New energy power plant centralized monitoring system time period reporting system

Abstract

The invention provides a new energy power plant centralized monitoring system time period reporting system, and relates to the technical field of new energy power generation, and the system comprises a time period analysis device which carries out the analysis processing of a time period selection instruction received by a monitoring human-computer interface, and forms a target time period parameter set. And the data screening device performs screening processing on the original operation data and the event records corresponding to the target time period to form a target information set, and the screening processing adopts a field white list mode. According to the time period reporting system of the centralized monitoring system of the new energy power plant, original operation data are accurately analyzed and screened through the synergistic effect of a plurality of modules such as the time period analysis device, the data screening device and the state arrangement device, a target information set is formed, and aggregation and arrangement of equipment-level state evolution sequences are achieved. By combining the event records of different sources, aligning the time labels and intensively arranging the cross-station data, the efficiency and accuracy of data processing are improved.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, specifically a time-period reporting system for centralized monitoring of new energy power plants. Background Technology

[0002] New energy power generation technologies have developed rapidly in recent years, especially in the fields of wind and solar power. With the expansion of equipment scale, power plant centralized monitoring systems face higher monitoring demands. These systems typically display the real-time status of power generation equipment, warning messages, and fault reports to help maintenance personnel respond quickly. However, existing monitoring systems have certain limitations. For example, traditional monitoring systems rely on manual operation, requiring monitoring personnel to be on duty around the clock to ensure real-time access to equipment operating status. Once the on-duty personnel leave, real-time equipment data cannot be transmitted in a timely manner, forcing staff to rely on reviewing operating logs afterward to obtain missed information, which is not only inefficient but also increases labor costs. Furthermore, traditional systems typically lack effective automatic reporting mechanisms, resulting in the inability to achieve immediate automated reporting when equipment operating status changes.

[0003] Chinese invention patent CN113190583A discloses a data acquisition system based on the MQTT protocol. This system uses a gateway acquisition module to obtain operational data from a photovoltaic power station and transmits the data to a storage module. It does not address automated reporting functionality after equipment is disconnected, focusing primarily on real-time data acquisition and storage. While it boasts high data transmission efficiency and system stability, it lacks an automatic reporting mechanism for equipment status after monitoring personnel are absent and lacks time-period reporting functionality.

[0004] The core flaw in existing technology lies in the system's inability to automatically report equipment operating status when on-duty personnel leave. Especially upon the return of monitoring personnel, the system cannot immediately obtain the equipment's status during the period of absence, impacting safe monitoring and timely maintenance. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a time-period reporting system for centralized monitoring of new energy power plants. The technical problem this invention aims to solve is: how to address the issues of low accuracy in time-period reporting, inaccurate fault diagnosis, and unclear information presentation in centralized monitoring systems for new energy power plants through methods and processes such as time-period analysis, data filtering, status arrangement, and initial fault determination.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a time-period reporting system for centralized monitoring of new energy power plants, comprising: A time period parsing device, wherein the time period parsing device parses and processes the time period selection command received by the monitoring human-machine interface to form a target time period parameter set; A data filtering device is used to filter and process the original operating data and event records corresponding to the target time period to form a target information set. The filtering and processing adopts a field whitelist method. A state orchestration device aggregates and orchestrates the target information set to form a device-level state evolution sequence. The aggregation and orchestration process includes merging of records from the same source, time tag alignment, and centralized orchestration across stations. The first-fault determination device performs first-fault determination processing on the device-level state evolution sequence to form a unique first-fault identifier. The first-fault determination processing includes a de-jittering time window, a fault priority matrix, and event causal chain extraction. The reporting and presentation device generates a time-period reporting page by processing the device-level state evolution sequence and the unique first fault identifier. The reporting and presentation process includes data association, hierarchical display, and interactive control.

[0007] Preferably, the parsing process includes time-stamped parsing and range parsing. The time-stamped parsing includes year, month, day, hour, and minute, and the range parsing includes station and unit.

[0008] Preferably, the field whitelist method is updated every 24 hours. The field whitelist method includes a device anomaly identification field, an operating status description field, and an alarm record field. The device anomaly identification field includes the abnormal device number and the initial fault candidate that caused the shutdown. The operating status description field includes the device status change type and a timestamp. The device status change type includes shutdown, standby, startup, fault, power-limited operation, communication loss, and communication recovery. The alarm record field includes the alarm time and alarm content.

[0009] Preferably, the merging of homologous records adopts an event similarity calculation model, and the technical formula of the event similarity calculation model is as follows: ; in, For the first Article and No. The similarity coefficient between events is dimensionless and ranges from 0 to 1. For the first The timestamp of each event record, in seconds. For the first The timestamp of each event record, in seconds. This is a time-dependent constant, in seconds. For the first A vector of event states, dimensionless. For the first A vector of event states, dimensionless. The time weighting coefficient is dimensionless. The state weight coefficients are dimensionless and satisfy the following conditions: .

[0010] Preferably, when the similarity coefficient is ≥0.85, the target information set is merged based on the same source records. The steps of aggregation and arrangement are as follows: S1. Merge the same-origin records in the target information set to form a unified event node; S2. The unified event nodes are aligned with time tags to form a state evolution sequence, wherein the time tag alignment is performed using a linear interpolation method; S3. Perform cross-site centralized arrangement of the state evolution sequence to form the equipment-level state evolution sequence.

[0011] Preferably, the de-jittering time window filters recurring triggered events in the device-level state evolution sequence, the fault priority matrix includes fault category, impact range and duration, the source of the first fault is determined according to the result of the fault priority matrix, and the event node corresponding to the source of the first fault is used as the root node of the event causal chain extraction.

[0012] Preferably, the unique first-fault identifier is generated according to the hierarchical structure of the root node, and the unique first-fault identifier includes a fault category code, a device number, and a trigger timestamp.

[0013] Preferably, the data association includes a two-way mapping between the unique initial fault identifier and the device operating status information; the hierarchical display includes a basic layer, an event layer, and a summary layer; and the interactive control includes pop-up display, scrolling view, floating prompts, and status replay.

[0014] Preferably, the base layer displays real-time status parameters of the equipment operation, the event layer marks the time and location of abnormal events and the first failure, and the summary layer displays site-level operation statistics and equipment comparison information.

[0015] Preferably, the time-period reporting page includes a basic display area, an event alarm area, a statistical analysis area, and an interactive control area.

[0016] This invention provides a time-period reporting system for centralized monitoring of new energy power plants. It has the following beneficial effects: This centralized monitoring system for new energy power plants utilizes a time-period reporting system. Through the coordinated operation of multiple modules, including a time-period analysis device, a data filtering device, and a status orchestration device, it accurately analyzes and filters raw operating data to form a target information set, enabling the aggregation and orchestration of equipment-level status evolution sequences. By merging event records from different sources, aligning time tags, and centrally orchestrating data across different power plants, it improves the efficiency and accuracy of data processing.

[0017] A primary fault identification device is employed to determine faults in the equipment-level state evolution sequence. Techniques such as de-jittering time windows, fault priority matrices, and event causal chain extraction are used to screen and locate the primary fault source. The de-jittering time window filters out repeatedly triggered events, reducing false positives. The fault priority matrix determines the source of the primary fault based on fault category, impact range, and duration, ensuring accurate fault source location. Event causal chain extraction generates a unique primary fault identifier, improving the accuracy and timeliness of fault identification. These techniques enable rapid fault identification and handling, reducing equipment downtime, improving power plant operating efficiency, providing a clear basis for subsequent fault analysis and handling, and enhancing the safety and stability of the power plant. Attached Figure Description

[0018] Figure 1 It is a system overall flowchart for realizing an invention; Figure 2 This is a flowchart of the time-segmentation processing for implementing the invention; Figure 3 This is a schematic diagram of a data filtering structure for implementing an invention; Figure 4 It is a flowchart of the state arrangement for realizing an invention; Figure 5 It is a flowchart for fault diagnosis and reporting in the implementation of an invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 like Figure 1-5As shown, this embodiment of the invention provides a time-period reporting system for a centralized monitoring system of a new energy power plant, including a time-period parsing device. The time-period parsing device parses and processes the time-period selection command received from the monitoring human-machine interface to form a target time-period parameter set. The parsing process includes time-stamp parsing and range parsing. Time-stamp parsing includes year, month, day, hour, and minute, while range parsing includes the power station and generating unit.

[0021] The time-period analysis device divides monitoring data into time periods, which helps to accurately analyze operational data according to user-specified time period parameters, ensuring that the monitoring system matches the requirements of the time period and improving the accuracy of data analysis.

[0022] The data filtering device processes the raw operational data and event records corresponding to the target time period to form a target information set. The filtering process uses a field whitelist method. The field whitelist method is updated every 24 hours. The field whitelist method includes equipment anomaly identification field, operating status description field, and alarm record field. The equipment anomaly identification field includes the abnormal equipment number and the initial fault candidate causing the shutdown. The operating status description field includes the equipment status change type and time stamp. The equipment status change type includes shutdown, standby, startup, fault, power-limited operation, communication loss, and communication recovery. The alarm record field includes the alarm time and alarm content.

[0023] The data filtering device filters out irrelevant data by screening key data such as equipment anomalies, operating status, and alarm records, ensuring that only important information is retained for analysis. This improves the efficiency and effectiveness of data processing and avoids information overload.

[0024] The state orchestration device aggregates and orchestrates the target information set to form a device-level state evolution sequence. The aggregation and orchestration process includes merging of similar records, time tag alignment, and centralized orchestration across stations. Merging of similar records employs an event similarity calculation model, the technical formula of which is: ; in, For the first Article and No. The similarity coefficient between events is dimensionless and ranges from 0 to 1. For the first The timestamp of each event record, in seconds. For the first The timestamp of each event record, in seconds. This is a time-dependent constant, in seconds. For the first A vector of event states, dimensionless. For the first A vector of event states, dimensionless. The time weighting coefficient is dimensionless. The state weight coefficients are dimensionless and satisfy the following conditions: .

[0025] When the similarity coefficient is ≥0.85, the target information set is merged based on records from the same source. The steps for aggregation and arrangement are as follows: S1. Merge records from the same source into a unified event node from the target information set.

[0026] S2. Time tag alignment is performed on unified event nodes to form a state evolution sequence. The time tag alignment adopts the linear interpolation method.

[0027] S3. The state evolution sequence is centrally arranged across stations to form an equipment-level state evolution sequence.

[0028] By aggregating and arranging equipment status, the evolution of equipment status is formed into a clear sequence, which facilitates the tracking and analysis of equipment behavior, helps to identify the changing trends and potential problems of equipment status, integrates equipment data across sites, and provides a more comprehensive monitoring view.

[0029] The initial fault determination device processes the equipment-level state evolution sequence to generate a unique initial fault identifier. This process includes a de-jittering time window, a fault priority matrix, and event causal chain extraction. The de-jittering time window filters out recurring events in the equipment-level state evolution sequence. The fault priority matrix includes fault category, impact range, and duration. Based on the results of the fault priority matrix, the source of the initial fault is determined, and the event node corresponding to the source of the initial fault is used as the root node for event causal chain extraction. A unique initial fault identifier is generated based on the hierarchical structure of the root node, and this identifier includes a fault category code, equipment number, and trigger timestamp.

[0030] By filtering duplicate events through a de-jitter time window and a fault priority matrix, and combining this with event causal chain extraction, the source of the fault can be accurately located, generating a unique fault identifier. This improves the accuracy of fault diagnosis and helps maintenance personnel quickly identify the root cause of the fault.

[0031] The reporting and presentation device processes the equipment-level status evolution sequence and unique initial fault identifier to generate a time-period reporting page. The reporting generation process includes data association, hierarchical display, and interactive control. Data association involves a two-way mapping between the unique initial fault identifier and equipment operating status information. Hierarchical display includes a basic layer, an event layer, and a summary layer. Interactive control includes pop-up displays, scrolling views, floating prompts, and status replay. The basic layer displays real-time status parameters of the equipment, the event layer marks the time and location of abnormal events and initial faults, and the summary layer displays site-level operating statistics and equipment comparison information. The time-period reporting page includes a basic display area, an event alarm area, a statistical analysis area, and an interactive control area.

[0032] The system associates equipment status evolution sequences with fault identifiers to generate structured time-period report pages. The reporting presentation device supports hierarchical display and interactive operation, providing maintenance personnel with real-time equipment status, event annotations, and system statistics, thus improving the operability of information presentation and decision support capabilities.

[0033] The centralized monitoring system for new energy power plants improves the efficiency and accuracy of data processing, fault diagnosis, and report generation through the coordinated operation of multiple key devices. The time-period analysis device accurately divides monitoring data into time periods, ensuring that monitoring results highly align with user needs. The data filtering device uses a field whitelist to filter key information, removing redundant data and ensuring the accuracy and relevance of the analyzed data. The status orchestration device aggregates and centrally orchestrates equipment status across stations, providing a comprehensive view of equipment status evolution and facilitating the timely detection of potential problems. The initial fault determination device utilizes de-jittering, fault priority matrices, and event causal chain extraction technologies to accurately locate the initial fault point, ensuring accurate identification of the fault source. The reporting and presentation device, through hierarchical display and interactive control functions, presents equipment operating status and fault information in real time, improving the system's operability and decision support capabilities. Overall, through the collaboration of these devices, the system optimizes the equipment monitoring, fault diagnosis, and information reporting processes, enhancing the management efficiency, fault response speed, and overall reliability of new energy power plants.

[0034] Example 2 This embodiment is based on the time-period reporting system of the centralized monitoring system for new energy power plants. It uses a time-period analysis device to analyze and filter the operating data of the units in wind farm A within a specific time period, providing accurate time-period and equipment information for subsequent data processing and fault analysis. The specific implementation method is as follows: 1. Background The operating data and fault records of turbines 1 and 3 in a wind farm A need to be extracted and analyzed by the monitoring system. The operator selected the following time periods on the monitoring human-machine interface: Time slot selection: The event will start at 08:00 on October 10, 2025 and end at 12:00 on October 10, 2025.

[0035] The system needs to select instructions based on the time period, parse the time period data, and provide accurate time ranges and device information for subsequent data filtering and event detection.

[0036] 2. Receive time period selection command The operator enters the following information from the monitoring system interface: Start time: 08:00 on October 10, 2025, end time: 12:00 on October 10, 2025, site: wind farm A, units: unit 1, unit 3.

[0037] 3. Timescale Analysis The time period parsing device begins parsing the received start and end times, performing time stamp parsing: Start time analysis: Year=2025, Month=10, Day=10, Hour=08, Minute=00.

[0038] End time analysis: Year = 2025, Month = 10, Day = 10, Hour = 12, Minute = 00.

[0039] Timescale resolution results: Start time parameter set: October 10, 2025, 08:00; End time parameter set: October 10, 2025, 12:00.

[0040] 4. Range Analysis Analysis of the wind farm and turbines: Select wind farm A as the wind farm, and select turbine 1 and turbine 3 as the turbines.

[0041] Range analysis results: The site is wind farm A, and the units are unit 1 and unit 3.

[0042] 5. Generate the parameter set for the target time period. The time period parsing device combines the time stamp parsing results with the range parsing results to generate a complete target time period parameter set. This target time period parameter set information is then transmitted to the subsequent data filtering device for processing the raw data and event records.

[0043] Target time period parameter set: Time scale parameter set: Start time: 08:00 on October 10, 2025, end time: 12:00 on October 10, 2025, site information: wind farm A, unit information: unit 1, unit 3.

[0044] 6. Specific data The actual equipment operation data and event logs during the time period are as follows: Raw data: Unit 1: October 10, 2025, 08:05: Equipment status change - Start-up; October 10, 2025, 09:20: Equipment fault - abnormal current; October 10, 2025, 11:45: Equipment status change - Shutdown.

[0045] Unit 3: October 10, 2025, 08:15: Equipment status change - Start-up; October 10, 2025, 09:50: Equipment malfunction - Overheating; October 10, 2025, 11:55: Equipment status change - Standby.

[0046] Data filtering results: Based on the target time period parameter set generated by the time period analysis device, the data filtering device will extract the equipment operation data and event records between 08:00 and 12:00 on October 10, 2025.

[0047] In this embodiment, the time period analysis device accurately analyzes the user's input time period selection command and filters out the operating data and event records of Unit 1 and Unit 3 based on the selected time period. This ensures that the monitoring system efficiently extracts the operating status and fault information of the equipment within the target time period, providing reliable data support for subsequent fault diagnosis, equipment status analysis and performance optimization, and improving the monitoring accuracy and response capability of the wind farm.

[0048] Example 3 This embodiment is based on the time-sharing reporting system of the centralized monitoring system for new energy power plants. By calculating the similarity of equipment status events, it rationally aggregates and arranges equipment status changes to generate an equipment-level status evolution sequence. The specific implementation method is as follows: 1. Background The monitoring system of a certain new energy power plant collects the operating status and event records of equipment in real time. The following data comes from the operating status of equipment numbered GT1 within a certain time period. Data collection is as follows: Table 1: Data Collection Table. 2. Merging of homogeneous records For the startup and failure events of device GT1, an event similarity calculation model is used to calculate the similarity. Merging of records from the same source also uses an event similarity calculation model. The technical formula for the event similarity calculation model is as follows: ; in, For the first Article and No. The similarity coefficient between events is dimensionless and ranges from 0 to 1. For the first The timestamp of each event record, in seconds. For the first The timestamp of each event record, in seconds. This is a time-dependent constant, in seconds. For the first A vector of event states, dimensionless. For the first A vector of event states, dimensionless. The time weighting coefficient is dimensionless. The state weight coefficients are dimensionless and satisfy the following conditions: .

[0049] During equipment failures and startups, events with small temporal variations tend to exhibit high similarity. Regarding the similarity of equipment state changes, changes in the state vector reflect the true state of the equipment and failure modes, but their similarity is slightly lower than that of the time factor. Therefore, we set... , .

[0050] Time-related constants This is used to measure the sensitivity of time differences, determining the degree to which time differences affect event similarity. In new energy power plants, equipment failures and recovery typically occur within a short period, usually on the order of hours. Based on the time cycle of equipment failures and recovery, and the characteristics of equipment status changes in actual operation, one hour is chosen as the time correlation constant. .

[0051] For GT1 startup and GT1 failure events: The time label difference is: State vector: Startup state Fault status .

[0052] Calculate similarity: The state vectors are completely different, and their dot product is 0.

[0053] Substitute into the formula to calculate: For GT1 startup and GT1 repair events: The time label difference is: State vector: Startup state Fault status .

[0054] Calculate similarity: The state vectors are completely different, and their dot product is 0.

[0055] Substitute into the formula to calculate: For device GT1 boot events and GT2 boot events: The time label difference is: State vector: Startup state Fault status .

[0056] Calculate similarity: The state vectors are exactly the same, and their dot product is 1.

[0057] Substitute into the formula to calculate: Regarding device GT1 failure and GT2 startup events: The time label difference is: State vector: Startup state Fault status .

[0058] Calculate similarity: The state vectors are completely different, and their dot product is 0.

[0059] Substitute into the formula to calculate: For equipment GT1 and GT2 fault events: The time label difference is: State vector: Startup state Fault status .

[0060] Calculate similarity: The state vectors are exactly the same, and their dot product is 1.

[0061] Substitute into the formula to calculate: 3. Time tag alignment Based on the above calculations, none of the events met the merging criteria, and all events remain independent. The time-stamp alignment step will sort the events according to their time differences and perform linear interpolation if necessary.

[0062] 4. Centralized marshalling across stations After time-stamp alignment, the events of devices GT1 and GT2 are summarized in chronological order to generate a device-level state evolution sequence. The specific state evolution sequence is as follows: Table 2: State evolution sequence list. 5. Data Validation and Output After the device-level state evolution sequence is generated, the system verifies the data to ensure that the merged events are accurate and the time stamps are correctly aligned. The following is the content of the report document: Device GT1 state evolution: Startup time: 1700000000 seconds, Fault time: 1700003600 seconds, Repair time: 1700007200 seconds.

[0063] Device GT2 state evolution: Startup: 1700001000 seconds, Fault: 1700004600 seconds, Repair: 1700008200 seconds.

[0064] Presentation levels: The base layer displays the real-time operating status of the devices, the event layer marks the time when device failures occurred, and the summary layer displays comparative information between devices, as well as the overall operating status of the devices.

[0065] In summary, this embodiment calculated the similarity of multiple state events for devices GT1 and GT2. The results showed that the similarity between all events was below the set threshold of 0.85, therefore no event merging occurred. Each event remained independent and was summarized and arranged in chronological order. The system accurately displays the state evolution of the devices, ensuring the accuracy and reliability of the data, and providing strong support for equipment fault diagnosis and maintenance decisions.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A time-period reporting system for centralized monitoring of new energy power plants, characterized in that: include: A time period parsing device, wherein the time period parsing device parses and processes the time period selection command received by the monitoring human-machine interface to form a target time period parameter set; A data filtering device is used to filter and process the original operating data and event records corresponding to the target time period to form a target information set. The filtering and processing adopts a field whitelist method. A state orchestration device aggregates and orchestrates the target information set to form a device-level state evolution sequence. The aggregation and orchestration process includes merging of records from the same source, time tag alignment, and centralized orchestration across stations. The first-fault determination device performs first-fault determination processing on the device-level state evolution sequence to form a unique first-fault identifier. The first-fault determination processing includes a de-jittering time window, a fault priority matrix, and event causal chain extraction. The reporting and presentation device generates a time-period reporting page by processing the device-level state evolution sequence and the unique first fault identifier. The reporting and presentation process includes data association, hierarchical display, and interactive control.

2. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 1, characterized in that: The parsing process includes time-scale parsing and range parsing. The time-scale parsing includes year, month, day, hour, and minute, while the range parsing includes station and unit.

3. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 1, characterized in that: The whitelist method for the fields is updated every 24 hours. The whitelist method for the fields includes a device anomaly identification field, an operating status description field, and an alarm record field. The device anomaly identification field includes the abnormal device number and the candidate initial fault that caused the shutdown. The operating status description field includes the device status change type and a timestamp. The device status change type includes shutdown, standby, startup, fault, power-limited operation, communication loss, and communication recovery. The alarm record field includes the alarm time and alarm content.

4. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 1, characterized in that: The merging of homologous records employs an event similarity calculation model, the technical formula of which is: ； in, For the first Article and No. The similarity coefficient between the events ranges from 0 to 1. For the first The time stamp of each event record For the first The time stamp of each event record For time-dependent constants, For the first Event state vector, For the first Event state vector, For time weighting coefficients, Let these be the state weight coefficients, satisfying... .

5. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 4, characterized in that: When the similarity coefficient is ≥0.85, the target information set is merged based on the same source records. The steps of aggregation and arrangement are as follows: S1. Merge the same-origin records in the target information set to form a unified event node; S2. The unified event nodes are aligned with time tags to form a state evolution sequence, wherein the time tag alignment is performed using a linear interpolation method; S3. Perform cross-site centralized arrangement of the state evolution sequence to form the equipment-level state evolution sequence.

6. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 1, characterized in that: The de-jitter time window filters repeated triggered events in the device-level state evolution sequence. The fault priority matrix includes fault category, impact range and duration. The source of the first fault is determined based on the result of the fault priority matrix, and the event node corresponding to the source of the first fault is used as the root node of the event causal chain extraction.

7. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 6, characterized in that: The unique first-fault identifier is generated based on the hierarchical structure of the root node. The unique first-fault identifier includes the fault category code, device number, and trigger timestamp.

8. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 1, characterized in that: The data association includes a two-way mapping between the unique initial fault identifier and the device operating status information. The layered display includes a basic layer, an event layer, and a summary layer. The interactive control includes pop-up display, scrolling view, floating prompts, and status replay.

9. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 8, characterized in that: The base layer displays the real-time status parameters of the equipment, the event layer marks the time and location of abnormal events and the first failure, and the summary layer displays the site-level operation statistics and equipment comparison information.

10. The time-period reporting system of the centralized monitoring system for new energy power plants according to claim 1, characterized in that: The time-period reporting page includes a basic display area, an event alarm area, a statistical analysis area, and an interactive control area.

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