A cloud-based method for remote real-time monitoring of gas turbines

Abstract

This invention discloses a cloud-based remote real-time monitoring method for gas turbines, comprising the following steps: acquiring raw gas turbine operating data in real time from the host computer of the gas turbine field control system; deploying intelligent devices at the edge of the gas turbine field to process the received raw data; the cloud platform receiving and verifying the data from the intelligent devices, and decrypting and parsing the data after successful verification; classifying and storing the parsed data on the cloud platform, and simultaneously forwarding the data to the display area for real-time monitoring and display; the display area providing a real-time monitoring interface for the gas turbine operating status, which can display the comprehensive scene distribution and operating overview of multiple gas turbines, as well as detailed operating parameters, trend information, and alarm events of a single gas turbine; the intelligent devices performing automatic fault judgment at the edge by monitoring equipment status and data reporting; and issuing alarm notifications; this invention reduces maintenance costs and improves interaction efficiency.

Description

Technical Field

[0001] This invention relates to the field of gas turbine health management technology, specifically to a remote real-time monitoring method for gas turbines based on a cloud platform. Background Technology

[0002] The application scenarios for civilian power generation gas turbines differ significantly from those for aircraft engines. Aircraft engines are primarily deployed in aircraft, characterized by short operating times, complex and variable operating conditions, and frequent start-stop cycles. Gas turbines, on the other hand, are mainly used in power plants and offshore oil refueling platforms, typically requiring long-term, steady-state operation. Therefore, the monitoring mode for gas turbines has shifted from the ultra-high-frequency monitoring (millisecond level), post-event analysis, and feedback improvement approach used for aircraft engines to a high-frequency monitoring (second level), online status assessment, and proactive intervention approach. Data storage has also changed from storing and managing data by sortie for aircraft engines to long-term, continuous online data storage. Furthermore, compared to aircraft engines, civilian power generation gas turbines require maintenance of units distributed across the country, necessitating faster communication and application to improve operational efficiency.

[0003] The changing application scenarios have created an urgent need to upgrade existing application systems designed for aero-engines. Gas turbine units are scattered across the country, often requiring a large number of personnel to monitor them 24 / 7. Furthermore, gas turbine operations are characterized by complex processes, high costs, numerous measurement parameters, dispersed resources, diverse equipment types, inconsistent data formats, and complex data structures. Traditional manual data processing and analysis is inefficient and prone to errors, and the demand for various experts is increasing, leading to rising maintenance costs year by year. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a cloud platform-based remote real-time monitoring method for gas turbines, which centralizes the data of gas turbines scattered in various locations, breaks down geographical barriers, achieves unified management, and then enables the monitoring and control of on-site operating equipment, thereby solving the problems existing in the background technology.

[0005] Technical solution: The present invention provides a remote real-time monitoring method for gas turbines based on a cloud platform, comprising the following steps: S1. Obtain raw operating data of the gas turbine from the host computer of the gas turbine field control system in real time; S2 deploys intelligent devices at the edge of the gas turbine site to process the received raw data, including: data integrity verification and completion, data downsampling, adding data tags, and data encryption and integrity verification, and then sends the processed data to the cloud platform. The S3 cloud platform receives and verifies data from smart devices. Once the verification is successful, the data is decrypted and the protocol is parsed. The S4 cloud platform categorizes and stores the parsed data, and simultaneously forwards the data to the display area for real-time monitoring and display. The S5 exhibition area provides a real-time monitoring interface for the gas turbine's operating status. The monitoring interface can display the comprehensive scene distribution and operating overview of multiple gas turbines, as well as detailed operating parameters, trend information, and alarm events of a single gas turbine. The S6 smart device performs automatic fault diagnosis at the edge. By monitoring the device status and data reporting, it identifies and distinguishes between device offline, power abnormality, network failure, or hardware program failure, and issues alarm notifications.

[0006] Furthermore, in step S2, the intelligent device processes the data collaboratively through multiple logical containers, specifically as follows: the received raw data is stored in the first container; the data in the first container is subjected to integrity verification and completion, and the result is stored in the third container; the data in the third container is downsampled, and the result is stored in the fourth container; data tags are added to the data in the fourth container, and the result is stored in the fifth container; the data in the fifth container is encrypted and integrity verification information is generated, and the result is stored in the sixth container; the data in the sixth container is backed up to the seventh container as historical data according to a predetermined period.

[0007] Furthermore, the data encryption and integrity verification specifically involves: calculating the hash value of the data to be sent and storing it in a hash tree structure; when it is necessary to verify data integrity, recalculating the hash value of the data and comparing it with the hash value stored in the hash tree.

[0008] Furthermore, in step S2, the intelligent device also supports receiving and updating the communication protocol from the host computer, and automatically adjusting the protocol matching information on subsequent data processing links according to the updated protocol. Furthermore, in step S3, if the cloud platform data verification fails, a data resend mechanism is triggered to retrieve the corresponding data from the smart device's historical data container and resend it to the cloud platform.

[0009] Furthermore, in step S4, the cloud platform automatically matches and identifies the project information and tag information to which the data belongs based on the hardware identification information carried in the data, and then classifies and stores the data accordingly. Furthermore, in step S5, the real-time monitoring display also includes: data-driven visual display based on the three-dimensional model of the gas turbine, dynamically displaying key information on the temperature field and pressure distribution of the gas turbine.

[0010] Furthermore, in step S6, the automatic fault judgment specifically involves: by connecting an uninterruptible power supply, and based on the equipment power supply status and network connectivity, combined with the data flow status, distinguishing between equipment power failure, network interruption, or equipment fault, and notifying the designated responsible person via email or SMS.

[0011] An electronic device according to the present invention includes a memory and a processor. The memory stores a computer program, and the processor executes the program to implement the steps of the method.

[0012] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method.

[0013] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: This invention utilizes cloud-edge collaboration, with edge-end data acquisition devices and centralized cloud processing, breaking geographical limitations and achieving unified management. It enables remote real-time monitoring of the operation of gas turbines across the country. Centralized expert support allows for remote operation and maintenance guidance in various locations, reducing maintenance costs and improving interaction efficiency. Attached Figure Description

[0014] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This invention relates to an intelligent device for the power plant edge. Figure 3 This invention features automatic fault detection at the edge. Detailed Implementation

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0016] This invention provides a method for remote real-time monitoring of gas turbines based on a cloud platform, comprising the following steps: Step 1: The data acquisition system is the host computer of the gas turbine field control system, which pushes and forwards the raw gas turbine data in real time. It also supports updating the data matching protocol and pushing it to the backend, automatically updating the changed protocol portions.

[0017] Step 2: As Figure 1 At the edge of the gas turbine site, intelligent devices are deployed to receive data forwarded from the host computer in real time and store it in container 1, or in container 2 via protocol storage. Data is subscribed to from containers 1 and 6, and its integrity is verified according to certain rules. Verified data is then stored in container 3. Data subscribed to from container 3 is downsampled according to certain rules and stored in container 4. Data subscribed to from container 4 has each frame tagged according to certain rules and stored in container 5. Data subscribed to from container 5 is encrypted, and its hash value is stored in a hash tree in container 6. When data integrity needs to be verified, the hash value can be recalculated and compared with the hash value stored in the hash tree to verify whether the data has been tampered with. Data subscribed to from container 6 is backed up and stored in container 7 at certain time intervals as historical data.

[0018] Step 3: As Figure 2The smart device sends data from container 6 to the cloud platform in parallel via multiple channels, and performs data verification according to certain rules. If a data verification anomaly is detected and the data fails to be sent normally, the data is resent from the subscribed container 7 according to certain rules and stored in container 8. Subscribed container 8 decrypts the data according to certain rules, parses it according to the data protocol, and stores it in container 9.

[0019] Step 4: Subscribe to the data in container 9, perform specific data processing and add tags, and then store the data in the underlying database according to preset rules based on the tags. Simultaneously, subscribe to the data in container 9, forward it to the display area, and perform real-time monitoring and display according to certain rules.

[0020] Step 5: The real-time monitoring section displays information such as the scene distribution of all gas turbines, platform overview, gas turbine distribution, operating efficiency, power level, operating percentage, and major unit events. It also displays individual gas turbine overview information, key parameter trends, control parameters, statistical information, unit status monitoring, vibration trends, temperature field, and alarm events. Furthermore, it allows users to jump to the health analysis of individual gas turbines, providing statistical analysis of equipment operation across multiple time dimensions such as year and month, including start-up, operating time, and the percentage of each operating condition.

[0021] Step 6: Automatic fault detection at the edge end, such as... Figure 3 As shown, the aim is to solve the problem of remotely diagnosing issues such as power outages and equipment malfunctions at the edge. By introducing a UPS backup power supply, certain rules are used to distinguish whether the forwarding device is offline or has no data reported. Further rules are used to determine the cause, such as UPS power depletion, UPS power supply abnormality, equipment network failure, equipment hardware failure, or equipment program failure. The responsible person is notified of the problem via email and SMS.

Claims

1. A method for remote real-time monitoring of gas turbines based on a cloud platform, characterized in that, Includes the following steps: S1. Obtain raw operating data of the gas turbine from the host computer of the gas turbine field control system in real time; S2 deploys intelligent devices at the edge of the gas turbine site to process the received raw data, including: data integrity verification and completion, data downsampling, adding data tags, and data encryption and integrity verification, and then sends the processed data to the cloud platform. The S3 cloud platform receives and verifies data from smart devices. Once the verification is successful, the data is decrypted and the protocol is parsed. The S4 cloud platform categorizes and stores the parsed data, and simultaneously forwards the data to the display area for real-time monitoring and display. The S5 exhibition area provides a real-time monitoring interface for the gas turbine's operating status. The monitoring interface can display the comprehensive scene distribution and operating overview of multiple gas turbines, as well as detailed operating parameters, trend information, and alarm events of a single gas turbine. The S6 smart device performs automatic fault diagnosis at the edge. By monitoring the device status and data reporting, it identifies and distinguishes between device offline, power abnormality, network failure, or hardware program failure, and issues alarm notifications.

2. The method for remote real-time monitoring of gas turbines based on a cloud platform according to claim 1, characterized in that, In step S2, the smart device processes the data collaboratively through multiple logical containers, as follows: the received raw data is stored in the first container; the data in the first container is verified and completed for integrity, and the result is stored in the third container; the data in the third container is downsampled, and the result is stored in the fourth container; data tags are added to the data in the fourth container, and the result is stored in the fifth container; the data in the fifth container is encrypted and integrity verification information is generated, and the result is stored in the sixth container. Data from the sixth container is backed up to the seventh container as historical data at predetermined intervals.

3. The method for remote real-time monitoring of gas turbines based on a cloud platform according to claim 2, characterized in that, The data encryption and integrity verification process specifically involves: calculating the hash value of the data to be sent and storing it in a hash tree structure; when data integrity needs to be verified, recalculating the hash value of the data and comparing it with the hash value stored in the hash tree.

4. The method for remote real-time monitoring of gas turbines based on a cloud platform according to claim 1, characterized in that, In step S2, the smart device also supports receiving and updating the communication protocol from the host computer, and automatically adjusting the protocol matching information on the subsequent data processing link according to the updated protocol.

5. The method for remote real-time monitoring of gas turbines based on a cloud platform according to claim 1, characterized in that, In step S3, if the cloud platform data verification fails, the data resend mechanism is triggered, and the corresponding data is retrieved from the historical data container of the smart device and resent to the cloud platform.

6. The method for remote real-time monitoring of gas turbines based on a cloud platform according to claim 1, characterized in that, In step S4, the cloud platform automatically matches and identifies the project information and tag information to which the data belongs based on the hardware identification information carried by the data, and then classifies and stores it accordingly.

7. The method for remote real-time monitoring of gas turbines based on a cloud platform according to claim 1, characterized in that, In step S5, the real-time monitoring display also includes: data-driven visual display based on the three-dimensional model of the gas turbine, dynamically displaying key information on the temperature field and pressure distribution of the gas turbine.

8. The method for remote real-time monitoring of gas turbines based on a cloud platform according to claim 1, characterized in that, In step S6, the automatic fault diagnosis specifically involves: by connecting an uninterruptible power supply, and based on the equipment power supply status and network connectivity, combined with the data flow status, distinguishing between equipment power failure, network interruption, or equipment fault, and notifying the designated responsible person via email or SMS.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the program to implement the steps of the method according to claims 1-8.

10. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the steps of the method described in claims 1-8.

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