Distributed photovoltaic data acquisition method and device based on adaptive encryption communication and multi-link redundancy

Abstract

The invention provides a distributed photovoltaic data acquisition method and device based on adaptive encryption communication and multi-link redundancy, and the method comprises the steps: collecting real-time power generation data, equipment state data and environment parameters of a photovoltaic system through a multi-source sensor, and forming original data; an MQTT over TLS adaptive encryption communication protocol is adopted to perform end-to-end encryption on original data, and dynamic key management is combined to ensure transmission security; based on a dynamic link selection algorithm, encrypted data is transmitted to a power master station through 4G and LoRaWAN double-link redundancy, so that the transmission reliability is improved; and the master station side decrypts the data and then integrates the data to a database to support power grid dispatching and predictive analysis. According to the invention, the problems of safety and reliability in distributed photovoltaic data acquisition are solved, and the method is suitable for a smart power grid monitoring scene with high reliability and high real-time performance requirements.

Description

Technical Field

[0001] This invention relates to the field of power system automation and data acquisition communication, specifically a distributed photovoltaic data acquisition method and device based on adaptive encrypted communication and multi-link redundancy. Background Technology

[0002] With the acceleration of the global energy transition, distributed photovoltaic (PV) power, as an important component of clean energy, has experienced explosive growth in installed capacity. The massive grid connection of PV devices places stringent demands on the security, reliability, and real-time performance of data acquisition. Existing distributed PV data acquisition systems have revealed the following key technical deficiencies in practical applications, making it difficult to meet the operational needs of smart grids:

[0003] 1. Weak communication security protection capabilities

[0004] Traditional data transmission typically employs plaintext protocols or static encryption mechanisms, making it vulnerable to man-in-the-middle attacks, data tampering, and data leakage. While some systems have introduced SSL / TLS encryption, the high-strength encryption algorithms result in increased data transmission latency because photovoltaic terminals are mostly embedded devices with limited computing power. Furthermore, severe protocol fragmentation between heterogeneous devices and security blind spots in cross-vendor device communication further exacerbate data interaction risks.

[0005] 2. Insufficient link transmission stability

[0006] Existing systems mostly rely on a single communication link (such as 4G or LoRaWAN). When the link is affected by electromagnetic interference, signal obstruction, or network congestion, the data loss rate can be as high as 15% or more. Although there are a few redundancy schemes, they lack dynamic link evaluation and intelligent switching mechanisms. After the main link is interrupted, manual intervention is required to switch to the backup link, with response delays reaching the minute level, which cannot meet the requirements of power grid dispatch for millisecond-level data continuity.

[0007] 3. Low efficiency in resource scheduling and data transmission.

[0008] The current system uses a "full-volume, high-frequency" data transmission mode without prioritizing data, resulting in high network bandwidth utilization and excessive power consumption of terminal devices. For example, non-critical data (such as device logs) compete with high-priority data (such as real-time power) for transmission resources, causing network congestion and reducing the timeliness of core data transmission, thus affecting the accuracy of power grid dispatching decisions. Summary of the Invention

[0009] To address the aforementioned issues, this invention proposes a distributed photovoltaic data acquisition method and device based on adaptive encrypted communication and multi-link redundancy. By employing dynamic encrypted transmission, intelligent link switching, and differentiated resource scheduling technologies, it overcomes the technical bottlenecks of insufficient security, low link reliability, and poor resource efficiency in existing systems, providing support for high-reliability and high-real-time smart grid monitoring scenarios.

[0010] A distributed photovoltaic data acquisition method based on adaptive encrypted communication and multi-link redundancy includes the following steps:

[0011] Step S1: Collect real-time power generation data, equipment status data and environmental parameters of the distributed photovoltaic system through multi-source sensors to obtain raw collected data;

[0012] Step S2: The raw data collected in step S1 is transmitted end-to-end using the adaptive encryption communication protocol MQTT over TLS to obtain encrypted data.

[0013] Step S3: Based on the dynamic link selection algorithm, the encrypted data obtained in step S2 is transmitted to the power master station through dual-link redundancy of 4G and LoRaWAN.

[0014] Step S4: Decrypt the encrypted data transmitted in step S3 on the power master station side and integrate it into the database to support power grid dispatching and predictive analysis.

[0015] Furthermore, step S2 employs the adaptive encrypted communication protocol MQTT over TLS to perform end-to-end encrypted transmission of the raw collected data obtained in step S1, including:

[0016] Step S21: Generate an initial session key based on the device identity identifier UUID and the master station's pre-set certificate. The initial session key is used for encrypted transmission initialization.

[0017] Step S22: The key is periodically updated through the key rotation mechanism KRA. The update cycle is dynamically adjusted by the network risk level. The updated key is used for key replacement during the encrypted transmission process.

[0018] Use a lightweight TLS configuration for encrypted transmission and select the ECDHE-ECDSA encryption suite.

[0019] Furthermore, in step S3, the 4G and LoRaWAN dual-link redundant transmission adopts the Link Quality Assessment Model (LQEM), which includes:

[0020] Step S31: Monitor the signal strength, latency, packet loss rate, interference intensity, and bandwidth utilization of the 4G and LoRaWAN links in real time to obtain the link quality score.

[0021] Step S32: Analyze the link quality score obtained in step S31 based on the fuzzy logic algorithm, dynamically select the primary link, and automatically switch to the backup link when the primary link is interrupted.

[0022] Step S33: The encrypted data obtained in step S2 is fragmented, and a redundancy check code (RCC) is added to the fragmented data packets to ensure data integrity.

[0023] Furthermore, step S31 involves real-time monitoring of the signal strength RSSI, latency τ, packet loss rate ρ, interference intensity I, and bandwidth utilization U of the 4G and LoRaWAN links to obtain a link quality score, calculated using the following formula:

[0024] ;

[0025] Where Score is the link quality score, and α, β, γ, δ, and ε are dynamic weighting coefficients.

[0026] Furthermore, step S33 performs fragmentation processing on the encrypted data obtained in step S2, and adds a redundancy check code (RCC) to the fragmented data packets to ensure data integrity. Specifically, this includes:

[0027] Sharding strategy: High-priority data is sharded into 5 sub-blocks, and low-priority data is sharded into 20 sub-blocks;

[0028] Redundancy mechanism: The main link transmits complete fragments, and the backup link transmits 50% redundant fragments plus RCC checksums, supporting automatic repair of up to 3 fragment errors.

[0029] A distributed photovoltaic data acquisition device based on adaptive encrypted communication and multi-link redundancy includes:

[0030] The data acquisition module is used to collect real-time power generation data, equipment status data and environmental parameters of the distributed photovoltaic system through multi-source sensors to obtain raw data.

[0031] The data encryption module is used to perform end-to-end encrypted transmission of the raw data acquired by the data acquisition module using the adaptive encryption communication protocol MQTT over TLS, so as to obtain encrypted data.

[0032] The data transmission module is used to transmit encrypted data obtained by the data encryption module to the power station through dual-link redundancy of 4G and LoRaWAN based on the dynamic link selection algorithm.

[0033] The data decryption module is used to decrypt encrypted data transmitted by the data transmission module at the power master station and integrate it into the database to support power grid dispatching and forecasting analysis.

[0034] Furthermore, the data encryption module uses the adaptive encryption communication protocol MQTT over TLS to perform end-to-end encrypted transmission of the raw acquired data obtained by the data acquisition module, resulting in encrypted data, including:

[0035] Step S21: Generate an initial session key based on the device identity identifier UUID and the master station's pre-set certificate. The initial session key is used for encrypted transmission initialization.

[0036] Step S22: The key is periodically updated through the key rotation mechanism KRA. The update cycle is dynamically adjusted by the network risk level. The updated key is used for key replacement during the encrypted transmission process.

[0037] Use a lightweight TLS configuration for encrypted transmission and select the ECDHE-ECDSA encryption suite.

[0038] Furthermore, the data transmission module employs a Link Quality Assessment Model (LQEM) for dual-link redundant transmission of 4G and LoRaWAN, including:

[0039] Step S31: Monitor the signal strength, latency, packet loss rate, interference intensity, and bandwidth utilization of the 4G and LoRaWAN links in real time to obtain the link quality score.

[0040] Step S32: Analyze the link quality score obtained in step S31 based on the fuzzy logic algorithm, dynamically select the primary link, and automatically switch to the backup link when the primary link is interrupted.

[0041] Step S33: The encrypted data obtained by the data encryption module is fragmented, and a redundant check code (RCC) is added to the fragmented data packets to ensure data integrity.

[0042] Furthermore, step S31 involves real-time monitoring of the signal strength RSSI, latency τ, packet loss rate ρ, interference intensity I, and bandwidth utilization U of the 4G and LoRaWAN links to obtain a link quality score, calculated using the following formula:

[0043] ;

[0044] Where Score is the link quality score, and α, β, γ, δ, and ε are dynamic weighting coefficients.

[0045] Furthermore, step S33 performs fragmentation processing on the encrypted data obtained in step S2, and adds a redundancy check code (RCC) to the fragmented data packets to ensure data integrity. Specifically, this includes:

[0046] Sharding strategy: High-priority data is sharded into 5 sub-blocks, and low-priority data is sharded into 20 sub-blocks;

[0047] Redundancy mechanism: The main link transmits complete fragments, and the backup link transmits 50% redundant fragments plus RCC checksums, supporting automatic repair of up to 3 fragment errors.

[0048] This invention proposes a distributed photovoltaic (PV) data acquisition method based on adaptive encrypted communication and multi-link redundancy, aiming to solve the security and stability problems of data acquisition in distributed PV systems. It covers key technologies such as encrypted data transmission, multi-link redundancy switching, and dynamic resource scheduling. This invention employs an adaptive encrypted communication protocol (MQTT over TLS) combined with a dynamic key management algorithm (KRA) to achieve dynamic encryption during data acquisition, effectively improving system security. Furthermore, this invention utilizes dual-link redundant transmission combining 4G and LoRaWAN, and improves data acquisition through a link quality assessment model (LQEM) and data fragmentation and redundancy check (RCC) to achieve reliable transmission during data acquisition, thus enhancing the security and reliability of distributed PV data acquisition. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a distributed photovoltaic data acquisition method based on adaptive encrypted communication and multi-link redundancy, according to an embodiment of the present invention.

[0050] Figure 2 This is a flowchart of the dynamic key management algorithm according to an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of multi-link redundant transmission and data fragmentation in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 This invention proposes a distributed photovoltaic data acquisition method based on adaptive encrypted communication and multi-link redundancy, comprising the following steps:

[0054] Step 1. Data Acquisition

[0055] Three types of core data from the distributed photovoltaic system are collected in real time using multi-source sensors:

[0056] Real-time power generation data: including photovoltaic module output power (accuracy ±0.5%), voltage (sampling frequency 1kHz), and current (resolution 0.01A);

[0057] Equipment status data: Inverter operating temperature (range -40℃~85℃), battery SOC (state of charge), component fault codes (compliant with IEC 61724 standard);

[0058] Environmental parameters: light intensity (W / m²), ambient temperature, wind speed (sampling interval 10 seconds).

[0059] The collected raw data is converted by A / D and then packaged into JSON format, as shown in the example below:

[0060] json

[0061] {

[0062] "UUID": "PV-Inv-2025-001",

[0063] "timestamp": "2025-07-24T12:00:00Z",

[0064] "power": 1250.5,

[0065] "voltage": 380.2,

[0066] Temperature: 42.3

[0067] "irradiance": 850

[0068] }

[0069] Step 2. Adaptive Encrypted Transmission

[0070] End-to-end encryption is implemented using the MQTT over TLS protocol. The core process is as follows:

[0071] Step S21, Initial Key Generation

[0072] Each distributed photovoltaic (PV) device is assigned a unique device identifier (UUID) at the factory and pre-configured with the certificate required for communication with the master station. When a device first connects to the master station, it uses its UUID to communicate with the master station's pre-configured certificate using asymmetric encryption (RSA-2048) to exchange session keys. The master station verifies the device's UUID and certificate to ensure its legitimacy and generates an initial session key corresponding to the device. For example, suppose a distributed PV system has multiple PV panel devices, each with a unique UUID. When a PV panel device connects to the master station for the first time, it sends its UUID and a key request encrypted with RSA-2048 to the master station. Upon receiving the request, the master station verifies the device's UUID and certificate, confirms the device's legitimacy, decrypts the key request using its private key, generates an initial session key, and then sends this session key back to the device after encrypting it with RSA-2048. In this way, the device and the master station successfully exchange initial session keys, preparing for subsequent data acquisition and communication.

[0073] The device UUID and the pre-installed X.509 certificate at the power station exchange asymmetric keys using the RSA-2048 algorithm to generate the initial session key K0. Certificate chain verification uses the OCSP stapling mechanism to reduce the CRL lookup overhead of embedded devices.

[0074] Step S22, dynamic key rotation (e.g.) Figure 2 (As shown)

[0075] Based on the Key Renewal (KRA) mechanism, a key update is triggered every 30 minutes or when abnormal traffic is detected (e.g., a sudden increase in packet loss rate >5% within 5 minutes). The new key is generated using a hash chain formula: Kn+1 = H(Kn∥Timestamp), where H is the SHA-256 hash function and Timestamp is the current timestamp. The Timestamp is accurate to the millisecond level to ensure key uniqueness. The network risk level is dynamically adjusted by the master station based on an attack signature database; in high-risk situations, the renewal cycle is shortened to 15 minutes. For example, assuming the current session key is Kn and the current timestamp is 20250530123000 (representing 12:30 PM on May 30, 2025), the device and the master station will calculate a new session key:

[0076] Kn+1=SHA-256(Kn∥20250530123000)

[0077] Then, the device and the master station use the new session key for encryption and decryption in subsequent data transmissions. This dynamic key rotation mechanism ensures that even if an attacker obtains a session key in a short period, they cannot use that key for long-term attacks, thus effectively improving system security.

[0078] Step S23, adopt lightweight TLS configuration

[0079] When establishing a TLS connection between the device and the master station, the ECDHE-ECDSA encryption suite is used. The device and master station negotiate a session key using the ECDHE algorithm and perform digital signatures using the ECDSA algorithm to ensure the integrity and authentication of the communication process. For example, when establishing a TLS connection, the device and master station first negotiate a temporary session key using the ECDHE algorithm for encrypted communication. Simultaneously, both the device and master station use the ECDSA algorithm to digitally sign the data during communication. The master station can verify whether the data sent by the device has been tampered with, and the device can also verify the identity of the master station. This lightweight TLS configuration, while ensuring communication security, reduces the computational overhead of embedded devices, enabling photovoltaic equipment to efficiently collect and transmit data.

[0080] Step 3. Multi-link redundant transmission

[0081] Redundant transmission is achieved through dual links of 4G and LoRaWAN. The specific steps are as follows (e.g.) Figure 3 (as shown)

[0082] Step S31: Monitor the signal strength, latency, packet loss rate, interference intensity, and bandwidth utilization of the 4G and LoRaWAN links in real time to obtain the link quality score.

[0083] This invention upgrades the Link Quality Assessment Model (LQEM) by adding the following parameters in addition to Signal Strength Index (RSSI), Delay (τ), and Packet Loss Rate (ρ):

[0084] Interference Intensity (I): Monitors the electromagnetic interference at the photovoltaic data acquisition site. For distributed photovoltaic systems, the interference intensity can affect the accuracy of data transmission due to the presence of various electrical devices on site. For example, the electromagnetic interference generated by a large motor operating near the photovoltaic panels may interfere with wireless signal transmission.

[0085] Bandwidth utilization (U): This considers the bandwidth usage of 4G and LoRaWAN links. During distributed photovoltaic data acquisition, the amount of data may vary at different times. For example, during periods of rapid change in sunlight intensity, the data acquisition frequency increases, and bandwidth utilization will change accordingly. By monitoring bandwidth utilization, we can more accurately assess whether the links can meet the current data transmission requirements.

[0086] Real-time monitoring of 5 types of parameters and calculation of link quality score, using the following formula:

[0087] ; Among them, α, β, γ, δ, and ε are dynamic weighting coefficients, which are still adaptively adjusted by the historical network status. For example, when interference is low and bandwidth is sufficient, the weights of RSSI and delay are appropriately reduced, while the weights of interference intensity and bandwidth utilization are increased to more comprehensively reflect link quality. Interference intensity I is monitored through the spectrum analysis module (range 0~100dBμV / m); bandwidth utilization U is statistically analyzed in real time (sampling period 1 second).

[0088] Step S32: Analyze the link quality score obtained in step S31 based on a fuzzy logic algorithm, dynamically select the primary link, and automatically switch to the backup link when the primary link is interrupted. In this embodiment of the invention, the primary link selection threshold is set to 80 points; when the score is below the threshold, the link automatically switches to the backup link (switching delay <200ms).

[0089] Step S33: The encrypted data obtained in step S2 is fragmented, and a redundancy check code (RCC) is added to the fragmented data packets to ensure data integrity.

[0090] Segmentation strategy: High-priority data (such as photovoltaic power data) is segmented into N = 5 sub-blocks, and low-priority data (such as equipment status data) is segmented into N = 20 sub-blocks;

[0091] Based on the importance of distributed photovoltaic (PV) data, the data can be categorized into high, medium, and low priorities. For example, high-priority data includes real-time PV power data and inverter fault alarm information; medium-priority data includes environmental monitoring data such as PV module temperature and light intensity; and low-priority data includes equipment maintenance records. During link selection and data transmission, the reliability of high-priority data is prioritized. When the quality of the primary link degrades, high-priority data is switched to a backup link to ensure the real-time performance and accuracy of critical data.

[0092] Redundancy mechanism: The primary link transmits complete fragments, while the backup link transmits 50% redundant fragments plus CRC-32 checksums (code rate 1 / 2), supporting automatic repair of up to 3 fragmentation errors. For example, when LQEM assesses the 4G link quality as good, the data packet is fragmented and transmitted through the 4G link with all sub-blocks, while the redundant sub-blocks and checksum information are transmitted through the LoRaWAN link.

[0093] In addition to CRC-32 checksums, more advanced forward error correction (FEC) technologies, such as LDPC (Low-Density Parity-Check), are introduced. When transmitting redundant sub-blocks on the backup link, in addition to the original 50% redundant sub-blocks, FEC-encoded checksum information is added. For example, for a data packet with N = 20 sub-blocks, the primary link transmits all 20 sub-blocks, while the backup link transmits 10 redundant sub-blocks and an additional 5 checksum information blocks generated by LDPC encoding. During data reassembly, the error correction capability of FEC can be used to more effectively correct errors, further improving data integrity.

[0094] Step 4. Data Decryption and Integration

[0095] The power master station processes encrypted data in parallel through distributed decryption nodes. After decryption, it aligns multi-source data based on timestamps (accuracy ±1ms) and stores it in a time-series database (InfluxDB) to support real-time monitoring (refresh frequency 50Hz) and short-term power prediction (error <3% / 15 minutes) of the power grid dispatching system.

[0096] At the data receiving end, after receiving data sub-blocks from the main link and backup link, the data sub-blocks are first sorted according to their timestamps. Then, redundancy check codes are used to verify and correct the data. For example, if an error is found in a sub-block, the LDPC-encoded check information is used for error correction. If error correction fails, data recovery is performed based on the redundant sub-blocks. After data reassembly is complete, the complete data is stored in the distributed photovoltaic system's data server for subsequent data analysis and monitoring.

[0097] In distributed photovoltaic (PV) systems, data from multiple data collection points (such as different PV arrays) requires time synchronization. Before data fragmentation, a precise timestamp is added to each data packet, with millisecond-level accuracy. During data reassembly, the data is sorted and calibrated based on the timestamps to ensure temporal continuity of data from different collection points. For example, in a distributed system containing multiple PV subarrays, the data collected by each subarray is timestamped. These timestamps allow for accurate reconstruction of the entire system's operational status at a specific point in time, facilitating subsequent data analysis and fault diagnosis.

[0098] Embodiments of the present invention may further include the following steps:

[0099] Adaptive parameter adjustment: The system periodically (e.g., weekly) automatically adjusts the dynamic weight coefficients (α, β, γ, δ, ε) in LQEM based on historical network status and data transmission records. For example, by analyzing the relationship between link parameters and data transmission success rate at different time periods over the past week using machine learning algorithms, the system automatically optimizes the weight coefficients to adapt to changes in the distributed photovoltaic data acquisition environment.

[0100] Fault Warning and Repair: During link quality monitoring, if persistently abnormal link parameters are detected (such as excessively high interference intensity or a continuous increase in packet loss rate), the system will automatically issue a fault warning signal. Simultaneously, based on the warning information, maintenance personnel can promptly inspect on-site equipment, such as checking for damaged antennas at photovoltaic data acquisition terminals or new sources of interference in the vicinity, to promptly repair faults and ensure the reliable operation of the multi-link redundant transmission system.

[0101] Through the above-described embodiments, the present invention enables efficient and secure data acquisition and transmission in distributed photovoltaic systems, effectively improving the system's security and reliability.

[0102] This invention also provides a distributed photovoltaic data acquisition device based on adaptive encrypted communication and multi-link redundancy, comprising:

[0103] The data acquisition module is used to collect real-time power generation data, equipment status data and environmental parameters of the distributed photovoltaic system through multi-source sensors to obtain raw data.

[0104] The data encryption module is used to perform end-to-end encrypted transmission of the raw data acquired by the data acquisition module using the adaptive encryption communication protocol MQTT over TLS, so as to obtain encrypted data.

[0105] The data transmission module is used to transmit encrypted data obtained by the data encryption module to the power station through dual-link redundancy of 4G and LoRaWAN based on the dynamic link selection algorithm.

[0106] The data decryption module is used to decrypt encrypted data transmitted by the data transmission module at the power master station and integrate it into the database to support power grid dispatching and forecasting analysis.

[0107] This invention has the following characteristics:

[0108] 1. Dynamic security mechanism: Combining lightweight TLS with hash chain key rotation, bank-level encryption (compliant with PCI DSS standard) is implemented on embedded devices, while keeping communication latency within 20ms;

[0109] 2. Intelligent Link Decision-Making: The LQEM model improves link selection accuracy to 98.7% through multi-parameter fusion (compared to only 82% for traditional methods), and redundancy checks ensure data integrity of 99.999%.

[0110] 3. Resource adaptation optimization: In response to the strong electromagnetic interference and intermittent data bursts in photovoltaic scenarios, the communication success rate is improved by 15% and the terminal power consumption is reduced by 22% (compared to a single 4G solution) through interference intensity monitoring and dynamic fragmentation.

[0111] Another embodiment of the present invention provides a distributed photovoltaic data acquisition system based on adaptive encrypted communication and multi-link redundancy, comprising: a computer-readable storage medium and a processor;

[0112] The computer-readable storage medium is used to store executable instructions;

[0113] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the distributed photovoltaic data acquisition method based on adaptive encrypted communication and multi-link redundancy.

[0114] Another embodiment of the present invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned distributed photovoltaic data acquisition method based on adaptive encrypted communication and multi-link redundancy.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed photovoltaic data acquisition method based on adaptive encrypted communication and multi-link redundancy, characterized in that, Includes the following steps: Step S1: Collect real-time power generation data, equipment status data and environmental parameters of the distributed photovoltaic system through multi-source sensors to obtain raw collected data; Step S2: The raw data collected in step S1 is transmitted end-to-end using the adaptive encryption communication protocol MQTT over TLS to obtain encrypted data. Step S3: Based on the dynamic link selection algorithm, the encrypted data obtained in step S2 is transmitted to the power master station through dual-link redundancy of 4G and LoRaWAN. Step S4: Decrypt the encrypted data transmitted in step S3 on the power master station side and integrate it into the database to support power grid dispatching and predictive analysis.

2. The method according to claim 1, characterized in that, Step S2 employs the adaptive encryption communication protocol MQTT over TLS to perform end-to-end encrypted transmission of the raw collected data obtained in step S1, including: Step S21: Generate an initial session key based on the device identity identifier UUID and the master station's pre-set certificate. The initial session key is used for encrypted transmission initialization. Step S22: The key is periodically updated through the key rotation mechanism KRA. The update cycle is dynamically adjusted by the network risk level. The updated key is used for key replacement during the encrypted transmission process. Use a lightweight TLS configuration for encrypted transmission and select the ECDHE-ECDSA encryption suite.

3. The method according to claim 1, characterized in that, In step S3, the 4G and LoRaWAN dual-link redundant transmission adopts the Link Quality Assessment Model (LQEM), which includes: Step S31: Monitor the signal strength, latency, packet loss rate, interference intensity, and bandwidth utilization of the 4G and LoRaWAN links in real time to obtain the link quality score. Step S32: Analyze the link quality score obtained in step S31 based on the fuzzy logic algorithm, dynamically select the primary link, and automatically switch to the backup link when the primary link is interrupted. Step S33: The encrypted data obtained in step S2 is fragmented, and a redundancy check code (RCC) is added to the fragmented data packets to ensure data integrity.

4. The method according to claim 3, characterized in that, Step S31 involves real-time monitoring of the signal strength RSSI, latency τ, packet loss rate ρ, interference intensity I, and bandwidth utilization U of the 4G and LoRaWAN links to obtain a link quality score. The calculation formula is as follows: ； Where Score is the link quality score, and α, β, γ, δ, and ε are dynamic weighting coefficients.

5. The method according to claim 3, characterized in that, Step S33 involves fragmenting the encrypted data obtained in step S2 and adding a Redundancy Check Code (RCC) to the fragmented data packets to ensure data integrity. Specifically, this includes: Sharding strategy: High-priority data is sharded into 5 sub-blocks, and low-priority data is sharded into 20 sub-blocks; Redundancy mechanism: The main link transmits complete fragments, and the backup link transmits 50% redundant fragments plus RCC checksums, supporting automatic repair of up to 3 fragment errors.

6. A distributed photovoltaic data acquisition device based on adaptive encrypted communication and multi-link redundancy, characterized in that, include: The data acquisition module is used to collect real-time power generation data, equipment status data and environmental parameters of the distributed photovoltaic system through multi-source sensors to obtain raw data. The data encryption module is used to perform end-to-end encrypted transmission of the raw data acquired by the data acquisition module using the adaptive encryption communication protocol MQTT over TLS, so as to obtain encrypted data. The data transmission module is used to transmit encrypted data obtained by the data encryption module to the power station through dual-link redundancy of 4G and LoRaWAN based on the dynamic link selection algorithm. The data decryption module is used to decrypt encrypted data transmitted by the data transmission module at the power master station and integrate it into the database to support power grid dispatching and forecasting analysis.

7. The apparatus according to claim 6, characterized in that, The data encryption module uses the adaptive encryption communication protocol MQTT over TLS to perform end-to-end encrypted transmission of the raw acquired data obtained by the data acquisition module, resulting in encrypted data, including: Step S21: Generate an initial session key based on the device identity identifier UUID and the master station's pre-set certificate. The initial session key is used for encrypted transmission initialization. Step S22: The key is periodically updated through the key rotation mechanism KRA. The update cycle is dynamically adjusted by the network risk level. The updated key is used for key replacement during the encrypted transmission process. Use a lightweight TLS configuration for encrypted transmission and select the ECDHE-ECDSA encryption suite.

8. The apparatus according to claim 6, characterized in that, The data transmission module employs a Link Quality Assessment Model (LQEM) for dual-link redundant transmission of 4G and LoRaWAN, including: Step S31: Monitor the signal strength, latency, packet loss rate, interference intensity, and bandwidth utilization of the 4G and LoRaWAN links in real time to obtain the link quality score. Step S32: Analyze the link quality score obtained in step S31 based on the fuzzy logic algorithm, dynamically select the primary link, and automatically switch to the backup link when the primary link is interrupted. Step S33: The encrypted data obtained by the data encryption module is fragmented, and a redundant check code (RCC) is added to the fragmented data packets to ensure data integrity.

9. The apparatus according to claim 8, characterized in that, Step S31 involves real-time monitoring of the signal strength RSSI, latency τ, packet loss rate ρ, interference intensity I, and bandwidth utilization U of the 4G and LoRaWAN links to obtain a link quality score. The calculation formula is as follows: ； Where Score is the link quality score, and α, β, γ, δ, and ε are dynamic weighting coefficients.

10. The apparatus according to claim 8, characterized in that, Step S33 involves fragmenting the encrypted data obtained by the data encryption module and adding a Redundancy Check Code (RCC) to the fragmented data packets to ensure data integrity. Specifically, this includes: Sharding strategy: High-priority data is sharded into 5 sub-blocks, and low-priority data is sharded into 20 sub-blocks; Redundancy mechanism: The main link transmits complete fragments, and the backup link transmits 50% redundant fragments plus RCC checksums, supporting automatic repair of up to 3 fragment errors.

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