abnormal air supply data is determined by an anomaly model, where the anomaly model is a machine learning model.

5. A smart air compressor control method based on deep learning and optimization algorithms, characterized in that, The method is executed by the processor of the intelligent air compressor control system based on deep learning and optimization algorithms as described in claim 1, and includes: Pressure characteristic data is acquired through pressure sensing devices, including gas supply pressure data and / or gas consumption pressure data. The power consumption data of the air compressor is obtained through power monitoring equipment; The air compressor temperature data is obtained through temperature sensing devices; The temperature of the air compressor is reduced by using cooling equipment; The intake air temperature is obtained through an environmental monitoring device; the intake air temperature is the air temperature drawn into the air compressor. as well as Based on the adjustment cycle, and according to the pressure characteristic data, the air compressor power consumption data, the intake air temperature, the air supply anomaly data, and the air compressor temperature data, the operating parameters of the air compressor and the cooling parameters of the cooling equipment are periodically determined through the air compressor model. The air supply anomaly data includes the location of the anomaly and the air compressor temperature at the time of the anomaly. The air supply anomaly includes the difference between the air supply pressure data and the air consumption pressure data exceeding a preset threshold, and the fluctuation value of the air supply volume at multiple time points exceeding a preset fluctuation threshold. The air compressor model is a reinforcement learning model. The air compressor model includes a working module and an optimal action determination module. The optimal action determination module is used to determine the reward value of each optional action in the set of optional actions based on the working state information. The working state information includes the pressure characteristic sequence, power consumption sequence, and temperature sequence corresponding to the air compressor. The optional actions include adjusting the valve opening and adjusting the motor power. The reward value is used to evaluate the impact of adjusting the operating parameters of the air compressor according to the optional actions on product production. The reward value is determined based on a second excitation function, which is related to the operating parameters of the air compressor, the cooling parameters of the cooling equipment, the energy efficiency coefficient of the air compressor, and the energy efficiency coefficient of the cooling equipment. The second excitation function is expressed by the following formula: in, The coefficient of performance (COP) of the air compressor after performing the optional action. The motor power after the air compressor performs the optional action. The energy efficiency ratio of the cooling equipment after performing the optional action. The power output after the optional action is performed on the cooling device. The reward value corresponding to the execution of the optional action by the air compressor and the cooling equipment. , As weight.

6. The method as described in claim 5, characterized in that, The method further includes: Based on the pressure characteristic data and the air compressor power consumption data, the abnormal air supply data is determined; In response to the gas supply anomaly data meeting the early warning conditions, an alarm message is generated and sent to the alarm component.

7. An intelligent air compressor control device based on deep learning and optimization algorithms, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is configured to execute at least a portion of the computer instructions to implement the method as described in any one of claims 5 to 6.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the method as described in any one of claims 5 to 6.

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