Multi-source data fusion pumped storage power station intelligent patrol method and system

Abstract

The invention belongs to the technical field of pumped storage power stations. According to the multi-source data fusion pumped storage power station intelligent patrol method and system, a primary equipment monitoring alarm signal, an auxiliary equipment monitoring alarm signal and a visual alarm signal which are associated with the same equipment or equipment group are obtained; dividing three types of strong space-time association, weak space-time association and no space-time association according to timestamp difference values among the signals, triggering a first-level alarm by strong association and type matching, pushing manual judgment if matching is not performed, triggering a second-level alarm by weak association based on a matching condition of two types of signals, and pushing manual judgment if not; the technical problems of low alarm accuracy and long fault positioning time consumption caused by cross-system splitting of multi-source alarm signals and lack of a space-time correlation analysis mechanism and hierarchical alarm logic in operation and maintenance of the existing pumped storage power station are solved, systematic correlation verification and hierarchical alarm of the multi-source alarm signals are realized, and the fault positioning accuracy is improved. And the efficiency and reliability of intelligent inspection of the pumped storage power station are improved.

Description

Technical Field

[0001] This invention relates to the field of pumped storage power station technology, specifically to an intelligent inspection method and system for pumped storage power stations using multi-source data fusion. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Pumped storage power stations, as core facilities ensuring the safe and stable operation of the power grid, undertake crucial functions such as peak shaving and valley filling, emergency backup, and frequency and voltage regulation within the power system. Their operation and maintenance quality directly determines the overall reliability of the power grid. With the deepening of the intelligent transformation of the power industry, the equipment system of pumped storage power stations is becoming increasingly complex, encompassing not only primary core equipment such as pumps, turbines, generators, and main transformers, but also auxiliary equipment such as security monitoring, fire alarms, and hydraulic safety monitoring. This places higher demands on the comprehensiveness of equipment status monitoring and the timeliness of operation and maintenance responses. Currently, the industry has widely recognized the importance of multi-source data integration in improving inspection efficiency, hoping to achieve accurate perception and prediction of equipment status by integrating information from different dimensions such as equipment operation monitoring data and video surveillance data. Therefore, how to establish linkage channels between data from different sources and construct scientific signal verification and alarm logic has become the core direction for promoting the development of intelligent inspection technology for pumped storage power stations.

[0004] The existing operation and maintenance schemes for pumped storage power stations have the following problems: the monitoring systems of different safety zones operate independently; the primary equipment monitoring system, auxiliary equipment monitoring system, and video monitoring system belong to different management categories, and their data formats and transmission protocols differ. This makes it difficult to integrate primary equipment monitoring alarms, auxiliary equipment monitoring alarms, and visual alarms related to the same equipment across systems, making it impossible to form a unified basis for judging equipment status and severing the inherent correlation between multiple source signals; there is a disconnect between monitoring data and inspection processes, and there is a lack of spatiotemporal correlation analysis and type matching mechanisms for multiple types of alarm signals. Traditional operation and maintenance relies on manual verification of alarm information from different systems one by one, which makes it difficult to quickly determine the correlation between signals and to achieve hierarchical alarms based on the degree of correlation. This not only results in insufficient alarm accuracy and a high false alarm rate, but also leads to long fault location time, seriously affecting the timeliness and effectiveness of operation and maintenance decisions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for intelligent inspection of pumped storage power stations based on multi-source data fusion. This method solves the technical problems in existing pumped storage power station operation and maintenance, such as the fragmentation of multi-source alarm signals across systems, the lack of spatiotemporal correlation analysis mechanisms, and hierarchical alarm logic, which leads to low alarm accuracy and long fault location times. It achieves systematic correlation verification and hierarchical alarming of multi-source alarm signals, opens up signal linkage channels between different monitoring systems, improves the scientific nature of alarm judgment and the targeted nature of fault response, reduces the workload of manually verifying information from multiple systems, and enhances the efficiency and reliability of intelligent inspection of pumped storage power stations.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an intelligent inspection method for pumped storage power stations using multi-source data fusion.

[0007] A method for intelligent inspection of pumped-storage power stations using multi-source data fusion includes the following processes: Acquire three types of alarm signals associated with the same device or device group. The three types of alarm signals include primary equipment monitoring alarm signals, auxiliary equipment monitoring alarm signals, and visual alarm signals. When the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, the three types of alarm signals are determined to be strongly spatiotemporally correlated. If the types of the three types of alarm signals match, a level one alarm is triggered directly. If they do not match, all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment. When the timestamp difference between only two of the three types of alarm signals is less than or equal to the first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than the second set threshold, the three types of alarm signals are determined to have weak spatiotemporal correlation. If the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, a level two alarm is triggered. When the timestamp difference between the three types of alarm signals is greater than the second set threshold, it is determined that the three types of alarm signals have no spatiotemporal correlation, and all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment.

[0008] In one implementation of the first aspect of the present invention, a level three alarm is triggered when only two of the three types of alarm signals are valid, the timestamp difference between the two valid alarm signals is less than or equal to a first set threshold, and the two valid alarm signals are associated with the same device or device group.

[0009] In one implementation of the first aspect of the present invention, when the types of two types of alarm signals with timestamp differences less than or equal to a set threshold do not match, historical data associated with the three types of alarm signals are retrieved, and it is determined whether a certain type of alarm signal is supported based on the historical data. If so, a conditional confirmation alarm is triggered; otherwise, all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment.

[0010] In one implementation of the first aspect of the present invention, under strong spatiotemporal correlation, if the types of the three types of alarm signals do not match, the three types of alarm signals are marked as contradictory signal groups, and the contradictory signal groups are pushed to the operation and maintenance terminal for manual judgment.

[0011] In one implementation of the first aspect of the present invention, the original data of the three types of alarm signals, the spatiotemporal correlation judgment process data, the alarm trigger time, the linkage control command and the manual review record are stored together.

[0012] In one implementation of the first aspect of the present invention, a manual confirmation alarm signal is triggered after a fault result is received by manual judgment.

[0013] Secondly, the present invention provides an intelligent inspection system for pumped storage power stations based on multi-source data fusion.

[0014] A multi-source data fusion intelligent inspection system for pumped storage power stations includes: The alarm signal acquisition unit is configured to acquire three types of alarm signals associated with the same device or device group. The three types of alarm signals include primary equipment monitoring alarm signals, auxiliary equipment monitoring alarm signals, and visual alarm signals. The strong spatiotemporal correlation judgment unit is configured to: when the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, determine that the three types of alarm signals are strongly spatiotemporally correlated; if the types of the three types of alarm signals match, directly trigger a level one alarm; if they do not match, push all three types of alarm signals to the operation and maintenance terminal for manual judgment. The weak spatiotemporal correlation judgment unit is configured to: determine that the three types of alarm signals are weakly spatiotemporally correlated when the timestamp difference between only two of the three types of alarm signals is less than or equal to the first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than the second set threshold; if the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, trigger a level 2 alarm. The unit for determining no spatiotemporal correlation is configured to: when the timestamp difference between the three types of alarm signals is greater than the second set threshold, determine that the three types of alarm signals have no spatiotemporal correlation, and push all three types of alarm signals to the operation and maintenance terminal for manual judgment.

[0015] Thirdly, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the intelligent inspection method for pumped-storage power stations based on multi-source data fusion, which is part of the first aspect of this invention.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the intelligent inspection method for pumped storage power stations based on multi-source data fusion of the first aspect of the present invention.

[0017] Fifthly, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the intelligent inspection method for pumped storage power stations based on multi-source data fusion according to the first aspect of the present invention.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention innovatively proposes a multi-source data fusion-based intelligent inspection method for pumped storage power stations. By acquiring primary equipment monitoring alarm signals, auxiliary equipment monitoring alarm signals, and visual alarm signals associated with the same equipment or equipment group, and classifying the signals into three types based on the timestamp difference: strong spatiotemporal correlation, weak spatiotemporal correlation, and no spatiotemporal correlation, a differentiated processing approach is implemented for different correlation types (strong correlation and type matching trigger a level-one alarm, mismatch pushes for manual judgment; weak correlation triggers a level-two alarm based on the matching of two types of signals, no correlation pushes for manual judgment). This solves the technical problems in existing pumped storage power station operation and maintenance, such as the fragmentation of multi-source alarm signals across systems, the lack of spatiotemporal correlation analysis mechanisms, and hierarchical alarm logic, leading to low alarm accuracy and long fault location time. It achieves systematic correlation verification and hierarchical alarm for multi-source alarm signals, opens up signal linkage channels between different monitoring systems, improves the scientific nature of alarm judgment and the pertinence of fault response, reduces the workload of manually verifying information from multiple systems, and enhances the efficiency and reliability of intelligent inspection of pumped storage power stations.

[0019] This invention innovatively proposes a multi-source data fusion-based intelligent inspection method for pumped storage power stations. When only two of the three types of alarm signals are valid, and these two valid signals are associated with the same device or device group, and the timestamp difference is less than or equal to a first set threshold, a level three alarm is triggered. This solves the problem in the existing operation and maintenance system where, when some alarm signals fail, the remaining valid signals lack a clear alarm triggering mechanism, which easily leads to missed judgments or disordered processing. It realizes ordered alarm output in scenarios with incomplete alarm signals, fills the alarm logic gap when only two types of valid signals are present, and ensures that even if some monitoring signals are abnormal, a standardized alarm prompt can still be formed based on the valid signals, avoiding missed fault reporting due to signal loss. This improves the adaptability and alarm coverage of the intelligent inspection system to complex signal scenarios.

[0020] This invention innovatively proposes a multi-source data fusion-based intelligent inspection method for pumped storage power stations. When two types of alarm signals with timestamp differences less than or equal to a set threshold are mismatched, historical data associated with the three types of alarm signals are retrieved. Based on the historical data, it is determined whether a certain type of alarm signal is supported. If supported, a conditional confirmation alarm is triggered; otherwise, a manual judgment scheme is pushed. This solves the problem of blind and inefficient manual judgment due to the lack of data support when two valid signal types conflict. It realizes alarm-assisted decision-making based on historical data, uses historical operating data to provide a scientific basis for judging conflicting signals, reduces the error of relying solely on human experience, lowers the frequency of unnecessary manual intervention, improves the rationality of alarm judgment and the efficiency of operation and maintenance decision-making, and further improves the logical system of multi-source signal verification.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 A schematic diagram of a secure partition deployment provided as an exemplary embodiment of the present invention; Figure 2 A flowchart illustrating an exemplary embodiment of the present invention for a method of intelligent inspection of pumped storage power stations using multi-source data fusion. Figure 3 A schematic diagram of a multi-source data fusion intelligent inspection system for pumped storage power stations provided as an exemplary embodiment of the present invention; Figure 4 A schematic diagram of a computer device provided for an exemplary embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] Pumped storage power stations (PSGs) are key facilities for ensuring the safe and stable operation of the power grid. They consist of a large number of diverse equipment, including primary equipment such as pumps and turbines, generators and motors, main transformers, and GIS (Gas Insulated Switchgear), as well as auxiliary equipment such as security and fire protection systems. Furthermore, their complex operating environment demands extremely high real-time performance and accuracy in monitoring and maintenance. Currently, the operation and maintenance management of PSGs faces the following prominent problems: the computer monitoring system in Safety Zone 1 (collecting real-time control data such as pump and turbine guide vane opening and generator active power), the unit status monitoring system in Safety Zone 2 (monitoring pump and turbine vibration, generator stator temperature, etc.), the substation equipment status monitoring system (main transformer oil chromatography, GIS partial discharge, etc.), and the hydrological monitoring system (upper reservoir water level, pressure steel pipe water pressure, etc.) operate independently from the hydraulic safety monitoring system (underground powerhouse surrounding rock displacement, dam settlement, etc.) and the auxiliary equipment monitoring system (gate hoist stroke, hydraulic pump pressure, etc.) in the Management Information Zone, resulting in significant data format heterogeneity. For example, water pump and turbine vibration data are transmitted using the Modbus protocol, main transformer oil chromatography data uses the IEC104 protocol, and security video streams follow the ONVIF protocol. This makes it impossible to perform cross-system correlation analysis such as "abnormal vibration of the same equipment and visible oil leakage" and "sudden change in water pressure of pressure steel pipe and deformation of surrounding rock". Traditional video surveillance systems and equipment monitoring data have no linkage mechanism, and defect alarms rely on manual verification of information from multiple systems, resulting in a high false alarm rate and long average time for fault location.

[0027] like Figure 1 The diagram shows the multi-area collaborative system architecture of a pumped storage power station, encompassing modules such as safety zoning, equipment monitoring, network isolation, and intelligent inspection systems. Specifically: A pumped-storage power station (monitoring subsystem or device, blue module) includes: local control, stability control, relay protection, speed control, excitation control, auxiliary equipment, unit status monitoring, substation main equipment status monitoring, fault recording, power monitoring, hydrological monitoring, hydraulic engineering monitoring, industrial video surveillance, fire protection, security, environmental monitoring, and intelligent inspection terminals. These are the various control, monitoring, and security monitoring systems and devices of the power station, undertaking core functions such as equipment control, status awareness, and safety protection.

[0028] Security Zone I (monitoring system layer) includes computer monitoring, automatic power generation control, and automatic voltage control. It connects to the underlying local control, stability control, relay protection, speed control, excitation control, and auxiliary equipment via the network to achieve automated monitoring and control of the power plant's core equipment. It is network isolated from Security Zone II through a firewall.

[0029] Safety Zone II (Monitoring System Layer) includes: unit status monitoring, substation equipment status monitoring, energy metering, fault recording, and hydrological monitoring. It primarily undertakes functions such as unit and substation status monitoring, energy metering, fault recording, and hydrological monitoring, and is a crucial sensing layer for the power plant's production status.

[0030] The Management Information Zone (Monitoring System Layer) includes: hydraulic engineering safety monitoring and auxiliary equipment monitoring. It integrates underlying hydraulic engineering monitoring, industrial video surveillance, fire protection, security, environmental monitoring, intelligent patrol terminals and other monitoring systems and devices, focusing on the safety of hydraulic engineering facilities, the status of auxiliary equipment and comprehensive management of security, fire protection, environment, etc. Network security isolation is achieved between zones through forward isolation and reverse isolation devices.

[0031] The intelligent inspection system (intelligent application layer) includes: intelligent inspection, intelligent linkage, intelligent analysis, equipment monitoring, as well as IoT access gateways and a comprehensive data center. This system is the core of intelligent operation and maintenance of the power plant. It integrates multi-source data through IoT access gateways, realizes data storage and management in the comprehensive data center, and then carries out intelligent applications such as intelligent inspection, linkage control, data analysis, and equipment monitoring.

[0032] Security Zone I is isolated from other zones via firewalls to ensure network security for core production control. The management information zone achieves secure cross-regional data exchange with other zones through forward isolation (one-way inbound) and reverse isolation (one-way outbound), ensuring the network boundary security between management information and production control domains. The overall architecture, through a hierarchical design of "lower-level device perception - zoned system control - intelligent application analysis," enables the coordinated operation of production control, status monitoring, comprehensive management, and intelligent inspection of pumped storage power stations, while simultaneously ensuring system security through multi-layered network isolation.

[0033] In this implementation, IoT access gateways are deployed based on security zones. The intelligent inspection module establishes bidirectional communication with the computer monitoring system in Security Zone 1, the unit status monitoring system, power equipment status monitoring system, and water situation monitoring system in Security Zone 2, and the hydraulic safety monitoring system and auxiliary equipment monitoring system in the Management Information Zone. It strictly follows the principle that data from Zone 2 flows unidirectionally into the Management Information Zone, and the control commands from the Management Information Zone are encrypted and fed back to Security Zone 2. Communication ports and data types are restricted through Access Control Lists (ACLs) to ensure the security of cross-zone data transmission.

[0034] In this implementation, the computer monitoring system in Safety Zone 1 collects real-time control data such as the guide vane opening of the water pump turbine and the active power of the generator motor; the unit status monitoring system in Safety Zone 2 monitors the vibration of the water pump turbine and the stator temperature of the generator motor, the power equipment status monitoring system collects data such as the main transformer oil chromatography and GIS partial discharge, and the water situation monitoring system collects data such as the upper reservoir water level and the pressure of the pressure steel pipe; the hydraulic safety monitoring system in Safety Zone 4 collects data such as the displacement of the surrounding rock of the underground powerhouse and the settlement of the dam body, and the auxiliary equipment monitoring system collects data such as the stroke of the hoist and the pressure of the hydraulic pump.

[0035] Standardization is achieved for the multi-source data collected from various security zones through the following steps: (1) Data format conversion: Convert unstructured video streams into timestamped frame sequences (25 frames per second), and convert structured data (such as XML format oil chromatography data and JSON format temperature data) into SCL format based on IEC61850 standard to ensure data structure consistency; (2) Timestamp alignment: Based on the power station Beidou clock synchronization system (time accuracy ≤ 1ms), a unified timestamp (format: YYYY-MM-DDHH:MM:SS.XXX) is added to all data to solve the problem of inconsistent time bases for cross-system data; (3) Outlier cleaning: The "3σ principle + sliding window filtering" combined algorithm is used to remove outliers that exceed the reasonable range. Missing values ​​are supplemented by interpolation of data from adjacent time points, and finally a standardized dataset is formed and stored in the time series database (InfluxDB) and relational database (MySQL) in the four regions.

[0036] In this implementation, based on the characteristics of pumped storage power stations—"primary equipment as the core, auxiliary equipment as the guarantee"—a dual-dimensional fusion monitoring and multi-modal verification system is constructed to achieve cross-verification of data and video, and output reliable equipment status signals. This implementation method designs a full-dimensional monitoring module for primary equipment, which adopts the fusion observation and cross-verification of online monitoring data and stereo vision to achieve multi-dimensional fusion of internal parameters and external status. Specifically, it includes: filtering and displaying online monitoring data, patrol data (point identification results of fixed-point camera monitoring), alarm data (online monitoring + patrol data alarms) associated with the main equipment based on the equipment tree structure (the root node is the station, and the lower-level nodes are the area, interval, and main equipment, forming a four-level equipment tree).

[0037] This implementation method designs an intelligent monitoring system for auxiliary equipment that integrates online monitoring data (remote signaling, remote control, and telemetry of measuring points), map display, and stereoscopic vision through observation and cross-verification. This achieves multi-dimensional fusion of internal parameters and external status, specifically including: maps associated with stations, business systems (environmental monitoring, fire monitoring, and security monitoring), interval filtering displays, online monitoring data, alarm data (online monitoring alarms), and real-time video. Each business system is divided into intervals according to physical areas (e.g., "pump and turbine layer," "generator and motor layer," "GIS room," etc.). The real-time location of each measuring point (e.g., temperature and humidity sensors, smoke detectors, infrared beam detectors) is dynamically marked on the map using vector icons. The operating status is displayed through color coding (green = normal, yellow = over-limit warning, red = alarm), and a pop-up window displays alarm details (including alarm type, occurrence time, associated device ID, and current measurement value) when the mouse hovers over the alarm.

[0038] In this implementation, a multimodal cross-validation mechanism is adopted, which integrates the visual alarm signals obtained by the built-in visual recognition algorithm of the intelligent inspection system with the accessed system alarm signals (primary equipment monitoring alarm signals and auxiliary equipment monitoring alarm signals) to construct a "triple verification" logic.

[0039] like Figure 2 As shown, this implementation proposes a multi-source data fusion intelligent inspection method for pumped storage power stations, which receives three types of alarm signals in real time: visual alarm signals, primary equipment monitoring alarm signals, and auxiliary equipment monitoring alarm signals. Figure 2 The diagram shows the situation where three types of signals can be received simultaneously. The validity of primary equipment monitoring alarm signals and auxiliary equipment monitoring alarm signals is verified, the confidence level of visual alarm signals is verified, and invalid signals are filtered out.

[0040] A spatiotemporal correlation determination is performed (assuming three types of alarm signals can be received). Specifically, this includes: the three types of alarm signals must be associated with the same device or device group, for example, all pointing to "main transformer #1". The timestamp difference (ΔT) of the signals is calculated. If the timestamp difference of the three types of alarm signals is less than or equal to 5s (i.e., the first set threshold), it is determined to be "strong spatiotemporal correlation". If only the timestamp difference between two types of alarm signals is less than or equal to 5s, and the timestamp difference between the third type of alarm signal and other types of signals is greater than 5s and less than or equal to 10s (i.e., the second set threshold), it is determined to be "weak spatiotemporal correlation". If the timestamp difference between all types of alarm signals is greater than 10s, it is determined to be "no spatiotemporal correlation".

[0041] Based on the spatiotemporal correlation determination result, the following verification logic is executed: Scenario 1: Strong spatiotemporal correlation, further determine whether the signal types match (e.g., “main transformer #1 acetylene concentration exceeds the standard (primary equipment monitoring alarm signal)”, “main transformer #1 top oil temperature exceeds the limit (auxiliary equipment monitoring alarm signal)”, “main transformer #1 oil tank leaks oil (visual alarm signal)” all point to “main transformer #1 fault”, and the types are determined to match). If the type matches, a "Level 1 Confirmation Alarm" is triggered directly and pushed synchronously to the Smart Patrol page (with the original data of the three types of alarm signals and associated video clips), and the pre-associated control command is automatically triggered (such as starting the main transformer standby cooler). If the type does not match (e.g., "main transformer #1 vibration exceeds limit", "main transformer #1 oil level is normal", "main transformer #1 appearance is normal"), it is marked as "contradictory signal group" and pushed to the operation and maintenance terminal to trigger manual review. After the manual judgment is that it is a real fault, the "confirmation alarm" and linkage control are triggered.

[0042] Scenario 2: In the case of weak spatiotemporal correlation, priority is given to matching the two types of signals with closer timestamp differences (i.e., two types of alarm signals with timestamp differences less than or equal to 5 seconds) as the core. When the two types of signals match (i.e., whether the core types match), a "second-level confirmation alarm" is triggered, pushed to the intelligent inspection page and marked "weak spatiotemporal correlation", prompting maintenance personnel to focus on checking the cause of the delay of the third type of alarm signal (such as insufficient video transmission bandwidth). When the two types of signals do not match, they are marked as "signal group to be reviewed", and historical data (last hour) of the correlation of the three types of alarm signals is automatically retrieved for auxiliary analysis. If the historical data supports the rationality of a certain type of signal (e.g., the main transformer oil temperature has been rising continuously in the last hour, supporting "oil temperature over-limit alarm"), a "conditional confirmation alarm" is triggered; otherwise, manual review is triggered.

[0043] Scenario 3: When there is no spatiotemporal correlation, the alarm is pushed to the operation and maintenance terminal to trigger manual review. After the manual review determines that it is a real fault, the "confirmation alarm" and linkage control are triggered.

[0044] When only two types of valid alarm signals can be received, if the devices associated with the two types of valid alarm signals are consistent (e.g., only one device monitoring alarm signal and visual alarm signal are valid and associated with the same device or device group, while the auxiliary control device monitoring alarm signal is invalid), and the timestamp difference between the two types of alarm signals is less than or equal to 5 seconds and the types match, a "Level 3 Confirmation Alarm" is triggered.

[0045] In other cases, such as when only one type of alarm signal is received, or when two valid alarm signals are received but not associated with the same device or device group, they are directly marked as "signals to be confirmed" and do not trigger automatic alarms. Instead, they are pushed to the "list to be reviewed" of the intelligent patrol system for manual verification by maintenance personnel (such as confirming whether it is a single point of failure of the sensor or whether the visual recognition is misjudged). After the manual determination that it is a real fault, a "manually confirmed alarm" is generated.

[0046] In this implementation, all verification results (including confirmed alarms, signals pending review, and contradictory signals) are synchronously stored in a relational database. The records include: the original data of the three types of alarm signals, the spatiotemporal correlation judgment process, the verification logic results, the alarm trigger time, the linkage control instructions, and the manual review records. Subsequently, the complete verification chain of any alarm can be traced by device ID or timestamp, providing data support for fault analysis and verification logic optimization.

[0047] Figure 3 This paper presents an intelligent inspection system for pumped-storage power stations, which integrates multi-source data. The alarm signal acquisition unit 301 is configured to acquire three types of alarm signals associated with the same device or device group. The three types of alarm signals include primary device monitoring alarm signals, auxiliary device monitoring alarm signals, and visual alarm signals. The strong spatiotemporal correlation judgment unit 302 is configured to: when the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, determine that the three types of alarm signals are strongly spatiotemporally correlated; if the types of the three types of alarm signals match, directly trigger a level one alarm; if they do not match, push all three types of alarm signals to the operation and maintenance terminal for manual judgment. The weak spatiotemporal correlation judgment unit 303 is configured to: determine that the three types of alarm signals are weakly spatiotemporally correlated when the timestamp difference between only two types of alarm signals is less than or equal to a first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than the second set threshold; if the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, trigger a secondary alarm. The no spatiotemporal correlation judgment unit 304 is configured to: when the timestamp difference between the three types of alarm signals is greater than the second set threshold, determine that the three types of alarm signals have no spatiotemporal correlation, and push all three types of alarm signals to the operation and maintenance terminal for manual judgment.

[0048] It is understood that the aforementioned units can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of the present invention. The aforementioned units are based on logical functional division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present invention, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0049] According to another embodiment of the present invention, the system of this embodiment can be constructed by running a computer program (including program code) capable of performing the steps involved in the corresponding method of the present invention on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the aforementioned computing device through the computer-readable recording medium, and run therein.

[0050] Figure 4 A computer device is shown, which includes a processor 401, a communication interface 402, and a computer-readable storage medium 403. The processor 401, communication interface 402, and computer-readable storage medium 403 can be connected via a bus or other means.

[0051] The communication interface 402 is used to receive and send data. The computer-readable storage medium 403 can be stored in the memory of the electronic device. The computer-readable storage medium 403 is used to store computer programs, which include program instructions. The processor 401 is used to execute the program instructions stored in the computer-readable storage medium 403.

[0052] The processor 401 is the computing and control core of the electronic device. It is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to achieve the corresponding method flow or corresponding function.

[0053] Processor 401 is configured to perform the following procedure: Acquire three types of alarm signals associated with the same device or device group. The three types of alarm signals include primary equipment monitoring alarm signals, auxiliary equipment monitoring alarm signals, and visual alarm signals. When the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, the three types of alarm signals are determined to be strongly spatiotemporally correlated. If the types of the three types of alarm signals match, a level one alarm is triggered directly. If they do not match, all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment. When the timestamp difference between only two of the three types of alarm signals is less than or equal to the first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than the second set threshold, the three types of alarm signals are determined to have weak spatiotemporal correlation. If the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, a level two alarm is triggered. When the timestamp difference between the three types of alarm signals is greater than the second set threshold, it is determined that the three types of alarm signals have no spatiotemporal correlation, and all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment.

[0054] This invention also provides a computer-readable storage medium, which is a memory device in an electronic device for storing programs and data. It is understood that the computer-readable storage medium here may include both built-in storage media in the electronic device and extended storage media supported by the electronic device. The computer-readable storage medium provides storage space for storing the processing system of the electronic device.

[0055] Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory; alternatively, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0056] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to perform the following process: Acquire three types of alarm signals associated with the same device or device group. The three types of alarm signals include primary equipment monitoring alarm signals, auxiliary equipment monitoring alarm signals, and visual alarm signals. When the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, the three types of alarm signals are determined to be strongly spatiotemporally correlated. If the types of the three types of alarm signals match, a level one alarm is triggered directly. If they do not match, all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment. When the timestamp difference between only two of the three types of alarm signals is less than or equal to the first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than the second set threshold, the three types of alarm signals are determined to have weak spatiotemporal correlation. If the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, a level two alarm is triggered. When the timestamp difference between the three types of alarm signals is greater than the second set threshold, it is determined that the three types of alarm signals have no spatiotemporal correlation, and all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment.

[0057] The present invention also provides a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process: Acquire three types of alarm signals associated with the same device or device group. The three types of alarm signals include primary equipment monitoring alarm signals, auxiliary equipment monitoring alarm signals, and visual alarm signals. When the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, the three types of alarm signals are determined to be strongly spatiotemporally correlated. If the types of the three types of alarm signals match, a level one alarm is triggered directly. If they do not match, all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment. When the timestamp difference between only two of the three types of alarm signals is less than or equal to the first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than the second set threshold, the three types of alarm signals are determined to have weak spatiotemporal correlation. If the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, a level two alarm is triggered. When the timestamp difference between the three types of alarm signals is greater than the second set threshold, it is determined that the three types of alarm signals have no spatiotemporal correlation, and all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment.

[0058] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can implement the described functions using different methods for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0059] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital cable) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent inspection of pumped-storage power stations using multi-source data fusion, characterized in that, Includes the following processes: Acquire three types of alarm signals associated with the same device or device group. The three types of alarm signals include primary equipment monitoring alarm signals, auxiliary equipment monitoring alarm signals, and visual alarm signals. When the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, the three types of alarm signals are determined to be strongly spatiotemporally correlated. If the types of the three types of alarm signals match, a level one alarm is triggered directly. If they do not match, all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment. When the timestamp difference between only two of the three types of alarm signals is less than or equal to the first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than the second set threshold, the three types of alarm signals are determined to have weak spatiotemporal correlation. If the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, a level two alarm is triggered. When the timestamp difference between the three types of alarm signals is greater than the second set threshold, it is determined that the three types of alarm signals have no spatiotemporal correlation, and all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment.

2. The intelligent inspection method for pumped-storage power stations based on multi-source data fusion as described in claim 1, characterized in that, A level 3 alarm is triggered when only two of the three types of alarm signals are valid, the timestamp difference between the two valid alarm signals is less than or equal to a first set threshold, and the two valid alarm signals are associated with the same device or device group.

3. The intelligent inspection method for pumped-storage power stations based on multi-source data fusion as described in claim 1 or 2, characterized in that, When the types of two alarm signals with timestamp differences less than or equal to a set threshold do not match, historical data associated with the three types of alarm signals are retrieved. Based on the historical data, it is determined whether a certain type of alarm signal is supported. If so, a conditional confirmation alarm is triggered. If not, all three types of alarm signals are pushed to the operation and maintenance terminal for manual judgment.

4. The intelligent inspection method for pumped-storage power stations based on multi-source data fusion as described in claim 1 or 2, characterized in that, Under strong spatiotemporal correlation, if the types of the three types of alarm signals do not match, the three types of alarm signals are marked as contradictory signal groups, and the contradictory signal groups are pushed to the operation and maintenance terminal for manual judgment.

5. The intelligent inspection method for pumped-storage power stations based on multi-source data fusion as described in claim 1 or 2, characterized in that, The raw data of the three types of alarm signals, the spatiotemporal correlation judgment process data, the alarm trigger time, the linkage control command and the manual review record are stored together.

6. The intelligent inspection method for pumped-storage power stations based on multi-source data fusion as described in claim 1 or 2, characterized in that, Upon receiving a fault result determined manually, a manual confirmation alarm signal is triggered.

7. A multi-source data fusion intelligent inspection system for pumped-storage power stations, characterized in that, include: The alarm signal acquisition unit is configured to acquire three types of alarm signals associated with the same device or device group, the three types of alarm signals including primary device monitoring alarm signals, auxiliary device monitoring alarm signals and visual alarm signals; The strong spatiotemporal correlation judgment unit is configured to: when the timestamp difference between the three types of alarm signals is less than or equal to the first set threshold, determine that the three types of alarm signals are strongly spatiotemporally correlated; if the types of the three types of alarm signals match, directly trigger a level one alarm; if they do not match, push all three types of alarm signals to the operation and maintenance terminal for manual judgment. The weak spatiotemporal correlation judgment unit is configured to: determine that the three types of alarm signals are weakly spatiotemporally correlated when the timestamp difference between only two of the three types of alarm signals is less than or equal to a first set threshold, and the timestamp difference between the third type of alarm signal and the other two types of alarm signals is greater than the first set threshold and less than a second set threshold; if the types of the two types of alarm signals with timestamp differences less than or equal to the set threshold match, trigger a secondary alarm. The unit for determining no spatiotemporal correlation is configured to: when the timestamp difference between the three types of alarm signals is greater than the second set threshold, determine that the three types of alarm signals have no spatiotemporal correlation, and push all three types of alarm signals to the operation and maintenance terminal for manual judgment.

8. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the intelligent inspection method for pumped-storage power stations based on multi-source data fusion as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1 to 6 for the intelligent inspection method of pumped storage power stations based on multi-source data fusion.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the intelligent inspection method for pumped storage power stations based on multi-source data fusion as described in any one of claims 1 to 6.

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