Encryption transmission method and system for distributed photovoltaic operation and maintenance data

Abstract

The invention relates to the technical field of encrypted transmission, in particular to an encrypted transmission method and system for distributed photovoltaic operation and maintenance data. The method comprises the following steps: installing edge computing equipment at each node of a distributed photovoltaic system, and collecting photovoltaic operation and maintenance data in real time through a photovoltaic sensor; on the edge computing device, photovoltaic operation and maintenance data of the multiple photovoltaic sensors are aggregated and preprocessed; establishing encryption connection through an encryption transmission protocol TLS and a network protocol HTTPS; based on the encrypted connection, the edge computing device encrypts the aggregated photovoltaic operation and maintenance data by using a symmetric encryption AES algorithm and an asymmetric encryption RSA algorithm; and through the SD-WAN technology, the encrypted photovoltaic operation and maintenance data are transmitted through different network paths. According to the design of the invention, an advanced encryption technology (such as TLS, AES and RSA) is adopted to ensure that the photovoltaic operation and maintenance data is not stolen or tampered in the transmission process from the edge computing device to the cloud platform.

Description

Technical Field

[0001] The present invention relates to the field of encrypted transmission technology, and in particular to an encrypted transmission method and system for distributed photovoltaic operation and maintenance data. Background Art

[0002] In distributed photovoltaic systems, operation and maintenance data (such as power generation, voltage, current, etc.) usually need to be uploaded from each node (such as photovoltaic inverters, sensors) to the central management system or cloud for centralized processing and analysis. However, during the data transmission process, if there is a lack of effective encryption measures, these data may be stolen or tampered with, resulting in sensitive information leakage or unstable system operation. As the scale of photovoltaic systems continues to expand, the amount of operation and maintenance data generated also increases, which puts higher requirements on network bandwidth. At the same time, due to the wide distribution of photovoltaic sites, the network conditions at different locations vary greatly, and how to ensure efficient and stable data transmission becomes a challenge. In addition, directly uploading a large amount of raw data will cause unnecessary network load and increase the data processing burden of the cloud platform. The normal operation of the photovoltaic system depends on accurate and timely data feedback. However, in the actual environment, factors such as noise interference and equipment failure may cause the quality of the collected data to be low. Moreover, when the data of multiple sensors are not completely synchronized, simple aggregation may introduce errors. Therefore, a method and system for encrypted transmission of distributed photovoltaic operation and maintenance data are provided. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for encrypting and transmitting distributed photovoltaic operation and maintenance data, so as to solve the problems of low photovoltaic system data transmission efficiency, lack of effective encryption measures, and low data quality and real-time performance proposed in the above background technology.

[0004] To achieve the above object, the present invention provides an encrypted transmission method for distributed photovoltaic operation and maintenance data, comprising: S1. Install edge computing devices at each node of the distributed photovoltaic system to collect photovoltaic operation and maintenance data in real time through photovoltaic sensors; S2: Aggregate and pre-process the photovoltaic operation and maintenance data of multiple photovoltaic sensors on the edge computing device; S3, establish an encrypted connection through the encrypted transmission protocol TLS and the network protocol HTTPS; S4. Based on the encrypted connection, the edge computing device uses the symmetric encryption AES and asymmetric encryption RSA algorithms to encrypt the aggregated photovoltaic operation and maintenance data; S5. Through SD-WAN technology, the encrypted photovoltaic operation and maintenance data is transmitted through different network paths, and encryption overhead is introduced during the path transmission process for optimization.

[0005] As a further improvement of this technical solution, in S1, photovoltaic operation and maintenance data is collected for: ; in, Indicates the amount of power generated, Indicates voltage, represents the current, Indicates time.

[0006] As a further improvement of the technical solution, in S2, on the edge computing device, the photovoltaic operation and maintenance data of multiple photovoltaic sensors are aggregated and preprocessed, including the following steps: S2.1. Collected photovoltaic operation and maintenance data Perform pre-processing; S2.2. The photovoltaic operation and maintenance data of multiple sensors are aggregated and calculated through weighted average aggregation to obtain aggregated photovoltaic operation and maintenance data. The weighted average aggregation is optimized considering the influence of light on the output data of the photovoltaic system, and the weighted average aggregation is further optimized for the time alignment problem of the sensor output data; S2.3. Detect and process outliers on the aggregated photovoltaic operation and maintenance data.

[0007] As a further improvement of the technical solution, in S2.2, the weighted average aggregation is: ; in, Indicates at time Weighted average photovoltaic operation and maintenance data; Indicates Sensors at time The weight of Indicates Sensors at time The collected raw photovoltaic operation and maintenance data; Indicates the total number of sensors; Indicates the index of the sensor; Considering the influence of sunlight on the output data of the photovoltaic system, the weighted average aggregation is optimized: ; in, Represents the weighted average photovoltaic operation and maintenance data after considering the sunlight; Indicates Sensors at time The standard deviation of noise; represents environmental impact factors; Indicates sensor The light intensity at the location; represents a constant; To address the time alignment problem of sensor output data, the weighted average aggregation is further optimized: ; in, Represents the weighted average PV operation and maintenance data after time alignment; represents a time window; Represents a time window a point in time.

[0008] As a further improvement of the technical solution, in S3, establishing an encrypted connection through the encrypted transmission protocol TLS and the network protocol HTTPS includes the following steps: S3.1. The edge computing device sends an encrypted HTTP request to the API endpoint of the cloud platform through the HTTPS protocol to establish a TLS connection. S3.2. The edge computing device and the cloud platform jointly select the symmetric encryption AES and asymmetric encryption RSA algorithms and verify the identity of the cloud platform; S3.3. After successfully verifying the identity of the cloud platform, the edge computing device and the cloud platform use the key exchange protocol RSA to generate a shared symmetric session key; S3.4. After the session key is generated, the edge computing device encrypts the aggregated PV operation and maintenance data based on the session key using the symmetric encryption AES and asymmetric encryption RSA algorithms.

[0009] As a further improvement of the technical solution, in S4, the edge computing device uses the symmetric encryption AES and asymmetric encryption RSA algorithms to encrypt the aggregated photovoltaic operation and maintenance data, including the following steps: S4.1. Generate a symmetric key : ; S4.2. Use the symmetric encryption algorithm AES to encrypt the aggregated data , to encrypt: ; in, Indicates the encrypted data. Indicates the AES symmetric encryption algorithm; S4.3, encrypt the generated symmetric key using the asymmetric encryption algorithm RSA; S4.4. Use the RSA public key of the cloud platform Encrypt the symmetric key: ; in, Represents the encrypted symmetric key.

[0010] As a further improvement of the technical solution, in S5, the encrypted photovoltaic operation and maintenance data is transmitted through different network paths by using SD-WAN technology, including the following steps: S5.1. Establish an SD-WAN network between the edge computing device and the cloud platform and select the optimal network path; S5.2. Monitor the network delay, packet loss rate, and bandwidth indicators of the path in real time, establish a path quality function to regularly evaluate the performance of each available path, and optimize the path quality function based on the calculation and delay overhead caused by encryption; S5.3. Use traffic compression and data deduplication technology to accelerate the transmission of photovoltaic operation and maintenance data; S5.4. Allocate bandwidth to each network path based on the real-time network load and path performance evaluation results.

[0011] As a further improvement of the technical solution, in S5.2, the quality function of the path is: ; in, Indicates the overall quality of the path; Indicates the path Delay Indicates the path Packet loss rate; Indicates the path bandwidth; Indicates the path; The weight coefficient representing the delay; Indicates the weight coefficient of packet loss rate; Indicates the weight coefficient of bandwidth; In view of the computational and delay overhead caused by encryption, the quality function of the path is optimized: ; in, represents the optimized path quality score; The weight coefficient representing the encryption overhead; Indicates the path encryption overhead.

[0012] As a further improvement of the technical solution, in S5.3, the transmission of photovoltaic operation and maintenance data is accelerated by using flow compression and data deduplication technology, including the following steps: S5.31, determine a time window t, compare the newly collected data with the most recently transmitted data within the time window t, and set a change threshold for each PV operation and maintenance data type; S5.32, inputting the processed photovoltaic operation and maintenance data block into the compression algorithm LZMA to obtain a compressed photovoltaic operation and maintenance data packet; S5.33. Before sending the compressed PV operation and maintenance data, check the integrity of the PV operation and maintenance data; S5.34. Integrate the compressed photovoltaic operation and maintenance data into the encrypted transmission protocol TLS framework.

[0013] On the other hand, the present invention provides an encrypted transmission system for distributed photovoltaic operation and maintenance data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned encrypted transmission method for distributed photovoltaic operation and maintenance data.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the encrypted transmission method and system of distributed photovoltaic operation and maintenance data, advanced encryption technologies (such as TLS, AES, and RSA) are used to ensure that photovoltaic operation and maintenance data is not stolen or tampered with during the transmission process from edge computing devices to cloud platforms. Symmetric encryption AES is used to efficiently encrypt large amounts of data, while asymmetric encryption RSA is used to securely exchange symmetric keys, thereby ensuring the security of the keys. In addition, the use of SD-WAN technology for multi-path optimization and end-to-end encryption further enhances the security of data transmission, making it possible to effectively protect the confidentiality and integrity of data even when there are potential threats in the network environment.

[0015] 2. In the encrypted transmission method and system of distributed photovoltaic operation and maintenance data, by performing data preprocessing (including denoising, missing value processing, data standardization, weighted average aggregation, etc.) on the edge computing device, the amount of data that needs to be uploaded to the cloud is reduced, and the network bandwidth occupancy and the processing burden of the cloud server are reduced. At the same time, the weighted average aggregation algorithm is optimized for the effects of light and time alignment, and traffic compression and data deduplication technology are used to accelerate data transmission, which not only improves the quality of data, but also optimizes the selection of transmission paths, taking into account the additional computing overhead brought by encryption, and ensuring the efficiency and stability of data transmission. This method is particularly suitable for the collection and analysis of sensor data that is widely distributed in distributed photovoltaic systems and may be under different network conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 As shown, this embodiment provides an encrypted transmission method for distributed photovoltaic operation and maintenance data, including the following steps: S1. Install edge computing devices (such as edge computing gateways or micro-computing devices) at each node of the distributed photovoltaic system (such as photovoltaic inverters, sensors, etc.) to collect photovoltaic operation and maintenance data in real time through photovoltaic sensors; In this embodiment, edge computing devices (such as edge computing gateways or micro-computing devices) are installed at various nodes of the distributed photovoltaic system (such as photovoltaic inverters, sensors, etc.), and these devices can process the collected data in real time. Edge computing devices can perform pre-processing tasks such as data denoising, formatting, and compression to reduce the burden of transmission to the cloud; edge computing devices can also monitor and analyze the data flow of the photovoltaic system in real time, and perform simple anomaly detection and fault warning. If an anomaly is found (such as equipment overheating, power fluctuations, etc.), local control measures can be taken directly without transmitting the data to the cloud for analysis; Collecting photovoltaic operation and maintenance data for: ; in, Indicates the amount of power generated, Indicates voltage, represents the current, Indicates time.

[0019] S2: Aggregate and pre-process the photovoltaic operation and maintenance data of multiple photovoltaic sensors on the edge computing device; In this embodiment, on the edge computing device, the photovoltaic operation and maintenance data of multiple photovoltaic sensors are aggregated and preprocessed, including the following steps: S2.1. Collected photovoltaic operation and maintenance data Perform preprocessing, including denoising, missing value processing, data standardization, etc. S2.2. The photovoltaic operation and maintenance data of multiple sensors are aggregated and calculated through weighted average aggregation to obtain aggregated photovoltaic operation and maintenance data. The weighted average aggregation is optimized considering the influence of light on the output data of the photovoltaic system, and the weighted average aggregation is further optimized for the time alignment problem of the sensor output data; Weighted average aggregation is a data processing method that assigns different weights to each data point according to its importance or relevance, and then calculates the average of these weighted data points to more accurately reflect the overall trend or characteristics. Distributed photovoltaics need to transmit data through the Internet because they do not have a dedicated data network. Photovoltaic power generation data and control instructions are confidential data, and the data volume is large and the real-time security requirements are high. Through weighted average aggregation, these data can be pre-processed on edge computing devices to reduce the amount of data that needs to be transmitted to the cloud, thereby reducing network bandwidth requirements and the data processing burden of the cloud platform; weighted average aggregation can comprehensively reflect the operating status of the entire system or region, rather than relying solely on sensor readings at a few specific locations. This method ensures that the results obtained are more representative of the overall situation and avoids the impact of local outliers on global evaluation; weighted average aggregation reduces the amount of data that needs to be transmitted to the cloud or central processing unit, reduces the demand for network bandwidth, and reduces the data processing burden on the server side, which helps save communication costs while improving the response speed and efficiency of the system; Furthermore, the weighted average aggregation is: ; in, Indicates at time Weighted average photovoltaic operation and maintenance data; Indicates Sensors at time The weight of Indicates Sensors at time The collected raw photovoltaic operation and maintenance data, including power generation, current, and voltage; Indicates the total number of sensors; Indicates the index of the sensor; The power generation of photovoltaic panels is closely related to light intensity. Under conditions of high light intensity, the power generation efficiency of photovoltaic panels is generally high, while when the light intensity is insufficient, the power generation may be significantly reduced. Therefore, simply weighting and averaging all sensor data and ignoring the impact of light may lead to errors and fail to accurately reflect the actual performance of the system; in distributed photovoltaic systems, sensors are often distributed in different locations. Each sensor may be located in a different geographical location and be affected by different degrees of light. For example, some sensors may be exposed to direct sunlight, while others may receive less light due to shading or other environmental factors. If the impact of light intensity on different sensor data is not taken into account, simple weighted averaging may lead to unfair contributions from different sensors, especially in low light conditions, where the contributions of some sensors may be overestimated; Considering the influence of sunlight on the output data of the photovoltaic system, the weighted average aggregation is optimized: ; in, It represents the weighted average photovoltaic operation and maintenance data after considering the sunlight; Indicates Sensors at time The standard deviation of noise; represents environmental impact factors; Indicates sensor The light intensity at the location; Represents a constant used to prevent division by zero errors, usually set to a very small positive number; In actual photovoltaic systems, sensors are located in different locations, and they may use different sampling periods or have different time synchronization accuracy. This means that although multiple sensors collect data at the same time, their data points may not be completely synchronized, and there may even be obvious time deviations. For example: one sensor may collect data every 10 seconds, while another may collect data every 30 seconds. This time inconsistency may cause the output data of the sensor to be processed incorrectly when performing data aggregation (such as weighted average calculation), especially when environmental factors (such as light intensity) have a significant impact on the data; after considering the impact of light, the sensor data needs to be weighted according to the light intensity, but if the data is not compared and aggregated at the same time point, the problem of wrong time window comparison may occur; To address the time alignment problem of sensor output data, the weighted average aggregation is further optimized: ; in, Represents the weighted average PV operation and maintenance data after time alignment. After time alignment, noise, environmental factors and the weighted average of multiple data points in the time window are considered. For example, the optimized results of power generation, voltage and current after time alignment are obtained; Represents a time window, that is, in time A collection of data within a certain period of time; Represents a time window A time point in the time window is a discrete time point. Other time points of sensor data collection within the time interval; S2.3. Perform outlier detection and processing on the aggregated PV operation and maintenance data to ensure data quality.

[0020] S3, establish an encrypted connection through the encrypted transmission protocol TLS and the network protocol HTTPS; In this embodiment, the encrypted transmission protocol TLS is a protocol that provides communication security and is used to encrypt data transmission between the client and the server; the network protocol HTTPS is an encrypted version of HTTP. TLS / HTTPS uses a combination of symmetric encryption and asymmetric encryption to encrypt the data in transmission, which means that even if the data is intercepted by a third party during transmission on the network, an attacker without the correct decryption key cannot read or tamper with the data content, thereby ensuring the privacy and integrity of the information; the TLS handshake process includes a certificate exchange mechanism, which allows the client to verify the identity of the server and prevent man-in-the-middle attacks (MITM). At the same time, under certain configurations, two-way authentication can also be achieved, that is, the server can also verify the identity of the client, thereby enhancing the trust between the communicating parties; HTTPS is an application layer protocol that adds an SSL / TLS encryption layer to HTTP, which simplifies the integration of security functions. Developers can quickly deploy secure data transmission channels without having to deeply understand the underlying encryption details, which is very beneficial for the rapid development and maintenance of photovoltaic operation and maintenance systems; Establishing an encrypted connection through the encrypted transmission protocol TLS and the network protocol HTTPS includes the following steps: S3.1. The edge computing device sends an encrypted HTTP request to the API endpoint of the cloud platform through the HTTPS protocol (the request will contain encrypted data and other necessary header information) to establish a TLS connection. This request starts to establish a secure connection through the TLS protocol. The first step in establishing the connection is the TLS handshake process, in which the client (edge ​​computing device) and the server (cloud platform) select the encryption algorithm through negotiation and exchange certificates to verify the identities of both parties, thereby ensuring the security of the data transmission process; S3.2. The edge computing device and the cloud platform jointly select the symmetric encryption AES and asymmetric encryption RSA algorithms and verify the identity of the cloud platform; S3.3. After successfully verifying the identity of the cloud platform, the edge computing device and the cloud platform use the key exchange protocol RSA to generate a shared symmetric session key. The client and the server generate a shared session key through the RSA key exchange protocol. The session key is a symmetric key used for subsequent data encryption and decryption processes. The client and the server generate a shared symmetric session key through the key exchange protocol. The session key is used to encrypt and decrypt subsequent data transmissions. The session key is usually a symmetric key, which means that the client and the server use the same key to encrypt and decrypt data. S3.4. After the session key is generated, the edge computing device encrypts the aggregated photovoltaic operation and maintenance data based on the session key through the symmetric encryption AES and asymmetric encryption RSA algorithms. Once the session key is generated, the client (edge ​​computing device) will use this session key to encrypt the data to be sent (such as photovoltaic operation and maintenance data). This encryption method uses a symmetric encryption algorithm (such as AES) for encryption because symmetric encryption is more efficient when processing large amounts of data.

[0021] S4. Based on the encrypted connection, the edge computing device uses the symmetric encryption AES and asymmetric encryption RSA algorithms to encrypt the aggregated photovoltaic operation and maintenance data; In this embodiment, symmetric encryption AES is a data encryption standard that uses the same key for encryption and decryption, while asymmetric encryption RSA uses a pair of public and private keys for encryption and decryption respectively. In distributed photovoltaic systems, edge computing devices need to process a large amount of sensor data. AES is an ideal choice with its fast encryption and decryption speed, ensuring that a large amount of photovoltaic operation and maintenance data can be quickly encrypted, thereby not affecting the real-time requirements; AES is a symmetric encryption algorithm, which is characterized by fast encryption and decryption speed and is suitable for processing a large amount of data. In distributed photovoltaic systems, AES can quickly encrypt a large amount of photovoltaic operation and maintenance data to ensure high efficiency during data transmission; Since RSA is an asymmetric encryption algorithm, it allows the symmetric key of AES to be securely transmitted on the network without sharing secrets in advance. Encrypting the AES key with RSA can ensure that it will not be stolen or tampered with even when transmitted on the network, thereby improving overall security; Combining AES and RSA not only utilizes the efficiency of AES in encrypting a large amount of data, but also uses RSA to securely exchange the symmetric key required by AES, which provides a high level of security for photovoltaic operation and maintenance data while maintaining good performance; The edge computing device uses the symmetric encryption AES and asymmetric encryption RSA algorithms to encrypt the aggregated photovoltaic operation and maintenance data, including the following steps: S4.1. Generate a symmetric key : , this key will be used when encrypting the aggregated data; S4.2. Use the symmetric encryption algorithm AES to encrypt the aggregated data , to encrypt: ; in, Indicates the encrypted data. Indicates the AES symmetric encryption algorithm; S4.3. Use the asymmetric encryption algorithm RSA to encrypt the generated symmetric key , to ensure that the key is not leaked during transmission; S4.4. Use the RSA public key of the cloud platform Encrypt the symmetric key: ; in, Represents the encrypted symmetric key.

[0022] S5. Through SD-WAN technology, the encrypted photovoltaic operation and maintenance data is transmitted through different network paths; In this embodiment, SD-WAN is a technology that optimizes wide area network connections in a software-defined manner. It can intelligently manage multiple network paths to improve performance, reliability and security. SD-WAN can monitor the quality of different network links (such as bandwidth, latency, packet loss rate, etc.) in real time and dynamically select the optimal path according to the current network conditions, which ensures that the best data transmission performance can be maintained even when network conditions change. SD-WAN can automatically distribute traffic to multiple available paths to avoid overloading a single link, thereby improving overall network utilization and stability; by utilizing multiple physical or logical independent network paths, even if a path fails, SD-WAN can quickly switch to other paths to ensure uninterrupted data transmission, thereby enhancing the fault tolerance and high availability of the system. For mission-critical applications, such as real-time monitoring and control functions in photovoltaic operation and maintenance systems, the uninterrupted connection characteristics provided by SD-WAN are crucial to ensure business continuity; although the data has been encrypted by AES and RSA, SD-WAN can maintain TLS-level security protection throughout the transmission process, further strengthening the data security barrier; Through SD-WAN technology, the encrypted photovoltaic operation and maintenance data is transmitted through different network paths, including the following steps: S5.1. Establish an SD-WAN network between the edge computing device and the cloud platform and select the optimal network path. SD-WAN monitors the network quality (such as bandwidth, latency, packet loss rate, etc.) in real time and selects the best transmission channel according to the real-time status of different network paths. If the quality of a network path is poor (such as too high latency or insufficient bandwidth), SD-WAN will automatically switch to another network path to ensure the stability and efficiency of data transmission. S5.2. Monitor the network delay, packet loss rate and bandwidth indicators of the path in real time, establish the quality function of the path and regularly evaluate the performance of each available path. Through multi-path optimization, SD-WAN can seamlessly switch between different network links (such as MPLS, VPN, LTE, 5G, etc.), thereby maintaining stable performance and high availability during the data upload process of the photovoltaic system, and optimize the quality function of the path for the calculation and delay overhead caused by encryption; Furthermore, the quality function helps the SD-WAN system intelligently select the best path that best suits the current data transmission needs by quantitatively evaluating the key performance indicators of different network paths (such as latency, packet loss rate, bandwidth, etc.). This ensures that the best data transmission performance can be maintained even when network conditions change. As network conditions change, the quality function can monitor and re-evaluate the performance of each path in real time, allowing SD-WAN to dynamically adjust traffic distribution and avoid using poor quality or unstable paths. By comprehensively considering multiple factors, the quality function helps to identify those paths with high reliability and reduce the risk of data transmission interruption due to single point failures. When a problem is detected on a path, the system can quickly switch to other better paths based on the quality function to ensure business continuity. For application scenarios that require real-time response, such as remote monitoring and control of photovoltaic systems, low latency and high throughput are essential. The quality function can help ensure that the selected path meets these strict requirements and provides a smooth and efficient data transmission experience. The quality function of the path is: ; in, Indicates the comprehensive quality of the path. The comprehensive quality of the path refers to the comprehensive performance index of the transmission path, covering latency, packet loss rate, and bandwidth. The comprehensive quality value is a comprehensive score used to compare the performance of different network paths. The higher the path quality, the more suitable the path is for data transmission and has better performance. Indicates the path Latency refers to the delay time from the sender to the receiver. A path with lower latency has better quality because it can provide faster transmission. Indicates the path The packet loss rate indicates the proportion of packets lost on the path. The lower the packet loss rate, the better the path quality. Indicates the path The bandwidth indicates the transmission rate of the path. The higher the bandwidth, the stronger the transmission capacity and the better the path quality. Indicates the path; The weight coefficient representing the delay; Indicates the weight coefficient of packet loss rate; Indicates the weight coefficient of bandwidth; Encryption and decryption require CPU resources, especially on edge computing devices, which may affect device performance. Encryption algorithms (especially asymmetric encryption) are usually more time-consuming than ordinary data transmission processes, resulting in increased data transmission latency. In traditional path selection algorithms, only network performance indicators such as latency, bandwidth, and packet loss rate are usually considered, but the additional impact of encryption is ignored. Especially when using encryption to transmit data, relying solely on traditional network indicators may lead to unreasonable path selection. A path with low latency and packet loss rate may have a higher computational burden and latency after adding encryption, resulting in a decrease in the performance of the path originally considered to be the preferred path. If the impact of encryption is not considered, the path selection result may be biased, thereby affecting the stability and efficiency of data transmission. To address the problem of slower data transmission due to the computational and latency overheads of encryption, the quality function of the path is optimized: ; ; ; ; in, represents the optimized path quality score, which takes into account the computational overhead and latency caused by encryption. This quality score more accurately reflects the additional burden brought by encryption; The weight coefficient of encryption overhead is used to adjust the importance of encryption in the path quality score. This weight reflects the relative impact of encryption in network transmission. Indicates the path The encryption overhead represents the computational overhead and delay introduced by the encryption operation on this path; represents the computational overhead; represents the delay overhead; The weight coefficient representing the computational overhead; The weight coefficient representing the delay overhead; Indicates the complexity of the encryption algorithm (for example, RSA is relatively complex, AES is relatively simple); Indicates the path The amount of data transmitted (in bytes, bits, etc.). The larger the amount of data, the higher the encryption overhead. The coefficient representing the encryption acceleration indicates the influence of encryption acceleration; represents a constant representing the effect of encryption delay; Indicates the time required to process data during encryption and decryption; S5.3. Use traffic compression and data deduplication technology to accelerate the transmission of photovoltaic operation and maintenance data. During the SD-WAN transmission process, all photovoltaic operation and maintenance data remains encrypted. When the data is transmitted through the SD-WAN network, end-to-end encryption (such as TLS) is used to ensure that the data is not stolen or tampered with during the transmission process; Among them, by adopting efficient compression algorithms (such as LZMA), the size of data packets can be significantly reduced, thereby reducing the demand for network bandwidth, which is particularly important for distributed photovoltaic systems because these systems are usually distributed over a wide geographical area and may rely on limited or expensive network connections (such as satellite communications, 4G / 5G, etc.); identifying and removing duplicate data blocks to ensure that only new or changed data is transmitted, further reducing the amount of data that needs to be transmitted, which is particularly helpful for improving network efficiency, especially when processing large amounts of similar or periodically updated data; smaller data packets mean faster transmission times, especially in high-latency or low-bandwidth network environments, which helps to achieve faster data synchronization, which is crucial for real-time monitoring and response. By reducing unnecessary data transmission, both edge computing devices and cloud platforms can better allocate their computing resources and avoid performance bottlenecks caused by processing redundant data; Using traffic compression and data deduplication technology to accelerate the transmission of photovoltaic operation and maintenance data includes the following steps: S5.31. Determine a time window t (e.g., 5 minutes), compare the newly collected data with the most recently transmitted data within the time window t, and set a change threshold for each PV operation and maintenance data type (e.g., power generation, voltage, current). If the difference between the new data and the old data is less than this threshold, they are considered to be "duplicates"; S5.32, input the processed photovoltaic operation and maintenance data block into the compression algorithm LZMA to obtain a compressed photovoltaic operation and maintenance data packet, ensuring that the compression operation does not destroy the data format so that the receiving end can correctly decompress and restore the original data, reduce the size of the data packet, and thus reduce the burden of network transmission. For each data block, the data is compressed using the above compression algorithm; S5.33. Before sending the compressed PV operation and maintenance data, check the integrity of the PV operation and maintenance data to ensure that there is no data loss or error caused by the compression process, and verify the compression result through checksum or other mechanisms; S5.34, integrate the compressed photovoltaic operation and maintenance data into the encrypted transmission protocol TLS framework to ensure that all transmitted data remains encrypted without affecting security; S5.4. Allocate bandwidth to each network path based on the network's real-time load and path performance evaluation results to ensure the transmission quality of photovoltaic operation and maintenance data. Sort the paths, prioritize the paths with higher quality scores, and allocate available bandwidth based on the priority of each path. For example, a proportional allocation strategy is adopted, that is, the total bandwidth is allocated to each path in proportion, and the proportion is adjusted based on the path quality score, available bandwidth, and current network load. Furthermore, a secure access area is established on the dispatching master station side of the photovoltaic system. The secure access area on the dispatching master station side must include forward and reverse safety isolation devices, which not only meet the protection requirements of different safety zones, but also provide a two-way physical channel for the data interaction system to realize the data transmission function of uplink and downlink messages. At the same time, security encryption devices are deployed at the exits of the public network and wireless network between the dispatching master station and the distributed photovoltaic power station, and technical measures such as authentication, encryption, and access control are used to realize remote secure transmission of data and security protection of the vertical boundary.

[0023] Embodiment 2: This embodiment provides an encrypted transmission system for distributed photovoltaic operation and maintenance data, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned encrypted transmission method for distributed photovoltaic operation and maintenance data.

[0024] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for encrypting and transmitting distributed photovoltaic operation and maintenance data, characterized in that: The following steps are involved: S1. Install edge computing devices at each node of the distributed photovoltaic system to collect photovoltaic operation and maintenance data in real time through photovoltaic sensors; S2: Aggregate and pre-process the photovoltaic operation and maintenance data of multiple photovoltaic sensors on the edge computing device; S3, establish an encrypted connection through the encrypted transmission protocol TLS and the network protocol HTTPS; S4. Based on the encrypted connection, the edge computing device uses the symmetric encryption AES and asymmetric encryption RSA algorithms to encrypt the aggregated photovoltaic operation and maintenance data; S5. Through SD-WAN technology, the encrypted photovoltaic operation and maintenance data is transmitted through different network paths, and encryption overhead is introduced during the path transmission process for optimization.

2. The method for encrypted transmission of distributed photovoltaic operation and maintenance data according to claim 1, characterized in that: In S1, photovoltaic operation and maintenance data is collected for: ； in, Indicates the amount of power generated, Indicates voltage, represents the current, Indicates time.

3. The method for encrypting and transmitting distributed photovoltaic operation and maintenance data according to claim 2 is characterized in that: In S2, on the edge computing device, the photovoltaic operation and maintenance data of multiple photovoltaic sensors are aggregated and preprocessed, including the following steps: S2.

1. Collected photovoltaic operation and maintenance data Perform pre-processing; S2.

2. The photovoltaic operation and maintenance data of multiple sensors are aggregated and calculated through weighted average aggregation to obtain aggregated photovoltaic operation and maintenance data. The weighted average aggregation is optimized considering the influence of light on the output data of the photovoltaic system, and the weighted average aggregation is further optimized for the time alignment problem of the sensor output data; S2.

3. Detect and process outliers on the aggregated photovoltaic operation and maintenance data.

4. The method for encrypting and transmitting distributed photovoltaic operation and maintenance data according to claim 3 is characterized in that: In S2.2, the weighted average aggregation is: ； in, Indicates at time Weighted average photovoltaic operation and maintenance data; Indicates Sensors at time The weight of Indicates Sensors at time The collected raw photovoltaic operation and maintenance data; Indicates the total number of sensors; Indicates the index of the sensor; Considering the influence of sunlight on the output data of the photovoltaic system, the weighted average aggregation is optimized: ； in, Represents the weighted average photovoltaic operation and maintenance data after considering the sunlight; Indicates Sensors at time The standard deviation of noise; represents environmental impact factors; Indicates sensor The light intensity at the location; represents a constant; To address the time alignment problem of sensor output data, the weighted average aggregation is further optimized: ； in, Represents the weighted average PV operation and maintenance data after time alignment; represents a time window; Represents a time window a point in time.

5. The method for encrypting and transmitting distributed photovoltaic operation and maintenance data according to claim 4 is characterized in that: In the S3, an encrypted connection is established through the encrypted transmission protocol TLS and the network protocol HTTPS, including the following steps: S3.

1. The edge computing device sends an encrypted HTTP request to the API endpoint of the cloud platform through the HTTPS protocol to establish a TLS connection. S3.

2. The edge computing device and the cloud platform jointly select the symmetric encryption AES and asymmetric encryption RSA algorithms and verify the identity of the cloud platform; S3.

3. After successfully verifying the identity of the cloud platform, the edge computing device and the cloud platform use the key exchange protocol RSA to generate a shared symmetric session key; S3.

4. After the session key is generated, the edge computing device encrypts the aggregated PV operation and maintenance data based on the session key using the symmetric encryption AES and asymmetric encryption RSA algorithms.

6. The method for encrypted transmission of distributed photovoltaic operation and maintenance data according to claim 5, characterized in that: In S4, the edge computing device uses the symmetric encryption AES and asymmetric encryption RSA algorithms to encrypt the aggregated photovoltaic operation and maintenance data, including the following steps: S4.

1. Generate a symmetric key : ; S4.

2. Use the symmetric encryption algorithm AES to encrypt the aggregated data , to encrypt: ； in, Indicates the encrypted data. Indicates the AES symmetric encryption algorithm; S4.3, encrypt the generated symmetric key using the asymmetric encryption algorithm RSA; S4.

4. Use the RSA public key of the cloud platform Encrypt the symmetric key: ； in, Represents the encrypted symmetric key.

7. The method for encrypted transmission of distributed photovoltaic operation and maintenance data according to claim 6, characterized in that: In S5, the encrypted photovoltaic operation and maintenance data is transmitted through different network paths through the SD-WAN technology, including the following steps: S5.

1. Establish an SD-WAN network between the edge computing device and the cloud platform and select the optimal network path; S5.

2. Monitor the network delay, packet loss rate, and bandwidth indicators of the path in real time, establish a path quality function to regularly evaluate the performance of each available path, and optimize the path quality function based on the calculation and delay overhead caused by encryption; S5.

3. Use traffic compression and data deduplication technology to accelerate the transmission of photovoltaic operation and maintenance data; S5.

4. Allocate bandwidth to each network path based on the real-time network load and path performance evaluation results.

8. The method for encrypting and transmitting distributed photovoltaic operation and maintenance data according to claim 7, characterized in that: In S5.2, the quality function of the path is: ； in, Indicates the overall quality of the path; Indicates the path Delay Indicates the path Packet loss rate; Indicates the path bandwidth; Indicates the path; The weight coefficient representing the delay; Indicates the weight coefficient of packet loss rate; Indicates the weight coefficient of bandwidth; In view of the computational and delay overhead caused by encryption, the quality function of the path is optimized: ； in, represents the optimized path quality score; The weight coefficient representing the encryption overhead; Indicates the path encryption overhead.

9. The method for encrypted transmission of distributed photovoltaic operation and maintenance data according to claim 8, characterized in that: In S5.3, the transmission of photovoltaic operation and maintenance data is accelerated by using traffic compression and data deduplication technology, including the following steps: S5.31, determine a time window t, compare the newly collected data with the most recently transmitted data within the time window t, and set a change threshold for each PV operation and maintenance data type; S5.32, inputting the processed photovoltaic operation and maintenance data block into the compression algorithm LZMA to obtain a compressed photovoltaic operation and maintenance data packet; S5.

33. Before sending the compressed PV operation and maintenance data, check the integrity of the PV operation and maintenance data; S5.

34. Integrate the compressed photovoltaic operation and maintenance data into the encrypted transmission protocol TLS framework.

10. An encrypted transmission system for distributed photovoltaic operation and maintenance data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the method for encrypting and transmitting distributed photovoltaic operation and maintenance data as described in any one of claims 1 to 9.

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