A phase modulator distributed cooperative monitoring and storage system, method and device

Abstract

The application provides a phase modifier distributed cooperative monitoring and storage system, method and device, comprising a collection module, a control module, an edge computing unit, a cloud processing unit, a local cache module and a cloud storage module. The edge computing unit is used for local real-time processing of high-frequency monitoring data and generation of control instructions, the local cache module realizes data redundancy backup, the cloud processing unit is responsible for global analysis and fault trend prediction, realizes low-delay response of data, reliable local storage, global cooperative analysis, and is adapted to be connected with a power grid dispatching system.

Description

Technical Field

[0001] This application relates to a distributed collaborative monitoring and storage system, method, and apparatus for synchronous condensers, belonging to the field of synchronous condenser monitoring technology. Background Technology

[0002] As a key device for reactive power compensation and stability support in new power systems, the real-time monitoring, data processing, and storage quality of synchronous condensers directly affect their operational safety and maintenance efficiency. Existing synchronous condenser monitoring systems generally employ a centralized architecture for data processing and storage, where all monitoring data is transmitted to a single centralized processor for processing and then stored in a single storage device. Relevant existing technologies include Chinese Patent Application No. 2025220045155, which discloses an integrated monitoring device for synchronous condensers, achieving the integration of various monitoring devices; Chinese Patent Application No. 2025111068260, which discloses an online monitoring method for synchronous condensers, improving the accuracy of status monitoring through multi-parameter fusion; and Chinese Patent Application No. 2019112237595, which designs a dedicated processing flow for vibration signals, optimizing storage efficiency. However, these existing technological solutions are all based on a centralized data processing and storage architecture, and are still limited by the inherent defects of centralized architecture, making it difficult to meet the needs of synchronous condensers for real-time processing, secure storage, and multi-system collaboration of high-frequency, multi-type data under complex operating conditions.

[0003] The existing centralized data processing and storage architecture suffers from the following problems when applied to synchronous condenser monitoring: The high-frequency monitoring data generated during synchronous condenser operation, such as vibration and excitation current, is voluminous and requires high real-time performance. Centralized processing requires transmitting all data to a remote processor, resulting in long transmission distances and data congestion, leading to high processing latency, hindering the rapid generation of control commands and early warning information, making it difficult to respond promptly to sudden parameter anomalies, and potentially exacerbating equipment failures. Centralized storage uses a single storage device, which is susceptible to damage and data loss due to the complex operating environment of synchronous condensers, including high temperatures, high vibrations, and strong electromagnetic interference. Furthermore, existing data encryption measures are inadequate, posing a risk of leakage to historical monitoring data during long-term storage. Centralized storage lacks standardized data format design, making seamless integration with power grid dispatching and maintenance management systems difficult, resulting in low data sharing efficiency. Historical data storage is disorganized, hindering rapid tracing, querying, and analysis, leading to a lack of effective data support for maintenance decisions. Moreover, in a centralized processing and storage architecture, a processor or storage device failure can paralyze the entire data processing and storage system, potentially causing the monitoring system to shut down.

[0004] Therefore, under the complex operating conditions of high temperature, high vibration, and strong electromagnetic interference of synchronous condensers, the existing monitoring systems based on centralized architecture still have significant shortcomings in terms of real-time response to high-frequency data processing, data storage security, and collaboration with external systems, making it difficult to meet the requirements of new power systems for the reliability and intelligent operation and maintenance of synchronous condensers. Summary of the Invention

[0005] The purpose of this application is to overcome the shortcomings of the prior art and provide a distributed collaborative monitoring and storage system, method and device for synchronous condensers, so as to realize local real-time response, dual secure storage and standardized collaboration of high-frequency monitoring data of synchronous condensers.

[0006] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0007] In a first aspect, this application provides a distributed collaborative monitoring and storage system for synchronous condensers, comprising:

[0008] The system comprises the following modules: a data acquisition module for real-time acquisition of raw high-frequency monitoring data from the synchronous condenser; a control module for receiving control commands to adjust the synchronous condenser; an edge computing unit connected to the acquisition and control modules and deployed locally on the synchronous condenser for real-time processing of the raw high-frequency monitoring data acquired by the acquisition module, generating control commands and early warning information, and sending control commands to the control module; and a cloud processing unit connected to the edge computing unit via a transmission medium that is resistant to electromagnetic interference and also serves as an electromagnetic isolation layer to block the impact of strong electromagnetic interference generated during the operation of the synchronous condenser on long-distance data transmission. A local cache module electrically connected to the edge computing unit stores the high-frequency monitoring data processed by the edge computing unit in real-time. A cloud storage module communicatively connected to the cloud processing unit stores historical monitoring data and provides a data service interface to external systems.

[0009] In conjunction with the first aspect, the edge computing unit further includes: a data receiving module, communicatively connected to the acquisition module, for receiving raw high-frequency monitoring data at a fixed sampling rate; a real-time processing module, electrically connected to the data receiving module, with a built-in feature extraction algorithm for the characteristics of the synchronous condenser, for real-time processing of the raw high-frequency monitoring data to generate control commands and early warning information, controlling the real-time processing delay to within 50ms; and a command issuing module, communicatively connected to the real-time processing module, the control module, and the cloud processing unit, for issuing control commands and synchronizing detected abnormal events and processed feature data to the cloud processing unit.

[0010] Furthermore, when the instruction issuing module synchronizes data with the cloud processing unit, it adopts a breakpoint resume and compressed transmission mechanism. This mechanism is used to temporarily store the data to be uploaded in the dedicated storage area of ​​the local cache module when the network is interrupted and automatically resume transmission after the network is restored, and to compress the data using a lossless compression algorithm before transmission.

[0011] Furthermore, the cloud processing unit includes: a data aggregation module, which is communicatively connected to the edge computing unit, for aggregating high-frequency monitoring data, early warning information, and integrating historical monitoring data from all synchronous condensers; a global analysis module, which is electrically connected to the data aggregation module, for comparing and analyzing global monitoring data and identifying abnormal correlations between different monitoring parameters; and a trend prediction module, which is electrically connected to the global analysis module, for predicting the development trend of synchronous condenser faults based on historical and real-time monitoring data using a trend fitting algorithm.

[0012] Furthermore, the data backup unit built into the local cache module adopts a cyclic overwrite backup mode or a dual backup mode. When the storage capacity reaches the upper limit, it automatically overwrites the earliest backup data or maintains two data copies at the same time to ensure that valid data within a preset period is always stored. This storage period matches the data time window required by the local historical trend analysis algorithm embedded in the edge computing unit.

[0013] Furthermore, the cloud storage module uses encryption technology to encrypt and store historical monitoring data, organizes the data according to the power system standardized data model, and provides standardized interfaces to the outside world.

[0014] Secondly, this application provides a distributed collaborative monitoring and storage method for synchronous condensers, applied to the system described in the first aspect. The method includes the following steps: real-time acquisition of raw high-frequency monitoring data from the synchronous condensers via an acquisition module; real-time processing of the raw high-frequency monitoring data via an edge computing unit deployed locally on the synchronous condenser to generate control commands and early warning information within a delay that meets the real-time control requirements of the synchronous condenser, and sending the control commands to the control module to regulate the synchronous condenser; real-time storage of the processed high-frequency monitoring data via a local caching module, with redundant backup using industrial-grade storage devices suitable for high-vibration environments; synchronization of the processed feature data to a cloud processing unit via a transmission medium, wherein the transmission medium also serves as an electromagnetic isolation layer to block the impact of strong electromagnetic interference generated during the operation of the synchronous condenser on long-distance data transmission; global analysis and trend prediction of the aggregated data from multiple synchronous condensers via the cloud processing unit; and encrypted storage of historical monitoring data via a cloud storage module using encryption technology, saving historical monitoring data according to a standardized data model for power systems, and providing data services to external systems through a standardized interface adapted to external systems.

[0015] Thirdly, this application provides a distributed collaborative monitoring and storage device for synchronous condensers, comprising: a data acquisition module for real-time acquisition of raw high-frequency monitoring data from the synchronous condensers; an edge computing unit deployed locally on the synchronous condenser and connected to the data acquisition module for real-time processing of the raw high-frequency monitoring data, generating control commands and early warning information within a preset delay that meets the real-time control requirements of the synchronous condenser, and synchronizing the processed feature data to the cloud via an electromagnetic interference-resistant transmission medium, wherein the transmission medium also serves as an electromagnetic isolation layer to block the impact of strong electromagnetic interference generated during the operation of the synchronous condenser on long-distance data transmission; a control module connected to the edge computing unit for receiving control commands and regulating the synchronous condenser; a local cache module electrically connected to the edge computing unit, employing industrial-grade storage devices suitable for high-vibration environments, and having a built-in data backup module for redundant backup of the processed high-frequency monitoring data; a cloud processing unit communicatively connected to the edge computing unit for global analysis and trend prediction of aggregated data from multiple synchronous condensers; and a cloud storage module connected to the cloud processing unit for storing historical monitoring data according to a standardized data model for power systems and providing data services to external systems through a standardized interface adapted to external systems.

[0016] Fourthly, this application provides an electronic device, including: at least one processor, and a memory communicatively connected to the processor;

[0017] The memory stores instructions that can be executed by a processor, which, when executed by the processor, enables the processor to perform the distributed collaborative monitoring and storage method for condensers described in the second aspect.

[0018] Fifthly, this application provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the distributed collaborative monitoring and storage method for condensers described in the second aspect.

[0019] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0020] This application provides a distributed collaborative monitoring and storage system, method, and apparatus for condensers. By integrating and deploying edge computing units, acquisition modules, and control modules locally, it achieves real-time processing of high-frequency monitoring data and local closed-loop control, reducing transmission latency. The local cache module uses an industrial-grade solid-state drive and has a built-in data backup unit to achieve data storage and cyclic overwrite backup in high-vibration environments.

[0021] The cloud processing unit aggregates data from multiple synchronous condensers for global analysis and trend prediction, enabling cross-device systemic anomaly identification and fault prediction. The cloud storage module adopts the IEC61850 standard data model and standardized interface to achieve native data interoperability with the power grid dispatching and operation and maintenance system.

[0022] The cloud storage module incorporates symmetric and asymmetric encryption to encrypt historical data storage and cross-system interaction; fiber optic communication is used between the edge computing unit and the cloud processing unit to achieve stable long-distance transmission under strong electromagnetic interference; the local cache cycle is matched with the algorithm time window of the edge computing unit to enable local historical data query and analysis. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the module connection of the distributed collaborative monitoring and storage system for synchronous condensers provided in the embodiments of this application;

[0024] Figure 2 This is a schematic diagram of the internal structure of the edge computing unit provided in an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of the internal structure of the cloud processing unit provided in the embodiments of this application;

[0026] In the diagram: 1-Edge computing unit, 2-Cloud processing unit, 3-Local cache module, 4-Acquisition module, 5-Control module, 6-Cloud storage module, 7-Power grid dispatching system, 8-Operation and maintenance management system, 11-Data receiving module, 12-Real-time computing module, 13-Command issuance module, 21-Data aggregation module, 22-Global analysis module, 23-Trend prediction module. Detailed Implementation

[0027] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and should not be used to limit the scope of protection of the present application.

[0028] Example 1:

[0029] This embodiment provides a distributed collaborative monitoring and storage system for synchronous condensers, such as... Figure 1 As shown, the system includes an edge computing unit 1, a cloud processing unit 2, a local cache module 3, a cloud storage module 6, a data acquisition module 4, and a control module 5.

[0030] The connection relationships between the various unit modules are as follows:

[0031] Edge computing unit 1 is integrated with acquisition module 4 and control module 5 and deployed locally on the synchronous condenser; cloud processing unit 2 is connected to edge computing unit 1 via fiber optic communication; local cache module 3 is electrically connected to edge computing unit 1; cloud storage module 6 is communicatively connected to cloud processing unit 2, and cloud storage module 6 is also adapted to connect with power grid dispatching system 7 and operation and maintenance management system 8.

[0032] like Figure 2As shown, the edge computing unit 1 includes a data receiving module 11, a real-time computing module 12, and an instruction issuing module 13.

[0033] The data receiving module 11 is communicatively connected to the acquisition module 4 and is used to receive raw high-frequency monitoring data such as synchronous condenser vibration data and excitation current data transmitted by the acquisition module 4 at a fixed high-frequency sampling rate.

[0034] The real-time computing module 12 adopts an industrial-grade high-speed computing chip and is electrically connected to the data receiving module 11. It has a built-in feature extraction algorithm for the characteristics of the camera modulator, which is used to perform real-time filtering, feature value calculation and anomaly detection on the raw data stream, identify data anomalies, generate control commands and early warning information, and control the real-time processing delay to within 50ms.

[0035] The instruction issuing module 13 is communicatively connected to the real-time computing module 12, the control module 5, and the cloud processing unit 2, respectively. It is used to issue control instructions to the control module 5 and synchronize the detected abnormal events and compressed periodic feature data to the cloud processing unit 2.

[0036] like Figure 3 As shown, the cloud processing unit 2 includes a data aggregation module 21, a global analysis module 22, and a trend prediction module 23.

[0037] The data aggregation module 21 is communicatively connected to the instruction issuing module 13 of the edge computing unit 1, and is used to aggregate high-frequency monitoring data, early warning information and integrate historical monitoring data of all synchronous condensers.

[0038] The global analysis module 22 is electrically connected to the data aggregation module 21 and is used to compare and analyze global monitoring data to identify abnormal correlations between different monitoring parameters.

[0039] The trend prediction module 23 is electrically connected to the global analysis module 22. Based on historical and real-time monitoring data, it uses a trend fitting algorithm to predict the development trend of synchronous condenser faults and achieve global control in the cloud.

[0040] In this embodiment, the local cache module 3 uses an industrial-grade solid-state drive with a storage capacity of 1TB, used to store high-frequency monitoring data from the past 1-3 months. The structural characteristics of the solid-state drive are adapted to the high-vibration operating environment of the camera condenser, thus avoiding data loss caused by vibration.

[0041] The local cache module 3 has a built-in data backup unit that uses a cyclic overwrite backup mode to perform local redundant backups of the stored high-frequency monitoring data. When the storage capacity reaches its limit, it automatically overwrites the oldest backup data, ensuring that valid data from the most recent 1-3 months is always stored. This storage cycle matches the shortest data time window required by the local historical trend analysis algorithm embedded in the edge computing unit 1, ensuring that the edge computing unit 1 can perform effective trend analysis based on locally stored historical data.

[0042] The cloud storage module 6 adopts a combination of symmetric and asymmetric encryption technologies for encrypted storage. Specifically, the AES-256 symmetric encryption algorithm is used for the storage encryption of historical monitoring data, and the RSA-2048 asymmetric encryption algorithm is used for encryption protection during data transmission. The dual encryption effectively prevents data leakage.

[0043] The cloud storage module 6 adopts the IEC 61850 standard for data storage format. Its external data service interface follows the remote communication protocol of the power grid dispatching system 7 and the data access protocol of the operation and maintenance management system 8, enabling native data interoperability and collaborative management with the power grid dispatching system 7 and the operation and maintenance management system 8. Simultaneously, the cloud storage module 6 is used for long-term storage of historical monitoring data, supporting data traceability, querying, and multi-dimensional analysis, providing data support for synchronous condenser fault review and operation and maintenance strategy optimization.

[0044] The edge computing unit 1 and the cloud processing unit 2 are connected by optical fiber transmission. This optical fiber communication network also serves as an electromagnetic isolation layer to block the strong electromagnetic interference generated during the operation of the synchronous condenser from affecting long-distance data transmission, reduce data transmission latency, and ensure the stable transmission of high-frequency monitoring data, early warning information, and control command feedback data.

[0045] Example 2:

[0046] This embodiment provides a distributed collaborative monitoring and storage method for synchronous condensers, the working process of which is as follows:

[0047] The acquisition module 4 collects high-frequency monitoring data such as vibration data and excitation current data of the synchronous condenser in real time, and transmits them to the real-time computing module 12 of the edge computing unit 1 through the data receiving module 11.

[0048] The real-time computing module 12 performs filtering, feature value calculation, and anomaly detection on the high-frequency monitoring data, identifies data anomalies, and generates control commands and early warning information. The control commands are sent to the control module 5 through the command sending module 13 to realize the real-time control of the synchronous condenser. The early warning information and compressed feature data are synchronized to the cloud processing unit 2 through the command sending module 13.

[0049] The local cache module 3 stores the high-frequency monitoring data processed by the edge computing unit 1 in real time, and the data backup unit performs redundant backups of the stored data.

[0050] The data aggregation module 21 of the cloud processing unit 2 aggregates high-frequency monitoring data and early warning information and integrates historical monitoring data; the global analysis module 22 performs comparative analysis on global data; and the trend prediction module 23 predicts the development trend of synchronous condenser faults.

[0051] The cloud storage module 6 uses encryption technology to encrypt and store historical monitoring data, and retains the historical monitoring data for a long time. It supports data traceability, query and analysis, and connects with the power grid dispatch system 7 and the operation and maintenance management system 8 through standardized interfaces to achieve data sharing.

[0052] Example 3:

[0053] This embodiment has a basically the same structure as Embodiment 1, the difference being that some components are selected or implemented using alternative solutions, as detailed below:

[0054] The solid-state drive in local cache module 3 can be replaced with a vibration-resistant industrial-grade storage card, which can also reliably store recent high-frequency monitoring data and ensure data integrity through redundant backup.

[0055] The fiber optic transmission between edge computing unit 1 and cloud processing unit 2 can be replaced with electromagnetic interference-resistant shielded cable transmission. This transmission medium also serves as an electromagnetic isolation layer, effectively blocking strong electromagnetic interference and meeting the requirements for low-latency transmission.

[0056] The encryption storage technology used in cloud storage module 6 can be replaced with encryption methods based on national cryptographic algorithms. Specifically, the SM2 asymmetric encryption algorithm can be used for transmission encryption, and the SM4 symmetric encryption algorithm can be used for storage encryption. This can also achieve encryption protection for the storage and transmission of historical monitoring data.

[0057] All of the above alternative solutions can achieve the purpose of this invention and are equally applicable to the complex working conditions of a synchronous condenser, including high temperature, high vibration, and strong electromagnetic interference.

[0058] Example 4:

[0059] This embodiment has a basically the same structure as Embodiment 1, the difference being the deployment method of the functional modules of the cloud processing unit 2.

[0060] In this embodiment, the cloud processing unit 2 adopts a distributed deployment architecture, wherein the data aggregation module 21, the global analysis module 22, and the trend prediction module 23 are deployed on different physical servers, and the modules communicate with each other through a high-speed internal network. Specifically, the data aggregation module 21 is deployed on the data access server and is responsible for handling the concurrent data access of massive edge nodes; the global analysis module 22 is deployed on the analysis and computing server and undertakes complex correlation analysis and computing tasks; the trend prediction module 23 is deployed on the AI ​​inference server and runs a deep learning model to predict fault trends.

[0061] The modules work together to achieve global cloud-based management and control. This deployment method is suitable for large-scale phase-modulation airport stations and can significantly improve the computing performance and scalability of cloud processing unit 2.

[0062] Example 5:

[0063] This embodiment has a basically the same structure as Embodiment 1, the difference being the redundancy backup mechanism of the local cache module 3.

[0064] In this embodiment, the data backup unit of the local cache module 3 adopts a dual backup mode, that is, it maintains two identical copies of the data simultaneously, stored in different storage areas. When one copy of the data is damaged due to vibration or other reasons, the system automatically switches to the other copy and rebuilds the damaged copy in the background. This mode further enhances the data reliability of the local cache module 3 under extreme vibration environments, ensuring that critical monitoring data is not lost.

[0065] Example 6:

[0066] This embodiment has a basically the same structure as Embodiment 1, the difference being the data transmission mechanism between the edge computing unit 1 and the cloud processing unit 2.

[0067] In this embodiment, the instruction issuing module 13 employs a breakpoint resumption and compressed transmission mechanism when synchronizing data with the cloud processing unit 2. When the fiber optic communication network is temporarily interrupted, the instruction issuing module 13 temporarily stores the data to be uploaded in the dedicated storage area of ​​the local cache module 3, and automatically resumes transmission after the network is restored. Simultaneously, for periodic characteristic data, a lossless compression algorithm is used for compression before transmission, further reducing network bandwidth usage and improving data transmission efficiency.

[0068] Example 7:

[0069] This application also provides a distributed collaborative monitoring and storage device for condensers, which includes: a data acquisition module 4, an edge computing unit 1, a control module 5, a local cache module 3, a cloud processing unit 2, and a cloud storage module 6.

[0070] The acquisition module 4 is used to acquire raw high-frequency monitoring data of the synchronous condenser in real time, including vibration data, excitation current data, etc.

[0071] Edge computing unit 1 is deployed locally on the synchronous condenser and connected to acquisition module 4 and control module 5. Edge computing unit 1 internally includes a data receiving module 11, a real-time processing module 12, and an instruction issuing module 13. Data receiving module 11 is communicatively connected to acquisition module 4 and is used to receive raw high-frequency monitoring data at a fixed high-frequency sampling rate. Real-time processing module 12 is electrically connected to data receiving module 11 and incorporates a feature extraction algorithm tailored to the characteristics of the synchronous condenser. It is used to perform real-time filtering, feature value calculation, and anomaly detection on the raw high-frequency monitoring data, identify data anomalies, generate control instructions and early warning information, and control the real-time processing latency to within 50ms. Instruction issuing module 13 is communicatively connected to real-time processing module 12, control module 5, and cloud processing unit 2, respectively. It is used to issue control instructions to control module 5 and synchronize detected abnormal events and compressed periodic feature data to cloud processing unit 2 through an electromagnetic interference-resistant transmission medium.

[0072] In this embodiment, the transmission medium is optical fiber, which also serves as an electromagnetic isolation layer to block the impact of strong electromagnetic interference generated during the operation of the synchronous condenser on long-distance data transmission.

[0073] The control module 5 is connected to the edge computing unit 1 and is used to receive control commands issued by the edge computing unit 1 and adjust the camera.

[0074] The local cache module 3 is electrically connected to the edge computing unit 1 and uses an industrial-grade solid-state drive suitable for high-vibration environments to store high-frequency monitoring data processed by the edge computing unit 1 in real time. The local cache module 3 has a built-in data backup unit that uses a cyclic overwrite backup mode or a dual backup mode to redundantly back up the stored data, ensuring that valid data within a preset period is always stored. This storage period matches the data time window required by the local historical trend analysis algorithm embedded in the edge computing unit 1.

[0075] The cloud processing unit 2 and the instruction delivery module 13 of the edge computing unit 1 are connected via optical fiber communication. For example... Figure 3 As shown, the cloud processing unit 2 includes a data aggregation module 21, a global analysis module 22, and a trend prediction module 23. The data aggregation module 21 aggregates high-frequency monitoring data and early warning information from multiple synchronous condenser edge computing units 1 and integrates historical monitoring data from all synchronous condensers. The global analysis module 22 is electrically connected to the data aggregation module 21 and is used to compare and analyze the global monitoring data to identify abnormal correlations between different monitoring parameters. The trend prediction module 23 is electrically connected to the global analysis module 22 and is used to predict the development trend of synchronous condenser faults based on historical and real-time monitoring data using a trend fitting algorithm.

[0076] The cloud storage module 6 is connected to the cloud processing unit 2 and is used to save historical monitoring data according to the standardized data model of the power system, and to encrypt and protect the stored data using encryption technology. The cloud storage module 6 provides a standardized data service interface, which follows the remote communication protocol of the power grid dispatching system 7 and the data access protocol of the operation and maintenance management system 8, so as to realize native data interoperability and collaborative management with the power grid dispatching system 7 and the operation and maintenance management system 8.

[0077] This device achieves local real-time processing and closed-loop control of high-frequency data through an edge computing unit, reliable data redundancy in high-vibration environments through a local caching module, global analysis and trend prediction of multiple machines through a cloud processing unit, and standardized encrypted storage and seamless integration with external systems through a cloud storage module, thereby meeting the distributed collaborative monitoring and storage needs of synchronous condensers under complex working conditions.

[0078] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art will understand the specific meaning of the above terms in this application based on the specific circumstances.

[0079] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A distributed collaborative monitoring and storage system for synchronous condensers, characterized in that, include: The acquisition module is used to acquire raw high-frequency monitoring data from the synchronous condenser in real time. The control module is used to receive control commands to adjust the synchronous condenser. The edge computing unit, connected to the acquisition module and the control module and deployed locally on the camera condenser, is used to process the raw high-frequency monitoring data acquired by the acquisition module in real time, generate control commands and early warning information, and send control commands to the control module. The cloud processing unit is connected to the edge computing unit via a transmission medium. This transmission medium is an electromagnetic interference resistant transmission medium, which also serves as an electromagnetic isolation layer to block the strong electromagnetic interference generated during the operation of the synchronous condenser from affecting long-distance data transmission. The local cache module is electrically connected to the edge computing unit and is used to store high-frequency monitoring data processed by the edge computing unit in real time. The cloud storage module communicates with the cloud processing unit to save historical monitoring data and provide data service interfaces to the outside world.

2. The system according to claim 1, characterized in that, The edge computing unit includes: The data receiving module is communicatively connected to the acquisition module and is used to receive raw high-frequency monitoring data at a fixed sampling rate. The real-time computing module is electrically connected to the data receiving module. It has a built-in feature extraction algorithm for the characteristics of the synchronous condenser, which is used to process the raw high-frequency monitoring data in real time to generate control commands and early warning information, and control the real-time processing delay to within 50ms. The instruction issuing module communicates with the real-time computing module, the control module, and the cloud processing unit, respectively, and is used to issue control instructions and synchronize the detected abnormal events and processed feature data to the cloud processing unit.

3. The distributed collaborative monitoring and storage system for synchronous condensers according to claim 2, characterized in that, When synchronizing data with the cloud processing unit, the instruction issuing module employs a breakpoint resume and compressed transmission mechanism. This mechanism temporarily stores the data to be uploaded in the dedicated storage area of ​​the local cache module when the network is interrupted and automatically resumes transmission after network recovery. It also uses a lossless compression algorithm to compress the data before transmission.

4. The system according to claim 1, characterized in that, The cloud processing unit includes: The data aggregation module communicates with the edge computing unit and is used to aggregate high-frequency monitoring data, early warning information and integrate historical monitoring data from all synchronous condensers; The global analysis module, electrically connected to the data aggregation module, is used to compare and analyze global monitoring data and identify abnormal correlations between different monitoring parameters. The trend prediction module is electrically connected to the global analysis module. It is used to predict the development trend of synchronous condenser faults based on historical and real-time monitoring data and through a trend fitting algorithm.

5. The system according to claim 1, characterized in that, The data backup unit built into the local cache module adopts a cyclic overwrite backup mode or a dual backup mode. When the storage capacity reaches the upper limit, it automatically overwrites the earliest backup data or maintains two data copies at the same time to ensure that valid data within a preset period is always stored. This storage period matches the data time window required by the local historical trend analysis algorithm embedded in the edge computing unit.

6. The system according to claim 1, characterized in that, The cloud storage module uses encryption technology to encrypt and store historical monitoring data, organizes the data according to the standardized data model of the power system, and provides standardized interfaces to the outside world.

7. A distributed collaborative monitoring and storage method for synchronous condensers, applied to the system described in any one of claims 1-6, characterized in that, Includes the following steps: The raw high-frequency monitoring data of the synchronous condenser is collected in real time through the acquisition module; The raw high-frequency monitoring data is processed in real time by an edge computing unit deployed locally on the synchronous condenser to generate control commands and early warning information within the delay required for real-time control of the synchronous condenser, and the control commands are sent to the control module to regulate the synchronous condenser. The processed high-frequency monitoring data is stored in real time through a local caching module, and redundant backup is performed using industrial-grade storage devices suitable for high-vibration environments. The processed feature data is synchronized to the cloud processing unit through the transmission medium, which also serves as an electromagnetic isolation layer to block the strong electromagnetic interference generated during the operation of the synchronous condenser from affecting long-distance data transmission. The cloud processing unit performs global analysis and trend prediction on the aggregated data from multiple synchronous condensers. The cloud storage module uses encryption technology to encrypt and store historical monitoring data, and saves historical monitoring data according to the standardized data model of the power system. It also provides data services to external systems through standardized interfaces that are compatible with external systems.

8. A distributed collaborative monitoring and storage device for synchronous condensers, characterized in that, include: The acquisition module is used to acquire raw high-frequency monitoring data from the synchronous condenser in real time. The edge computing unit, deployed locally on the synchronous condenser and connected to the acquisition module, is used to process the raw high-frequency monitoring data in real time, generate control commands and early warning information within a preset delay that meets the real-time control requirements of the synchronous condenser, and synchronize the processed feature data to the cloud through an electromagnetic interference-resistant transmission medium. The transmission medium also serves as an electromagnetic isolation layer to block the impact of strong electromagnetic interference generated during the operation of the synchronous condenser on long-distance data transmission. The control module, connected to the edge computing unit, is used to receive control commands and adjust the phase shifter. The local cache module is electrically connected to the edge computing unit. It uses industrial-grade storage devices suitable for high-vibration environments and has a built-in data backup module for redundant backup of the processed high-frequency monitoring data. The cloud processing unit communicates with the edge computing unit and is used to perform global analysis and trend prediction on aggregated data from multiple synchronous condensers. The cloud storage module, connected to the cloud processing unit, is used to save historical monitoring data according to the standardized data model of the power system, and to provide data services to external systems through standardized interfaces adapted to external systems.

9. An electronic device, characterized in that, include: At least one processor; And the memory that is connected in communication with the processor; The memory stores instructions that can be executed by a processor, which are then executed by the processor to enable the processor to perform the distributed collaborative monitoring and storage method for condenser cameras as described in claim 7.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the distributed collaborative monitoring and storage method for condensers as described in claim 7.

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