Big data management method and system applied to digital air compression station

Abstract

The invention discloses a big data management method and system applied to a digital air compression station, and belongs to the technical field of big data, and the method comprises the steps: obtaining the data of the digital air compression station, carrying out the data preprocessing and data cleaning of the data, obtaining first data, carrying out the encryption operation of the first data, and uploading the first data to a distributed database for storage; a real-time energy consumption monitoring system is established to monitor and adjust output power, a fault prediction model and an intelligent early warning mechanism are established, potential faults are predicted, and if the potential faults exist, early warning information is sent out; and constructing a state prediction model, predicting the future operation state of the digital air compression station, and visually displaying the output power of the digital air compression station, the early warning information and the future operation state of the digital air compression station. According to the invention, storage is carried out through a distributed database, multi-aspect data management is realized, and real-time monitoring and predictive maintenance are carried out on the digital air compression station by establishing a real-time energy consumption monitoring system, a fault prediction model and a data prediction optimization model.

Description

Technical Field

[0001] The present invention belongs to the field of big data technology, and specifically relates to a big data management method and system applied to a digital air compressor station. Background Art

[0002] As an important power source in industrial production, air compressor stations are widely used in manufacturing, chemical engineering, and energy. They consist of air compressors, post-processing equipment, pressure sensors, dew point meters (humidity sensors), temperature sensors, flow meters, and other equipment. With the advancement and development of intelligent manufacturing, the digitization of air compressor stations and their intelligent management have become an industry trend. Traditional air compressor station management relies on manual monitoring and regular inspections and maintenance, which suffers from low efficiency, untimely fault warnings, and high energy consumption. With the rapid development of the Internet of Things and big data technologies, the data collection and analysis capabilities of air compressor stations have been significantly improved. The inability of existing technologies to efficiently manage massive amounts of data and achieve real-time monitoring and maintenance is due to two reasons. First, data management at air compressor stations is often limited to monitoring a single device, lacking global analysis and optimization of the entire system. Second, the inefficiency of data storage, processing, and analysis makes real-time monitoring and predictive maintenance difficult to achieve.

[0003] Therefore, the traditional air compressor station management method has obvious shortcomings. Based on this, how to provide an effective solution to achieve multi-faceted data management, real-time monitoring and predictive maintenance has become a difficult problem that needs to be solved urgently in existing technologies. Summary of the Invention

[0004] The purpose of the present invention is to provide a big data management method and system for digital air compressor stations to solve the problems of multi-faceted data management, real-time monitoring and predictive maintenance existing in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a big data management method applied to a digital air compressor station, comprising:

[0007] Acquire data of a digital air compression station, wherein the data of the digital air compression station includes temperature data, pressure data, air supply volume data, and flow data;

[0008] Performing data preprocessing and data cleaning on the temperature data, pressure data, gas supply volume data, and flow rate data to obtain first data, performing an encryption operation on the first data, and uploading the first data to a distributed database for storage;

[0009] Build a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establish a fault prediction model and an intelligent early warning mechanism to predict potential faults in the digital air compressor station, and if there are potential faults, send out early warning information;

[0010] Build a state prediction model based on the first data, and use the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes no-load operating state, partial-load operating state, stable operating state, and full-load operating state;

[0011] Visualize the output power of the digital air compressor station, early warning information, and the future operating state of the digital air compressor station.

[0012] In a possible design, perform data preprocessing and data cleaning on temperature data, pressure data, air supply data, and flow data to obtain the first data, and perform an encryption operation on the first data, including:

[0013] Perform denoising, missing value, and outlier processing on temperature data, pressure data, air supply data, and flow data to obtain the first data;

[0014] Use an asymmetric encryption algorithm to encrypt the first data.

[0015] In a possible design, build a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station, including:

[0016] Screen the first data according to the correlation analysis method, extract the data related to energy consumption in the first data to obtain the second data;

[0017] Build a real-time energy consumption monitoring system based on the second data, and dynamically adjust the output power of the digital air compressor station according to the real-time monitored energy consumption data.

[0018] In a possible design, build a fault prediction model and an intelligent early warning mechanism to predict potential faults in the digital air compressor station, and if there are potential faults, send out early warning information, including:

[0019] Build a fault prediction model based on the first data and deep learning algorithm;

[0020] Use the fault prediction model to predict potential faults in the digital air compressor station, establish an intelligent early warning mechanism, and automatically trigger an early warning signal when potential faults are predicted.

[0021] In a possible design, build a state prediction model based on the first data, including:

[0022] Build a state prediction model based on the first data and BPNN model;

[0023] Optimizing the state prediction model using the whale optimization algorithm, where the calculation expression of the state prediction model is:

[0024] m = σ(w'n + B');

[0025] In the above formula, m represents the output value, n represents the input value, σ represents the activation function, w' represents the weight, and B' represents the bias vector. Among them, the input value is the first data, and the output value is the predicted data of the digital air compressor station.

[0026] In a possible design, obtaining the data of the digital air compressor station includes:

[0027] Obtaining the geographical location information and connection relationships of the pipelines and several devices in the digital air compressor station, and constructing a visual pipe network model of the digital air compressor station based on the geographical location information and connection relationships of the pipelines and several devices in the digital air compressor station, where the devices include air compressors, pressure gauges, and flow meters;

[0028] Dividing the digital air compressor station into a gas - using area and a gas - supply area based on the visual pipe network model of the digital air compressor station, and obtaining the temperature data, pressure data, gas supply volume data, and flow data in the gas - using area and the gas - supply area.

[0029] In a possible design, the method further includes:

[0030] Installing a proportional - integral regulating valve in the gas - supply area, collecting the real - time gas - using pressure within a preset time period, and taking the real - time gas - using pressure as the preset maximum gas - using pressure;

[0031] Performing constant - pressure control on the proportional - integral regulating valve according to the preset maximum gas - using pressure, pressure data, and PI control strategy.

[0032] In a second aspect, the present invention provides a big data management system applied to a digital air compressor station, including:

[0033] An acquisition module for acquiring the data of the digital air compressor station, where the data of the digital air compressor station includes temperature data, pressure data, gas supply volume data, and flow data;

[0034] A pre - processing and uploading module for performing data pre - processing, data cleaning, and encryption operations on the temperature data, pressure data, gas supply volume data, and flow data to obtain first data, and uploading the first data to a distributed database for storage;

[0035] A first monitoring and prediction module for establishing a real - time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establishing a fault prediction model and an intelligent early - warning mechanism to predict potential faults existing in the digital air compressor station, and if there are potential faults, sending out early - warning information;

[0036] A second prediction module constructs a state prediction model based on the first data and uses the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes an unloaded operating state, a partial load operating state, a stable operating state, and a full load operating state;

[0037] A display module visually displays the output power of the digital air compressor station, the warning information, and the future operating state of the digital air compressor station.

[0038] In a third aspect, the present invention provides a big data management device applied to a digital air compressor station, including a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the big data management method applied to the digital air compressor station as described in any one of the above.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium, on which instructions are stored. When the instructions are run on a computer, the big data management method applied to the digital air compressor station as described in any one of the above is executed.

[0040] The beneficial effects of the present invention are as follows:

[0041] The present invention discloses a big data management method and system applied to a digital air compressor station, including: obtaining data of the digital air compressor station, where the data of the digital air compressor station includes temperature data, pressure data, air supply volume data, and flow data; performing data preprocessing, data cleaning, and encryption operations on the temperature data, pressure data, air supply volume data, and flow data to obtain first data, and uploading the first data to a distributed database for storage; establishing a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establishing a fault prediction model and an intelligent warning mechanism to predict potential faults existing in the digital air compressor station, and if there are potential faults, sending out warning information; constructing a state prediction model based on the first data and using the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes an unloaded operating state, a partial load operating state, a stable operating state, and a full load operating state; visually displaying the output power of the digital air compressor station, the warning information, and the future operating state of the digital air compressor station. The present invention obtains various data in the digital air compressor station, performs preprocessing operations and encryption operations on the data to improve the integrity, accuracy, and security of the data, uploads it to a distributed database for storage, establishes a real-time energy consumption monitoring system, monitors and adjusts the output power in real time to reduce the energy consumption of the digital air compressor station, and at the same time establishes a fault prediction model to predict fault information for early prevention. Staff can perform maintenance in advance. Using a distributed database to store the data of the digital air compressor station can uniformly manage various types of data and improve the efficiency of data storage and processing. Description of the Drawings

[0042] Figure 1 It is a flowchart of the big data management method applied to the data air compressor station provided in the first aspect of this embodiment;

[0043] Figure 2 It is a block diagram of the modules of the big data management system applied to the data air compressor station provided in the second aspect of this embodiment. Detailed Embodiments

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the present invention in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structures of the drawings is only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. It should be noted here that the description of these embodiments is used to help understand the present invention, but does not limit the present invention.

[0045] It should be understood that although terms such as first and second etc. may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object can be called the second object, and similarly, the second object can be called the first object, without departing from the scope of the exemplary embodiments of the present invention.

[0046] It should be understood that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that when the terms "comprise", "comprises", "include" and / or "includes" are used herein, they specify the presence of the stated features, integers, steps, operations, units and / or components, and do not exclude the presence or addition of one or more other features, quantities, steps, operations, units, components and / or their combinations.

[0047] Embodiment:

[0048] As Figure 1 shown, the first aspect of this embodiment provides a big data management method applied to a data air compressor station, which can but is not limited to be executed by a computer device or virtual machine with certain computing resources, such as an electronic device such as a personal computer, or a virtual machine; as Figure 1 shown, the big data management method applied to the data air compressor station can but is not limited to include the following steps:

[0049] S1. Obtain the data of the digital air compressor station, where the data includes temperature data, pressure data, air supply volume data, and flow rate data;

[0050] Specifically, in step S1, obtaining the data of the digital air compressor station includes:

[0051] S101. Obtain the geographical location information and connection relationships of the pipelines and several devices in the digital air compressor station, and construct a visual pipe network model of the digital air compressor station based on the geographical location information and connection relationships of the pipelines and several devices in the digital air compressor station. Among them, the devices include air compressors, pressure gauges, and flow meters;

[0052] S102. Divide the digital air compressor station into a gas - using area and a gas - supply area based on the visual pipe network model of the digital air compressor station, and obtain the temperature data, pressure data, air supply volume data, and flow rate data in the gas - using area and the gas - supply area.

[0053] S2. Perform data pre - processing and data cleaning on the temperature data, pressure data, air supply volume data, and flow rate data to obtain first - stage data, perform an encryption operation on the first - stage data, and upload the first - stage data to a distributed database for storage;

[0054] Among them, in the distributed database, the data is split into multiple data blocks and stored on different nodes. Through mechanisms such as data sharding, data replication, and distributed query, high availability, scalability, and fault tolerance are achieved, ensuring the integrity and security of the data.

[0055] Specifically, in step S2, performing data pre - processing and data cleaning on the temperature data, pressure data, air supply volume data, and flow rate data to obtain first - stage data, and performing an encryption operation on the first - stage data includes:

[0056] S201. Perform denoising, missing - value, and outlier processing on the temperature data, pressure data, air supply volume data, and flow rate data to obtain first - stage data;

[0057] Receive the specified target cleaning rules, and perform pre - processing operations on the temperature data, pressure data, air supply volume data, and flow rate data according to the target cleaning rules, including denoising, deleting or filling missing values, and deleting or replacing outliers, to ensure the integrity and accuracy of the data. Among them, the denoising operation uses an adaptive filtering algorithm. The adaptive filtering algorithm automatically adjusts the parameters of the filter according to the statistical characteristics of the current signal to achieve the best filtering effect. Common adaptive filtering algorithms include the least mean square error algorithm, the normalized least mean square algorithm, and the recursive least squares algorithm, which can be selected according to actual needs and will not be elaborated in detail here.

[0058] S202. Perform encryption processing on the first - stage data using an asymmetric encryption algorithm.

[0059] Specifically, the principle of the asymmetric encryption algorithm is to use the public key for encryption and the private key for decryption, or vice versa. Common asymmetric encryption algorithms include the RSA algorithm and the DSA (Digital Signature Algorithm) algorithm, which will not be elaborated in detail here.

[0060] S3. Establish a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establish a fault prediction model and an intelligent early warning mechanism to predict potential faults in the digital air compressor station, and if there are potential faults, send out early warning information;

[0061] Specifically, in step S3, establishing a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time includes:

[0062] S301. Screen the first data according to the correlation analysis method, extract the data related to energy consumption in the first data to obtain the second data;

[0063] Specifically, the principle of the correlation analysis method is to analyze two or more correlated variable elements and calculate the correlation coefficient to measure the linear relationship or degree of association between variables.

[0064] S302. Establish a real-time energy consumption monitoring system based on the second data, and dynamically adjust the output power of the digital air compressor station according to the real-time monitored energy consumption data.

[0065] When the monitored energy consumption data is higher than the preset energy consumption value, reduce the output power of the digital air compressor station; when the monitored energy consumption data is lower than the preset energy consumption value, increase the output power of the digital air compressor station.

[0066] Specifically, in step S3, establishing a fault prediction model and an intelligent early warning mechanism to predict potential faults in the digital air compressor station, and if there are potential faults, send out early warning information, includes:

[0067] S303. Construct a fault prediction model based on the first data and the deep learning algorithm;

[0068] S304. Use the fault prediction model to predict potential faults in the digital air compressor station, establish an intelligent early warning mechanism, and automatically trigger an early warning signal when potential faults are predicted.

[0069] The fault prediction model is constructed based on the random forest. The principle of the random forest is to construct multiple decision trees and integrate their prediction results to obtain the final prediction result.

[0070] S4. Build a state prediction model based on the first data, and use the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes no-load operating state, partial-load operating state, stable operating state, and full-load operating state;

[0071] Specifically, in step S4, building a data prediction optimization model based on the first data includes:

[0072] S401. Build a state prediction model based on the first data and the BPNN model;

[0073] Among them, the BPNN model (Backpropagation Neural Network) is a feedforward neural network, which is trained through the backpropagation algorithm, continuously adjusting the weights to minimize the error between the predicted output and the actual output.

[0074] S402. Use the whale optimization algorithm to optimize the state prediction model. The calculation expression of the state prediction model is:

[0075] m = σ(w'n + B');

[0076] In the above formula, m represents the output value, n represents the input value, σ represents the activation function, w' represents the weight, and B' represents the bias vector. Among them, the input value is the first data, and the output value is the data of the predicted digital air compressor station. The future operating state is judged according to the data of the predicted digital air compressor station.

[0077] The whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm that simulates the foraging behavior of humpback whales. By simulating the foraging behavior of whales, including strategies such as surrounding prey, spiral attack, and random search, it finds the optimal solution to the problem.

[0078] The whale optimization algorithm mainly includes three main stages: surrounding prey, bubble net attack, and searching for prey. Surrounding prey means that the whale group approaches the best whale, that is, the current optimal solution; the bubble net attack means that the whales make spiral movements around the prey, simulating the formation of a bubble net. As the whale group gradually narrows the encirclement, it more precisely locates the prey. At the same time, the whales will randomly search for new positions to increase the diversity of exploration.

[0079] The whale optimization algorithm is used to optimize the weights and bias vectors of the BPNN model to achieve global parameter optimization, aiming to improve the prediction accuracy and calculation speed of the state prediction model.

[0080] S5. Visualize the output power of the digital air compressor station, the warning information, and the future operating state of the digital air compressor station.

[0081] Further, the method further includes: installing a proportional-integral regulating valve in the air supply area, collecting the real-time gas pressure within a preset time period, and using the real-time gas pressure as the preset maximum gas pressure;

[0082] Performing constant pressure control on the proportional-integral regulating valve according to the preset maximum gas pressure, pressure data, and PI control strategy.

[0083] The PI control strategy is a control method based on feedback control theory, which combines proportional control (P) and integral control (I). The principle of the PI control strategy is to add the integral part of the feedback error to the proportional gain coefficient of the feedback control. By calculating the error, performing proportional control and integral control, the output of the PI controller is finally obtained. Constant pressure control is implemented for the digital air compressor station to achieve stable gas output, extend the service life of the equipment, and achieve energy conservation and consumption reduction.

[0084] In summary, this embodiment discloses a big data management method applied to a digital air compressor station, including obtaining the data of the digital air compressor station, where the data of the digital air compressor station includes temperature data, pressure data, gas supply data, and flow data; performing data preprocessing and data cleaning on the temperature data, pressure data, gas supply data, and flow data to obtain first data, performing an encryption operation on the first data, and uploading the first data to a distributed database for storage; establishing a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establishing a fault prediction model and an intelligent early warning mechanism to predict potential faults existing in the digital air compressor station, and if there are potential faults, sending out early warning information; constructing a state prediction model based on the first data, and using the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes no-load operating state, partial-load operating state, stable operating state, and full-load operating state; visually displaying the output power of the digital air compressor station, early warning information, and the future operating state of the digital air compressor station, managing multiple data of the digital air compressor station, and constructing a real-time energy consumption monitoring system, a fault prediction model, and a data prediction model to perform energy consumption monitoring, fault prediction, and state prediction on the digital air compressor station, so as to monitor the equipment state of the digital air compressor station in real time and facilitate timely maintenance and repair of faults.

[0085] As Figure 2 shown, in the second aspect of this embodiment, a big data management system applied to a digital air compressor station is provided for implementing the big data management method applied to a digital air compressor station in the first aspect of the embodiment. The big data management system includes:

[0086] An acquisition module that acquires the data of the digital air compressor station, where the data of the digital air compressor station includes temperature data, pressure data, gas supply data, and flow data;

[0087] The preprocessing and uploading module preprocesses and cleans the temperature data, pressure data, gas supply data, and flow rate data to obtain the first data, encrypts the first data, and uploads it to the distributed database for storage;

[0088] The first monitoring and prediction module establishes a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establishes a fault prediction model and an intelligent early warning mechanism to predict potential faults existing in the digital air compressor station, and issues a warning message if there are potential faults;

[0089] The second prediction module constructs a state prediction model based on the first data and uses the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes no-load operating state, partial-load operating state, stable operating state, and full-load operating state;

[0090] The display module visually displays the output power of the digital air compressor station, the warning message, and the future operating state of the digital air compressor station.

[0091] In summary, this embodiment discloses a big data management system applied to a digital air compressor station, including an acquisition module, a preprocessing and uploading module, a first monitoring and prediction module, a second prediction module, and a display module. The acquisition module acquires the data of the digital air compressor station, where the data of the digital air compressor station includes temperature data, pressure data, gas supply data, and flow rate data. The preprocessing and uploading module preprocesses and cleans the temperature data, pressure data, gas supply data, and flow rate data to obtain the first data, encrypts the first data, and uploads it to the distributed database for storage. The first monitoring and prediction module establishes a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establishes a fault prediction model and an intelligent early warning mechanism to predict potential faults existing in the digital air compressor station, and issues a warning message if there are potential faults. The second prediction module constructs a state prediction model based on the first data and uses the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes no-load operating state, partial-load operating state, stable operating state, and full-load operating state. The display module visually displays the output power of the digital air compressor station, the warning message, and the future operating state of the digital air compressor station, facilitating the staff to intuitively understand the real-time operating state of the equipment of the digital air compressor station, predict the location of faults, and facilitate equipment maintenance and repair.

[0092] In the third aspect of this embodiment, a big data management device applied to a digital air compressor station is provided, including a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the big data management method applied to the digital air compressor station as described in the first aspect of the embodiment.

[0093] Specifically, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out memory (FIFO), and / or first-in-last-out memory (FILO), etc.; the processor may not be limited to using a microprocessor of the STM32F105 series, an ARM (Advanced RISC Machines), an X86 architecture processor, or a processor integrated with an NPU (neural-network processing units); the transceiver may include, but is not limited to, a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee (a low-power local area network protocol based on the IEEE802.15.4 standard) wireless transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver, etc.

[0094] For the working process, working details, and technical effects of the device provided in the third aspect of this embodiment, reference may be made to the big data management method applied to the digital air compressor station as described in the first aspect of the embodiment, which will not be elaborated here.

[0095] In the fourth aspect of this embodiment, a computer-readable storage medium is provided. Instructions are stored on the computer-readable storage medium, and when the instructions are run on a computer, they are used to execute the big data management method applied to the digital air compressor station as described in the first aspect of the embodiment. Among them, the computer-readable storage medium refers to a carrier for storing data, which may include, but is not limited to, computer-readable storage media such as floppy disks, optical discs, hard disks, flash memories, USB flash drives, and / or Memory Sticks, and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0096] For the working process, working details, and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment, reference may be made to the big data management method applied to the digital air compressor station as described in the first aspect, which will not be elaborated here.

[0097] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A big data management method applied to a digital air compressor station, characterized in that, Including: Obtain the data of the digital air compressor station, where the data of the digital air compressor station includes temperature data, pressure data, air supply volume data, and flow rate data; Perform data preprocessing and data cleaning on the temperature data, pressure data, air supply volume data, and flow rate data to obtain first data, perform an encryption operation on the first data, and upload it to a distributed database for storage; Establish a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, establish a fault prediction model and an intelligent early warning mechanism to predict potential faults existing in the digital air compressor station, and if there are potential faults, send out early warning information; Construct a state prediction model based on the first data, and use the state prediction model to predict the future operating state of the digital air compressor station, where the operating state includes no-load operating state, partial-load operating state, stable operating state, and full-load operating state; Visually display the output power of the digital air compressor station, early warning information, and the future operating state of the digital air compressor station.

2. The big data management method applied to a digital air compressor station according to claim 1, wherein Perform data preprocessing and data cleaning on the temperature data, pressure data, air supply volume data, and flow rate data to obtain first data, and perform an encryption operation on the first data, including: Perform denoising, missing value, and outlier processing on the temperature data, pressure data, air supply volume data, and flow rate data to obtain first data; Use an asymmetric encryption algorithm to encrypt the first data.

3. The big data management method applied to a digital air compressor station according to claim 1, characterized in that Establish a real-time energy consumption monitoring system to monitor and dynamically adjust the output power of the digital air compressor station in real time, including: Screen the first data according to the correlation analysis method, extract the data related to energy consumption in the first data to obtain second data; Establish a real-time energy consumption monitoring system based on the second data, and dynamically adjust the output power of the digital air compressor station according to the real-time monitored energy consumption data.

4. A big data management method applied to a digital air compressor station according to claim 1, characterized in that Establish a fault prediction model and an intelligent early warning mechanism to predict potential faults of the digital air compressor station, and if there are potential faults, send out early warning information, including: Construct a fault prediction model based on the first data and deep learning algorithm; Use the fault prediction model to predict potential faults existing in the digital air compressor station, establish an intelligent early warning mechanism, and automatically trigger an early warning signal when potential faults are predicted.

5. A big data management method applied to a digital air compressor station according to claim 1, characterized in that, Construct a state prediction model based on the first data, including: Construct a state prediction model based on the first data and the BPNN model; Use the whale optimization algorithm to optimize the state prediction model, where the calculation expression of the state prediction model is: m = σ(w'n + B'); In the above formula, m represents the output value, n represents the input value, σ represents the activation function, w' represents the weight, and B' represents the bias vector.

6. The big data management method applied to a digital air compressor station according to claim 1, wherein, Obtain the data of the digital air compressor station, including: Obtain the geographical location information and connection relationships of the pipelines and several devices in the digital air compressor station, and construct a visual pipe network model of the digital air compressor station based on the geographical location information and connection relationships of the pipelines and several devices in the digital air compressor station, where the devices include air compressors, pressure gauges, and flow meters; Divide the digital air compressor station into a gas-using area and a gas-supplying area based on the visual pipe network model of the digital air compressor station, and obtain the temperature data, pressure data, air supply volume data, and flow rate data in the gas-using area and the gas-supplying area.

7. A big data management method applied to a digital air compressor station according to claim 6, characterized in that, The method further includes: Install a proportional-integral regulating valve in the air supply area, collect the real-time gas pressure within a preset time period, and use the real-time gas pressure as the preset maximum gas pressure; Conduct constant pressure control on the proportional-integral regulating valve according to the preset maximum gas pressure, pressure data, and PI control strategy.

8. A big data management system applied to a digital air compressor station, characterized in that, It includes: An acquisition module that acquires the data of the digital air compressor station, and the data of the digital air compressor station includes temperature data, pressure data, gas supply volume data, and flow rate data; A preprocessing and uploading module that performs data preprocessing and data cleaning on the temperature data, pressure data, gas supply volume data, and flow rate data to obtain first data, encrypts the first data, and uploads it to a distributed database for storage; A first monitoring and prediction module that establishes a real-time energy consumption monitoring system, monitors and dynamically adjusts the output power of the digital air compressor station in real time, establishes a fault prediction model and an intelligent early warning mechanism, predicts potential faults existing in the digital air compressor station, and issues a warning message if there are potential faults; A second prediction module that constructs a state prediction model based on the first data and uses the state prediction model to predict the future operating state of the digital air compressor station, and the operating state includes no-load operating state, partial-load operating state, stable operating state, and full-load operating state; A display module that visually displays the output power of the digital air compressor station, warning messages, and the future operating state of the digital air compressor station.

9. A big data management device applied to a digital air compressor station, characterized in that, It includes a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the big data management method applied to the digital air compressor station according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Instructions are stored on the computer-readable storage medium, and when the instructions run on the computer, they execute the big data management method applied to the digital air compressor station according to any one of claims 1 to 7.

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