Multi-modal anomaly detection edge computing platform for power equipment

Abstract

The invention discloses a power equipment multi-mode anomaly detection edge computing platform, and relates to the technical field of power equipment anomaly detection, the power equipment multi-mode anomaly detection edge computing platform comprises a hardware platform and a software platform, and the software platform comprises data fusion management, equipment state real-time monitoring, equipment fault overview, block chain security service, edge computing service, security and privacy protection. The hardware platform comprises a data acquisition module, an equipment access module and an edge gateway module; according to the method, the data can be processed in a place close to a data source through the edge computing service, data transmission delay is reduced, which means that anomaly detection and response of the power equipment can be performed at the first time, decentralized management and non-tampering of the data are provided by using the block chain technology to enable the edge computing, and the reliability of the power equipment is improved. Hash processing is performed on the data and the data are stored on the block chain, so that the data cannot be tampered in the transmission and storage process, and meanwhile, the data security is further enhanced through an encryption technology.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment anomaly detection, and in particular to a multi-modal anomaly detection edge computing platform for power equipment. Background Art

[0002] Multimodal anomaly detection is an advanced anomaly detection method that can use data from different sources or modes to identify anomalies from a more comprehensive perspective. Compared with traditional anomaly detection methods that rely on a single data source, multimodal anomaly detection uses a variety of data forms to integrate multi-dimensional information, thereby significantly improving the accuracy and robustness of detection. This method can capture more complex anomaly patterns, making its application effect more significant in complex environments.

[0003] Edge computing is a cutting-edge distributed computing paradigm that pushes computing tasks from centralized cloud data centers to edge devices on the network, allowing them to be processed closer to the data source. This approach not only significantly reduces data transmission latency, but also improves response speed and enhances security during data processing. This localized data processing method is particularly suitable for scenarios that require real-time response, such as status monitoring of power equipment.

[0004] Combining the above two technologies, the edge computing platform for multimodal anomaly detection of power equipment has become an efficient real-time monitoring system. The platform integrates edge computing technology with data from various sensors, and realizes rapid anomaly detection of power equipment status by performing data analysis locally on the device. This platform can effectively process multiple data types from different sensors, including temperature, humidity, current, voltage and other information, thus showing good characteristics in data transmission delay, real-time response capability of the system and data security.

[0005] However, in the traditional edge computing platform for multimodal anomaly detection of power equipment, the data transmission and storage process may be exposed to the risk of tampering or leakage, especially when the data needs to flow between multiple nodes and systems, which means that the security and integrity of the data are often not fully guaranteed. In view of this, it is particularly important to explore and study a safer and more reliable edge computing platform for multimodal anomaly detection of power equipment. Summary of the invention

[0006] The present invention proposes an edge computing platform for multimodal anomaly detection of power equipment to solve the technical problems existing in the background technology.

[0007] To solve the above problems, the technical solution of the present invention is:

[0008] A multimodal anomaly detection edge computing platform for power equipment includes a hardware platform and a software platform, wherein the software platform includes modules such as data fusion management, real-time monitoring of equipment status, equipment fault overview, blockchain security service, edge computing service, security and privacy protection, etc.; data fusion management is used to integrate data from different sources to build a comprehensive global view; real-time monitoring of equipment status is used to monitor the operating status of the equipment in real time, and when an anomaly is detected, a corresponding early warning mechanism will be triggered; equipment fault overview is used to provide summary information of equipment faults; blockchain security service is used to ensure the security and immutability of data; edge computing service is used to process data instantly at a location close to the equipment; security and privacy protection is used to protect data and systems by implementing encryption, identity authentication and access control measures.

[0009] Preferably, the hardware platform includes a data acquisition module, a device access module, and an edge gateway module; the data acquisition module is used to collect data from various power equipment, including sensor data, operating status, fault information, etc., and the collected data serves as the basis for subsequent analysis and processing; the device access module is used to support multiple devices to access and connect to the edge computing platform; the edge gateway module is used to act as an intermediary for data aggregation, processing and transmission to a cloud platform or other systems, providing data preprocessing, storage and edge computing capabilities.

[0010] Preferably, the data fusion management module includes data cleaning, feature extraction, data association, and data modeling; data cleaning is used to remove outliers, duplicate values, missing values, etc. in the data; feature extraction is used to extract meaningful features from the original data; data association is used to associate data from different sources; data modeling is used to establish a data model.

[0011] Preferably, the real-time monitoring module for equipment status includes data preprocessing, status parameter extraction, anomaly detection algorithm, and alarm mechanism; data preprocessing is used to perform preprocessing such as filtering, denoising, and format conversion on the collected data; status parameter extraction is used to extract equipment operation status parameters from the preprocessed data; anomaly detection algorithm is used to detect abnormal conditions of equipment operation status parameters using methods such as thresholds, statistical models, and machine learning models; and the alarm mechanism is used to trigger a corresponding early warning mechanism based on the anomaly detection results.

[0012] Preferably, the blockchain security service module includes blockchain nodes, smart contracts, data hashing and verification; blockchain nodes are used for distributed nodes to ensure decentralization and tamper-proofing of data transmission; smart contracts are used to automatically execute protocols; data hashing and verification are used to hash each piece of data and record it in the blockchain.

[0013] Preferably, the implementation steps of the smart contract are: first select the Ethereum blockchain platform, then use Solidity programming to write the smart contract code, deploy it to the blockchain and call it on the edge computing platform through the API interface.

[0014] Preferably, the implementation steps of the data hash are: first use the SHA-256 algorithm to hash the collected data to generate a hash value, and then store the data hash value and the original data in the blockchain. When the data integrity needs to be verified, the original data is hashed again, and the generated hash value is compared with the hash value stored in the blockchain. If the two hash values ​​are consistent, it means that the data has not been tampered with.

[0015] Preferably, the edge computing service module includes data acquisition and preprocessing, equipment status monitoring and analysis, edge computing and decision-making; data acquisition and preprocessing are used to perform data acquisition and preliminary processing in the edge gateway module; equipment status monitoring and analysis are used to analyze data in real time by utilizing the advantages of edge computing; edge computing and decision-making are used to make decisions on the edge side based on the results of anomaly detection.

[0016] Preferably, the security and privacy protection module includes encryption, identity authentication, and access control; encryption is used to adopt strong encryption algorithms during data transmission and storage; identity authentication is used to ensure that only authorized users and devices can access and use system resources; access control is used to formulate access control policies based on user roles and permissions to limit user access rights to data.

[0017] Preferably, the multimodal anomaly detection of power equipment based on the edge computing platform includes the following steps:

[0018] Step 1: First collect sensor data, operating status and fault information from various power equipment, and pre-process the collected data in the edge gateway module;

[0019] Step 2: Then perform fusion management operations such as data cleaning, feature extraction, association and modeling;

[0020] Step 3: Extract device status information from the data, use algorithms to identify abnormal situations in real time, and send abnormal alerts to operation and maintenance personnel;

[0021] Step 4: Further data processing is performed at the edge gateway to reduce latency, analyze device status in real time, quickly identify anomalies, and adjust device parameters or trigger response processes based on the analysis results;

[0022] Step 5: Use blockchain technology to enable edge computing, then automatically execute the data transmission protocol, hash the data and record it;

[0023] Step 6: Encrypt data during transmission and storage, and authenticate and control access to users or devices.

[0024] The above-mentioned technical scheme of the present invention has the following beneficial technical effects: the present invention enables data to be processed close to the data source through edge computing services, reducing data transmission delays, which means that abnormal detection and response of power equipment can be carried out as soon as possible, improving the response speed and real-time performance of the system, and the use of blockchain technology to enable edge computing provides decentralized management and non-tamperability of data. By hashing the data and storing it on the blockchain, it can be ensured that the data will not be tampered with during transmission and storage. Even if a node fails or is attacked, the data on other nodes is still complete and reliable, ensuring the security and consistency of the data, while further enhancing data security through encryption technology. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0026] An edge computing platform for multimodal anomaly detection of power equipment, including a hardware platform and a software platform, wherein the software platform includes modules such as data fusion management, real-time monitoring of equipment status, equipment fault overview, blockchain security services, edge computing services, security and privacy protection, etc.

[0027] Data fusion management, which is used to integrate data from different sources to build a comprehensive global view;

[0028] Real-time monitoring of equipment status is used to monitor the operating status of the equipment in real time. When an abnormality is detected, the corresponding early warning mechanism will be triggered;

[0029] Equipment failure overview, which is used to provide summary information of equipment failures;

[0030] Blockchain security services to ensure data security and immutability;

[0031] Edge computing services for instant data processing close to the device;

[0032] Security and privacy protection to protect data and systems by implementing encryption, authentication, and access control measures.

[0033] Furthermore, the hardware platform includes a data acquisition module, a device access module, and an edge gateway module; the data acquisition module is used to collect data from various power equipment, including sensor data, operating status, fault information, etc., and the collected data serves as the basis for subsequent analysis and processing; the device access module is used to support multiple devices to access and connect to the edge computing platform; the edge gateway module is used to act as an intermediary for data aggregation, processing and transmission to the cloud platform or other systems, providing data preprocessing, storage and edge computing capabilities.

[0034] Furthermore, the data fusion management module includes data cleaning, feature extraction, data association, and data modeling; data cleaning is used to remove outliers, duplicate values, missing values, etc. in the data; feature extraction is used to extract meaningful features from the original data; data association is used to associate data from different sources; data modeling is used to establish a data model.

[0035] The specific steps for collecting temperature sensor data, current sensor data, voltage sensor data and operation log data from a power transformer are as follows:

[0036] First, clean the data to remove outliers, duplicate values, and missing values, then extract characteristic values ​​such as temperature, current, and voltage, and then associate the sensor data with the operation log data. For example, associate the temperature data with the operation status data, analyze the relationship between temperature changes and operation status, and then establish a time series model to predict future temperature change trends.

[0037] Through data fusion management, the ability to integrate and analyze data from different sources is provided. This comprehensive data processing method makes anomaly detection more accurate and reliable.

[0038] Furthermore, the real-time monitoring module for equipment status includes data preprocessing, status parameter extraction, anomaly detection algorithm, and alarm mechanism; data preprocessing is used to perform filtering, denoising, format conversion and other preprocessing on the collected data; status parameter extraction is used to extract equipment operation status parameters from the preprocessed data; anomaly detection algorithm is used to detect abnormal conditions of equipment operation status parameters using methods such as thresholds, statistical models, and machine learning models; the alarm mechanism is used to trigger a corresponding early warning mechanism based on the anomaly detection results.

[0039] The specific steps for real-time monitoring of the blade speed and wind speed data of the wind turbine are as follows:

[0040] First, the blade speed and wind speed data are preprocessed, filtered and denoised, and then the state parameters such as blade speed and wind speed are extracted. Then, the threshold method or machine learning model is used to monitor in real time whether the blade speed and wind speed data are abnormal. If an abnormality is detected, the alarm mechanism is triggered to notify the operation and maintenance personnel.

[0041] Furthermore, the blockchain security service module includes blockchain nodes, smart contracts, data hashing and verification; blockchain nodes are used for distributed nodes to ensure the decentralization and tamper-proofing of data transmission; smart contracts are used to automatically execute protocols; data hashing and verification are used to hash each piece of data and record it in the blockchain.

[0042] Furthermore, the implementation steps of the smart contract are: first select the Ethereum platform, then use the Solidity programming language provided by the Ethereum platform to write the smart contract code, and then deploy the written smart contract to the blockchain platform, and call the deployed smart contract in the edge computing platform through the API provided by the platform to realize functions such as data transmission, permission management and data verification.

[0043] Furthermore, the implementation steps of the data hash are: first use the SHA-256 algorithm to hash the collected data to generate a hash value, and then store the data hash value and the original data in the blockchain. When the data integrity needs to be verified, the original data is hashed again, and the generated hash value is compared with the hash value stored in the blockchain. If the two hash values ​​are consistent, it means that the data has not been tampered with.

[0044] Assuming that sensor data is to be stored in the blockchain and smart contracts are used to control data access rights, the specific steps are as follows:

[0045] Step 1: Define a smart contract, which includes functions such as storing sensor data, controlling data access rights, and verifying data integrity.

[0046] Step 2: Deploy the smart contract to the blockchain platform;

[0047] Step 3: Collect data from the sensor, and then use the SHA-256 algorithm to hash the collected data to generate a hash value;

[0048] Step 4: Store the original data and hash value in the blockchain;

[0049] Step 5: When data integrity needs to be verified, the original data is hashed again and the resulting hash value is compared with the hash value stored in the blockchain to ensure that the data has not been tampered with.

[0050] Blockchain security services provide decentralized management and immutability of data. By hashing the data and storing it on the blockchain, it can be ensured that the data will not be tampered with during transmission and storage. The use of smart contracts can also automate data access rights control, further enhancing data security.

[0051] Furthermore, the edge computing service module includes data collection and preprocessing, equipment status monitoring and analysis, edge computing and decision-making; data collection and preprocessing are used to perform data collection and preliminary processing in the edge gateway module; equipment status monitoring and analysis are used to utilize the advantages of edge computing to analyze data in real time; edge computing and decision-making are used to make decisions on the edge side based on the results of anomaly detection.

[0052] The specific steps of analyzing the temperature data of the power transformer in the edge gateway module and automatically adjusting the fan speed according to the temperature change are as follows:

[0053] First, data is collected from the temperature sensor and preprocessed in the edge gateway module. Then, the temperature data is analyzed using a time series model to predict future temperature change trends. Then, the fan speed is automatically adjusted based on the temperature changes. For example, when the temperature rises, the fan speed is accelerated to reduce the transformer temperature.

[0054] Edge computing services enable data to be processed close to the data source, reducing data transmission delays. This means that abnormal detection and response of power equipment can be carried out in the first place, improving the response speed and real-time performance of the system.

[0055] Furthermore, the security and privacy protection module includes encryption, identity authentication, and access control; encryption is used to adopt strong encryption algorithms during data transmission and storage; identity authentication is used to ensure that only authorized users and devices can access and use system resources; access control is used to formulate access control policies based on user roles and permissions to limit user access rights to data.

[0056] The specific steps of using encryption technology to protect sensitive data of power equipment and using authentication mechanism to control user access rights are as follows:

[0057] During data transmission, strong encryption algorithms are used to encrypt data, and access control policies are formulated to limit user access rights to data. When users access the system, they need to be authenticated to ensure that only authorized users can access the system.

[0058] Encryption technology is used to protect the security of data during transmission and storage, ensuring that sensitive information is not accessed by unauthorized persons.

[0059] Furthermore, the multimodal anomaly detection of power equipment based on the edge computing platform includes the following steps:

[0060] Step 1: First collect sensor data, operating status and fault information from various power equipment, and pre-process the collected data in the edge gateway module;

[0061] Step 2: Then perform fusion management such as data cleaning, feature extraction, association and modeling;

[0062] Step 3: Extract device status information from the data, use algorithms to identify abnormal situations in real time, and send abnormal alerts to operation and maintenance personnel;

[0063] Step 4: Further data processing is performed at the edge gateway to reduce latency, analyze device status in real time, quickly identify anomalies, and adjust device parameters or trigger response processes based on the analysis results;

[0064] Step 5: Use blockchain technology to enable edge computing, then automatically execute the data transmission protocol, hash the data and record it;

[0065] Step 6: Encrypt data during transmission and storage, and authenticate and control access to users or devices.

[0066] The above are only preferred embodiments of the present invention, which are only used to help understand the method and core ideas of the present application. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention. The above is only a preferred embodiment of the present invention, and it is not used to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-modal anomaly detection edge computing platform for power equipment, characterized in that: It includes a hardware platform and a software platform. The software platform includes modules such as data fusion management, real-time monitoring of equipment status, equipment failure overview, blockchain security services, edge computing services, security and privacy protection, etc. Data fusion management, which is used to integrate data from different sources to build a comprehensive global view; Real-time monitoring of equipment status is used to monitor the operating status of the equipment in real time. When an abnormality is detected, the corresponding early warning mechanism will be triggered; Equipment failure overview, which is used to provide summary information of equipment failures; Blockchain security services to ensure data security and immutability; Edge computing services for instant data processing close to the device; Security and privacy protection to protect data and systems by implementing encryption, authentication, and access control measures.

2. The multi-modal anomaly detection edge computing platform for power equipment according to claim 1 is characterized in that: The hardware platform includes a data acquisition module, a device access module, and an edge gateway module; Data acquisition module, used to collect data from various power equipment, including sensor data, operating status, fault information, etc. The collected data serves as the basis for subsequent analysis and processing; Device access module, used to support multiple devices to access and connect to the edge computing platform; The edge gateway module acts as an intermediary for data aggregation, processing and transmission to the cloud platform or other systems, providing data preprocessing, storage and edge computing capabilities.

3. The multi-modal anomaly detection edge computing platform for power equipment according to claim 1, characterized in that: The data fusion management module includes data cleaning, feature extraction, data association, and data modeling; Data cleaning, used to remove outliers, duplicate values, missing values, etc. in the data; Feature extraction, which is used to extract meaningful features from raw data; Data association, used to associate data from different sources; Data modeling, used to build data models.

4. The multi-modal anomaly detection edge computing platform for power equipment according to claim 1, characterized in that: The device status real-time monitoring module includes data preprocessing, status parameter extraction, anomaly detection algorithm, and alarm mechanism; Data preprocessing, used to perform filtering, denoising, format conversion and other preprocessing on the collected data; Status parameter extraction, used to extract equipment operation status parameters from preprocessed data; Anomaly detection algorithms are used to detect anomalies in equipment operating status parameters using methods such as thresholds, statistical models, and machine learning models; The alarm mechanism is used to trigger the corresponding early warning mechanism based on the abnormal detection results.

5. The multi-modal anomaly detection edge computing platform for power equipment according to claim 1, characterized in that: The blockchain security service module includes blockchain nodes, smart contracts, data hashing and verification; Blockchain nodes are used for distributed nodes to ensure decentralization and tamper-proof data transmission; Smart contracts, which automatically execute agreements; Data hashing and verification is used to hash each piece of data and record it in the blockchain.

6. The multi-modal anomaly detection edge computing platform for power equipment according to claim 5, characterized in that: The implementation steps of the smart contract are: first select the Ethereum blockchain platform, then use Solidity programming to write the smart contract code, deploy it to the blockchain and call it on the edge computing platform through the API interface.

7. The power equipment multimodal anomaly detection edge computing platform according to claim 5, characterized in that: The implementation steps of the data hash are: first use the SHA-256 algorithm to hash the collected data to generate a hash value, and then store the data hash value and the original data in the blockchain. When the data integrity needs to be verified, the original data is hashed again, and the generated hash value is compared with the hash value stored in the blockchain. If the two hash values ​​are consistent, it means that the data has not been tampered with.

8. The power equipment multimodal anomaly detection edge computing platform according to claim 1, characterized in that: The edge computing service module includes data collection and preprocessing, equipment status monitoring and analysis, edge computing and decision-making; Data collection and preprocessing, used for data collection and preliminary processing in the edge gateway module; Equipment status monitoring and analysis, which is used to analyze data in real time using the advantages of edge computing; Edge computing and decision-making are used to make decisions on the edge side based on anomaly detection results.

9. The multi-modal anomaly detection edge computing platform for power equipment according to claim 1, characterized in that: The security and privacy protection module includes encryption, identity authentication, and access control; Encryption, which uses strong encryption algorithms during data transmission and storage; Authentication, which is used to ensure that only authorized users and devices can access and use system resources; Access control is used to formulate access control policies based on user roles and permissions to limit user access rights to data.

10. The power equipment multi-modal anomaly detection edge computing platform according to claim 1, characterized in that: The multimodal anomaly detection of power equipment based on the edge computing platform includes the following steps: Step 1: First collect sensor data, operating status and fault information from various power equipment, and pre-process the collected data in the edge gateway module; Step 2: Then perform fusion management operations such as data cleaning, feature extraction, association and modeling; Step 3: Extract device status information from the data, use algorithms to identify abnormal situations in real time, and send abnormal alerts to operation and maintenance personnel; Step 4: Further data processing is performed at the edge gateway to reduce latency, analyze device status in real time, quickly identify anomalies, and adjust device parameters or trigger response processes based on the analysis results; Step 5: Use blockchain technology to enable edge computing, then automatically execute the data transmission protocol, hash the data and record it; Step 6: Encrypt data during transmission and storage, and authenticate and control access to users or devices.