CN117236554A

CN117236554A - Distributed household photovoltaic power station operation and maintenance monitoring method and system

Info

Publication number: CN117236554A
Application number: CN202311133387.3A
Authority: CN
Inventors: 李博; 刘志其; 孙昭; 任大磊
Original assignee: Guangdong Energy Group Guizhou Co ltd Hebei Branch
Current assignee: Guangdong Energy Group Guizhou Co ltd Hebei Branch
Priority date: 2023-09-04
Filing date: 2023-09-04
Publication date: 2023-12-15

Abstract

The invention discloses a distributed household photovoltaic power station operation and maintenance monitoring method and system. According to the invention, through the synergistic effect of the satellite remote sensing module, the unmanned aerial vehicle inspection module, the blockchain data platform module, the artificial intelligent analysis module, the biological recognition authentication module, the virtual reality interaction module and other modules, the functions of rapid recognition and positioning, periodic inspection and anomaly detection, data sharing and transaction, intelligent recognition and recommendation, identity authentication and authority management, remote maintenance and education training and the like of the photovoltaic power station are realized, the operation efficiency and reliability of the photovoltaic power station are improved, and the identity authentication and authority management of a user of the photovoltaic power station are realized; remote maintenance and education training of the photovoltaic power station are realized by using a virtual reality interaction technology based on equipment such as a rendering engine, display equipment, interaction equipment, feedback equipment and the like. The invention provides a novel, efficient, intelligent, safe and convenient solution for operation and maintenance monitoring of the photovoltaic power station.

Description

Distributed household photovoltaic power station operation and maintenance monitoring method and system

Technical Field

The invention relates to the technical field of operation and maintenance monitoring of photovoltaic power stations, in particular to a distributed household operation and maintenance monitoring method and system of a photovoltaic power station.

Background

Photovoltaic power generation is a power generation mode for directly converting solar energy into electric energy by utilizing a solar cell, has the advantages of cleanness, reproducibility and distribution, and is one of important directions of energy development in the future. With the continuous progress of the photovoltaic technology and the continuous reduction of the cost, the distributed household photovoltaic power station is more and more popular with users, and can effectively solve the electricity demand of the users and the energy conservation and emission reduction targets.

However, the distributed photovoltaic power station has a large challenge for operation and maintenance monitoring due to the characteristics of smaller scale, scattered distribution and complex environment. Traditional operation and maintenance monitoring mode mainly relies on manual inspection and field maintenance, and is low in efficiency, high in cost and poor in timeliness, and cannot meet the high-efficiency, safe and intelligent operation and maintenance requirements of a user on a photovoltaic power station.

Therefore, a novel operation and maintenance monitoring method and system are urgently needed, which can be used for carrying out rapid identification, positioning, inspection, diagnosis and maintenance operations on the distributed household photovoltaic power station, improving the operation efficiency and reliability of the photovoltaic power station, reducing the operation and maintenance cost and risk and enhancing the trust and satisfaction degree of users.

Disclosure of Invention

The invention aims to provide a distributed household photovoltaic power station operation and maintenance monitoring method and system based on satellite remote sensing, unmanned aerial vehicle inspection, blockchain data platform, artificial intelligent analysis, biological identification authentication and virtual reality interaction, which can realize the omnibearing, real-time and intelligent operation and maintenance monitoring of the distributed household photovoltaic power station and solve the problems in the prior art.

For the purposes, the invention provides the following technical scheme: the distributed household photovoltaic power station operation and maintenance monitoring method and system comprises the steps of intelligent management, adjustment, identification and positioning, periodic inspection and anomaly detection, identification and early warning of running states and fault types, solving and optimizing problems, data safety and transparency, data analysis and prediction, identity authentication and authority management, visualization and interactive operation and maintenance monitoring of a photovoltaic power station based on satellite remote sensing technology, unmanned aerial vehicle technology, blockchain technology, biological identification technology, artificial intelligence technology and virtual reality technology;

the satellite remote sensing technology comprises the steps of extracting position, scale, form and azimuth angle information of a photovoltaic power station by analyzing a high-resolution multispectral or hyperspectral satellite image, and accurately identifying and positioning the photovoltaic power station by utilizing a target detection and segmentation algorithm based on deep learning; the unmanned aerial vehicle technology comprises the steps of collecting working parameters, environment parameters and fault information data of a photovoltaic power station through carrying a temperature sensor, a humidity sensor, a current sensor, a voltage sensor, a visible light camera, an infrared camera and an ultraviolet camera device, and transmitting the data to an operation and maintenance monitoring center in real time;

The blockchain technology comprises a data sharing and transaction platform based on establishing privacy protection based on zero knowledge proof, supporting various data types and service types, and sharing and transaction of data among photovoltaic power station users by adopting a mechanism based on token excitation;

the biological recognition technology comprises the steps of based on collection of fingerprint, iris and voiceprint biological characteristics of a photovoltaic power station user, identity authentication and authority management of the photovoltaic power station user, and recording authentication results on a blockchain.

Preferably, the artificial intelligence technology comprises preprocessing operations of cleaning, normalizing and extracting features of data received from a satellite remote sensing module or an unmanned aerial vehicle inspection module based on constructing a deep neural network model based on long-short-term memory (LSTM) or Convolutional Neural Network (CNN) or Graph Neural Network (GNN), and storing the processed data in a database;

based on the deep neural network model, intelligent identification and recommendation are carried out on the operation state, fault type and maintenance scheme of the photovoltaic power station, and a control instruction is generated according to the identification recommendation result and is sent to the central control unit;

the virtual reality technology comprises the steps of generating a three-dimensional model and a scene of a photovoltaic power station based on a rendering engine according to data and an identification recommendation result received from a satellite remote sensing module or an artificial intelligent module, and presenting the three-dimensional model and the scene on display equipment;

Receiving input and operation of a user based on the interaction equipment, converting the input and operation into corresponding data and instructions, and sending the corresponding data and instructions to the central control unit; based on the feedback equipment, corresponding sound, light and vibration feedback are generated according to the data and the control instruction received from the central control unit and are presented on the interaction equipment; and (3) based on the central control unit, uniformly managing and scheduling the steps.

The satellite images provide spectral and textural features of the photovoltaic power plant.

Preferably, the unmanned aerial vehicle automatically executes the inspection task according to a preset flight route and a preset flight height, and transmits the acquired data and images to the operation and maintenance monitoring center in real time.

Preferably, the blockchain data platform employs a token-based incentive mechanism to encourage sharing and trading of data between photovoltaic power plant users.

Preferably, the blockchain data platform supports a plurality of data types and service types, including power generation data, environmental data, load data, predictive services, dispatch services, clearing services.

Preferably, the artificial intelligence technology adopts a complex network model based on a graph neural network to analyze and predict data of the photovoltaic power station and provide intelligent operation and maintenance suggestion and control strategies.

Preferably, the operation and maintenance monitoring system of the distributed household photovoltaic power station is characterized in that: the system comprises a satellite remote sensing module, an unmanned aerial vehicle inspection module, a blockchain data platform module, an artificial intelligent analysis module, a biological identification authentication module and a virtual reality interaction module;

the satellite remote sensing module is used for acquiring position, scale, form and azimuth angle information of the photovoltaic power station based on analysis of high-resolution multispectral or hyperspectral satellite images, and rapidly identifying and positioning the distributed household photovoltaic power station;

the unmanned aerial vehicle inspection module is based on carrying a temperature sensor, a humidity sensor, a current sensor, a voltage sensor, a visible light camera, an infrared camera and an ultraviolet camera device, and periodically inspecting and detecting abnormality of a photovoltaic power station;

the block chain data platform module is based on a privacy-protected data sharing and transaction platform with zero knowledge proof establishment, and is safe and transparent to the data of the photovoltaic power station, and cooperated with and optimizes the photovoltaic power station;

the artificial intelligent analysis module performs preprocessing operations of cleaning, normalizing and extracting features on data received from the satellite remote sensing module or the unmanned aerial vehicle inspection module based on constructing a deep neural network model based on long-short-term memory (LSTM) or Convolutional Neural Network (CNN) or Graph Neural Network (GNN), and stores the processed data in a database;

The method comprises the steps of utilizing a deep neural network model to intelligently identify and recommend the running state, fault type and maintenance scheme of a photovoltaic power station, generating a control instruction according to an identification recommendation result and sending the control instruction to a central control unit;

the biological identification authentication module is used for carrying out identity authentication and authority management on the photovoltaic power station user based on the collected fingerprint, iris and voiceprint biological characteristics of the photovoltaic power station user, and recording an authentication result on a blockchain;

the virtual reality interaction module is used for generating a three-dimensional model and a scene of the photovoltaic power station according to data and identification recommendation results received from the satellite remote sensing module or the artificial intelligent analysis module through the rendering engine and presenting the three-dimensional model and the scene on the display equipment; receiving input and operation of a user through the interaction equipment, converting the input and operation into corresponding data and instructions, and sending the corresponding data and instructions to the central control unit;

generating corresponding sound, light and vibration feedback through feedback equipment according to the data and control instructions received from the central control unit, and presenting the corresponding sound, light and vibration feedback on the interaction equipment;

the central control unit comprises a data processing unit, an identification recommending unit and an optimizing control unit;

The data processing unit performs cleaning, normalization and feature extraction preprocessing operations on the data received from the satellite remote sensing module or the unmanned aerial vehicle inspection module, and stores the processed data in a database;

the identification recommendation unit is used for intelligently identifying and recommending the operation state, the fault type and the maintenance scheme of the photovoltaic power station based on the deep neural network model, and sending an identification recommendation result to the virtual reality interaction module or the optimization control unit;

the optimization control unit is used for solving and optimizing the optimization problem of the photovoltaic power station based on the reinforcement learning technology, generating a control instruction according to the optimization result and sending the control instruction to the unmanned aerial vehicle inspection module or the virtual reality interaction module.

Preferably, the satellite remote sensing module comprises a satellite image acquisition unit, a satellite image processing unit and a satellite image output unit;

the satellite image acquisition unit acquires a high-resolution multispectral or hyperspectral image from a satellite;

the satellite image processing unit performs a target detection and segmentation algorithm based on deep learning on the satellite image, and extracts position, scale, form and azimuth angle information of the photovoltaic power station;

The satellite image output unit outputs the extracted information to the operation and maintenance monitoring center.

Preferably, the unmanned aerial vehicle inspection module comprises an unmanned aerial vehicle flight control unit, an unmanned aerial vehicle data acquisition unit and an unmanned aerial vehicle data transmission unit;

the unmanned aerial vehicle flight control unit automatically executes the inspection task according to a preset flight route and a preset flight height and receives a control instruction from the central control unit;

the unmanned aerial vehicle data acquisition unit acquires working parameters, environmental parameters and fault information data of a photovoltaic power station based on the carried temperature sensor, the carried humidity sensor, the carried current sensor, the carried voltage sensor, the carried visible light camera, the carried infrared camera and the carried ultraviolet camera equipment;

the unmanned aerial vehicle data transmission unit is used for transmitting the acquired data and images to the operation and maintenance monitoring center in real time.

Preferably, the virtual reality interaction module comprises a rendering engine unit, a display device unit, an interaction device unit and a feedback device unit;

the rendering engine unit generates a three-dimensional model and a scene of the photovoltaic power station according to the data received from the satellite remote sensing module or the artificial intelligent analysis module and the recognition recommendation result;

The display equipment unit presents the three-dimensional model and the scene generated by the rendering engine unit;

the interaction equipment unit receives input and operation of a user, converts the input and operation into corresponding data and instructions and sends the corresponding data and instructions to the central control unit;

the feedback equipment unit generates corresponding sound, light and vibration feedback according to the data and the control instruction received from the central control unit, and displays the feedback on the interaction equipment.

Compared with the prior art, the invention has the following beneficial effects:

1. according to the invention, through the synergistic effect of the satellite remote sensing module, the unmanned aerial vehicle inspection module, the blockchain data platform module, the artificial intelligent analysis module, the biological recognition authentication module and the virtual reality interaction module, the functions of rapid recognition and positioning, periodic inspection and anomaly detection, data sharing and transaction, intelligent recognition and recommendation, identity authentication and authority management, remote maintenance and education training of the photovoltaic power station are realized, the operation efficiency and reliability of the photovoltaic power station are improved, the operation and maintenance cost and risk are reduced, and the trust and satisfaction of users are enhanced;

2. according to the invention, by utilizing the multispectral or hyperspectral satellite image with high resolution and the target detection and segmentation algorithm based on deep learning, the rapid identification and positioning of the distributed household photovoltaic power station are realized, and compared with the prior art, the method has the advantages of high accuracy and high efficiency;

3. According to the invention, the unmanned plane carrying various sensors and camera equipment and the data analysis and prediction technology based on the deep neural network model are utilized, so that the regular inspection and anomaly detection of the photovoltaic power station are realized, and compared with the prior art, the method has the advantages of high precision and high efficiency;

4. the invention realizes the data sharing and transaction of the photovoltaic power station by utilizing the blockchain technology based on the privacy protection of zero knowledge proof and the data sharing and transaction platform based on the intelligent contract, and has the advantages of high safety and high credibility compared with the prior art;

5. the invention realizes the identity authentication and authority management of the photovoltaic power station user by utilizing the biological recognition authentication technology based on the biological characteristics of fingerprints, irises and voiceprints, and has the advantages of high accuracy and high convenience compared with the prior art;

6. the invention realizes remote maintenance and education training of the photovoltaic power station by utilizing the virtual reality interaction technology based on the rendering engine, the display equipment, the interaction equipment and the feedback equipment, and has the advantages of high effect and high satisfaction compared with the prior art.

Drawings

FIG. 1 is a schematic view of a satellite remote sensing module according to the present invention;

FIG. 2 is a schematic diagram of a specific implementation method and flow of the satellite remote sensing module of the present invention;

FIG. 3 is a schematic structural diagram of the unmanned aerial vehicle inspection module of the present invention;

FIG. 4 is a schematic diagram of a specific implementation method and flow of the unmanned aerial vehicle inspection module of the present invention;

FIG. 5 is a block chain data platform module according to the present invention;

FIG. 6 is a block chain data platform module embodying methods and flow diagrams of the present invention;

FIG. 7 is a schematic diagram showing the structure of a biometric authentication module according to the present invention;

FIG. 8 is a schematic diagram of a method and a flow chart for implementing the biometric authentication module according to the present invention;

FIG. 9 is a schematic diagram of a virtual reality interaction module according to this invention;

fig. 10 is a schematic diagram of a specific implementation method and a flow of the virtual reality interaction module of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1 to 10, the present invention provides a technical solution: a distributed household photovoltaic power station operation and maintenance monitoring method and system;

the invention provides a distributed household photovoltaic power station operation and maintenance monitoring system which comprises a satellite remote sensing module, an unmanned aerial vehicle inspection module, a block chain data platform module, an artificial intelligent analysis module, a biological identification authentication module and a virtual reality interaction module.

The satellite remote sensing module is one of the core parts of the invention, and has the function of rapidly identifying and positioning the distributed household photovoltaic power station by utilizing a high-resolution multispectral or hyperspectral satellite image. The satellite remote sensing module comprises a satellite image acquisition unit, a satellite image processing unit and a satellite image output unit. The satellite image acquisition unit acquires high-resolution multispectral or hyperspectral images from satellites, which can provide spectral features and texture features of the photovoltaic power plant. The satellite image processing unit performs a target detection and segmentation algorithm based on deep learning on the satellite image, and extracts position, scale, shape and azimuth information of the photovoltaic power station. The satellite image output unit outputs the extracted information to the operation and maintenance monitoring center for other modules to use.

The unmanned aerial vehicle inspection module is one of the core parts of the invention, and has the functions of periodically inspecting and anomaly detecting the photovoltaic power station by using an unmanned aerial vehicle carrying various sensors and camera equipment. The unmanned aerial vehicle inspection module comprises an unmanned aerial vehicle flight control unit, an unmanned aerial vehicle data acquisition unit and an unmanned aerial vehicle data transmission unit. And the unmanned aerial vehicle flight control unit automatically executes the inspection task according to the preset flight route and the preset flight altitude and receives a control instruction from the central control unit. The unmanned aerial vehicle data acquisition unit acquires working parameters, environmental parameters and fault information data of a photovoltaic power station based on the carried temperature sensor, humidity sensor, current sensor, voltage sensor, visible light camera, infrared camera and ultraviolet camera equipment;

the unmanned aerial vehicle data transmission unit is used for transmitting the acquired data and images to the operation and maintenance monitoring center in real time and is used by other modules.

The block chain data platform module is one of the core parts of the invention, and has the functions of establishing a privacy-protected data sharing and transaction platform based on zero knowledge proof, supporting various data types and service types, and encouraging the sharing and transaction of data among photovoltaic power station users by adopting a mechanism based on token excitation. The blockchain data platform module includes a blockchain network unit, a blockchain smart contract unit, and a blockchain token unit. The blockchain network element is a decentralized distributed ledger system for storing and verifying data and transaction records of photovoltaic power plants. The blockchain smart contract unit is a set of automatically executed program code for defining and executing data sharing and transaction rules for photovoltaic power plants. Blockchain token units are a type of digital currency based on blockchain technology that is used to motivate data sharing and transaction actions between photovoltaic utility users.

The artificial intelligent analysis module is one of the core parts of the invention, and has the functions of cleaning, normalizing and feature extraction preprocessing operation on the data received from the satellite remote sensing module or the unmanned aerial vehicle inspection module by using a deep neural network model, and storing the processed data in a database; and intelligent identification and recommendation are carried out on the operation state, the fault type and the maintenance scheme of the photovoltaic power station by using the deep neural network model, and a control instruction is generated according to the identification recommendation result and is sent to the central control unit. The artificial intelligent analysis module comprises a data preprocessing unit, a deep neural network model unit, an identification recommending unit and a control instruction generating unit. The data preprocessing unit performs cleaning, normalization and feature extraction preprocessing operations on the data received from the satellite remote sensing module or the unmanned aerial vehicle inspection module, and stores the processed data in a database. The deep neural network model unit builds a deep neural network model based on long-short-term memory (LSTM) or Convolutional Neural Network (CNN) or Graph Neural Network (GNN) and is used for intelligently identifying and recommending the operation state, fault type and maintenance scheme of the photovoltaic power station. And the identification recommendation unit generates a corresponding identification recommendation report according to the output result of the deep neural network model unit, and sends the report to the virtual reality interaction module or the control instruction generation unit. And the control instruction generating unit generates a corresponding control instruction according to the identification recommendation report or the input and operation of the user, and sends the instruction to the unmanned aerial vehicle inspection module or the virtual reality interaction module.

The biological identification authentication module is one of the core parts of the invention, and has the functions of utilizing the biological characteristics of fingerprints, irises and voiceprints to authenticate the identity and manage the authority of the user of the photovoltaic power station, recording the authentication result on a blockchain and guaranteeing the safety and privacy of the photovoltaic power station. The biological identification authentication module comprises a biological characteristic acquisition unit, a biological characteristic processing unit, a biological characteristic matching unit and a biological characteristic recording unit. The biological characteristic collecting unit collects the biological characteristics of fingerprints, irises and voiceprints of the photovoltaic power station user through a fingerprint scanner, an iris scanner and microphone equipment. The biological feature processing unit performs feature extraction and coding on the collected biological features to generate corresponding biological feature templates. The biological feature matching unit compares the biological feature template with the biological feature template pre-stored in the database, and judges the identity and authority of the photovoltaic power station user. The biological characteristic recording unit records the authentication result on the blockchain, and guarantees the non-falsification and traceability of the authentication result.

The virtual reality interaction module is one of the core parts of the invention, and has the functions of generating a three-dimensional model and a scene of the photovoltaic power station by using a rendering engine according to data and an identification recommendation result received from the satellite remote sensing module or the artificial intelligent analysis module, and presenting the three-dimensional model and the scene on display equipment; receiving input and operation of a user by using interaction equipment, converting the input and operation into corresponding data and instructions, and sending the corresponding data and instructions to a central control unit; and generating corresponding sound, light and vibration feedback by using the feedback equipment according to the data and the control instruction received from the central control unit, and presenting the feedback on the interaction equipment. The virtual reality interaction module comprises a rendering engine unit, a display device unit, an interaction device unit and a feedback device unit. The rendering engine unit generates a three-dimensional model and a scene of the photovoltaic power station according to the data received from the satellite remote sensing module or the artificial intelligence analysis module and the recognition recommendation result. The display device unit presents the three-dimensional model and scene generated by the rendering engine unit, for example using a Head Mounted Display (HMD), projector, tablet computer device. The interactive device unit receives user inputs and operations and converts the inputs and operations into corresponding data and instructions to be sent to the central control unit, for example using a handle, a touch screen, a speech recognition device. The feedback device unit generates corresponding sound, light, vibration feedback according to the data and control instructions received from the central control unit and presents them on the interactive device, for example using a loudspeaker, a light, a vibrator device.

The central control unit is one of the core parts of the invention, and the function of the central control unit is unified management and scheduling of the steps. The central control unit comprises a data processing unit, an identification recommending unit and an optimizing control unit. The data processing unit performs cleaning, normalization and feature extraction preprocessing operations on the data received from the satellite remote sensing module or the unmanned aerial vehicle inspection module, and stores the processed data in a database. The identification recommendation unit is used for intelligently identifying and recommending the operation state, the fault type and the maintenance scheme of the photovoltaic power station based on the deep neural network model, and sending the identification recommendation result to the virtual reality interaction module or the optimization control unit. The optimization control unit is used for solving and optimizing the optimization problem of the photovoltaic power station based on the reinforcement learning technology, generating a control instruction according to the optimization result and sending the control instruction to the unmanned aerial vehicle inspection module or the virtual reality interaction module.

The specific implementation method and flow of each part of the invention will be described in detail below:

the specific implementation method and flow of the satellite remote sensing module are shown in fig. 2, and the method comprises the following steps:

(1) And (5) satellite image acquisition. The invention adopts high-resolution multispectral or hyperspectral satellite images, such as high-resolution one-size, high-resolution two-size, high-resolution three-size, high-resolution four-size, high-resolution five-size and high-resolution six-size satellite images, as data sources of the photovoltaic power station. The satellite image acquisition unit acquires these images from the satellite through the satellite signal receiver and transmits the images to the satellite image processing unit.

(2) Satellite image processing. The invention adopts a target detection and segmentation algorithm based on deep learning to process satellite images and extract the position, scale, shape and azimuth information of the photovoltaic power station. The satellite image processing unit firstly carries out preprocessing on the satellite image, including denoising, enhancing and clipping operations, so as to improve the quality and definition of the image. Then, the satellite image processing unit utilizes the deep neural network model to detect and divide the target of the preprocessed image, identifies the boundary and the area of the photovoltaic power station, and calculates the position coordinate, the area, the perimeter, the shape and the azimuth information of the photovoltaic power station. Finally, the satellite image processing unit outputs the extracted information to the satellite image output unit.

(3) And outputting satellite images. The invention outputs the information extracted by the satellite image processing unit to the operation and maintenance monitoring center for other modules to use. The satellite image output unit sends the information to a database of the operation and maintenance monitoring center through the network transmitter, and displays the information on a display of the operation and maintenance monitoring center.

The specific implementation method and flow of the unmanned aerial vehicle inspection module are shown in fig. 3 and 4, and the method comprises the following steps:

(1) Unmanned aerial vehicle flight control. The invention adopts unmanned aerial vehicles carrying various sensors and camera equipment, such as four-axis aircrafts, six-axis aircrafts and fixed wing aircrafts, as inspection tools of photovoltaic power stations. And the unmanned aerial vehicle flight control unit automatically executes the inspection task according to the preset flight route and the preset flight altitude and receives a control instruction from the central control unit. The unmanned aerial vehicle flight control unit performs data interaction with the operation and maintenance monitoring center through the wireless remote controller or the network communicator, and realizes the operations of taking off, landing, cruising, steering, accelerating, decelerating and stopping of the unmanned aerial vehicle.

(2) And (5) unmanned aerial vehicle data acquisition. According to the invention, unmanned aerial vehicles carrying temperature sensors, humidity sensors, current sensors, voltage sensors, visible light cameras, infrared cameras and ultraviolet camera equipment are adopted to collect working parameters, environmental parameters and fault information data of a photovoltaic power station. The unmanned aerial vehicle data acquisition unit monitors and shoots the temperature, humidity, current and voltage working parameters of the photovoltaic power station, and the visible light image, the infrared thermal imaging image and the ultraviolet fluorescence image fault information of the photovoltaic power station in real time through the sensor and the camera equipment, and transmits data and images to the unmanned aerial vehicle data transmission unit.

(3) Unmanned aerial vehicle data transmission. The invention transmits the data and the image acquired by the unmanned aerial vehicle data acquisition unit to the operation and maintenance monitoring center in real time for other modules to use. The unmanned aerial vehicle data transmission unit transmits the data and the image to a database of the operation and maintenance monitoring center through the network transmitter, and displays the data and the image on a display of the operation and maintenance monitoring center.

The specific implementation method and flow of the blockchain data platform module are shown in fig. 5 and 6, and include the following steps:

(1) And establishing a block chain network. The invention adopts a blockchain technology based on privacy protection of zero knowledge proof to establish a decentralized distributed account book system for storing and verifying data and transaction records of a photovoltaic power station. The block chain network unit connects the operation and maintenance monitoring center with the photovoltaic power station user through the network node to form a point-to-point network. The blockchain network element uses a consensus algorithm, such as a workload proof (PoW), a rights and interests proof (PoS), a bayer occupational fault tolerance (BFT) algorithm, to secure and stabilize the blockchain network. The blockchain network element uses an encryption algorithm, such as asymmetric encryption, a hash function, a zero knowledge proof algorithm, to ensure privacy and trustworthiness of the blockchain network.

(2) Blockchain smart contracts are written. The invention adopts a data sharing and transaction platform based on intelligent contracts, supports various data types and service types, and adopts a mechanism based on token excitation to encourage the sharing and transaction of data among photovoltaic power station users. The blockchain smart contract unit is a set of automatically executed program code for defining and executing data sharing and transaction rules for photovoltaic power plants. The blockchain smart contract unit uses a smart contract language, such as Solidity, vyper, bamboo, to write smart contracts. The blockchain intelligent contract unit deploys the compiled intelligent contracts on the blockchain network and broadcasts the intelligent contracts to all network nodes through the blockchain network unit.

(3) Blockchain token issuance. The invention adopts a mechanism based on token incentive to encourage sharing and transaction of data among photovoltaic power station users. Blockchain token units are a type of digital currency based on blockchain technology that is used to motivate data sharing and transaction actions between photovoltaic utility users. The blockchain token unit uses a token standard, such as the ERC-20, ERC-721, ERC-1155 standards, to define the attributes and functions of tokens. The block chain token unit issues tokens according to preset token total amount, allocation mode and issuance rule parameters, and distributes the tokens to photovoltaic power station users through the block chain network unit.

The method comprises the following steps:

(1) And (5) preprocessing data. The invention adopts a data analysis and prediction technology based on a deep neural network model to carry out cleaning, normalization and feature extraction preprocessing operations on data received from a satellite remote sensing module or an unmanned aerial vehicle inspection module, and stores the processed data in a database. The data preprocessing unit firstly carries out operations of removing noise, missing values and abnormal values on the received data, and improves the quality and the integrity of the data. And then, the data preprocessing unit performs normalization or standardization operation on the data to enable the data to meet the input requirement of the deep neural network model. And then, the data preprocessing unit performs feature extraction or dimension reduction operation on the data, and extracts effective information and key features of the data. And finally, the data preprocessing unit stores the processed data in a database for the deep neural network model unit to use.

(2) And (6) constructing a deep neural network model. The invention adopts a deep neural network model based on long-short-term memory (LSTM) or Convolutional Neural Network (CNN) or Graph Neural Network (GNN) for intelligently identifying and recommending the operation state, fault type and maintenance scheme of the photovoltaic power station. The deep neural network model unit selects a proper deep neural network model according to different data types and task types, and trains and optimizes the model according to a training data set and a verification data set. For example, for time series data and prediction tasks, an LSTM model may be selected; for image data and classification tasks, a CNN model may be selected; for graph structure data and clustering tasks, the GNN model may be selected.

(3) Recommendation generation is identified. According to the output result of the deep neural network model unit, the corresponding identification recommendation report is generated, and the report is sent to the virtual reality interaction module or the control instruction generation unit. The identification recommendation unit firstly acquires the output result of the deep neural network model unit, such as the operation state, the fault type and the maintenance scheme information of the photovoltaic power station, from the database. And then, generating a corresponding identification recommendation report by the identification recommendation unit according to the output result, wherein the identification recommendation report comprises the operation condition assessment, fault cause analysis and maintenance recommendation providing contents of the photovoltaic power station. And finally, the identification recommendation unit sends the identification recommendation report to the virtual reality interaction module or the control instruction generation unit for the user to check or execute.

(4) And (5) generating a control instruction. According to the method, corresponding control instructions are generated according to the identification recommendation report or the input and operation of the user, and the instructions are sent to the unmanned aerial vehicle inspection module or the virtual reality interaction module. The control instruction generating unit first obtains an identification recommendation report from a database or obtains user input and operation, such as maintenance scheme of the photovoltaic power station or patrol request information of the user, from the interactive device. And then, the control instruction generating unit generates corresponding control instructions according to the information content, wherein the corresponding control instructions comprise flight routes, heights, speeds and action parameters of the unmanned aerial vehicle or sound, light and vibration feedback parameters of the virtual reality interaction equipment. And finally, the control instruction generating unit sends the control instruction to the unmanned aerial vehicle inspection module or the virtual reality interaction module for execution by the unmanned aerial vehicle or a user.

The specific implementation method and flow of the biological recognition authentication module are shown in fig. 7 and 8, and the method comprises the following steps:

(1) And (5) biological characteristic collection. The invention adopts the biological characteristics of fingerprints, irises and voiceprints to authenticate the identity and manage the authority of the photovoltaic power station user, and records the authentication result on the blockchain to ensure the safety and privacy of the photovoltaic power station. The biological characteristic collecting unit collects biological characteristics of fingerprints, irises and voiceprints of the photovoltaic power station user through a fingerprint scanner, an iris scanner and microphone equipment, and transmits the biological characteristics to the biological characteristic processing unit.

(2) And (5) biological characteristic treatment. The invention performs feature extraction and coding on the collected biological features to generate corresponding biological feature templates. The biometric processing unit extracts valid information and key features from the biometric using a feature extraction algorithm, such as Principal Component Analysis (PCA), linear Discriminant Analysis (LDA), local Binary Pattern (LBP) algorithm. The biometric processing unit then converts the extracted features into binary or numerical biometric templates using a coding algorithm, such as a hash function, a coding tree, a coding matrix algorithm, and transmits the templates to the biometric matching unit.

(3) Biometric matching. The invention compares the biological characteristic template with the biological characteristic template pre-stored in the database to judge the identity and authority of the photovoltaic power station user. The biometric matching unit calculates the similarity or difference between the input biometric template and the biometric template in the database by using a matching algorithm, such as euclidean distance, hamming distance, cosine similarity algorithm, and determines whether the photovoltaic power station user passes authentication according to a set threshold or standard, and transmits the authentication result to the biometric recording unit.

(4) And (5) biological characteristic record. The invention records the authentication result on the blockchain, and ensures the non-falsification and traceability of the authentication result. The biometric recording unit sends the authentication result as a transaction record to the blockchain network through the blockchain network unit, receives other transaction records on the blockchain network through the blockchain network unit, and stores the transaction records in the local database.

The specific implementation method and flow of the virtual reality interaction module are shown in fig. 9 and 10, and include the following steps:

(1) Three-dimensional model and scene generation. According to the invention, a rendering engine is utilized to generate a three-dimensional model and a scene of the photovoltaic power station according to data and an identification recommendation result received from a satellite remote sensing module or an artificial intelligent analysis module, and the three-dimensional model and the scene are presented on display equipment. The rendering engine unit generates a three-dimensional model and scene of the photovoltaic power plant, including appearance, structure, texture, color, illumination, shadow effect of the photovoltaic power plant, based on the content of the data and results, using a rendering technique, such as ray tracing, rasterization, illumination calculation techniques, and transmits the three-dimensional model and scene to the display device unit.

(2) Three-dimensional models and scene presentations. The invention presents the three-dimensional model and the scene generated by the rendering engine unit for the user to watch and operate. The display device unit displays the three-dimensional model and the scene in the field of view of the user using a display technology such as a Head Mounted Display (HMD), a projector, a tablet computer device, and adjusts the viewing angle and the viewing distance of the three-dimensional model and the scene according to the movement of the head or eyes of the user, realizes an immersive virtual reality experience, and transmits the state of the display device unit to the interactive device unit.

(3) User input and operation reception. The invention receives the input and operation of the user by using the interaction equipment, converts the input and operation into corresponding data and instructions and sends the corresponding data and instructions to the central control unit. The interactive device unit receives user inputs and operations, such as rotation, scaling, movement, selection, clicking operations, of the three-dimensional model and scene using an interactive technology, such as a handle, touch screen, voice recognition device, and converts the inputs and operations into corresponding data and instructions, and transmits the data and instructions to the control instruction generation unit.

(4) User feedback is generated. The invention uses the feedback device to generate corresponding sound, light and vibration feedback according to the data and control instructions received from the central control unit, and presents the feedback on the interaction device. The feedback equipment unit uses a feedback technology, such as a loudspeaker, a lamplight and a vibrator, generates corresponding sound, light and vibration feedback effects according to the content of the data and the instructions, transmits the feedback effects to the interaction equipment unit, and presents the feedback effects on the interaction equipment, so that the virtual reality experience of a user is enhanced.

Next, three examples of the present invention will be given, showing the effects and advantages of the present invention in various cases, respectively.

Example 1: photovoltaic power station quick identification and positioning based on satellite remote sensing

The embodiment demonstrates how to quickly identify and locate distributed household photovoltaic power stations using the satellite remote sensing module of the present invention. The operation and maintenance monitoring center is assumed to need to count and manage all photovoltaic power stations in a certain area, but because the area is large and photovoltaic power stations are distributed more scattered, effective identification and positioning are difficult to carry out through manual or ground equipment. Therefore, the operation and maintenance monitoring center can use the satellite remote sensing module to acquire the high-resolution multispectral or hyperspectral image of the region from the satellite, process the image by utilizing a target detection and segmentation algorithm based on deep learning, extract the position, scale, form and azimuth information of the photovoltaic power station, and output the information to the operation and maintenance monitoring center. The operation and maintenance monitoring center can count and manage the photovoltaic power stations in the area according to the output information, for example, calculate the number and the total area of the photovoltaic power stations, and display the indexes on a display of the operation and maintenance monitoring center. Meanwhile, the operation and maintenance monitoring center can visualize and interact the photovoltaic power stations in the area according to the output information, for example, the positions of the photovoltaic power stations are marked on a map, the appearance, structure and material effect of the photovoltaic power stations are displayed, and the photovoltaic power stations can be amplified, reduced and rotated through a mouse or a touch screen. Through the satellite remote sensing module, the operation and maintenance monitoring center can realize the rapid identification and positioning of the distributed household photovoltaic power station, and the working efficiency and accuracy of the operation and maintenance monitoring center are improved.

Example 2: photovoltaic power station periodic inspection and anomaly detection based on unmanned aerial vehicle inspection

The embodiment demonstrates how to use the unmanned aerial vehicle inspection module of the invention to periodically inspect and detect anomalies of a photovoltaic power station. The operation and maintenance monitoring center is assumed to need to carry out regular inspection and anomaly detection on a certain photovoltaic power station, but because the photovoltaic power station is large in scale and complex in environment, effective inspection and detection are difficult to carry out through manual or ground equipment. Therefore, the unmanned aerial vehicle inspection module can be used by an operation and maintenance monitoring center to dispatch unmanned aerial vehicles carrying various sensors and camera equipment to automatically inspect and detect abnormality of the photovoltaic power station. And the unmanned aerial vehicle flight control unit automatically executes the inspection task according to the preset flight route and the preset flight altitude and receives a control instruction from the central control unit. Unmanned aerial vehicle data acquisition unit carries out real-time supervision and shoots to this photovoltaic power plant's operating parameter, environmental parameter, trouble information data based on carrying temperature sensor, humidity transducer, current sensor, voltage sensor, visible light camera, infrared camera, ultraviolet camera equipment to with data and image transmission to unmanned aerial vehicle data transmission unit. The unmanned aerial vehicle data transmission unit transmits the acquired data and images to the operation and maintenance monitoring center in real time for other modules to use. The operation and maintenance monitoring center can intelligently identify and recommend the operation state, the fault type and the maintenance scheme of the photovoltaic power station according to the transmitted data and images, and generates a control instruction according to the identification recommendation result to be sent to the unmanned aerial vehicle flight control unit or the virtual reality interaction module. Through the unmanned aerial vehicle inspection module, the operation and maintenance monitoring center can realize regular inspection and anomaly detection of the photovoltaic power station, and the operation efficiency and reliability of the photovoltaic power station are improved.

Example 3: photovoltaic power station remote maintenance and education training based on virtual reality interaction

The embodiment demonstrates how the virtual reality interaction module of the invention can be used for remote maintenance and educational training of the photovoltaic power station. Assuming that the operation and maintenance monitoring center needs to remotely repair or educate a certain photovoltaic power station, the photovoltaic power station is far away and inconvenient in traffic, and effective repair or training is difficult to be performed through manual or ground equipment. Therefore, the operation and maintenance monitoring center can use the virtual reality interaction module to enter a virtual reality environment through a head-mounted display (HMD), a handle and a loudspeaker device, and remotely maintain or educate and train the photovoltaic power station. The virtual reality interaction module generates a three-dimensional model and a scene of the photovoltaic power station according to data and recognition recommendation results received from the satellite remote sensing module or the artificial intelligence analysis module by using a rendering engine, and displays the three-dimensional model and the scene on display equipment. The user can operate the three-dimensional model and the scene through the handle interaction device, such as rotation, zooming, moving, selecting and clicking, and receive corresponding sound, light and vibration feedback effects through the speaker feedback device. The user can carry out remote maintenance or education training on the photovoltaic power station according to the identification recommendation report or own requirements, such as replacing damaged components, adjusting unreasonable parameters, learning new technical knowledge operation, and sending the operation result to the central control unit or the blockchain data platform module. Through the virtual reality interaction module, the operation and maintenance monitoring center can realize remote maintenance and education training of the photovoltaic power station, reduce operation and maintenance cost and risk and enhance trust and satisfaction of users.

Some comparative experiments of the present invention are given below, showing the performance differences and advantages of the present invention over the prior art with tables and graphs.

Comparative experiment 1: accuracy and efficiency comparison of quick identification and positioning of photovoltaic power station based on satellite remote sensing

The comparison experiment aims at comparing the accuracy and the efficiency of the rapid identification and the positioning of the satellite remote sensing module and the photovoltaic power station in the prior art. The prior art mainly comprises the following two methods:

(1) Manual identification and positioning methods. The method manually observes satellite images, manually marks the position and information of the photovoltaic power station, and then analyzes and counts the position and information through Geographic Information System (GIS) software. The method has the advantages that the photovoltaic power station can be accurately identified and positioned according to manual experience and judgment. The method has the defects of low efficiency, time and labor consumption, and susceptibility to human factors, so that the identification and positioning results are unstable and unreliable.

(2) Traditional machine learning identification and localization methods. The method is used for carrying out feature extraction and classification on satellite images by using a traditional machine learning algorithm, such as a Support Vector Machine (SVM), a Random Forest (RF) and a K Nearest Neighbor (KNN) algorithm, so that the identification and the positioning of the photovoltaic power station are realized. The method has the advantages that the processing capacity of the computer can be utilized, and the recognition and positioning efficiency is improved. The method has the defects that a large amount of labeling data is required for training, and for complex and changeable satellite images, the traditional machine learning algorithm is difficult to extract effective and robust features, so that the accuracy of identification and positioning is low.

The satellite remote sensing module processes satellite images by adopting a target detection and segmentation algorithm based on deep learning, and extracts position, scale, form and azimuth information of the photovoltaic power station. The method has the advantages that the characteristics of the photovoltaic power station in the satellite image can be automatically learned by utilizing the strong expression capacity and generalization capacity of the deep neural network model, and the high-precision and high-efficiency identification and positioning are realized.

In order to compare the performance difference and advantages of the invention with the prior art, the present comparison experiment selects some typical satellite images as test data sets, and uses the following indexes to evaluate the accuracy and efficiency of identification and positioning:

(1) Accuracy (Accuracy). The index represents the proportion of the identified photovoltaic power station in accordance with the real situation, and the calculation formula is as follows:

TP represents a real example, namely a correctly identified photovoltaic power station; TN represents true negative examples, i.e. correctly excluded non-photovoltaic power stations; FP represents a false positive, i.e. a misidentified non-photovoltaic power plant; FN represents a false negative example, i.e. a misexcluded photovoltaic power plant.

(2) Recall (Recall). The index represents the proportion of the truly existing photovoltaic power station to be identified, and the calculation formula is as follows:

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(3) Precision (Precision). The index represents the proportion of the identified photovoltaic power station in real existence, and the calculation formula is as follows:

(4) F1 value (F1-score). The index represents a harmonic average value of the accuracy and the recall, and the calculation formula is as follows:

(5) Processing Time (Processing Time). The index represents the time in seconds required to identify and locate a satellite image.

Table 1 gives the results of comparison of accuracy and efficiency of the identification and localization of the present invention on a test dataset with the prior art.

As can be seen from Table 1, compared with the prior art, the satellite remote sensing module has the obvious advantages that the processing efficiency is greatly improved while the high accuracy is ensured, and the quick identification and positioning of the photovoltaic power station are realized.

TABLE 1 comparison of accuracy and efficiency of identification and location of the present invention with the prior art

Comparative experiment 2: the method comprises the steps of comparing the accuracy and the efficiency of regular inspection and anomaly detection of a photovoltaic power station based on unmanned aerial vehicle inspection;

the comparison experiment aims at comparing the accuracy and the efficiency of the regular inspection and anomaly detection of the unmanned aerial vehicle inspection module and the photovoltaic power station in the prior art. The prior art mainly comprises the following two methods:

(1) Manual inspection and detection methods. The method collects and analyzes working parameters, environment parameters and fault information data of the photovoltaic power station through manual on-site inspection and examination. The method has the advantages that detailed inspection and detection can be carried out on the photovoltaic power station according to manual experience and judgment. The method has the defects of low efficiency, time and labor consumption, safety risk, unstable and unreliable inspection and detection results.

(2) Traditional sensor and camera equipment inspection and detection methods. According to the method, working parameters, environmental parameters and fault information data of the photovoltaic power station are collected and analyzed through temperature sensors, humidity sensors, current sensors, voltage sensors, visible light cameras, infrared cameras and ultraviolet camera equipment which are arranged on the photovoltaic power station. The method has the advantages that the monitoring capability of the equipment can be utilized, and the inspection and detection efficiency is improved. The disadvantage of this method is that it requires a lot of equipment installation and maintenance and for complex and varied photovoltaic power plant environments, the equipment is difficult to cover all areas and angles, resulting in low inspection and detection accuracy.

The unmanned aerial vehicle inspection module adopts an unmanned aerial vehicle carrying various sensors and camera equipment to carry out periodic inspection and anomaly detection on a photovoltaic power station. The invention has the advantages that the flight capability and the flexibility of the unmanned aerial vehicle can be utilized to cover all areas and angles of the photovoltaic power station, and the high-precision and high-efficiency inspection and detection can be realized.

In order to compare the performance difference and advantages of the invention with the prior art, the comparison experiment selects some typical photovoltaic power stations as test objects, and the following indexes are used for evaluating the inspection and detection accuracy and efficiency:

(1) Accuracy (Accuracy). The index represents the proportion of the data or information detected by inspection to the real situation, and the calculation formula is as follows:

TP represents real examples, namely data or information which is correctly inspected or detected; TN represents true negative examples, i.e., correctly excluded non-data or information; FP represents a false positive, i.e., false inspection or detected non-data or information; FN represents a false negative example, i.e. data or information that is incorrectly excluded.

(2) Recall (Recall). The index represents the ratio of the data or information which exists truly and is inspected or detected, and the calculation formula is as follows:

(3) Precision (Precision). The index represents the proportion of the real existence in the data or information detected by inspection, and the calculation formula is as follows:

processing Time (Processing Time). The index represents the time in seconds required for inspection or detection of a photovoltaic power plant.

Table 2 shows the results of comparing the inspection and detection accuracy and efficiency of the present invention with the prior art on the test object.

As can be seen from Table 2, compared with the prior art, the unmanned aerial vehicle inspection module has the obvious advantages that the processing efficiency is greatly improved while the high accuracy is ensured, and the regular inspection and the anomaly detection of the photovoltaic power station are realized.

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TABLE 2 comparison of the accuracy and efficiency of inspection and detection of the present invention with the prior art

Comparative experiment 3: photovoltaic power station remote maintenance and education training effects and satisfaction contrast based on virtual reality interaction;

the comparison experiment aims at comparing the effect and satisfaction of remote maintenance and education training of the virtual reality interaction module and the photovoltaic power station in the prior art. The prior art mainly comprises the following two methods:

(1) Video call and remote control methods. The method guides the personnel on the photovoltaic power station to carry out maintenance or training by carrying out voice or video conversation with the personnel on the photovoltaic power station site through video conversation software such as Skype, zoom, weChat software. The method may also be used for maintenance or training by remote control software, such as TeamViewer, anyDesk, VNC software, remotely controlling the computer or equipment on the photovoltaic power plant site. The method has the advantages that the remote maintenance and education training of the photovoltaic power station can be realized by using the existing software and equipment. The disadvantages of this approach are limited network bandwidth and device performance, low quality and stability of video calls and remote controls, and inability to provide immersive and interactive virtual reality experiences, resulting in low maintenance or training effectiveness and satisfaction.

(2) Online courses and video tutorial methods. The method provides a photovoltaic power plant-related course or video tutorial for a user to learn or watch online for maintenance or training through an online course platform, such as Coursera, edX, udemy platform, or a video website, such as YouTube, bilibili, tikTok website. The method has the advantages that abundant network resources can be utilized to provide diversified courses or video contents, and different learning or watching requirements of users are met. The method has the defects of lack of pertinence and practicality, inability to provide customized and real-time maintenance or training schemes according to specific photovoltaic power station conditions and problems of users, and inability to provide immersive and interactive virtual reality experience, resulting in low maintenance or training effects and satisfaction.

The virtual reality interaction module of the invention adopts a head-mounted display (HMD), a handle and speaker equipment to enter a virtual reality environment for remote maintenance or education training of the photovoltaic power station. The invention has the advantages that the rendering engine can be utilized to generate a three-dimensional model and a scene of the photovoltaic power station according to the data received from the satellite remote sensing module or the artificial intelligent analysis module and the identification recommendation result, the three-dimensional model and the scene are presented on the display device, the three-dimensional model and the scene can be operated and fed back through the interaction device and the feedback device, the remote maintenance or education training is carried out on the photovoltaic power station according to the identification recommendation report or the own requirement, and the operation result is sent to the central control unit or the blockchain data platform module. The invention can provide immersive and interactive virtual reality experience, and improves the effect and satisfaction of remote maintenance and education training of the photovoltaic power station.

In order to compare the performance differences and advantages of the present invention with the prior art, the present comparison experiment selected some typical photovoltaic power station users as test subjects and used the following indicators to evaluate the effectiveness and satisfaction of remote maintenance and educational training:

(1) Maintenance or training Success Rate (Success Rate). The index represents the proportion that a user can successfully solve the problem of the photovoltaic power station or master the knowledge of the photovoltaic power station after using different methods for remote maintenance or education training, and the calculation formula is as follows:

wherein S represents a successful user, i.e. a user who can successfully solve the problems of the photovoltaic power station or who is knowledgeable about the photovoltaic power station; f represents a failed user, i.e. a user who is not able to successfully solve the problem of the photovoltaic power plant or to grasp knowledge of the photovoltaic power plant.

(2) User satisfaction (User Satisfaction). The index represents the satisfaction degree of users for remote maintenance or education training of different methods, and the user adopts a five-point scale method for scoring, and is classified into very dissatisfaction (1 score), dissatisfaction (2 score), general (3 score), satisfaction (4 score) and very satisfaction (5 score), and the calculation formula is as follows:

wherein N represents the total number of test objects; score _i Representing the satisfaction score of the ith test subject with the method used.

Table 3 shows the results of the invention compared to the prior art for remote maintenance and educational training effects and satisfaction on the test subjects;

as can be seen from Table 3, the virtual reality interaction module has obvious advantages compared with the prior art, namely, the maintenance or training success rate is improved, the user satisfaction is improved, and the remote maintenance and education training of the photovoltaic power station is realized.

Table 3 results of the invention in comparison with the effects and satisfaction of the prior art remote maintenance and educational training.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.

Claims

1. A distributed household photovoltaic power station operation and maintenance monitoring method is characterized in that: the method comprises the steps of intelligent management, adjustment, identification and positioning, periodic inspection and anomaly detection, identification and early warning of operation states and fault types, solving and optimization of optimization problems, data safety and transparency, data analysis and prediction, identity authentication and authority management, visualization and interactive operation and maintenance monitoring of a photovoltaic power station based on satellite remote sensing technology, unmanned plane technology, blockchain technology, biological identification technology, artificial intelligence technology and virtual reality technology;

2. The distributed household photovoltaic power plant operation and maintenance monitoring method according to claim 1, wherein: the artificial intelligence technology comprises the steps of performing preprocessing operations of cleaning, normalizing and extracting features on data received from a satellite remote sensing module or an unmanned aerial vehicle inspection module based on constructing a deep neural network model based on long-short-term memory (LSTM) or Convolutional Neural Network (CNN) or Graph Neural Network (GNN), and storing the processed data in a database;

3. The distributed household photovoltaic power plant operation and maintenance monitoring method according to claim 1, wherein: the unmanned aerial vehicle automatically executes the inspection task according to a preset flight route and a preset flight altitude, and transmits the acquired data and images to the operation and maintenance monitoring center in real time.

4. The distributed household photovoltaic power plant operation and maintenance monitoring method according to claim 1, wherein: the blockchain data platform encourages sharing and trading of data among photovoltaic power station users by adopting a mechanism based on token incentive.

5. The distributed household photovoltaic power plant operation and maintenance monitoring method according to claim 1, wherein: the blockchain data platform supports a variety of data types and service types including power generation data, environmental data, load data, predictive services, dispatch services, clearing services.

6. The distributed household photovoltaic power plant operation and maintenance monitoring method according to claim 1, wherein: the artificial intelligence technology adopts a complex network model based on a graph neural network to analyze and predict the data of the photovoltaic power station and provide an intelligent operation and maintenance suggestion and control strategy.

7. A distributed household photovoltaic power station operation and maintenance monitoring system is characterized in that: the system comprises a satellite remote sensing module, an unmanned aerial vehicle inspection module, a blockchain data platform module, an artificial intelligent analysis module, a biological identification authentication module and a virtual reality interaction module;

8. The distributed household photovoltaic power plant operation and maintenance monitoring system of claim 7, wherein: the satellite remote sensing module comprises a satellite image acquisition unit, a satellite image processing unit and a satellite image output unit;

9. The distributed household photovoltaic power plant operation and maintenance monitoring system of claim 7, wherein: the unmanned aerial vehicle inspection module comprises an unmanned aerial vehicle flight control unit, an unmanned aerial vehicle data acquisition unit and an unmanned aerial vehicle data transmission unit;

10. The distributed household photovoltaic power plant operation and maintenance monitoring system of claim 7, wherein: the virtual reality interaction module comprises a rendering engine unit, a display device unit, an interaction device unit and a feedback device unit;