Electric power informatization data model terminal

Abstract

The invention relates to the technical field of electric power informatization, in particular to an electric power informatization data model terminal which comprises a core processing unit, a sensor module, an edge computing unit, a data transmission module, a cloud data center and a user interaction module. The core processing unit is electrically connected with the sensor module, the edge computing unit, the data transmission module, the cloud data center and the user interaction module through a data bus. The sensor module collects operation data of power equipment in real time, the edge computing unit performs primary processing on the data, and the data transmission module transmits the processed data to the cloud data center. Through the intelligent task scheduling system, the intelligent transmission optimization system, the intelligent data cleaning module and the voice recognition optimization system, the data processing capability, the edge calculation efficiency and the data transmission speed are improved, and the accuracy and the intelligent level of equipment state evaluation are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power informatization, and specifically relates to a power informatization data model terminal. Background Art

[0002] With the continuous development of the field of power informatization data model terminals, existing power informatization data model terminal products have been widely used. However, there are still some problems in their actual use. For example, existing power informatization data model terminals usually have problems such as limited data processing capabilities, low edge computing efficiency, and high data transmission delays, which lead to inaccuracies in equipment status assessment and insufficient intelligence levels.

[0003] To solve these problems, there have been some attempts, such as increasing edge computing nodes and optimizing data transmission protocols to improve data processing capabilities and transmission efficiency. Although these methods have improved the relevant aspects to a certain extent, they still have limitations. For example, the computing power of edge computing nodes is limited and cannot handle complex data analysis tasks, and data transmission protocols are prone to delays and packet loss under high bandwidth requirements.

[0004] Specifically, after retrieval, a GIS device status intelligent monitoring system and method based on edge computing technology with the publication number CN110401262B was disclosed, and the publication date was March 30, 2021. This design adopts an architecture of a status sensor terminal, a data node device, and an intelligent monitoring center, and realizes the preliminary processing and transmission of data through edge computing technology. Although it can achieve basic device status monitoring functions, due to the limited computing power of its edge computing nodes, it cannot handle complex device status assessment algorithms, resulting in poor performance in scenarios with high precision and real-time requirements. In addition, this system relies on a fixed edge computing model and is difficult to dynamically adjust computing tasks according to actual working conditions, thus affecting the flexibility and adaptability of the system.

[0005] After retrieval, a power equipment status online monitoring system based on the Internet of Things with the publication number CN112217283B was disclosed, and the publication date was August 27, 2024. This product uses Internet of Things technology to achieve real-time monitoring and data analysis of equipment status through a sensor network and a cloud data center. Although it has a certain degree of automation, due to its data transmission mainly relying on cloud processing, the data transmission delay is high and it cannot meet application scenarios with high real-time requirements. At the same time, this system is relatively weak in edge computing capabilities and cannot perform complex data processing and analysis locally, thus affecting the overall system's response speed and intelligence level.

[0006] The above problems indicate that the power informatization data model terminals on the current market are difficult to effectively meet the new requirements for data processing capabilities and real-time response speeds under complex working conditions. Therefore, the present invention provides a power informatization data model terminal to overcome these deficiencies and provide a new solution that is more intelligent, efficient, and adaptable to changing environments. Summary of the Invention

[0007] The present invention provides a power informatization data model terminal, which solves the problems existing in the existing power informatization data model terminals, such as limited data processing capabilities, low edge computing efficiency, and high data transmission delays, thereby effectively improving the accuracy and intelligence level of equipment status assessment.

[0008] The technical solution adopted by the present invention to solve the above technical problems is: a power informatization data model terminal, including a core processing unit, a sensor module, an edge computing unit, a data transmission module, a cloud data center, and a user interaction module. The core processing unit is electrically connected to the sensor module, the edge computing unit, the data transmission module, the cloud data center, and the user interaction module. The sensor module is used to collect the operation data of power equipment in real time. The edge computing unit is used to perform preliminary processing on the collected data. The data transmission module is used to transmit the processed data. The cloud data center is used for cloud analysis and storage. The user interaction module is used for user operation and display.

[0009] The core processing unit includes a central processor, a data memory, a signal transceiver, and a power management module. The central processor is connected to the data memory through a data bus and is used to store and process the data collected by the sensor module, and to judge the equipment status according to preset algorithms and logics and issue corresponding instructions. The signal transceiver is connected to the central processor and is used to receive and send instruction signals. The power management module is connected to the central processor and is used to manage and distribute the power provided by the power module.

[0010] The sensor module includes a temperature sensor, a current sensor, a voltage sensor, a vibration sensor, and a humidity sensor, which are installed at key positions of power equipment. The temperature sensor is used to detect the equipment temperature. The current sensor is used to detect the equipment current. The voltage sensor is used to detect the equipment voltage. The vibration sensor is used to detect the vibration condition of the equipment. The humidity sensor is used to detect the environmental humidity. The data collected in real time by the above sensors is transmitted to the core processing unit through the signal transceiver and analyzed and processed by the central processor.

[0011] The described edge computing unit includes a high-performance computing chip, a data buffer, and an algorithm module. The high-performance computing chip is connected to the data buffer through a data bus and is used for performing complex data analysis tasks; the data buffer is used for storing intermediate calculation results; the algorithm module contains a variety of device status evaluation algorithms, which are called by the central processing unit and are used for evaluating the device status according to the collected data. When the core processing unit receives the real-time data from the sensor module, it transmits the data to the edge computing unit through the signal transceiver. The high-performance computing chip processes the data according to the preset algorithms in the algorithm module to evaluate the device status.

[0012] The described data transmission module includes a 5G communication module and an optical fiber communication module. The 5G communication module is used for realizing high-speed wireless data transmission, and the optical fiber communication module is used for realizing high-speed wired data transmission. The data transmission module is connected to the core processing unit and the cloud data center and is used for transmitting the data processed by the edge computing unit to the cloud data center for further analysis. The core processing unit sends instructions to the data transmission module through the signal transceiver, and the data transmission module selects an appropriate transmission method according to the instructions and transmits the data to the cloud data center.

[0013] The described cloud data center includes cloud servers, a data storage module, and a data analysis module. The cloud servers are connected to the data transmission module through a network and are used for receiving the data transmitted from the data transmission module; the data storage module is used for storing a large amount of historical data; the data analysis module is used for performing in-depth analysis and modeling on the data. After the cloud data center receives the data transmitted from the data transmission module, it stores the data in the data storage module, and the data analysis module performs deep learning and modeling on the data to generate a device status report.

[0014] The described user interaction module includes a touch screen, a voice recognition module, and a remote control module. The touch screen is installed on the terminal device and is used for users to input instructions and view the device status; the voice recognition module is used for receiving user voice instructions, and the remote control module is used for receiving remote control instructions. The user interaction module is connected to the core processing unit, and the core processing unit receives user instructions through the signal transceiver and executes corresponding operations, and at the same time displays the device status to the user through the touch screen.

[0015] Preferably, the edge computing unit further includes an intelligent task scheduling system, which includes a task scheduling algorithm and a real-time task allocation module. The task scheduling algorithm is installed in the high-performance computing chip, and the real-time task allocation module is connected to the central processing unit and is used to dynamically adjust the allocation of computing tasks according to the current device status and data processing requirements. The central processing unit sends instructions to the real-time task allocation module through the signal transceiver according to the real-time data of the sensor module. The real-time task allocation module adjusts the computing tasks according to the task scheduling algorithm to ensure efficient data processing under different working conditions. The task scheduling algorithm can give priority to key tasks under high load conditions, improving the response speed and efficiency of the overall system.

[0016] Preferably, the data transmission module further includes an intelligent transmission optimization system, which includes a transmission protocol optimization algorithm and a bandwidth dynamic adjustment module. The transmission protocol optimization algorithm is installed in the 5G communication module and the optical fiber communication module, and the bandwidth dynamic adjustment module is connected to the central processing unit and is used to dynamically adjust the transmission bandwidth according to the current network condition and data transmission requirements. The central processing unit sends instructions to the bandwidth dynamic adjustment module through the signal transceiver according to the real-time data of the sensor module and the feedback from the cloud data center. The bandwidth dynamic adjustment module adjusts the transmission bandwidth according to the transmission protocol optimization algorithm to ensure low latency and high reliability even under high bandwidth requirements. The transmission protocol optimization algorithm can automatically select the optimal transmission protocol in different network environments, improving the stability and speed of data transmission.

[0017] Preferably, the cloud data center further includes an intelligent data cleaning module, which includes a data cleaning algorithm and an anomaly detection module. The data cleaning algorithm is installed in the cloud server, and the anomaly detection module is connected to the cloud server and is used to perform real-time cleaning and anomaly detection on the transmitted data. After the cloud server receives the data transmitted from the data transmission module, the anomaly detection module detects the abnormal values in the data in real time, and the data cleaning algorithm processes the abnormal values to generate a clean data set. The data cleaning algorithm can automatically identify and process abnormal data during data transmission, improving the accuracy and reliability of data analysis.

[0018] Preferably, the user interaction module further includes a voice recognition optimization system, which includes a noise suppression algorithm and a voiceprint recognition module. The noise suppression algorithm is installed in the voice recognition module, and the voiceprint recognition module is connected to the central processing unit and is used to accurately recognize user voice commands in a noisy environment. The central processing unit receives the user voice commands through the signal transceiver. The voiceprint recognition module processes the voice signal according to the noise suppression algorithm to extract user features, ensuring accurate recognition of user commands even in a noisy environment. The noise suppression algorithm can automatically adjust parameters under various environmental conditions, improving the accuracy and stability of voice recognition.

[0019] The described intelligent task scheduling system is connected to the task scheduling algorithm and the real-time task allocation module through a data bus, and is connected to the central processing unit through a signal transceiver. It receives the instructions of the central processing unit in real time and adjusts the allocation of computing tasks.

[0020] The described intelligent transmission optimization system is connected to the transmission protocol optimization algorithm and the bandwidth dynamic adjustment module through a network, and is connected to the central processing unit through a signal transceiver. It receives the instructions of the central processing unit in real time and adjusts the transmission bandwidth.

[0021] The described intelligent data cleaning module is connected to the data cleaning algorithm and the anomaly detection module through a data bus, and is connected to the cloud server. It processes the transmitted data in real time.

[0022] The described speech recognition optimization system is connected to the noise suppression algorithm and the voiceprint recognition module through a data bus, and is connected to the central processing unit through a signal transceiver. It receives the instructions of the central processing unit in real time and processes the user's voice instructions.

[0023] The structural composition, implementation method, and operating principle of the present invention are as follows:

[0024] 1. Intelligent task scheduling system: When the core processing unit receives the real-time data from the sensor module, it transmits the data to the edge computing unit through the signal transceiver. The high-performance computing chip processes the data according to the preset algorithm in the algorithm module to evaluate the device status. Under high load conditions, the central processing unit sends instructions to the real-time task allocation module through the signal transceiver. The real-time task allocation module adjusts the computing tasks according to the task scheduling algorithm, giving priority to key tasks to ensure efficient data processing under different working conditions. The task scheduling algorithm can give priority to key tasks under high load conditions, improving the response speed and efficiency of the overall system.

[0025] 2. Intelligent transmission optimization system: After the data transmission module receives the data transmitted from the edge computing unit, the central processing unit, based on the real-time data from the sensor module and the feedback from the cloud data center, sends instructions to the bandwidth dynamic adjustment module through the signal transceiver. The bandwidth dynamic adjustment module adjusts the transmission bandwidth according to the transmission protocol optimization algorithm to ensure low latency and high reliability even under high bandwidth requirements. The transmission protocol optimization algorithm can automatically select the optimal transmission protocol in different network environments, improving the stability and speed of data transmission.

[0026] 3. Intelligent data cleaning module: After the cloud server receives the data transmitted from the data transmission module, the anomaly detection module detects the outliers in the data in real time, and the data cleaning algorithm processes the outliers to generate a clean data set. The data cleaning algorithm can automatically identify and process abnormal data during data transmission, improving the accuracy and reliability of data analysis.

[0027] 4. Voice Recognition Optimization System: The user interacts with the user interaction module through the touch screen or voice commands. The central processor receives the user's voice commands through the signal transceiver. The voiceprint recognition module processes the voice signal according to the noise suppression algorithm, extracts the user's features, and ensures accurate recognition of the user's commands even in a noisy environment. The noise suppression algorithm can automatically adjust parameters under various environmental conditions, improving the accuracy and stability of voice recognition.

[0028] Advantages of the present invention:

[0029] 1. The intelligent task scheduling system improves the response speed and efficiency of the edge computing unit under high load by dynamically adjusting the allocation of computing tasks, ensuring efficient data processing under complex working conditions.

[0030] 2. The intelligent transmission optimization system improves the stability and speed of data transmission by dynamically adjusting the transmission bandwidth and optimizing the transmission protocol, ensuring low latency and high reliability even under high bandwidth requirements.

[0031] 3. The intelligent data cleaning module improves the accuracy and reliability of data analysis through real-time cleaning and anomaly detection, ensuring more accurate device status reports generated by the cloud data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is the overall structure schematic diagram of the present invention;

[0033] Figure 2 is the structure schematic diagram of the core processing unit of the present invention;

[0034] Figure 3 is the structure schematic diagram of the sensor module of the present invention;

[0035] Figure 4 is the structure schematic diagram of the edge computing unit of the present invention;

[0036] Figure 5 is the structure schematic diagram of the data transmission module of the present invention;

[0037] Figure 6 is the structure schematic diagram of the cloud data center of the present invention;

[0038] Figure 7 is the structure schematic diagram of the user interaction module of the present invention;

[0039] Figure 8 is the structure schematic diagram of the intelligent task scheduling system of the present invention.

[0040] In the figure:

[0041] 1. Core processing unit; 11. Central processing unit; 12. Data memory; 13. Signal transceiver; 14. Power management module;

[0042] 2. Sensor module; 21. Temperature sensor; 22. Current sensor; 23. Voltage sensor; 24. Vibration sensor; 25. Humidity sensor;

[0043] 3. Edge computing unit; 31. High-performance computing chip; 32. Data buffer; 33. Algorithm module; 34. Intelligent task scheduling system; 341. Task scheduling algorithm; 342. Real-time task allocation module;

[0044] 4. Data transmission module; 41. 5G communication module; 42. Fiber optic communication module; 43. Intelligent transmission optimization system; 431. Transmission protocol optimization algorithm; 432. Bandwidth dynamic adjustment module;

[0045] 5. Cloud data center; 51. Cloud server; 52. Data storage module; 53. Data analysis module; 54. Intelligent data cleaning module; 541. Data cleaning algorithm; 542. Anomaly detection module;

[0046] 6. User interaction module; 61. Touch screen; 62. Voice recognition module; 63. Remote control module; 64. Voice recognition optimization system; 641. Noise suppression algorithm; 642. Voiceprint recognition module. Detailed implementation manners

[0047] In order to make the technical means, creative features, achieved purposes and functions of the present invention easy to understand, the present invention will be further described below in conjunction with specific implementation manners.

[0048] Embodiments of the present invention provide a power informatization data model terminal, which solves the problems of limited data processing capacity, low edge computing efficiency, high data transmission delay, etc. existing in existing power informatization data model terminals, thereby effectively improving the accuracy and intelligent level of equipment status assessment.

[0049] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the specification drawings and specific implementation manners.

[0050] Refer to Figures 1 to 8, a power informatization data model terminal, including a core processing unit 1, a sensor module 2, an edge computing unit 3, a data transmission module 4, a cloud data center 5, and a user interaction module 6. The core processing unit 1 is electrically connected to the sensor module 2, the edge computing unit 3, the data transmission module 4, the cloud data center 5, and the user interaction module 6. The sensor module 2 is used to collect the operation data of power equipment in real time. The edge computing unit 3 is used to perform preliminary processing on the collected data. The data transmission module 4 is used to transmit the processed data. The cloud data center 5 is used for cloud analysis and storage. The user interaction module 6 is used for user operation and display.

[0051] Refer to Figure 2 , the core processing unit 1 includes a central processor 11, a data memory 12, a signal transceiver 13, and a power management module 14. The central processor 11 is connected to the data memory 12 through a data bus, and is used to store and process the data collected by the sensor module 2, and judge the device state according to preset algorithms and logics and issue corresponding instructions. The signal transceiver 13 is connected to the central processor 11 and is used to receive and send instruction signals. The power management module 14 is connected to the central processor 11 and is used to manage and distribute the power provided by the power module.

[0052] Refer to Figure 3 , the sensor module 2 includes a temperature sensor 21, a current sensor 22, a voltage sensor 23, a vibration sensor 24, and a humidity sensor 25, which are installed at key positions of power equipment. The temperature sensor 21 is used to detect the device temperature. The current sensor 22 is used to detect the device current. The voltage sensor 23 is used to detect the device voltage. The vibration sensor 24 is used to detect the vibration condition of the device. The humidity sensor 25 is used to detect the environmental humidity. The data collected by the above sensors in real time are transmitted to the core processing unit 1 through the signal transceiver 13 and analyzed and processed by the central processor 11.

[0053] Refer to Figure 4 , the edge computing unit 3 includes a high-performance computing chip 31, a data buffer 32, and an algorithm module 33. The high-performance computing chip 31 is connected to the data buffer 32 through a data bus and is used to perform complex data analysis tasks. The data buffer 32 is used to store intermediate calculation results. The algorithm module 33 contains various device state evaluation algorithms, which are called by the central processor 11 and are used to evaluate the device state according to the collected data. When the core processing unit 1 receives the real-time data of the sensor module 2, the data is transmitted to the edge computing unit 3 through the signal transceiver 13, and the high-performance computing chip 31 processes the data according to the preset algorithms in the algorithm module 33 to evaluate the device state.

[0054] Refer to Figure 5, the data transmission module 4 includes a 5G communication module 41 and an optical fiber communication module 42. The 5G communication module 41 is used to achieve high-speed wireless data transmission, and the optical fiber communication module 42 is used to achieve high-speed wired data transmission. The data transmission module 4 is connected to the core processing unit 1 and the cloud data center 5, and is used to transmit the data processed by the edge computing unit 3 to the cloud data center 5 for further analysis. The core processing unit 1 sends instructions to the data transmission module 4 through the signal transceiver 13. The data transmission module 4 selects an appropriate transmission method according to the instructions and transmits the data to the cloud data center 5.

[0055] See Figure 6 , the cloud data center 5 includes a cloud server 51, a data storage module 52, and a data analysis module 53. The cloud server 51 is connected to the data transmission module 4 through the network and is used to receive the data transmitted from the data transmission module 4; the data storage module 52 is used to store a large amount of historical data; the data analysis module 53 is used to perform in-depth analysis and modeling on the data. After receiving the data transmitted from the data transmission module 4, the cloud data center 5 stores the data in the data storage module 52, and the data analysis module 53 performs deep learning and modeling on the data to generate a device status report.

[0056] See Figure 7 , the user interaction module 6 includes a touch screen 61, a voice recognition module 62, and a remote control module 63. The touch screen 61 is installed on the terminal device and is used for users to input instructions and view the device status; the voice recognition module 62 is used to receive user voice instructions, and the remote control module 63 is used to receive remote control instructions. The user interaction module 6 is connected to the core processing unit 1. The core processing unit 1 receives user instructions through the signal transceiver 13 and performs corresponding operations, and at the same time displays the device status to the user through the touch screen 61.

[0057] Preferably, the edge computing unit 3 further includes an intelligent task scheduling system 34. The intelligent task scheduling system 34 includes a task scheduling algorithm 341 and a real-time task allocation module 342. The task scheduling algorithm 341 is installed in the high-performance computing chip 31. The real-time task allocation module 342 is connected to the central processing unit 11 and is used to dynamically adjust the allocation of computing tasks according to the current device status and data processing requirements. The central processing unit 11 sends instructions to the real-time task allocation module 342 through the signal transceiver 13 according to the real-time data of the sensor module 2. The real-time task allocation module 342 adjusts the computing tasks according to the task scheduling algorithm 341 to ensure efficient data processing under different working conditions. The task scheduling algorithm 341 can give priority to processing key tasks under high load conditions, improving the response speed and efficiency of the overall system.

[0058] Preferably, the data transmission module 4 further includes an intelligent transmission optimization system 43, and the intelligent transmission optimization system 43 includes a transmission protocol optimization algorithm 431 and a bandwidth dynamic adjustment module 432. The transmission protocol optimization algorithm 431 is installed in the 5G communication module 41 and the optical fiber communication module 42. The bandwidth dynamic adjustment module 432 is connected to the central processor 11 and is used to dynamically adjust the transmission bandwidth according to the current network condition and data transmission requirements. The central processor 11 sends an instruction to the bandwidth dynamic adjustment module 432 through the signal transceiver 13 according to the real-time data of the sensor module 2 and the feedback of the cloud data center 5. The bandwidth dynamic adjustment module 432 adjusts the transmission bandwidth according to the transmission protocol optimization algorithm 431 to ensure low latency and high reliability even under high bandwidth requirements. The transmission protocol optimization algorithm 431 can automatically select the optimal transmission protocol in different network environments, improving the stability and speed of data transmission.

[0059] Preferably, the cloud data center 5 further includes an intelligent data cleaning module 54, and the intelligent data cleaning module 54 includes a data cleaning algorithm 541 and an anomaly detection module 542. The data cleaning algorithm 541 is installed in the cloud server 51. The anomaly detection module 542 is connected to the cloud server 51 and is used to perform real-time cleaning and anomaly detection on the transmitted data. After the cloud server 51 receives the data transmitted from the data transmission module 4, the anomaly detection module 542 detects the outliers in the data in real time, and the data cleaning algorithm 541 processes the outliers to generate a clean data set. The data cleaning algorithm 541 can automatically identify and process abnormal data during data transmission, improving the accuracy and reliability of data analysis.

[0060] Preferably, the user interaction module 6 further includes a voice recognition optimization system 64, and the voice recognition optimization system 64 includes a noise suppression algorithm 641 and a voiceprint recognition module 642. The noise suppression algorithm 641 is installed in the voice recognition module 62. The voiceprint recognition module 642 is connected to the central processor 11 and is used to accurately identify the user voice command in a noisy environment. The central processor 11 receives the user voice command through the signal transceiver 13. The voiceprint recognition module 642 processes the voice signal according to the noise suppression algorithm 641 to extract the user features, ensuring that the user command can be accurately recognized even in a noisy environment. The noise suppression algorithm 641 can automatically adjust parameters under various environmental conditions, improving the accuracy and stability of voice recognition.

[0061] The intelligent task scheduling system 34 is connected to the task scheduling algorithm 341 and the real-time task allocation module 342 through a data bus and is connected to the central processor 11 through a signal transceiver 13, and receives the instruction of the central processor 11 in real time to adjust the allocation of computing tasks.

[0062] The described intelligent transmission optimization system 43 is connected to the transmission protocol optimization algorithm 431 and the bandwidth dynamic adjustment module 432 through a network, and is connected to the central processing unit 11 through a signal transceiver 13, receiving the instructions of the central processing unit 11 in real time to adjust the transmission bandwidth.

[0063] The described intelligent data cleaning module 54 is connected to the data cleaning algorithm 541 and the anomaly detection module 542 through a data bus, and is connected to the cloud server 51, processing the transmitted data in real time.

[0064] The described speech recognition optimization system 64 is connected to the noise suppression algorithm 641 and the voiceprint recognition module 642 through a data bus, and is connected to the central processing unit 11 through a signal transceiver 13, receiving the instructions of the central processing unit 11 in real time to process the user voice instructions.

[0065] The structural composition, implementation method and operation principle of the present invention are as follows:

[0066] Intelligent task scheduling system: When the core processing unit 1 receives the real-time data from the sensor module 2, it transmits the data to the edge computing unit 3 through the signal transceiver 13. The high-performance computing chip 31 processes the data according to the preset algorithm in the algorithm module 33 to evaluate the device status. Under high load conditions, the central processing unit 11 sends instructions to the real-time task allocation module 342 through the signal transceiver 13, and the real-time task allocation module 342 adjusts the computing tasks according to the task scheduling algorithm 341 to give priority to processing key tasks. This ensures that data can be efficiently processed under different working conditions, improving the response speed and efficiency of the overall system.

[0067] Intelligent transmission optimization system: After the data transmission module 4 receives the data transmitted from the edge computing unit 3, the central processing unit 11 sends instructions to the bandwidth dynamic adjustment module 432 through the signal transceiver 13 according to the real-time data of the sensor module 2 and the feedback from the cloud data center 5. The bandwidth dynamic adjustment module 432 adjusts the transmission bandwidth according to the transmission protocol optimization algorithm 431. This ensures low latency and high reliability even under high bandwidth requirements. The transmission protocol optimization algorithm 431 can automatically select the optimal transmission protocol in different network environments, improving the stability and speed of data transmission.

[0068] Intelligent data cleaning module: After the cloud server 51 receives the data transmitted from the data transmission module 4, the anomaly detection module 542 detects the abnormal values in the data in real time. The data cleaning algorithm 541 processes the abnormal values to generate a clean data set. This ensures the accuracy and reliability of data analysis.

[0069] Voice Recognition Optimization System: The user interacts with the user interaction module 6 through the touch screen 61 or voice commands. The central processing unit 11 receives the user voice commands through the signal transceiver 13. The voiceprint recognition module 642 processes the voice signal according to the noise suppression algorithm 641 to extract user features. This ensures accurate recognition of user commands even in a noisy environment. The noise suppression algorithm 641 can automatically adjust parameters under various environmental conditions, improving the accuracy and stability of voice recognition.

[0070] Advantages of the present invention:

[0071] The intelligent task scheduling system improves the response speed and efficiency of the edge computing unit under high load by dynamically adjusting the allocation of computing tasks, ensuring efficient data processing under complex working conditions.

[0072] The intelligent transmission optimization system improves the stability and speed of data transmission by dynamically adjusting the transmission bandwidth and optimizing the transmission protocol, ensuring low latency and high reliability even under high bandwidth requirements.

[0073] The intelligent data cleaning module improves the accuracy and reliability of data analysis through real-time cleaning and anomaly detection, ensuring more accurate device status reports generated by the cloud data center.

[0074] The voice recognition optimization system can accurately recognize user commands even in a noisy environment through noise suppression and voiceprint recognition technologies, improving the user's operation experience.

[0075] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of protection required by the present invention. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

[0076] As used in the specification and claims, certain terms are used to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. The specification and claims do not use the difference in names as a way to distinguish components, but use the difference in functions of components as the criterion for distinction. As mentioned throughout the specification and claims, "comprising" is an open-ended term and should be interpreted as "including but not limited to". "Substantially" means within an acceptable error range. Those skilled in the art can solve technical problems within a certain error range and basically achieve the technical effects.

[0077] It should be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a commodity or system comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such commodity or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the commodity or system comprising the element.

[0078] The foregoing description has shown and described several preferred embodiments of the present invention. However, as previously mentioned, it should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be altered within the scope of the inventive concept described herein through the above teachings or the skills or knowledge in the relevant field. Any alterations and changes made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A power informatization data model terminal, characterized in that, It includes a core processing unit (1), a sensor module (2), an edge computing unit (3), a data transmission module (4), a cloud data center (5), and a user interaction module (6). The core processing unit (1) is electrically connected to the sensor module (2), the edge computing unit (3), the data transmission module (4), the cloud data center (5), and the user interaction module (6). The sensor module (2) is used to collect the operation data of power equipment in real time. The edge computing unit (3) is used to perform preliminary processing on the collected data. The data transmission module (4) is used to transmit the processed data. The cloud data center (5) is used for cloud analysis and storage. The user interaction module (6) is used for user operation and display.

2. The power informatization data model terminal according to claim 1, wherein The core processing unit (1) includes a central processing unit (11), a data memory (12), a signal transceiver (13), and a power management module (14). The central processing unit (11) is connected to the data memory (12) through a data bus. The signal transceiver (13) is connected to the central processing unit (11). The power management module (14) is connected to the central processing unit (11).

3. The power informatization data model terminal according to claim 1, wherein The sensor module (2) includes a temperature sensor (21), a current sensor (22), a voltage sensor (23), a vibration sensor (24), and a humidity sensor (25). The temperature sensor (21) is used to detect the equipment temperature. The current sensor (22) is used to detect the equipment current. The voltage sensor (23) is used to detect the equipment voltage. The vibration sensor (24) is used to detect the vibration condition of the equipment. The humidity sensor (25) is used to detect the environmental humidity.

4. The power information data model terminal according to claim 1, characterized in that, The edge computing unit (3) includes a high-performance computing chip (31), a data buffer (32), and an algorithm module (33). The high-performance computing chip (31) is connected to the data buffer (32) through a data bus. The algorithm module (33) contains various device state evaluation algorithms.

5. The power informatization data model terminal according to claim 1, characterized in that, The data transmission module (4) includes a 5G communication module (41) and an optical fiber communication module (42). The 5G communication module (41) is used to achieve high-speed wireless transmission of data. The optical fiber communication module (42) is used to achieve high-speed wired transmission of data.

6. The power informatization data model terminal according to claim 1, wherein The cloud data center (5) includes a cloud server (51), a data storage module (52), and a data analysis module (53). The cloud server (51) is connected to the data transmission module (4) through a network. The data storage module (52) is used to store a large amount of historical data. The data analysis module (53) is used to perform in-depth analysis and modeling on the data.

7. The power information data model terminal according to claim 1, wherein The user interaction module (6) includes a touch screen (61), a voice recognition module (62), and a remote control module (63). The touch screen (61) is installed on the terminal device. The voice recognition module (62) is used to receive user voice commands. The remote control module (63) is used to receive remote control commands.

8. The power information data model terminal according to claim 4, wherein The described edge computing unit (3) further includes an intelligent task scheduling system (34). The intelligent task scheduling system (34) includes a task scheduling algorithm (341) and a real-time task allocation module (342). The task scheduling algorithm (341) is installed in the high-performance computing chip (31), and the real-time task allocation module (342) is connected to the central processing unit (11).

9. The power informatization data model terminal according to claim 5, wherein The described data transmission module (4) further includes an intelligent transmission optimization system (43). The intelligent transmission optimization system (43) includes a transmission protocol optimization algorithm (431) and a bandwidth dynamic adjustment module (432). The transmission protocol optimization algorithm (431) is installed in the 5G communication module (41) and the optical fiber communication module (42), and the bandwidth dynamic adjustment module (432) is connected to the central processing unit (11).

10. The power informationization data model terminal according to claim 6, wherein The described cloud data center (5) further includes an intelligent data cleaning module (54). The intelligent data cleaning module (54) includes a data cleaning algorithm (541) and an anomaly detection module (542). The data cleaning algorithm (541) is installed in the cloud server (51), and the anomaly detection module (542) is connected to the cloud server (51).

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