Power generation equipment detection system based on intelligent analysis

Abstract

The invention discloses a power generation equipment detection system based on intelligent analysis, and relates to the technical field of power generation equipment detection. The system specifically comprises a data acquisition module, a data processing and storage module, an intelligent diagnosis module, an early warning and decision support module, a man-machine interaction module and a communication interface module, and the collected electric signal data are transmitted to the data processing and storage module. Key feature parameters are extracted from processed data through the fault feature extraction sub-module, and the fault state of equipment is reflected; and then, the fault diagnosis model sub-module establishes a diagnosis model by using the characteristics, and quickly matches a known fault mode through a method based on fault rules, or finds similar cases from a historical case library through a method based on cases so as to judge potential fault types and reasons of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation equipment detection, and in particular to a power generation equipment detection system based on intelligent analysis. Background Art

[0002] With the continuous growth of electricity demand and the optimization and upgrading of the energy structure, the operating efficiency and reliability of power generation equipment have become key factors in ensuring the stable operation of the power system. Traditional methods for detecting power generation equipment mainly rely on manual inspections and regular maintenance. This method is not only time-consuming and labor-intensive, but also difficult to detect potential faults in real time, resulting in high equipment failure rates and high maintenance costs. Especially in large power plants, with a wide variety of equipment and complex structures, the limitations of manual inspection are even more obvious. In addition, as power generation equipment develops towards intelligence and automation, traditional detection methods can no longer meet the needs of modern power systems for efficient, accurate, and real-time monitoring.

[0003] Nowadays, the development of all walks of life in society depends on electricity. Once a large-scale power accident occurs, the consequences will be unimaginable. In recent years, the State Grid has continuously strengthened the inspection and maintenance of power equipment to ensure the safe and stable supply of electricity. Therefore, ensuring the safe and stable operation of the power system is of great significance to national development.

[0004] Substations are a crucial component of power systems. The equipment within them is complex and diverse, and traditional manual inspections are labor-intensive and inefficient. To improve substation inspections, a growing number of researchers are exploring data monitoring models that are compatible with smart grids and can enhance inspection quality. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a power generation equipment detection system based on intelligent analysis.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A power generation equipment detection system based on intelligent analysis includes a data acquisition module, a data processing and storage module, an intelligent diagnosis module, an early warning and decision support module, a human-computer interaction module, and a communication interface module. The data acquisition module monitors and senses the operating status of the power generation equipment through multiple types of sensors distributed at key locations of the power generation equipment, and transmits the collected electrical signal data to the data processing and storage module.

[0008] After receiving the raw data, the data processing and storage module pre-processes the data and stores it in the database, and performs feature analysis on the data;

[0009] The intelligent diagnosis module extracts characteristic parameters that can reflect the fault status of the equipment from the processed and analyzed data, and uses different diagnostic methods to perform fault diagnosis based on the extracted fault characteristics;

[0010] The early warning and decision support module sets early warning rules based on the normal operating parameters of the equipment, and provides decision-making suggestions to operation and maintenance personnel based on fault diagnosis results and early warning information;

[0011] The human-computer interaction module displays the equipment operating status in real time and displays it on the mobile terminal interface according to the needs of operation and maintenance personnel. Operation and maintenance personnel can remotely control the equipment and its parameter instructions through the human-computer interaction interface;

[0012] The communication interface module ensures smooth data communication between modules within the system and exchanges data with other external systems.

[0013] Preferably, the data acquisition module includes a sensor submodule and a data transmission submodule, and its specific working steps are as follows:

[0014] A1: Sensor submodule: The temperature sensor converts the monitored device temperature signal into an electrical signal; the vibration sensor converts the device vibration amplitude and frequency into an electrical signal; the pressure sensor monitors the pressure of the device's hydraulic system and cooling system in real time and converts it into an electrical signal;

[0015] A2: Data transmission submodule: The electrical signals collected by various sensors are transmitted to the data processing and analysis module through wired and wireless transmission. Wired transmission and wireless transmission work together, with wired transmission as the main method and wireless transmission as the auxiliary method. A data caching mechanism is set during the transmission process.

[0016] Furthermore, during the data transmission process, the data buffering mechanism allows the sensor to cache the data in the local memory due to the instability of the wireless signal, and then send it via wireless transmission after the signal is restored.

[0017] Based on the above solution: the data processing and storage module includes a data preprocessing submodule, a data storage submodule and a data analysis submodule, and its specific working steps are as follows:

[0018] B1: Data preprocessing submodule: Receives raw data transmitted by various sensors and performs cleaning, filtering, and normalization on the raw data;

[0019] B2: Data storage submodule: stores the processed data in a relational database and organizes and classifies different types of data according to time.

[0020] B3: Data analysis submodule: Use machine learning and deep learning models to analyze stored data, and iteratively optimize the model to correct and update data parameters.

[0021] A more preferred solution among the above solutions is that the intelligent diagnosis module includes a fault feature extraction submodule, a fault diagnosis model submodule and a diagnosis result output submodule, and its specific working steps are as follows:

[0022] C1: Fault feature extraction submodule: extracts characteristic parameters that can reflect the fault status of the equipment from the processed data;

[0023] C2: Fault diagnosis model submodule: Based on the extracted fault features, a fault diagnosis model is established to determine the possible faults of the equipment according to different fault diagnosis methods;

[0024] C3: Diagnosis result output submodule: Outputs the fault diagnosis results in an intuitive manner and displays the fault type, fault location, and fault severity information on the monitoring interface. At the same time, the fault information is promptly notified to the operation and maintenance personnel via SMS.

[0025] As a further solution of the present invention: In the fault diagnosis model submodule, the fault rule-based diagnosis method:

[0026] IF(f1∈R1)∩(f2∈R2)∩…∩(f n ∈R n )THEN Fault=C k ;

[0027] Among them, (f1, f2,,, f n ) represents the fault characteristics, (R1, R2,,, R n ) represents the feature threshold range, C k Indicates the fault type.

[0028] At the same time, in the fault diagnosis model submodule, the case-based diagnosis method:

[0029] C i =(f i1 , f i2 ,…,f in , Fault i ,Solution i );

[0030] Among them, f ij represents the (j)th eigenvalue of the (i)th case, Fault i Indicates the fault type, Solution i represents the solution, C i Indicates historical cases;

[0031] Similarity calculation:

[0032] Among them, (C new , C i ) represents the similarity with historical cases, w j represents the weight of the (j)th feature, sim j Represents the similarity function of a single feature;

[0033] Numerical features (such as vibration amplitude):

[0034] Categorical features (such as fault location):

[0035] Diagnosis results: Select the top (K) cases with the highest similarity and weight them to get the fault type: Among them, δ(Fault i =C k ) is an exponential function, which takes 1 when there is a match and 0 otherwise.

[0036] As a preferred embodiment of the present invention, the early warning and decision support module includes an early warning rule setting submodule, an early warning information release submodule and a decision support submodule, and its specific working steps are as follows:

[0037] D1: Early warning rule setting submodule: Based on the historical operation data of the equipment, set reasonable early warning rules for various operating parameters of the equipment;

[0038] D2: Early warning information release submodule: When the operating status of the equipment triggers the early warning rule, the system will immediately start the early warning information release process. The sound and light alarm will sound an alarm and flash the light. At the same time, the system will automatically send the early warning information to the operation and maintenance personnel;

[0039] D3: Decision support submodule: Based on the fault diagnosis results and early warning information, the system provides decision-making suggestions for operation and maintenance personnel. The system will analyze the severity of the fault and the possible impact, and recommend appropriate maintenance plans, such as whether simple debugging or component replacement is required. The submodule will also list the required spare parts and calculate the optimal maintenance time based on the equipment's operating conditions and maintenance resources.

[0040] At the same time, the human-computer interaction module includes a monitoring interface submodule, an operation control submodule and a report generation submodule, and its specific working steps are as follows:

[0041] E1: Monitoring interface submodule: provides an intuitive graphical interface to display the operating status of power generation equipment in real time;

[0042] E2: Operation control submodule: remotely controls the start, stop and adjustment operations of power generation equipment through the human-computer interaction interface;

[0043] E3: Report generation submodule: extract relevant data from the database and generate various types of reports based on user needs.

[0044] As a more optimal solution of the present invention: the communication interface module includes an internal communication submodule and an external communication submodule, and its specific working steps are as follows:

[0045] F1: Internal communication submodule: When bus communication is used, each module transmits and shares data through the bus. Each module has its own address, and address recognition is used to achieve accurate data transmission. Network communication connects each module through a local area network and uses a unified communication protocol for data exchange.

[0046] F2: External communication submodule: Transmits the operating data and fault information of power equipment to the enterprise's production management system and energy management system through the network in the format and interface protocol required by the external system. At the same time, it receives instructions and control signals from the external system and passes them to the relevant modules within the system.

[0047] The beneficial effects of the present invention are:

[0048] 1. This is a power generation equipment detection system based on intelligent analysis. The intelligent diagnosis module extracts key characteristic parameters from the processed data through the fault feature extraction submodule to reflect the fault status of the equipment. Then, the fault diagnosis model submodule uses these features to establish a diagnostic model. It quickly matches known fault patterns through a fault rule-based method, or finds similar cases from the historical case library through a case-based method to determine the potential fault type and cause of the equipment. Finally, the diagnosis result output submodule displays the fault type, location and severity in an intuitive manner, and promptly notifies the operation and maintenance personnel via text message, realizing rapid identification and response to faults, thereby improving the operational reliability and maintenance efficiency of the equipment.

[0049] 2. This is a power generation equipment detection system based on intelligent analysis. The early warning and decision support module uses the early warning rule setting submodule to set reasonable early warning rules for various operating parameters based on the historical operating data of the equipment to achieve early identification of abnormal conditions; when the equipment status triggers the early warning rule, the early warning information release submodule immediately starts the alarm process and notifies the operation and maintenance personnel through sound and light alarms and text messages to ensure timely response; the decision support submodule combines the fault diagnosis results and early warning information to analyze the severity of the fault and its potential impact, provide maintenance suggestions and spare parts lists, and calculate the optimal maintenance time, thereby helping operation and maintenance personnel make more accurate and efficient decisions and improve equipment reliability and operation and maintenance efficiency.

[0050] 3. This is a power generation equipment detection system based on intelligent analysis. The human-computer interaction module provides an intuitive monitoring interface, allowing operation and maintenance personnel to understand the operating status and fault information of the power generation equipment in real time. It also enables remote management of the equipment and adjustment of system parameters through the operation control interface. At the same time, the report generation submodule extracts data from the database to generate various reports, providing reliable data support for enterprise management decisions. The communication interface module ensures smooth data communication within and outside the system, enabling data sharing and interaction between modules through the internal communication submodule. The external communication submodule is responsible for transmitting operating data and fault information to external systems and receiving external commands to ensure effective connection and collaboration between the system and the enterprise management system. This integrated mechanism improves the transparency of equipment management, the convenience of operation, and the overall efficiency of the system.

[0051] 4. This is a power generation equipment detection system based on intelligent analysis. The data processing and storage module receives the raw data transmitted by the sensor, and first cleans, filters and normalizes the data in the data preprocessing submodule to improve data quality and consistency; then, the data storage submodule systematically stores the processed data in a relational database, organizes and classifies it in chronological order to ensure orderly data management and efficient query; finally, the data analysis submodule uses machine learning and deep learning models to conduct in-depth analysis of the stored data, and corrects and updates data parameters through iterative optimization models, thereby providing accurate data support and decision-making basis for equipment fault diagnosis and performance optimization.

[0052] 5. This power generation equipment detection system, based on intelligent analysis, consists of a sensor submodule within the data acquisition module that converts the equipment's temperature, vibration, and pressure signals into electrical signals, providing essential data for real-time monitoring and fault warning. The data transmission submodule transmits these signals to the data processing module via both wired and wireless transmission methods, with wired transmission primarily ensuring stability and wireless transmission providing flexibility. To address wireless signal instability, a data buffering mechanism caches data locally and transmits it only after the signal recovers, ensuring data integrity and system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is the main flow chart of a power generation equipment detection system based on intelligent analysis proposed by the present invention;

[0054] Figure 2 This is a flow chart of a data acquisition module in a power generation equipment detection system based on intelligent analysis proposed by the present invention;

[0055] Figure 3 This is a flow chart of a data processing and storage module in a power generation equipment detection system based on intelligent analysis proposed by the present invention;

[0056] Figure 4 This is a flow chart of an intelligent diagnosis module in a power generation equipment detection system based on intelligent analysis proposed by the present invention;

[0057] Figure 5 This is a flow chart of the early warning and decision support module in the power generation equipment detection system based on intelligent analysis proposed by the present invention;

[0058] Figure 6 This is a flow chart of a human-computer interaction module in a power generation equipment detection system based on intelligent analysis proposed by the present invention;

[0059] Figure 7 This is a flow chart of a communication interface module in a power generation equipment detection system based on intelligent analysis proposed by the present invention. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] A power generation equipment detection system based on intelligent analysis, such as Figure 1 As shown, it includes data acquisition module, data processing and storage module, intelligent diagnosis module, early warning and decision support module, human-computer interaction module and communication interface module;

[0062] The data acquisition module monitors and senses the operating status of the power generation equipment through various types of sensors distributed in key parts of the power generation equipment, and transmits the collected electrical signal data to the data processing and storage module. After receiving the original data, the data processing and storage module pre-processes the data and stores it in the database, and performs feature analysis on the data. The intelligent diagnosis module extracts characteristic parameters that can reflect the fault status of the equipment from the processed and analyzed data, and uses different diagnostic methods to perform fault diagnosis based on the extracted fault characteristics. The early warning and decision support module sets early warning rules based on the normal operating parameters of the equipment, and provides decision suggestions to the operation and maintenance personnel based on the fault diagnosis results and early warning information. The human-computer interaction module displays the equipment operation status in real time according to the needs of the operation and maintenance personnel and displays it on the mobile terminal interface. The operation and maintenance personnel can remotely control the equipment and its parameter instructions through the human-computer interaction interface. The communication interface module ensures smooth data communication between the various modules within the system and interacts with other external systems.

[0063] In order to monitor the status of the equipment in real time and efficiently grasp the real-time data of the equipment; Figure 2 As shown, the data acquisition module includes a sensor submodule and a data transmission submodule, and its specific working steps are as follows:

[0064] A1: Sensor submodule: The temperature sensor converts the monitored device temperature signal into an electrical signal; the vibration sensor converts the device vibration amplitude and frequency into an electrical signal; the pressure sensor monitors the pressure of the device's hydraulic system and cooling system in real time and converts it into an electrical signal;

[0065] A2: Data transmission submodule: The electrical signals collected by various sensors are transmitted to the data processing and analysis module through wired and wireless transmission. Wired transmission and wireless transmission work together, with wired transmission as the main method and wireless transmission as the auxiliary method. A data caching mechanism is set during the transmission process.

[0066] During the data transmission process, the data buffering mechanism allows the sensor to cache the data in the local memory due to the instability of the wireless signal, and then send it through wireless transmission after the signal is restored.

[0067] In order to improve the data quality and the accuracy of subsequent analysis; Figure 3 As shown, the data processing and storage module includes a data preprocessing submodule, a data storage submodule and a data analysis submodule, and its specific working steps are as follows:

[0068] B1: Data preprocessing submodule: Receives raw data transmitted by various sensors and performs cleaning, filtering, and normalization on the raw data;

[0069] B2: Data storage submodule: stores the processed data in a relational database and organizes and classifies different types of data according to time.

[0070] B3: Data analysis submodule: Use machine learning and deep learning models to analyze stored data, and iteratively optimize the model to correct and update data parameters.

[0071] In order to achieve accurate identification, location and response of power equipment faults; Figure 4 As shown, the intelligent diagnosis module includes a fault feature extraction submodule, a fault diagnosis model submodule and a diagnosis result output submodule, and its specific working steps are as follows:

[0072] C1: Fault feature extraction submodule: extracts characteristic parameters that can reflect the fault status of the equipment from the processed data;

[0073] C2: Fault diagnosis model submodule: Based on the extracted fault features, a fault diagnosis model is established to determine the possible faults of the equipment according to different fault diagnosis methods;

[0074] C3: Diagnosis result output submodule: Outputs the fault diagnosis results in an intuitive manner and displays the fault type, fault location, and fault severity information on the monitoring interface. At the same time, the fault information is promptly notified to the operation and maintenance personnel via SMS;

[0075] The fault diagnosis model submodule adopts the following algorithm formula:

[0076] Fault rule-based diagnostic method:

[0077] IF(f1∈R1)∩(f2∈R2)∩…∩(f n ∈R n )THEN Fault=C k ;

[0078] Among them, (f1, f2,,, f n ) represents the fault characteristics (such as vibration amplitude, temperature change rate), (R1, R2,,, R n ) represents the feature threshold range, C k Indicates the fault type (such as bearing wear, winding overheating);

[0079] The fault rule-based diagnosis method is suitable for scenarios with clear characteristics and clear thresholds (such as overvoltage and overheating). The rule matching calculation amount is small and it is suitable for edge device deployment.

[0080] Case-based diagnostic approach:

[0081] C i =(f i1 , f i2 ,…,f in , Fault i ,Solution i );

[0082] Among them, f ij represents the (j)th eigenvalue of the (i)th case, Fault i Indicates the fault type, Solution i represents the solution, C i Indicates historical cases;

[0083] Similarity calculation:

[0084] Among them, (C new , C i ) represents the similarity with historical cases, w j represents the weight of the (j)th feature, sim j Represents the similarity function of a single feature;

[0085] Numerical features (such as vibration amplitude):

[0086] Categorical features (such as fault location):

[0087] Diagnosis results: Select the top (K) cases with the highest similarity and weight them to get the fault type: Among them, δ(Fault i =C k ) is an exponential function, which takes 1 when there is a match and 0 otherwise;

[0088] The case-based diagnostic method provides reference solutions through case matching when the rule library is not covered, and the case library is continuously expanded to support adaptive learning.

[0089] In order to reduce the time of troubleshooting; Figure 5 As shown, the early warning and decision support module includes an early warning rule setting submodule, an early warning information release submodule and a decision support submodule. The specific working steps are as follows:

[0090] D1: Warning rule setting submodule: Based on the historical operation data of the equipment, reasonable warning rules are set for each operating parameter of the equipment. For example, for the temperature parameter of the equipment, when the temperature exceeds 80% of the normal range, a level 1 warning is triggered, and when it exceeds 90%, a level 2 warning is triggered.

[0091] D2: Early warning information release submodule: When the operating status of the equipment triggers the early warning rule, the system will immediately start the early warning information release process. The sound and light alarm will sound an alarm and flash the light. At the same time, the system will automatically send the early warning information to the operation and maintenance personnel;

[0092] D3: Decision support submodule: Based on the fault diagnosis results and early warning information, the system provides decision-making suggestions for operation and maintenance personnel. The system will analyze the severity of the fault and the possible impact, and recommend appropriate maintenance plans, such as whether simple debugging or component replacement is required. The submodule will also list the required spare parts and calculate the optimal maintenance time based on the equipment's operating conditions and maintenance resources.

[0093] In order to realize the human-computer interaction function, the equipment can be controlled quickly and effectively; Figure 6 As shown, the human-computer interaction module includes a monitoring interface submodule, an operation control submodule and a report generation submodule, and its specific working steps are as follows:

[0094] E1: Monitoring Interface Submodule: This module provides an intuitive graphical interface that displays the operating status of power generation equipment in real time. Various charts and data displays, such as temperature curves and pressure bar graphs, allow operators to clearly understand the equipment's operating status. Fault diagnosis results and early warning information are also displayed, allowing operators to promptly identify equipment anomalies.

[0095] E2: Operation Control Submodule: This module remotely controls the start, stop, and adjustment of power generation equipment through a human-machine interface. The interface features buttons and parameter setting boxes, allowing operators to enter commands, which are then sent to the equipment's control unit, enabling remote operation of the equipment. Operators can also set and modify system parameters, such as adjusting warning thresholds and changing data collection frequency.

[0096] E3: Report Generation Submodule: This module extracts relevant data from the database and generates various reports based on user needs. The equipment operation report records the equipment's operating parameters, start and stop times, and other information over a period of time. The fault statistics report counts the type and frequency of faults within a certain period. The maintenance record report details equipment maintenance, including maintenance time, maintenance content, and replaced parts. These reports can provide data support for enterprise management decisions.

[0097] In order to ensure smooth data communication within and outside the system; Figure 7 As shown, the communication interface module includes an internal communication submodule and an external communication submodule, and its specific working steps are as follows:

[0098] F1: Internal communication submodule: When bus communication is used, each module transmits and shares data through the bus. Each module has its own address, and address recognition is used to achieve accurate data transmission. Network communication connects each module through a local area network and uses a unified communication protocol for data exchange.

[0099] F2: External communication submodule: Transmits the operating data and fault information of power equipment to the enterprise's production management system and energy management system through the network in the format and interface protocol required by the external system. At the same time, it receives instructions and control signals from the external system and passes them to the relevant modules within the system.

[0100] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A power generation equipment detection system based on intelligent analysis, comprising a data acquisition module, a data processing and storage module, an intelligent diagnosis module, an early warning and decision support module, a human-computer interaction module and a communication interface module, characterized in that: The data acquisition module monitors and senses the operating status of the power generation equipment through various sensors distributed at key locations of the power generation equipment, and transmits the collected electrical signal data to the data processing and storage module; After receiving the raw data, the data processing and storage module pre-processes the data and stores it in the database, and performs feature analysis on the data; The intelligent diagnosis module extracts characteristic parameters that can reflect the fault status of the equipment from the processed and analyzed data, and uses different diagnostic methods to perform fault diagnosis based on the extracted fault characteristics; The early warning and decision support module sets early warning rules based on the normal operating parameters of the equipment, and provides decision-making suggestions to operation and maintenance personnel based on fault diagnosis results and early warning information; The human-computer interaction module displays the equipment operating status in real time and displays it on the mobile terminal interface according to the needs of operation and maintenance personnel. Operation and maintenance personnel can remotely control the equipment and its parameter instructions through the human-computer interaction interface; The communication interface module ensures smooth data communication between modules within the system and exchanges data with other external systems.

2. The power generation equipment detection system based on intelligent analysis according to claim 1, characterized in that: The data acquisition module includes a sensor submodule and a data transmission submodule, and its specific working steps are as follows: A1: Sensor submodule: The temperature sensor converts the monitored device temperature signal into an electrical signal; the vibration sensor converts the device vibration amplitude and frequency into an electrical signal; the pressure sensor monitors the pressure of the device's hydraulic system and cooling system in real time and converts it into an electrical signal; A2: Data transmission submodule: The electrical signals collected by various sensors are transmitted to the data processing and analysis module through wired and wireless transmission. Wired transmission and wireless transmission work together, with wired transmission as the main method and wireless transmission as the auxiliary method. A data caching mechanism is set during the transmission process.

3. The power generation equipment detection system based on intelligent analysis according to claim 2, characterized in that: During the data transmission process, the data buffering mechanism allows the sensor to cache the data in the local memory due to the instability of the wireless signal, and then send it through wireless transmission after the signal is restored.

4. The power generation equipment detection system based on intelligent analysis according to claim 1, characterized in that: The data processing and storage module includes a data preprocessing submodule, a data storage submodule, and a data analysis submodule. The specific working steps are as follows: B1: Data preprocessing submodule: Receives raw data transmitted by various sensors and performs cleaning, filtering, and normalization on the raw data; B2: Data storage submodule: stores the processed data in a relational database and organizes and classifies different types of data according to time. B3: Data analysis submodule: Use machine learning and deep learning models to analyze stored data, and iteratively optimize the model to correct and update data parameters.

5. The power generation equipment detection system based on intelligent analysis according to claim 1, characterized in that: The intelligent diagnosis module includes a fault feature extraction submodule, a fault diagnosis model submodule and a diagnosis result output submodule. The specific working steps are as follows: C1: Fault feature extraction submodule: extracts characteristic parameters that can reflect the fault status of the equipment from the processed data; C2: Fault diagnosis model submodule: Based on the extracted fault features, a fault diagnosis model is established to determine the possible faults of the equipment according to different fault diagnosis methods; C3: Diagnosis result output submodule: Outputs the fault diagnosis results in an intuitive manner and displays the fault type, fault location, and fault severity information on the monitoring interface. At the same time, the fault information is promptly notified to the operation and maintenance personnel via SMS.

6. The power generation equipment detection system based on intelligent analysis according to claim 5, characterized in that: In the fault diagnosis model submodule, the fault rule-based diagnosis method: IF(f1∈R1)∩(f2∈R2)∩…∩(f n ∈R n )THEN Fault=C k ; Among them, (f1, f2,,, f n ) represents the fault characteristics, (R1, R2,,, R n ) represents the feature threshold range, C k Indicates the fault type.

7. The power generation equipment detection system based on intelligent analysis according to claim 5, characterized in that: In the fault diagnosis model submodule, the case-based diagnosis method: C i =(f i1 , f i2 …, f in , Fault i ,Solution i ); Among them, f ij represents the (j)th eigenvalue of the (i)th case, Fault i Indicates the fault type, Solution i represents the solution, C i Indicates historical cases; Similarity calculation: Among them, (C new , C i ) represents the similarity with historical cases, w j represents the weight of the (j)th feature, sim j Represents the similarity function of a single feature; Numerical features: Categorical features: Diagnosis results: Select the top (K) cases with the highest similarity and weight them to get the fault type: Among them, δ(Fault i =C k ) is an exponential function, which takes 1 when there is a match and 0 otherwise.

8. The power generation equipment detection system based on intelligent analysis according to claim 1, characterized in that: The early warning and decision support module includes an early warning rule setting submodule, an early warning information release submodule, and a decision support submodule. The specific working steps are as follows: D1: Early warning rule setting submodule: Based on the historical operation data of the equipment, set reasonable early warning rules for various operating parameters of the equipment; D2: Early warning information release submodule: When the operating status of the equipment triggers the early warning rule, the system will immediately start the early warning information release process. The sound and light alarm will sound an alarm and flash the light. At the same time, the system will automatically send the early warning information to the operation and maintenance personnel; D3: Decision support submodule: Based on the fault diagnosis results and early warning information, the system provides decision-making suggestions for operation and maintenance personnel. The system will analyze the severity of the fault and the possible impact, and recommend appropriate maintenance plans, such as whether simple debugging or component replacement is required. The submodule will also list the required spare parts and calculate the optimal maintenance time based on the equipment's operating conditions and maintenance resources.

9. The power generation equipment detection system based on intelligent analysis according to claim 1, characterized in that: The human-computer interaction module includes a monitoring interface submodule, an operation control submodule, and a report generation submodule. The specific working steps are as follows: E1: Monitoring interface submodule: provides an intuitive graphical interface to display the operating status of power generation equipment in real time; E2: Operation control submodule: remotely controls the start, stop and adjustment operations of power generation equipment through the human-computer interaction interface; E3: Report generation submodule: extract relevant data from the database and generate various types of reports based on user needs.

10. The power generation equipment detection system based on intelligent analysis according to claim 1, characterized in that: The communication interface module includes an internal communication submodule and an external communication submodule, and its specific working steps are as follows: F1: Internal communication submodule: When bus communication is used, each module transmits and shares data through the bus. Each module has its own address, and address recognition is used to achieve accurate data transmission. Network communication connects each module through a local area network and uses a unified communication protocol for data exchange. F2: External communication submodule: Transmits the operating data and fault information of power equipment to the enterprise's production management system and energy management system through the network in the format and interface protocol required by the external system. At the same time, it receives instructions and control signals from the external system and passes them to the relevant modules within the system.

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