Intelligent early warning system and method for insulation risk of high-voltage system under Flink driving

Abstract

The invention relates to the technical field of data processing, and discloses a Flink-driven high-voltage system insulation risk intelligent early warning system and method, the early warning system is composed of a data acquisition module, a data processing module, a data storage module, an analysis module and an early warning module; according to the method, abnormal events such as voltage fluctuation or current abnormity can be quickly identified by using the complex event processing capability of Flink, potential fault risks can be found as soon as possible, and the system can perform risk prediction and give out early warning before potential faults occur through combined analysis of historical data and real-time data, so that the fault prevention capability is improved, and the fault risk is reduced. The beneficial effect of monitoring and analyzing the insulation state of the high-voltage system in real time by using the Flink is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a Flink-driven intelligent early warning system and method for insulation risk of a high-voltage system. Background Art

[0002] With the rapid development of power grids, live working technology has become an important means of ultra-high voltage line maintenance, power grid equipment inspection and maintenance. However, live working is also a very dangerous job. The working voltage of overhead ultra-high voltage lines is high, the number of conductors is large, the tower head size is large, and the structure is complex. The actual live working conditions are very complex, with many working points and a wide distribution area, and they are often located in harsh environmental areas, which puts extremely high demands on the safety of live working sites and the quality of personnel. Live working itself has a very high risk factor, and personal risk is the most important risk. The impact of high-intensity working methods and bad weather on operators, the existence of strong electric fields, transient electric shocks and steady-state electric shocks, and the discharge of short-circuited air gaps due to misoperation during the operation process, threaten the safety of live working personnel at all times, which may directly cause casualties and accidents, and affect operators, regional power supply, ultra-high voltage line operation, production and life, etc.

[0003] After searching, it was found that the publication number of CN 115423387 A is an intelligent early warning method and system for on-site behavior of live workers on ultra-ultra-high voltage lines. The on-site behavior characteristic data of live workers are obtained through wearable devices worn by live workers. Then, the current safety level of live workers is determined based on the on-site behavior characteristic data and a preset intelligent early warning algorithm model. Then, early warning prompts are given based on the current safety level and the preset early warning mechanism, which can effectively avoid the occurrence of safety accidents and reduce casualties, thereby improving the safety of live working.

[0004] Although the aforementioned technical solution can determine the safety level according to the behavioral characteristics of the operators and provide early warning prompts, this technical solution cannot provide early warning for overhead ultra-high voltage / UHV line equipment. The extremely high voltage and current can break through the protective equipment worn by the operators, and the failure of overhead ultra-high voltage / UHV line equipment needs to be calculated and predicted in real time, otherwise accidents will directly pose a huge threat to the operators. Summary of the invention

[0005] Technical issues solved: In view of the deficiencies in the prior art, the present invention provides a high-voltage system insulation risk intelligent early warning system and method driven by Flink, which has the advantages of using Flink to monitor and analyze the insulation status of the high-voltage system in real time; timely discover and warn of potential risks, etc., and solves the problems of the above-mentioned technologies.

[0006] Technical solution: To achieve the above object, the present invention provides the following technical solution: an intelligent early warning system for insulation risk of a high-voltage system driven by Flink, wherein the early warning system is composed of a data acquisition module, a data processing module, a data storage module, an analysis module and an early warning module; The data acquisition module is used to collect data on high-voltage line equipment. Sensors are installed on overhead ultra-high / ultra-high voltage line equipment: transformers, transmission lines, and insulators to collect real-time operating status data of high-voltage line equipment: voltage, current, temperature, humidity, and insulation status. The data is transmitted to the data processing module via wireless: LoRa and 5G or optical fiber; The data processing module is used to process the voltage, current, temperature, humidity and insulation status data collected by the data acquisition module, perform real-time data reception and processing, support event-time-based processing, process delayed and out-of-order data, detect abnormal events in the equipment operation status, including voltage anomalies, insulation material aging and partial discharge detection, and calculate the insulation risk level of the equipment in real time based on the equipment status data and a preset early warning algorithm model; The data storage module uses a database to store device status data; The analysis module is used to store processing results, including insulation risk levels and alarm records of equipment; The warning module is used to perform real-time analysis based on the data processed by Flink, evaluate the status of the equipment and the insulation risk, and trigger different levels of warning prompts according to the insulation risk level of the equipment: low risk, medium risk and high risk. The warning strategy is set as follows: if the insulation risk level of the equipment exceeds the preset threshold, the system takes measures to automatically send warning information, and uses historical data and real-time data to establish a prediction model, conduct risk assessment on the equipment based on conditions, identify potential failure risks in advance, and issue warnings.

[0007] Preferably, there are N devices in the data acquisition module: transformer, transmission line, insulator, the operating status data of each device is represented by multiple parameters: voltage, current, temperature, humidity, insulation state, and the state vector of the device is defined as:

[0008] in: Indicates Devices at time Voltage; Indicates Devices at time Temperature; Indicates Devices at time Insulation status; Indicates Devices at time The current; Indicates Devices at time Humidity value; Indicates Devices at time Multiple operating status data.

[0009] Preferably, the data collected by the sensor in the data acquisition module is transmitted to the data processing module via wireless: LoRa, 5G or optical fiber. If there is a delay in the transmission process , then the data after transmission is calculated as:

[0010] in: is a vector, indicating the Devices at time data; Indicates Devices at original time Status data; is a scalar, indicating the Data transmission delay between devices.

[0011] Preferably, for N devices in the data acquisition module, the overall data acquisition system is expressed as:

[0012] in: Indicates time The overall data collected.

[0013] Preferably, the real-time data processing calculation in the data processing module is:

[0014] in: Representative Devices at time Status data; The processing calculation based on event time in the data processing module is: set up Indicates The event time of the data of each device is used to process delayed and out-of-order data. The data processing module is based on event time. Processing of device data:

[0015] in: Indicates that according to the event time Processed device data.

[0016] Preferably, the abnormal event detection calculation in the data processing module is:

[0017] in: Indicates at time Device detected abnormal events, otherwise it means no abnormality is detected; The insulation risk level in the data processing module is calculated as follows: And the preset early warning algorithm model , calculate the insulation risk level of the equipment in real time:

[0018] in: Indicates Devices at time Insulation risk level.

[0019] Preferably, the insulation risk level and warning threshold in the early warning module are: Indicates Devices at time Insulation risk level, define warning threshold , including low risk thresholds , Medium Risk Threshold and high risk threshold ; Low risk: ; Medium risk: ; High risk: .

[0020] Preferably, the early warning trigger mechanism expression in the early warning module is: Define the warning trigger function When the insulation risk level of the equipment exceeds the preset threshold, the system automatically sends an early warning message, which is expressed as:

[0021] The risk assessment and prediction model is:

[0022] in: represents the historical data matrix, Indicates the Devices at time Predict insulation risks; For time series data.

[0023] Preferably, the early warning module is based on a prediction model , identify potential failure risks in advance and issue early warnings, and define early warning thresholds When the predicted risk exceeds the threshold, the system issues an early warning: .

[0024] Intelligent early warning system and method for insulation risk of high-voltage system driven by Flink, the early warning method is: Step 1: Data collection: Install sensors on high-voltage line equipment: transformers, transmission lines and insulators to collect real-time operating status data of high-voltage line equipment, including voltage, current, temperature, humidity and insulation status; Step 2: Data transmission: The collected data is transmitted to the data processing module through appropriate communication methods: wireless or optical fiber, and the transmission delay is processed; Step 3: Data processing: Organize the status data of each device into a state vector for subsequent analysis. According to the transmission delay and event time, adjust and process the data to reflect the real-time status of the device. Step 4: Abnormal detection and risk assessment: Identify whether the equipment is in an abnormal state, make judgments based on the set detection standards, calculate the insulation risk level of the equipment based on the collected status data, and assess the degree of risk; Step 5. Early warning mechanism design: Set the early warning threshold of the risk level to divide the risk level into low, medium and high risks. When the equipment insulation risk level exceeds the set threshold, the early warning information is automatically sent to notify the relevant personnel; Step 6: Risk prediction and early warning: Establish risk assessment and prediction models based on historical data and real-time data, define early warning thresholds, and set trigger conditions. When the predicted risk reaches the threshold, issue a warning in a timely manner.

[0025] Compared with the prior art, the present invention provides a Flink-driven intelligent early warning system and method for insulation risk in a high-voltage system, which has the following beneficial effects: The present invention utilizes Flink's complex event processing capabilities to quickly identify abnormal events: voltage fluctuations or current anomalies, and discover potential fault risks early. Flink supports real-time calculation and analysis, and can dynamically evaluate the insulation status of the equipment based on the collected real-time data and historical data, thereby improving the accuracy of risk assessment. By utilizing Flink's real-time processing capabilities, the system can quickly trigger an early warning mechanism and notify relevant personnel to take measures when it detects that the equipment status is abnormal or the risk level exceeds a preset threshold. By combining and analyzing historical data and real-time data, the system can predict risks and issue early warnings before potential faults occur, thereby improving fault prevention capabilities and achieving the beneficial effect of using Flink to monitor and analyze the insulation status of high-voltage systems in real time.

[0026] By utilizing Flink's complex event processing capabilities, the present invention enables the system to quickly identify abnormal events: voltage fluctuations, current anomalies. Through preset detection functions, the system can respond immediately when an abnormal state occurs. Based on Flink's real-time processing capabilities, the system can combine historical data and real-time data to establish a risk prediction model. By dynamically updating model parameters, the system can improve the accuracy of risk prediction. The system sets an early warning threshold. When the predicted risk reaches the threshold, early warning information is issued in time, so that relevant personnel can take preventive measures before potential failures occur and reduce the risk of failures. By utilizing Flink's fault-tolerant mechanism, the system can continue to operate stably when some nodes fail, ensuring the continuity and accuracy of data processing, and achieving the beneficial effect of timely discovering and warning of potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a structural diagram of the intelligent early warning system for insulation risk of a high-voltage system driven by Flink in an embodiment of the present invention.

[0028] Figure 2 The figure is a flow chart of an intelligent early warning method for insulation risk of a high-voltage system driven by Flink in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] See also Figure 1 , the present invention provides a high-voltage system insulation risk intelligent early warning system driven by Flink, the early warning system includes a data acquisition module, a data processing module, a data storage module, an analysis module and an early warning module; The data acquisition module is used to collect data on high-voltage line equipment. Sensors are installed on overhead ultra-high / ultra-high voltage line equipment: transformers, transmission lines, and insulators to collect real-time operating status data of high-voltage line equipment: voltage, current, temperature, humidity, and insulation status. The data is transmitted to the data processing module via wireless: LoRa and 5G or optical fiber; The data processing module is used to process the voltage, current, temperature, humidity and insulation status data collected by the data acquisition module, perform real-time data reception and processing, support event-time-based processing, process delayed and out-of-order data, detect abnormal events in the equipment operation status, including voltage anomalies, insulation material aging and partial discharge detection, and calculate the insulation risk level of the equipment in real time based on the equipment status data and a preset early warning algorithm model; The data storage module uses a database to store device status data; The analysis module is used to store processing results, including insulation risk levels and alarm records of equipment; The warning module is used to perform real-time analysis based on the data processed by Flink, evaluate the status of the equipment and the insulation risk, and trigger different levels of warning prompts according to the insulation risk level of the equipment: low risk, medium risk and high risk. The warning strategy is set as follows: if the insulation risk level of the equipment exceeds the preset threshold, the system takes measures to automatically send warning information, and uses historical data and real-time data to establish a prediction model, conduct risk assessment on the equipment based on conditions, identify potential failure risks in advance, and issue warnings.

[0032] Flink has powerful parallel processing capabilities and can efficiently process massive amounts of data transmitted from multiple sensors and devices. In this way, the system can quickly process and analyze real-time status data.

[0033] Flink supports data processing based on event time, which means that the system can process data based on the time when the data is actually generated, rather than the physical time when the data arrives. This processing method can more accurately reflect the actual status of the device.

[0034] Flink can provide low-latency data processing and streaming computing, ensuring that data can be processed and analyzed in a short time. This is crucial for real-time monitoring and early warning systems that require immediate response.

[0035] Flink supports continuous data stream processing, which ensures that the system continuously obtains and processes data from the data source to ensure real-time performance.

[0036] Flink provides a powerful fault-tolerance mechanism. Even if some nodes fail, the system can still ensure the continuity and accuracy of data processing.

[0037] Flink is designed to ensure high availability of the system, ensuring that the system can run stably even in the event of hardware failures or other unpredictable situations.

[0038] By leveraging Flink’s complex event processing (CEP) capabilities, the system can quickly identify abnormal events, such as voltage fluctuations, current anomalies, etc. In this way, the system can detect potential failure risks early.

[0039] Flink supports real-time computing and analysis. It can dynamically evaluate the insulation status of equipment based on the collected real-time and historical data, thereby improving the accuracy of risk assessment.

[0040] By leveraging Flink’s real-time processing capabilities, the system can quickly trigger an early warning mechanism and notify relevant personnel to take action when it detects abnormal equipment status or the risk level exceeds a preset threshold.

[0041] By combining and analyzing historical data with real-time data, the system can predict risks and issue early warnings before potential failures occur, thereby improving failure prevention capabilities.

[0042] Flink supports modular design and can flexibly expand functional modules according to different business needs, such as adding new sensor data processing, optimizing risk assessment algorithms, etc.

[0043] Through Flink, the system can be seamlessly integrated with existing monitoring and management systems, further improving the overall efficiency and accuracy of the system.

[0044] In another embodiment of the present invention, a Flink-driven intelligent early warning system and method for insulation risk in a high-voltage system are provided, wherein the early warning method is: Step 1: Data collection: Install sensors on high-voltage line equipment: transformers, transmission lines and insulators to collect real-time operating status data of high-voltage line equipment, including voltage, current, temperature, humidity and insulation status; Step 2: Data transmission: The collected data is transmitted to the data processing module through appropriate communication methods: wireless or optical fiber, and the transmission delay is processed; Step 3: Data processing: Organize the status data of each device into a state vector for subsequent analysis. According to the transmission delay and event time, adjust and process the data to reflect the real-time status of the device. Step 4: Abnormal detection and risk assessment: Identify whether the equipment is in an abnormal state, make judgments based on the set detection standards, calculate the insulation risk level of the equipment based on the collected status data, and assess the degree of risk; Step 5. Early warning mechanism design: Set the early warning threshold of the risk level to divide the risk level into low, medium and high risks. When the equipment insulation risk level exceeds the set threshold, the early warning information is automatically sent to notify the relevant personnel; Step 6: Risk prediction and early warning: Establish risk assessment and prediction models based on historical data and real-time data, define early warning thresholds, and set trigger conditions. When the predicted risk reaches the threshold, issue a warning in a timely manner.

[0045] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the intelligent early warning method for insulation risk of high-voltage systems driven by Flink, including the following steps: obtaining the operating status data of the equipment, including voltage, current, temperature, humidity and insulation status, transmitting the collected data to the data processing module through a suitable communication method: wireless or optical fiber, and processing the transmission delay; The status data of each device is organized into a state vector for subsequent analysis. According to the transmission delay and event time, the data is adjusted and processed to reflect the real-time status of the device. Identify whether the device is in an abnormal state and make a judgment based on the set detection standards. According to the collected status data, the insulation risk level of the device is calculated to assess the degree of risk. According to the early warning threshold of the risk level, the risk level is divided into low, medium and high risks. When the insulation risk level of the device exceeds the set threshold, an early warning message is automatically sent to notify relevant personnel. A risk assessment and prediction model is established based on historical data and real-time data, an early warning threshold is defined, and trigger conditions are set. When the predicted risk reaches the threshold, a warning is issued in a timely manner.

[0046] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory.

[0047] The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the intelligent early warning method for insulation risk of high-voltage system driven by Flink in the above embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and execute the following steps: obtain the operating status data of the equipment, including voltage, current, temperature, humidity and insulation status, transmit the collected data to the data processing module through a suitable communication method: wireless or optical fiber, and process the transmission delay; organize the status data of each device into a state vector for subsequent analysis, adjust and process the data according to the transmission delay and event time to reflect the real-time status of the device; identify whether the equipment is in an abnormal state, judge it according to the set detection standard, calculate the insulation risk level of the equipment according to the collected status data, and evaluate the risk level; divide the risk level into low, medium and high risks according to the early warning threshold of the risk level, and automatically send early warning information to notify relevant personnel when the insulation risk level of the equipment exceeds the set threshold; establish a risk assessment and prediction model based on historical data and real-time data, define an early warning threshold, and set a trigger condition. When the predicted risk reaches the threshold, a warning is issued in time.

[0048] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0049] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0053] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The intelligent early warning system for insulation risk of high-voltage system driven by Flink is characterized by: The early warning system is composed of a data acquisition module, a data processing module, a data storage module, an analysis module and an early warning module; The data acquisition module is used to collect data on high-voltage line equipment. Sensors are installed on overhead ultra-high / ultra-high voltage line equipment: transformers, transmission lines, and insulators to collect real-time operating status data of high-voltage line equipment: voltage, current, temperature, humidity, and insulation status. The data is transmitted to the data processing module via wireless: LoRa and 5G or optical fiber; The data processing module is used to process the voltage, current, temperature, humidity and insulation status data collected by the data acquisition module, perform real-time data reception and processing, support event-time-based processing, process delayed and out-of-order data, detect abnormal events in the equipment operation status, including voltage anomalies, insulation material aging and partial discharge detection, and calculate the insulation risk level of the equipment in real time based on the equipment status data and a preset early warning algorithm model; The data storage module uses a database to store device status data; The analysis module is used to store processing results, including insulation risk levels and alarm records of equipment; The warning module is used to perform real-time analysis based on the data processed by Flink, evaluate the status of the equipment and the insulation risk, and trigger different levels of warning prompts according to the insulation risk level of the equipment: low risk, medium risk and high risk. The warning strategy is set as follows: if the insulation risk level of the equipment exceeds the preset threshold, the system takes measures to automatically send warning information, and uses historical data and real-time data to establish a prediction model, conduct risk assessment on the equipment based on conditions, identify potential failure risks in advance, and issue warnings.

2. According to the Flink-driven high-voltage system insulation risk intelligent early warning system of claim 1, it is characterized by: There are N devices in the data acquisition module: transformers, transmission lines, insulators. The operating status data of each device is represented by multiple parameters: voltage, current, temperature, humidity, insulation status. The state vector of the device is defined as: in: Indicates Devices at time Voltage; Indicates Devices at time Temperature; Indicates Devices at time Insulation status; Indicates Devices at time The current; Indicates Devices at time Humidity value; Indicates Devices at time Multiple operating status data.

3. The Flink-driven high-voltage system insulation risk intelligent early warning system according to claim 2 is characterized by: The data collected by the sensor in the data acquisition module is transmitted to the data processing module via wireless: LoRa, 5G or optical fiber. If there is a delay in the transmission process , then the data after transmission is calculated as: in: is a vector, indicating the Devices at time data; Indicates Devices at original time Status data; is a scalar, indicating the Data transmission delay between devices.

4. The Flink-driven high-voltage system insulation risk intelligent early warning system according to claim 3 is characterized by: For N devices in the data acquisition module, the overall data acquisition system is expressed as: in: Indicates time The overall data collected.

5. The Flink-driven high-voltage system insulation risk intelligent early warning system according to claim 4 is characterized by: The real-time data processing calculation in the data processing module is: in: Representative Devices at time Status data; The processing calculation based on event time in the data processing module is: set up Indicates The event time of the data of each device is used to process delayed and out-of-order data. The data processing module is based on event time. Processing of device data: in: Indicates that according to the event time Processed device data.

6. The Flink-driven high-voltage system insulation risk intelligent early warning system according to claim 5 is characterized by: The abnormal event detection calculation in the data processing module is: in: Indicates at time Device detected abnormal events, otherwise it means no abnormality is detected; The insulation risk level in the data processing module is calculated as follows: And the preset early warning algorithm model , calculate the insulation risk level of the equipment in real time: in: Indicates Devices at time Insulation risk level.

7. The Flink-driven high-voltage system insulation risk intelligent early warning system according to claim 6 is characterized by: The insulation risk level and warning threshold in the warning module are: Indicates Devices at time Insulation risk level, define warning threshold , including low risk thresholds , Medium Risk Threshold and high risk threshold ; Low risk: ; Medium risk: ; High risk: .

8. The Flink-driven high-voltage system insulation risk intelligent early warning system according to claim 7 is characterized by: The early warning trigger mechanism expression in the early warning module is: Define the warning trigger function When the insulation risk level of the equipment exceeds the preset threshold, the system automatically sends an early warning message, which is expressed as: The risk assessment and prediction model is: in: represents the historical data matrix, Indicates the Devices at time Predict insulation risks; For time series data.

9. The Flink-driven high-voltage system insulation risk intelligent early warning system according to claim 8 is characterized by: The early warning module is based on the prediction model , identify potential failure risks in advance and issue early warnings, and define early warning thresholds When the predicted risk exceeds the threshold, the system issues an early warning: 。 10. The intelligent early warning method for insulation risk of high-voltage system driven by Flink is characterized by: The early warning method is applied to the early warning system according to claim 9, and the early warning method is: S1. Real-time collection of operating status data of high-voltage line equipment, including voltage, current, temperature, humidity and insulation status; S2, transmitting the collected data to the data processing module through appropriate communication methods: wireless or optical fiber, and processing transmission delay; S3, organize the state data of each device into a state vector for subsequent analysis, and adjust and process the data according to the transmission delay and event time to reflect the real-time state of the device; S4. Identify whether the equipment is in an abnormal state, make a judgment based on the set detection standards, calculate the insulation risk level of the equipment based on the collected status data, and assess the risk level; S5. According to the early warning threshold of the risk level, the risk level is divided into low, medium and high risks. When the equipment insulation risk level exceeds the set threshold, an early warning message is automatically sent to notify relevant personnel; S6. Establish risk assessment and prediction models based on historical data and real-time data, define early warning thresholds, and set trigger conditions. When the predicted risk reaches the threshold, issue a warning in a timely manner.

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