Power station data protocol automatic adaptation fusion device and method based on machine learning

Abstract

The invention relates to the field of power systems, in particular to a power station data protocol automatic adaptation fusion device and method based on machine learning, and the device comprises an edge calculation module which is used for analyzing, learning and processing data in real time, performing a network communication test, analyzing key parameter information of a test point configuration file, and automatically calling data protocol communication parameters and the like; and the storage module is used for storing real-time acquired data and historical data, and comprises a power supply module, a data interface group, a liquid crystal display, an indicating lamp group and a box body. The data protocol is simple in adaptation, low in implementation cost, short in debugging time and low in professional requirement, the risk of inaccurate data caused by manual input requirements and human errors is remarkably reduced, a large amount of human resource cost is saved, meanwhile, the data processing capacity is enhanced, the data collection quality is improved, the machine learning diagnosis function is integrated, and the data processing efficiency is improved. And the data can be quickly analyzed, processed, diagnosed and decided, so that problems can be timely found and solved, and potential risks can be avoided.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a device and method for automatic adaptation and fusion of power plant data protocols based on machine learning. Background Art

[0002] The new power system is the primary vehicle for building a new energy system. Power plants are the core link in the new power system's "source, grid, load, and storage" architecture. Efficient data exchange between power plants is fundamental to ensuring the new power system's safety, efficiency, cleanliness, low carbon footprint, flexibility, and intelligent integration.

[0003] Relevant patents exist in the prior art, such as the Chinese invention patent application number 201410838561.9, entitled "A Power Dispatching Automation Master Station System Test Platform and Test Method Thereof." This patent discloses: The present invention relates to a power dispatching automation master station system test platform and test method therefor, organically integrating the support layer, power system simulation layer, dispatching automation system simulation layer, test verification layer, and interface adaptation layer into a test platform. This provides a standard test environment configuration, enabling source code testing, control software closed-loop testing, power system avalanche testing, protocol testing, and other test items for the dispatching automation master station system. While this patent effectively integrates various test verification resources to achieve standardized and automated testing of the test object, given the complex and diverse data protocols of power plants, problems still exist, such as complex data protocol adaptation, high implementation costs, long debugging time, and high professional requirements. Summary of the Invention

[0004] The present invention provides a device and method for automatic adaptation and fusion of power plant data protocols based on machine learning. It automatically adapts and integrates power plant data protocols, and uses artificial intelligence machine learning diagnostic algorithms to analyze and diagnose data, which can enhance the intelligent fusion capabilities of regional power generation bases.

[0005] To achieve the above technical effects, the present invention is implemented through the following technical solutions: The machine learning-based automatic adaptation and fusion device for power plant data protocols includes: Edge computing module: used for real-time analysis, learning, and data processing, network communication testing, parsing measurement point configuration file parameter information, automatically calling data protocol communication parameters, data cleaning, data storage, machine learning diagnosis, reading database data in the storage module to obtain historical data of the measurement point to be diagnosed and related measurement points, calculating typical working condition benchmark values, comparing measurement point data with working condition benchmark values, and performing abnormality judgment and data forwarding; Storage module: used to store programs and data, including real-time collected data and historical data; Power module: used to provide power to each part; Data interface group: used to connect various data devices or debugging equipment; LCD: used for function settings; Indicator light group: used to display the status of each module and data interface in the fusion device; Box: used to place the edge computing module, storage module, power module, data interface, and LCD display.

[0006] Furthermore, the LCD display and indicator light group are arranged on the front of the box; the edge computing module, storage module, and power supply module are arranged inside the box; and the data interface group is located on the back of the box.

[0007] Furthermore, the liquid crystal display is a touch liquid crystal display, which performs parameter settings on the automatic adaptive fusion, data storage function, data forwarding function, and device configuration information function of the fusion device.

[0008] Furthermore, the data interface group includes an Ethernet interface, a serial interface, a switch input / output interface and an external display interface.

[0009] The automatic adaptation and fusion method of power plant data specifications based on machine learning includes the following steps: A. Use the LCD display to configure the network attribute parameters of the fusion device and external data source device. The network attribute parameters include: IP address, subnet mask, gateway, and DNS server address. B. The fusion device and the external data source device automatically perform a network communication test by sending a network connection program command, wherein the network connection program command includes an ICMP Echo request message command; C. If the network communication is normal, automatically parse the parameter information of the measurement point configuration file and call the data protocol communication parameters from the parameter configuration database for automatic adaptation; if the network communication is abnormal, return to step A; D. Clean and store the collected data; E. Perform machine learning diagnosis on the selected data and forward the result data after diagnosis.

[0010] Furthermore, in step D, the data cleaning improves the quality of the collected data according to the steps of data review, missing value processing, outlier processing, data consistency check, and data verification; the data storage is to store the cleaned data in the database in the storage module.

[0011] Further, The machine learning diagnostic steps in step E include: EA. Use the LCD to set and determine the measurement point data that needs to be diagnosed, and mark the associated measurement point data that affects the value of the measurement point data to be diagnosed; EB. Read the database data in the storage module to obtain the historical data of the measurement point to be diagnosed and the associated measurement points, and determine the baseline value of the data of the measurement point to be diagnosed under the corresponding numerical conditions of each associated measurement point; EC. Compare the current value of the data at the measurement point to be diagnosed with the reference value under the current working conditions, determine whether the data value at the measurement point to be diagnosed is abnormal, and forward the diagnosis result data.

[0012] Furthermore, step E is specifically as follows: Perform machine learning diagnosis on the measurement point A to be diagnosed. The associated data B and associated data C are the associated data of the measurement point A to be diagnosed. The values ​​of the associated data B and associated data C will affect the value of the measurement point A to be diagnosed. The data values ​​(A, B, C) at this moment are considered as one working condition. The LCD display is used to set the measurement point A to be diagnosed, and the associated data B and associated data C associated with the measurement point A are selected. The edge computing module reads the database data in the storage module to obtain the historical data of the measurement point A to be diagnosed and the associated measurement points B and C, and determines the reference value of the measurement point A to be diagnosed under the corresponding numerical conditions of each associated measurement point. The steps for determining the reference value are as follows: Set the threshold ε, and the values ​​of the measurement point A to be diagnosed under three typical working conditions are A1, A2, and A3 respectively. The corresponding values ​​of the associated data B and associated data C are (B1, C1), (B2, C2), and (B3, C3). Search the historical database for the corresponding values ​​of the measurement point A to be diagnosed under the working conditions where the associated data B and associated data C values ​​are in the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), and (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε). Take 100 times, and then (A1 1 , A1 2 , A1 3 ,……,A1 99 , A1 100 ), (A2 1 , A2 2 , A2 3 ,……,A2 99 , A2 100 ), (A3 1 , A3 2 , A3 3 ,……,A3 99 , A3 100 ), then its average value The following are the baseline values ​​of the test point A to be diagnosed under three typical working conditions: ;

[0013] Compare the current value of the data at the measurement point to be diagnosed with the reference value under the current working conditions and set the threshold , that is, when the current associated data B and associated data C values ​​are within the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε), if the current value of the measurement point A to be diagnosed exceeds , , Range, it means that under three typical working conditions, the A value of the measurement point to be diagnosed is abnormal, and the current A value of the measurement point to be diagnosed and the abnormal alarm identification value of the diagnosis result data are forwarded.

[0014] Furthermore, the automatically parsed measuring point configuration file in step C includes measuring point name, measuring point type, measuring point coefficient, function code, register address, word length and offset parameters, and the above parameters are automatically configured.

[0015] Furthermore, the calling data protocol communication parameters in step C specifically automatically configure the port, baud rate, data bits, check mode, stop bits, interval time, byte stream mode, sending delay, timeout, packet length, byte order and public address parameters.

[0016] The beneficial effects of the present invention are: 1. The data protocol adaptation of this device and method is simple, with low implementation cost, short debugging time and low professional requirements. It significantly reduces the need for manual input and the risk of inaccurate data caused by human errors, saving a lot of human resource costs. At the same time, it enhances data processing capabilities, improves data collection quality, integrates machine learning diagnostic functions, and quickly analyzes, processes, diagnoses and makes decisions on key data, helping to discover and solve problems in a timely manner and avoid potential risks.

[0017] 2. This application can be applied to thermal power plants, hydroelectric power plants, nuclear power plants, geothermal power plants, wind power plants, solar power plants, waste-to-energy power plants, ocean power plants, and the like. Furthermore, the data protocols applicable to this application include national standard protocols, power protocols, Internet of Things protocols, industrial protocols, and other protocols. National standard protocols include Modbus RTU, Modbus TCP, and Modbus ASCII. Power protocols include DLT645, IEC-61850, IEC-101, ICE103, and IEC104. Internet of Things protocols include MQTT. Industrial protocols include CAN. Other protocols include PLC protocols and protocols related to smart meters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of the fusion device of the present application.

[0019] Figure 2 It is a flowchart of the fusion method of this application.

[0020] Among them, the figures are marked as: 1-edge computing module, 2-storage module, 3-power module, 4-data interface group, 5-LCD display, 6-indicator light group, 7-box. DETAILED DESCRIPTION

[0021] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0022] It should be pointed out that all directional indications in the embodiments of the present invention (such as two sides, edges, up, down, left, right, front, back, middle, top, bottom, tail, axial, radial...) are only used to explain the relative position relationship, movement state, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0023] Example 1 like Figure 1 As shown, the automatic adaptation and fusion device for power plant data protocols based on machine learning includes: Edge computing module: used for real-time analysis, learning, and data processing, network communication testing, parsing measurement point configuration file parameter information, automatically calling data protocol communication parameters, data cleaning, data storage, machine learning diagnosis, reading database data in the storage module to obtain historical data of the measurement point to be diagnosed and related measurement points, calculating typical working condition benchmark values, comparing measurement point data with working condition benchmark values, and performing abnormality judgment and data forwarding; Storage module: used to store programs and data, including real-time collected data and historical data, in a time series database or other similar databases; Power module: used to provide power to each part; Data interface group: used to connect various data devices or debugging equipment; LCD: used for function settings; Indicator light group: used to display the status of each module and data interface in the fusion device; Box: used to place the edge computing module, storage module, power module, data interface, and LCD display.

[0024] The liquid crystal display and indicator light group are arranged on the front of the box; the edge computing module, storage module, and power supply module are arranged inside the box; and the data interface group is located on the back of the box.

[0025] The power module is provided with a power switch button and an external power connection terminal. The power switch button is used to start or shut down the fusion device. The external power connection terminal is connected to an external AC power source via a power connection line.

[0026] The LCD is a touch LCD, which is used to set parameters for the automatic adaptation and fusion, data storage function, data forwarding function, and equipment configuration information function of the fusion device.

[0027] The data interface group includes an Ethernet interface, a serial interface, a switch input / output interface and an external display interface.

[0028] The data protocol adaptation of this device is simple, with low implementation cost, short debugging time and low professional requirements. It significantly reduces the need for manual input and the risk of inaccurate data caused by human errors, saving a lot of human resource costs. At the same time, it enhances data processing capabilities, improves data collection quality, integrates machine learning diagnostic functions, and quickly analyzes, processes, diagnoses and makes decisions on key data, helping to discover and solve problems in a timely manner and avoid potential risks.

[0029] Example 2 like Figure 2 As shown in FIG, the automatic adaptation and fusion method of power plant data specifications based on machine learning includes the following steps: A. Use the LCD display to configure the network attribute parameters of the fusion device and external data source device. The network attribute parameters include: IP address, subnet mask, gateway, and DNS server address; B. The fusion device and the external data source device automatically perform a network communication test by sending a network connection program command, wherein the network connection program command includes an ICMP Echo request message command; C. If the network communication is normal, automatically parse the parameter information of the measurement point configuration file and call the data protocol communication parameters from the parameter configuration database for automatic adaptation; if the network communication is abnormal, return to step A; D. Clean and store the collected data; E. Perform machine learning diagnosis on the selected data and forward the result data after diagnosis.

[0030] In step D, the data cleaning improves the quality of the collected data according to the steps of data review, missing value processing, outlier processing, data consistency check, and data verification; the data storage is to store the cleaned data in the database in the storage module.

[0031] The machine learning diagnostic steps in step E include: EA. Use the LCD to set and determine the measurement point data that needs to be diagnosed, and mark the associated measurement point data that affects the value of the measurement point data to be diagnosed; EB. Read the database data in the storage module to obtain the historical data of the measurement point to be diagnosed and the associated measurement points, and determine the baseline value of the data of the measurement point to be diagnosed under the corresponding numerical conditions of each associated measurement point; EC. Compare the current value of the data at the measurement point to be diagnosed with the reference value under the current working conditions, determine whether the data value at the measurement point to be diagnosed is abnormal, and forward the diagnosis result data.

[0032] Step E is specifically as follows: Perform machine learning diagnosis on the measurement point A to be diagnosed. The associated data B and associated data C are the associated data of the measurement point A to be diagnosed. The values ​​of the associated data B and associated data C will affect the value of the measurement point A to be diagnosed. The data values ​​(A, B, C) at this moment are considered as one working condition. The LCD display is used to set the measurement point A to be diagnosed, and the associated data B and associated data C associated with the measurement point A are selected. The edge computing module reads the database data in the storage module to obtain the historical data of the measurement point A to be diagnosed and the associated measurement points B and C, and determines the reference value of the measurement point A to be diagnosed under the corresponding numerical conditions of each associated measurement point. The steps for determining the reference value are as follows: Set the threshold ε, and the values ​​of the measurement point A to be diagnosed under three typical working conditions are A1, A2, and A3 respectively. The corresponding values ​​of the associated data B and associated data C are (B1, C1), (B2, C2), and (B3, C3). Search the historical database for the corresponding values ​​of the measurement point A to be diagnosed under the working conditions where the associated data B and associated data C values ​​are in the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), and (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε). Take 100 times, and then (A1 1 , A1 2 , A1 3 ,……,A1 99 , A1 100 ), (A2 1 , A2 2 , A2 3 ,……,A2 99 , A2 100 ), (A3 1 , A3 2 , A3 3 ,……,A3 99 , A3 100), then its average value The following are the baseline values ​​of the test point A to be diagnosed under three typical working conditions: ;

[0033] Compare the current value of the data at the measurement point to be diagnosed with the reference value under the current working conditions and set the threshold , that is, when the current associated data B and associated data C values ​​are within the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε), if the current value of the measurement point A to be diagnosed exceeds , , Range, it means that under three typical working conditions, the A value of the measurement point to be diagnosed is abnormal, and the current A value of the measurement point to be diagnosed and the abnormal alarm identification value of the diagnosis result data are forwarded.

[0034] Automatically parsing the measurement point configuration file eliminates the need for parameter configuration on a liquid crystal display or on a web page accessed through the debug interface. The automatically parsed measurement point configuration file in step C includes measurement point name, measurement point type, measurement point coefficient, function code, register address, word length, and offset parameters. The above parameters are automatically configured, and the configuration parameters for different data protocols may be different.

[0035] The calling data protocol communication parameters in step C specifically automatically configure parameters such as port, baud rate, data bits, check mode, stop bits, interval time, byte stream mode, sending delay, timeout, packet length, byte order and public address. The communication parameters of different data protocols are different.

[0036] The data protocol targeted by this method is simple to adapt, has low implementation cost, short debugging time, and low professional requirements. It significantly reduces the need for manual input and the risk of inaccurate data caused by human errors, saves a lot of human resource costs, and at the same time enhances data processing capabilities, improves data collection quality, integrates machine learning diagnostic functions, and quickly analyzes, processes, diagnoses, and makes decisions on key data, helping to promptly discover and solve problems and avoid potential risks. This application can be applied to thermal power stations, hydropower stations, nuclear power stations, geothermal power stations, wind power stations, solar power stations, garbage power stations, ocean power stations, etc. In addition, the data protocols applicable to this application include national standard protocols, power protocols, Internet of Things protocols, industrial protocols, and other protocols. National standard protocols include Modbus RTU, Modbus TCP, and Modbus ASCII. The power protocols include DLT645, IEC-61850, IEC-101, ICE103, and IEC104. The Internet of Things protocols include MQTT, the industrial protocols include CAN, and the other protocols include PLC protocols and smart meter related protocols.

[0037] Example 3 See attached Figure 1 The power station data protocol automatic adaptation and fusion device based on machine learning includes an edge computing module, a storage module, a power module, a data interface group, a liquid crystal display, an indicator light group and a cabinet.

[0038] Connect the power module to an external power source. Press the power button on the module to turn on the module, and the power indicator in the indicator group will illuminate. Connect the RJ45 port in the data interface group to an external data source device directly via a network cable, or indirectly through a networking device (such as a switch or firewall). Configure the network IP address of the external data source device via the LCD display. The edge computing module first performs a network communication test. If network communication is normal, it automatically adapts to multiple data protocols, automatically parses key parameters in the measurement point configuration file, and retrieves data protocol communication parameters from the parameter configuration database. For example, if the Modbus TCP data protocol is used, it automatically sets parameters such as data bits, parity, stop bits, interval time, byte stream mode, transmission delay, timeout, packet length, and byte order. If the IEC104 data protocol is used, it automatically sets parameters such as the time calibration mode, total call mode, cumulative data collection mode, and communication public address. Configure data forwarding function parameters via the LCD display, automatically generate a forwarding data point table based on actual needs, and forward data to the external data receiving device according to the configured forwarding protocol.

[0039] If a machine learning diagnosis is performed on a measurement point A to be diagnosed, and the associated data B and associated data C are the associated data of the measurement point A to be diagnosed, and the values ​​of the associated data B and associated data C will affect the value of the measurement point A to be diagnosed, then the data value (A, B, C) at this moment is regarded as one working condition; The LCD display is used to set the measurement point A to be diagnosed, and the associated data B and associated data C associated with the measurement point A are selected. The edge computing module reads the database data in the storage module to obtain the historical data of the measurement point A to be diagnosed and the associated measurement points B and C, and determines the reference value of the measurement point A to be diagnosed under the corresponding numerical conditions of each associated measurement point. The steps for determining the reference value are as follows: Set the threshold ε, and the values ​​of the measurement point A to be diagnosed under three typical working conditions are A1, A2, and A3 respectively. The corresponding values ​​of the associated data B and associated data C are (B1, C1), (B2, C2), and (B3, C3). Search the historical database for the corresponding values ​​of the measurement point A to be diagnosed under the working conditions where the associated data B and associated data C values ​​are in the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), and (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε). Take 100 times, and then (A1 1 , A1 2 , A1 3 ,……,A1 99 , A1 100 ), (A2 1 , A2 2 , A2 3 ,……,A2 99 , A2 100 ), (A3 1 , A3 2 , A3 3 ,……,A3 99 , A3 100 ), then its average value The following are the baseline values ​​of the test point A to be diagnosed under three typical working conditions: ;

[0040] Compare the current value of the data at the measurement point to be diagnosed with the reference value under the current working conditions and set the threshold , that is, when the current associated data B and associated data C values ​​are within the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε), if the current value of the measurement point A to be diagnosed exceeds , , Range, it means that under three typical working conditions, the A value of the measurement point to be diagnosed is abnormal, and the current A value of the measurement point to be diagnosed and the abnormal alarm identification value of the diagnosis result data are forwarded.

[0041] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a structural diagram of the part related to the fusion device disclosed in the present invention, and does not constitute a limitation on the fusion device of the present invention. The specific fusion device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0042] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A power plant data protocol automatic adaptation and fusion device based on machine learning, characterized in that: include: Edge computing module: used for real-time analysis, learning and data processing, network communication testing, parsing parameter information of measurement point configuration files, automatically calling data protocol communication parameters, data cleaning, data storage, machine learning diagnosis, reading database data in the storage module to obtain historical data of the measurement point to be diagnosed and related measurement points, calculating typical working condition benchmark values, comparing measurement point data with working condition benchmark values, and making abnormal judgments and data forwarding; Storage module: used to store programs and data, including real-time collected data and historical data; Power module: used to provide power to each part; Data interface group: used to connect various data devices or debugging equipment; LCD display: used for function settings; Indicator light group: used to display the status of each module and data interface in the fusion device; Box: used to place the edge computing module, storage module, power module, data interface, and LCD display.

2. The device for automatic adaptation and fusion of power plant data specifications based on machine learning according to claim 1 is characterized in that: The liquid crystal display and indicator light group are arranged on the front side of the box; the edge computing module, storage module, and power supply module are arranged inside the box; and the data interface group is located on the back side of the box.

3. The automatic adaptation and fusion device for power plant data protocol based on machine learning according to claim 1 is characterized in that: The liquid crystal display is a touch liquid crystal display, which performs parameter settings on the automatic adaptive fusion, data storage function, data forwarding function, and equipment configuration information function of the fusion device.

4. The automatic adaptation and fusion device for power plant data protocol based on machine learning according to claim 1 is characterized in that: The data interface group includes an Ethernet interface, a serial interface, a switch input / output interface and an external display interface.

5. The automatic adaptation and fusion method of power plant data specifications based on machine learning is characterized by: The steps include: A. Use the LCD display to configure information functions, respectively configure the network attribute parameters of the fusion device and the external data source device, the network attribute parameters include: IP address, subnet mask, gateway and DNS server address; B. The fusion device and the external data source device automatically perform a network communication test by sending a network connection program command, and the network connection program command includes an ICMP Echo request message command; C. If the network communication is normal, the parameter information of the measurement point configuration file is automatically parsed, and the data protocol communication parameters are called from the parameter configuration database for automatic adaptation; if the network communication is abnormal, return to step A; D. Clean and store the collected data; E. Perform machine learning diagnosis on the selected data and forward the result data after diagnosis.

6. The method for automatic adaptation and fusion of power plant data specifications based on machine learning according to claim 5 is characterized in that: In step D, the data cleaning improves the quality of the collected data according to the steps of data review, missing value processing, outlier processing, data consistency check, and data verification; the data storage is to store the cleaned data into a database in the storage module.

7. The method for automatic adaptation and fusion of power plant data specifications based on machine learning according to claim 5 is characterized in that: The machine learning diagnostic steps in step E include: EA. Determine the measuring point data to be diagnosed through the LCD display settings, and mark the associated measuring point data that affects the numerical value of the measuring point data to be diagnosed; EB. Read the database data in the storage module to obtain the historical data of the measurement point to be diagnosed and the associated measurement points, and determine the reference value of the data of the measurement point to be diagnosed under the corresponding numerical conditions of each associated measurement point; EC. Compare the current value of the data at the measurement point to be diagnosed with the reference value under the current working condition, determine whether the data value of the measurement point to be diagnosed is abnormal, and forward the diagnosis result data.

8. The method for automatic adaptation and fusion of power plant data specifications based on machine learning according to claim 7 is characterized in that: Step E is specifically as follows: Perform machine learning diagnosis on the measurement point A to be diagnosed. The associated data B and the associated data C are the associated data of the measurement point A to be diagnosed. The values ​​of the associated data B and the associated data C will affect the value of the measurement point A to be diagnosed. The data value (A, B, C) at this moment is regarded as a working condition. The measurement point A to be diagnosed is determined by the LCD display setting, and the associated data B and associated data C associated with the measurement point A to be diagnosed are selected. The edge computing module reads the database data in the storage module to obtain the historical data of the measurement point A to be diagnosed and the associated measurement points B and C, and determines the reference value of the measurement point A to be diagnosed under the corresponding numerical conditions of each associated measurement point. The steps for determining the reference value are as follows: Setting the Threshold , the values ​​of the measurement point A to be diagnosed under three typical working conditions are A1, A2, and A3, and the corresponding values ​​of the associated data B and the associated data C are (B1, C1), (B2, C2), and (B3, C3), respectively. Search the historical database for the values ​​of the associated data B and the associated data C in the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), and (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε) corresponding to the measurement point A to be diagnosed. Take 100 times, and we have (A1 1 , A1 2 , A1 3 , ..., A1 99 , A1 100 ), (A2 1 , A2 2 , A2 3 , ..., A2 99 , A2 100 ), (A3 1 , A3 2 , A3 3 , ..., A3 99 , A3 100 ), then its average The following are the benchmark values ​​of the test point A to be diagnosed under three typical working conditions: ； Compare the current value of the data at the point to be diagnosed with the reference value under the current working condition and set the threshold , that is, when the current associated data B and associated data C values ​​are within the range of (B1-ε≤B≤B1+ε, C1-ε≤C≤C1+ε), (B2-ε≤B≤B2+ε, C2-ε≤C≤C2+ε), (B3-ε≤B≤B3+ε, C3-ε≤C≤C3+ε), if the current value of the measurement point A to be diagnosed exceeds , , range, it means that under the three typical working conditions, the A value of the measurement point to be diagnosed is abnormal, and the current A value of the measurement point to be diagnosed and the abnormal alarm identification value of the diagnosis result data are forwarded.

9. The method for automatic adaptation and fusion of power plant data specifications based on machine learning according to claim 5 is characterized in that: The automatically parsed measuring point configuration file in step C includes measuring point name, measuring point type, measuring point coefficient, function code, register address, word length and offset parameters, and the above parameters are automatically configured.

10. The method for automatic adaptation and fusion of power plant data specifications based on machine learning according to claim 5 is characterized in that: The calling data protocol communication parameters in step C are specifically to automatically configure the port, baud rate, data bit, check mode, stop bit, interval time, byte stream mode, transmission delay, timeout, packet length, byte order and public address.

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