Electric power big data-based central monitoring platform and monitoring analysis method

Abstract

The invention discloses a center monitoring platform based on electric power big data and a monitoring analysis method, and relates to the technical field of electric power monitoring, and the center monitoring platform comprises a data acquisition system, a data transmission and preprocessing system, a storage management system, a data processing system and a prediction scheduling system. The data acquisition system is used for collecting power utilization data, power grid operation parameters, geographic information data and meteorological environment information in real time, and the data transmission and preprocessing system is responsible for preprocessing the collected data. The power utilization information of users is collected through the intelligent electric meter, the temperature sensor, the humidity sensor and the pressure sensor are installed on the key nodes of the power grid, the operation state of the power grid is monitored in real time, the meteorological condition of the power grid needs to be monitored, and the meteorological environment is comprehensively monitored, so that the power utilization efficiency is improved. The method provides data support for subsequent natural disaster identification, and helps a monitoring platform to analyze how to carry out a better scheduling strategy on a power grid when a natural disaster occurs.

Description

Technical Field

[0001] The present invention relates to the field of power monitoring technology, and in particular to a central monitoring platform and a monitoring and analysis method based on power big data. Background Art

[0002] The power monitoring platform is a comprehensive system used to monitor and manage data in real time, and to monitor and analyze the power system in real time. The process of monitoring power involves data collection from multiple links, including power generation, transmission, transformation, distribution and consumption.

[0003] The defects of the existing central monitoring platform are: Patent document CN115296422B discloses a power cable operation status monitoring and control system and method based on big data. It mainly considers monitoring and analyzing the operation status of the power cable through operation monitoring data, and can give early warning in time when the power cable is abnormal. However, it does not consider how to obtain comprehensive data, making it difficult to provide sufficient and accurate data support for subsequent dispatch analysis of faults. Patent document CN112653241B discloses a power grid monitoring method and device, and an IoT metering unit, which mainly considers how to improve the accuracy of monitoring data, but does not consider whether natural disasters occur and the impact of different types of natural disasters on dispatching decisions; Application document CN115169598A discloses a smart grid monitoring and management system, which mainly considers faster troubleshooting of faulty lines through the regulation of each module program, but does not consider how to evaluate the effect of the dispatching decision after a period of time, making it difficult to ensure whether the operation effect of the power system after the decision is made meets expectations; Application document CN116545106A discloses an Internet of Things-based power system monitoring system, which mainly considers how to learn about fault conditions, locate faults and solve faults in the first place, but does not consider how to ensure data security and reliability. Summary of the invention

[0004] The purpose of the present invention is to provide a central monitoring platform and a monitoring and analysis method based on power big data to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a central monitoring platform based on power big data, including a data acquisition system, a data transmission and preprocessing system, a storage management system, a data processing system and a prediction and scheduling system, wherein the data acquisition system is used to collect power consumption data, power grid operation parameters, geographic information data and meteorological environment information in real time, the data transmission and preprocessing system is responsible for preprocessing the collected data and transmitting the preprocessed data to the storage management system, the storage management system performs distributed storage and backup of the preprocessed data, the data processing system uses a machine learning model and big data processing technology to perform in-depth analysis on the data stored in the storage management system, and the prediction and scheduling system performs fault prediction based on the analyzed data and gives corresponding response scheduling decisions; The data acquisition system includes power grid acquisition and environmental acquisition. The power grid acquisition uses smart meters and sensors to obtain power consumption data and power grid operation status in real time. The environmental acquisition collects meteorological data and geographic information data in real time by accessing meteorological stations, rainfall stations, water condition stations and earthquake monitoring networks. The electricity consumption data includes the user's total electricity consumption, time-based electricity consumption, electricity consumption time, voltage level and current intensity; The meteorological data include wind speed, rainfall, water level changes, typhoon path, typhoon intensity, earthquake time, earthquake location, earthquake magnitude, fire conditions, snowfall and temperature drop, and lightning activities.

[0006] Preferably, the prediction and dispatching system includes fault diagnosis, load forecasting and dispatching analysis. The fault diagnosis uses a fault diagnosis model to trace the faults that have occurred, and determines the source of the fault in combination with the disaster identification results. The load forecasting uses historical data to establish a time series model and combines the fault diagnosis results to simulate the load changes of the power grid under different scenarios through scenario simulation technology. The dispatching analysis uses an expert decision-making system and combines the results of load forecasting to formulate corresponding dispatching decisions, and evaluates the effects after dispatching. The dispatch analysis includes dispatch decision, dispatch evaluation and dispatch optimization. The dispatch decision is responsible for formulating reasonable power generation plans, load distribution plans and spare capacity configurations to ensure the stable operation of the power grid. The dispatch evaluation is used to evaluate the use effect of the power grid after the execution of the dispatch decision. The dispatch optimization uses machine learning algorithms and combines the dispatch evaluation results to find a better dispatch plan.

[0007] Preferably, the central monitoring platform also includes a user interface, a security management system and maintenance support. The user interface includes a desktop client, a mobile application and a web portal. The user interface is used to realize the interaction between the central monitoring platform and supervisors. The security management system ensures the security of data through strict access control policies. The maintenance support provides technical support services and updates system software to optimize the central monitoring platform.

[0008] Preferably, the data transmission and preprocessing system includes data preprocessing, data encryption and data transmission, wherein the data preprocessing is responsible for cleaning, integrating and standardizing the collected data, the data encryption uses encryption technology to encrypt the preprocessed data, and the data transmission uses wireless communication technology to transmit the encrypted data; The data preprocessing includes data cleaning, data integration, data conversion and data normalization.

[0009] Preferably, the storage management system includes data storage and data backup, the data storage uses a distributed database to store and manage a large amount of collected data, and the data backup is responsible for regular data backup.

[0010] Preferably, the data processing system includes data monitoring, alarm mechanism and disaster identification. The data monitoring uses data scanning technology to perform real-time scanning on the collected power consumption data and power grid operation parameters. When the data exceeds a set threshold, the alarm mechanism is triggered. The disaster identification uses big data analysis technology and combines historical data to predict whether a natural disaster will occur and determine the occurrence of the disaster. The disaster occurrence conditions include the type of disaster, the disaster level, and the time and place of occurrence; The types of disasters mentioned include typhoons, earthquakes, wildfires, ice cover, lightning and floods.

[0011] Preferably, the security management system includes access control and audit logs, wherein the access control is used to ensure that only authorized users can access sensitive data, and the audit log is responsible for recording all user operations and generating record reports.

[0012] Preferably, the maintenance support includes technical support and system upgrades. The technical support utilizes technical support services to solve problems encountered by users during use, and the system upgrades repair and improve the system by regularly updating system software.

[0013] Preferably, the sensor includes a temperature sensor, a humidity sensor and a pressure sensor.

[0014] Preferably, the monitoring and analysis method of the central monitoring platform comprises the following steps: Step S1: collecting power consumption data, power grid operation parameters, geographic information data and meteorological environment information in real time, and preprocessing the collected data; Step S2: then storing the preprocessed data in a distributed database and backing up the data; Step S3: Conduct in-depth analysis of the data, then use the fault diagnosis model and the disaster identification results to perform fault diagnosis, and then formulate corresponding response scheduling decisions; Step S4: Realize the interaction between supervisors and the monitoring platform through the user interface, and ensure data security through strict access control policies.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects users' electricity consumption information through smart meters, including users' total electricity consumption, time-sharing electricity consumption, electricity consumption time, voltage level and current intensity, and then installs temperature sensors, humidity sensors and pressure sensors at key nodes of the power grid to collect the temperature, humidity and pressure status of transmission lines and equipment in the power grid, and monitor the operating status of the power grid in real time, which is helpful for subsequent predictions, can promptly discover potential problems, reduce the time of unexpected downtime, and improve the overall reliability of the power grid. It is also necessary to monitor the meteorological conditions in which the power grid is located. By comprehensively monitoring the meteorological environment, data support is provided for the identification of subsequent natural disasters, which helps the monitoring platform analyze how to implement a better scheduling strategy for the power grid when a natural disaster occurs.

[0016] The present invention uses an expert decision-making system and combines the results of load forecasting to formulate corresponding response scheduling decisions according to whether a natural disaster occurs and the type of natural disaster. When no natural disaster occurs but a power grid fault is diagnosed, the load forecast directly uses a time series model and combines the fault results for simulation. When a natural disaster occurs and a power grid fault is diagnosed, the load forecast uses a time series model combined with the fault results and the natural disaster situation to predict the load change according to different disaster types and the fault conditions that occur. Then the dispatch analysis formulates corresponding response scheduling decisions based on the results of the load forecast.

[0017] The present invention evaluates the effectiveness of the implemented dispatching schemes, and maintains targeted performance monitoring and evaluation for a period of time after the dispatching decision is executed, to evaluate whether the dispatching scheme has achieved the expected effect, and to provide a basis for subsequent dispatching optimization. According to the dispatching evaluation results, the dispatching optimization will use machine learning algorithms to further find better dispatching schemes based on the dispatching decisions. Through continuous dispatching decisions, evaluations and optimizations, the operating efficiency and stability of the power system can be gradually improved, which will help to improve the perfection of the dispatching strategy of the future power grid when different natural disasters occur.

[0018] The present invention improves the security and reliability of data through data encryption, data backup and security management systems, adopts advanced encryption technology to encrypt and protect pre-processed data, improves the security of data during transmission, ensures the quality of data, provides an accurate data basis for subsequent encryption and analysis work, regularly backs up data, and effectively prevents data loss or damage. The security management system ensures that only authorized personnel can access some sensitive data through strict access control. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system diagram of the present invention; Figure 2 It is a structural diagram of the forecasting and scheduling system of the present invention; Figure 3 It is a structural diagram of the data preprocessing of the present invention; Figure 4 It is a structural diagram of the data processing system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention. Embodiment 1

[0021] See also Figure 1 , an embodiment provided by the present invention: a central monitoring platform and monitoring and analysis method based on power big data, including a data acquisition system, a data transmission and preprocessing system, a storage management system, a data processing system and a prediction and scheduling system, the data acquisition system is used to collect power consumption data, power grid operation parameters, geographic information data and meteorological environment information in real time, the data transmission and preprocessing system is responsible for preprocessing the collected data, and transmitting the preprocessed data to the storage management system, the storage management system performs distributed storage and backup of the preprocessed data, the data processing system uses a machine learning model and big data processing technology to perform in-depth analysis on the data stored in the storage management system, the prediction and scheduling system performs fault prediction based on the analyzed data, and gives corresponding response scheduling decisions; The data acquisition system includes power grid acquisition and environmental acquisition. The power grid acquisition uses smart meters and sensors to obtain power consumption data and power grid operation status in real time. The environmental acquisition collects meteorological data and geographic information data in real time by accessing meteorological stations, rainfall stations, water condition stations and earthquake monitoring networks. The electricity consumption data includes the user's total electricity consumption, time-based electricity consumption, electricity consumption time, voltage level and current intensity; The meteorological data include wind speed, rainfall, water level changes, typhoon path, typhoon intensity, earthquake time, earthquake location, earthquake magnitude, fire conditions, snowfall, temperature drop, and lightning activities; The sensors include a temperature sensor, a humidity sensor and a pressure sensor.

[0022] Furthermore, the data acquisition system is the cornerstone of the entire central monitoring platform. First, the user's electricity consumption information must be collected through smart meters, including the user's total electricity consumption, time-sharing electricity consumption, electricity consumption time, voltage level and current intensity data. Then, temperature sensors, humidity sensors and pressure sensors are installed at key nodes of the power grid to collect the temperature, humidity and pressure status of the transmission lines and equipment in the power grid. Real-time monitoring of the operation status of the power grid will help with subsequent predictions, detect potential problems in a timely manner, reduce unexpected downtime, and improve the overall reliability of the power grid. It is also necessary to monitor the meteorological conditions of the power grid. By connecting to multiple sources such as meteorological stations, rainfall stations, and water condition stations, The data, combined with the location information of the power grid equipment, the subsequent platform can simulate future weather conditions, evaluate the possible impact on the power grid, and send warning information to the operation and maintenance personnel in a timely manner. By comprehensively monitoring the meteorological environment, it provides data support for the identification of subsequent natural disasters, which helps the monitoring platform analyze how to implement better scheduling strategies for the power grid when natural disasters occur. In actual use, under the premise of accessing multi-source data such as meteorological stations, rainfall stations, and water condition stations, a variety of meteorological monitoring devices can be used to conduct more detailed and accurate monitoring of the environment around key points of the power grid, which further improves the data accuracy of the monitoring system to a certain extent and helps the subsequent analysis of the monitoring platform. Embodiment 2

[0023] See also Figure 2 , an embodiment provided by the present invention: a central monitoring platform and monitoring and analysis method based on power big data, the prediction and dispatching system includes fault diagnosis, load forecasting and dispatching analysis, the fault diagnosis uses a fault diagnosis model to trace the fault that has occurred, and determines the source of the fault in combination with the disaster identification result, the load forecasting uses historical data to establish a time series model and combines the fault diagnosis result, and simulates the power grid load changes under different scenarios through scenario simulation technology, the dispatching analysis uses an expert decision system and combines the results of load forecasting to formulate corresponding dispatching decisions, and evaluates the effect after dispatching; The dispatch analysis includes dispatch decision, dispatch evaluation and dispatch optimization. The dispatch decision is responsible for formulating reasonable power generation plans, load distribution plans and spare capacity configurations to ensure the stable operation of the power grid. The dispatch evaluation is used to evaluate the use effect of the power grid after the execution of the dispatch decision. The dispatch optimization uses machine learning algorithms and combines the dispatch evaluation results to find a better dispatch plan.

[0024] Furthermore, when no natural disaster occurs but a power grid fault is diagnosed, the load forecast directly uses the time series model and combines it with the fault results for simulation. When a natural disaster occurs and a power grid fault is diagnosed, the load forecast uses the time series model combined with the fault results and the natural disaster situation to predict the load change according to the different disaster types and the fault conditions. Then the dispatch analysis formulates corresponding response dispatch decisions based on the results of the load forecast. By accessing the data of meteorological stations, rainfall stations and water condition stations, the wind speed, rainfall and water level changes are monitored in real time, and the typhoon path and intensity are predicted. Once the typhoon is detected approaching, Immediately initiate the emergency plan, deploy resources and personnel to carry out emergency repairs and restore power supply. Access the earthquake monitoring network to obtain the time, location and magnitude of the earthquake in real time, assess the extent of damage to power grid facilities, determine the priority of emergency repairs, and develop a detailed power grid restoration plan. Coordinate the forces of all parties to carry out emergency repairs. Use satellite remote sensing technology to monitor the occurrence and development of wildfires, and use drones equipped with radars for patrols to promptly discover fire points. Cooperate with the fire department to quickly respond to wildfire incidents and protect power grid facilities. Access data from the meteorological department to obtain weather information such as low temperature and snowfall, and take direct current ice melting measures based on the monitoring results. Technical measures such as lightning protection and lightning rod protection are used to prevent line failures caused by icing. Lightning conductors and lightning rods are installed, and lightning positioning systems are used to monitor lightning activities. Damage assessments are conducted on substations and transmission lines struck by lightning, and repairs are carried out in a timely manner. The layout of lightning protection facilities is optimized based on monitoring data to improve the lightning protection effect. Dispatch analysis uses monitoring data and expert decision-making systems to formulate reasonable power generation plans, load distribution plans, and spare capacity configuration strategies to ensure the stable operation of the power system, and evaluates the effectiveness of the implemented dispatching plans to assess whether the dispatching plans have achieved the expected results, such as whether the load demand is met and whether the power grid is safe. After the dispatching decision is executed, the dispatching evaluation will continue to monitor and evaluate the performance for a period of time to evaluate the effect after the dispatching. In addition, the dispatching evaluation can also identify existing problems and deficiencies, and provide a basis for subsequent dispatching optimization. According to the dispatching evaluation results, the dispatching optimization will use machine learning algorithms to further find better dispatching plans based on the dispatching decision to improve the efficiency and economy of the power system, reduce operating costs, and ensure the safety and reliability of the power grid. Through continuous dispatching decisions, evaluations, and optimizations, the operating efficiency and stability of the power system can be gradually improved. Embodiment 3

[0025] See also Figure 1 and 3 , an embodiment provided by the present invention: a central monitoring platform and monitoring and analysis method based on power big data, the data transmission and preprocessing system includes data preprocessing, data encryption and data transmission, the data preprocessing is responsible for cleaning, integrating and standardizing the collected data, the data encryption uses encryption technology to encrypt the preprocessed data, and the data transmission uses wireless communication technology to transmit the encrypted data; The data preprocessing includes data cleaning, data integration, data conversion and data normalization; The storage management system includes data storage and data backup. The data storage uses a distributed database to store and manage a large amount of collected data, and the data backup is responsible for regularly backing up data.

[0026] Furthermore, the collected data is cleaned to remove invalid values, outliers and duplicate data to ensure the accuracy and reliability of the data and provide a solid foundation for subsequent analysis. Then, the data from different data sources are merged into a unified data set to ensure the uniqueness and accuracy of the data. The data is then format converted, attribute transformed, and normalized or standardized to better meet the needs of subsequent analysis or modeling work. Finally, the data is converted into a unified format and unit for comparison and application. Normalization helps to eliminate differences between different data sources and improve data consistency. The quality and accuracy of subsequent analysis are ensured by preprocessing data from different sources. Advanced encryption technology is then used to The preprocessed data is encrypted and protected, which improves the security of the data during transmission, ensures the quality of the data, and provides an accurate data basis for subsequent encryption and analysis work. When subsequent personnel need to access the data, they only need to access the data through the corresponding identity authentication mechanism at the user interface. Wireless communication technologies such as 4G / 5G and LoRa are used to transmit the encrypted data to the storage management system. In actual use, wired communication technologies such as Ethernet can also be used to achieve real-time data transmission. The storage management system uses a distributed database management system to store massive data, provide support for subsequent analysis and decision-making, ensure high availability and scalability of the data, and also need to perform regular data backup to prevent data loss or damage. Embodiment 4

[0027] See also Figure 4, an embodiment provided by the present invention: a central monitoring platform and monitoring and analysis method based on power big data, the data processing system includes data monitoring, alarm mechanism and disaster identification, the data monitoring uses data scanning technology to perform real-time scanning on the collected power consumption data and power grid operation parameters, and triggers the alarm mechanism when the data exceeds the set threshold, and the disaster identification uses big data analysis technology and combines historical data to predict whether a natural disaster will occur and determine the occurrence of the disaster; The disaster occurrence conditions include the type of disaster, the disaster level, and the time and place of occurrence; The types of disasters mentioned include typhoons, earthquakes, wildfires, ice cover, lightning and floods.

[0028] Furthermore, thresholds are set in advance, and big data analysis technology is used to analyze meteorological data to determine whether a natural disaster has occurred. If a natural disaster is determined to have occurred, the specific circumstances of the disaster are determined. At the same time, a large amount of electricity consumption data and power grid operation status data stored in the storage management system are scanned in real time. When the monitored data exceeds the set threshold range, the alarm mechanism is immediately triggered, and the disaster identification results and geographic location are immediately sent to the user interface in text form, prompting management personnel in time so that they can quickly take countermeasures. Embodiment 5

[0029] See also Figure 1 , an embodiment provided by the present invention: a central monitoring platform based on electric power big data and a monitoring and analysis method, the central monitoring platform also includes a user interface, a security management system and maintenance support, the user interface includes a desktop client, a mobile application and a web portal, the user interface is used to realize the interaction between the central monitoring platform and the supervisor, the security management system ensures the security of data through a strict access control policy, and the maintenance support provides technical support services and updates the system software to optimize the central monitoring platform; The security management system includes access control and audit logs. The access control is used to ensure that only authorized users can access sensitive data. The audit log is responsible for recording all user operations and generating record reports. The maintenance support includes technical support and system upgrades. The technical support utilizes technical support services to solve problems encountered by users during use, and the system upgrade repairs and improves the system by regularly updating system software.

[0030] Furthermore, the user interface is used to realize the interaction between personnel and the monitoring center, including the desktop client. The various data in the monitoring platform can be aggregated into a three-dimensional dynamic image using the modeling software, and then displayed through the desktop application of the desktop client, which is convenient for in-depth analysis and report generation. The mobile application allows staff to access the key information of the monitoring platform anytime and anywhere, and also facilitates the monitoring platform to promptly notify the on-site staff of the diagnosis situation, reducing processing time. In addition, the web portal can provide non-staff with a general overview of the power grid operation status, so that non-staff can understand whether the power grid is operating normally. The security management system is used to ensure that sensitive data can only be accessed by authorized personnel or users, thereby ensuring the security of the data. In addition, all access operations are recorded and record reports are generated to facilitate subsequent traceability and security analysis. Maintenance support will regularly update the system software, fix known vulnerabilities, and add new functions.

[0031] Furthermore, the monitoring and analysis method of the central monitoring platform includes the following steps: Step S1: collecting power consumption data, power grid operation parameters, geographic information data and meteorological environment information in real time, and preprocessing the collected data; Step S2: then storing the preprocessed data in a distributed database and backing up the data; Step S3: Conduct in-depth analysis of the data, then use the fault diagnosis model and the disaster identification results to perform fault diagnosis, and then formulate corresponding response scheduling decisions; Step S4: Realize the interaction between supervisors and the monitoring platform through the user interface, and ensure data security through strict access control policies.

[0032] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A central monitoring platform based on power big data, characterized by: It includes a data acquisition system, a data transmission and preprocessing system, a storage management system, a data processing system and a prediction and scheduling system. The data acquisition system is used to collect power consumption data, power grid operation parameters, geographic information data and meteorological environment information in real time. The data transmission and preprocessing system is responsible for preprocessing the collected data and transmitting the preprocessed data to the storage management system. The storage management system performs distributed storage and backup of the preprocessed data. The data processing system uses machine learning models and big data processing technology to perform in-depth analysis on the data stored in the storage management system. The prediction and scheduling system predicts faults based on the analyzed data and gives corresponding response scheduling decisions. The data acquisition system includes power grid acquisition and environmental acquisition. The power grid acquisition uses smart meters and sensors to obtain power consumption data and power grid operation status in real time. The environmental acquisition collects meteorological data and geographic information data in real time by accessing meteorological stations, rainfall stations, water condition stations and earthquake monitoring networks. The electricity consumption data includes the user's total electricity consumption, time-based electricity consumption, electricity consumption time, voltage level and current intensity; The meteorological data include wind speed, rainfall, water level changes, typhoon path, typhoon intensity, earthquake time, earthquake location, earthquake magnitude, fire conditions, snowfall and temperature drop, and lightning activities.

2. According to claim 1, a central monitoring platform based on power big data is characterized by: The prediction and dispatching system includes fault diagnosis, load forecasting and dispatching analysis. The fault diagnosis uses a fault diagnosis model to trace the faults that have occurred, and determines the source of the fault in combination with the disaster identification results. The load forecasting uses historical data to establish a time series model and combines the fault diagnosis results to simulate the load changes of the power grid under different scenarios through scenario simulation technology. The dispatching analysis uses an expert decision-making system and combines the results of load forecasting to formulate corresponding dispatching decisions, and evaluates the effects after dispatching. The dispatch analysis includes dispatch decision, dispatch evaluation and dispatch optimization. The dispatch decision is responsible for formulating reasonable power generation plans, load distribution plans and spare capacity configurations to ensure the stable operation of the power grid. The dispatch evaluation is used to evaluate the use effect of the power grid after the execution of the dispatch decision. The dispatch optimization uses machine learning algorithms and combines the dispatch evaluation results to find a better dispatch plan.

3. According to claim 1, a central monitoring platform based on power big data is characterized by: The central monitoring platform also includes a user interface, a security management system and maintenance support. The user interface includes a desktop client, a mobile application and a web portal. The user interface is used to realize the interaction between the central monitoring platform and supervisors. The security management system ensures the security of data through strict access control policies. The maintenance support provides technical support services and updates system software to optimize the central monitoring platform.

4. The central monitoring platform based on power big data according to claim 1 is characterized by: The data transmission and preprocessing system includes data preprocessing, data encryption and data transmission. The data preprocessing is responsible for cleaning, integrating and standardizing the collected data. The data encryption uses encryption technology to encrypt the preprocessed data. The data transmission uses wireless communication technology to transmit the encrypted data. The data preprocessing includes data cleaning, data integration, data conversion and data normalization.

5. The central monitoring platform based on power big data according to claim 1 is characterized by: The storage management system includes data storage and data backup. The data storage uses a distributed database to store and manage a large amount of collected data, and the data backup is responsible for regularly backing up data.

6. The central monitoring platform based on power big data according to claim 1 is characterized by: The data processing system includes data monitoring, alarm mechanism and disaster identification. The data monitoring uses data scanning technology to scan the collected power consumption data and power grid operation parameters in real time. When the data exceeds the set threshold, the alarm mechanism is triggered. The disaster identification uses big data analysis technology and combines historical data to predict whether a natural disaster will occur and determine the occurrence of the disaster. The disaster occurrence conditions include the type of disaster, the disaster level, and the time and place of occurrence; The types of disasters mentioned include typhoons, earthquakes, wildfires, ice cover, lightning and floods.

7. The central monitoring platform based on power big data according to claim 3 is characterized by: The security management system includes access control and audit logs. The access control is used to ensure that only authorized users can access sensitive data. The audit log is responsible for recording all user operations and generating record reports.

8. The central monitoring platform based on power big data according to claim 3 is characterized by: The maintenance support includes technical support and system upgrades. The technical support utilizes technical support services to solve problems encountered by users during use, and the system upgrade repairs and improves the system by regularly updating system software.

9. The central monitoring platform based on power big data according to claim 1 is characterized by: The sensors include a temperature sensor, a humidity sensor and a pressure sensor.

10. A monitoring and analysis method for a central monitoring platform based on electric power big data, applicable to a central monitoring platform based on electric power big data according to claims 1-7, characterized in that: The monitoring and analysis method of the center's monitoring platform includes the following steps: Step S1: collecting power consumption data, power grid operation parameters, geographic information data and meteorological environment information in real time, and preprocessing the collected data; Step S2: then storing the preprocessed data in a distributed database and backing up the data; Step S3: Conduct in-depth analysis of the data, then use the fault diagnosis model and the disaster identification results to perform fault diagnosis, and then formulate corresponding response scheduling decisions; Step S4: Realize the interaction between supervisors and the monitoring platform through the user interface, and ensure data security through strict access control policies.

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