A high-efficiency photovoltaic inverter communication monitoring and conversion method and system

Abstract

This invention discloses a high-efficiency photovoltaic inverter communication monitoring and conversion method and system, belonging to the field of communication monitoring service technology. The method and system include: S1, collecting and preprocessing link structure data and field feature data to construct a standardized communication transmission dataset; S2, analyzing the transmission delay of communication nodes at each level and dynamically adjusting the data encapsulation structure and primary / backup channel forwarding strategy; S3, assessing the risk of mismatch between the primary and redundant channels and triggering path switching and structural reorganization processes; and S4, comprehensively analyzing the delay and mismatch risk results to dynamically optimize the link rhythm distribution structure. This solves the problem that multiple encapsulation and decoding of inverter data transmission in different gateway levels easily introduces delay and distortion, and that existing systems lack an end-to-end link performance collaborative perception and optimization control mechanism from the edge to the cloud platform.

Description

Technical Field

[0001] This invention relates to the field of communication monitoring service technology, specifically to a high-efficiency photovoltaic inverter communication monitoring and conversion method and system. Background Technology

[0002] With the increasing prevalence of photovoltaic (PV) power generation systems deployed across multiple regions, inverter communication monitoring has become a crucial element for achieving remote operation and maintenance and status awareness. Currently, PV inverter communication monitoring typically relies on RS-485, CAN bus, Ethernet, or wireless networks to establish data links, achieving bidirectional communication with the monitoring host through master-slave polling or event reporting. During communication, the inverter sends data frames containing key fields such as voltage, current, power, temperature, and fault status at fixed intervals. The host acquisition terminal parses the protocol and extracts the necessary fields for operational status assessment and energy efficiency analysis. To adapt to complex scenarios such as multi-string access and multi-device grid connection, some systems also employ redundant primary / backup channel configurations, segmented encapsulation structures, and automatic protocol identification mechanisms to achieve real-time monitoring and protocol conversion control of the data transmission path. Furthermore, data synchronization verification and cache comparison enhance communication integrity and field consistency. During operation, the communication monitoring module continuously records field transmission status, response latency changes, and link stability parameters, forming a basic dataset of communication status that supports remote diagnostics and dynamic scheduling.

[0003] For example, invention patent CN117675006B discloses a multi-GNSS receiver optical communication monitoring system and method. In this system, a base station receiving device receives satellite signals and calculates base station data, then transmits the base station data to a GNSS optical host via a splitter using optical fiber. The GNSS optical host receives the base station data and transmits it to each rover receiving device via optical fiber. The rover receiving device receives satellite signals, calculates its own site data, and performs a differential calculation between its own site data and the received base station data to obtain positioning data. The GNSS optical host also receives the positioning data transmitted by the rover receiving devices and performs packet uploading. This application, through optical fiber transmission and the use of a splitter and GNSS optical host, improves data transmission quality while reducing costs, and also enables the expansion of the number of optical hosts in multi-antenna scenarios.

[0004] For example, invention patent CN115549780B discloses a method and apparatus for monitoring the performance parameters of optical communication networks. The method includes: acquiring the original target-polarized coherent light signal received by the receiver of the optical communication system; inputting the original target-polarized coherent light signal into a preset spatiotemporal feature network, so that the spatiotemporal feature network outputs the modulation format and optical signal-to-noise ratio corresponding to the original target-polarized coherent light signal, and using the modulation format and optical signal-to-noise ratio as the current monitoring result of the optical communication network performance parameters. This application can effectively shorten the time required for monitoring the performance parameters of optical communication networks, improve the efficiency and real-time performance of optical communication network performance parameter monitoring; at the same time, it can ensure the accuracy of the identification of the monitoring results of optical communication network performance parameters, thereby meeting the real-time and high-precision requirements of optical communication systems for monitoring the performance parameters of optical communication networks.

[0005] However, existing communication monitoring methods mainly focus on static link status statistics or post-fault diagnosis and backtracking, lacking the ability to track and dynamically control link fluctuation behavior, field reconstruction patterns, and protocol conversion response paths during communication. Especially during sudden changes in data load, link switching, or redundant path takeover, it is difficult to simultaneously identify the data consistency status and structural compression requirements of primary and backup channels, and it is impossible to build end-to-end bidirectional control strategies and rhythm matching mechanisms, thus limiting the continuity of communication and the stability maintenance of data conversion.

[0006] To address the above issues, there is an urgent need for a high-efficiency photovoltaic inverter communication monitoring and conversion method and system. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a high-efficiency photovoltaic inverter communication monitoring and conversion method and system, which solves the problems of inverter data transmission being encapsulated and decoded multiple times in different level gateways, which easily introduces delays and distortions, and the lack of an end-to-end link performance collaborative perception and optimization control mechanism from the edge to the cloud platform in existing systems.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a high-efficiency photovoltaic inverter communication monitoring and conversion method and system, comprising: S1, collecting link structure data and field feature data, and preprocessing the collected link structure data and field feature data to construct a standardized communication transmission dataset; S2, analyzing the transmission delay of communication nodes at each level in the link based on the standardized communication transmission dataset, and dynamically adjusting the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results; S3, assessing the structural mismatch risk in the primary and redundant channels based on the standardized communication transmission dataset, and dynamically triggering path switching control and structural reorganization processes based on the assessment results; S4, comprehensively analyzing the current link transmission status using the transmission delay analysis results and mismatch risk assessment results as input, and dynamically adjusting the rhythm distribution structure based on the analysis results.

[0011] Further, the specific steps for collecting link structure data and field feature data are as follows: Structured parsing of the encapsulated content at each level is performed to collect link structure data, which includes: the number of communication segments, the receive and send timestamps of each node, the communication distance of each path segment, the number of data frames forwarded by each path segment in each period, the resource occupancy ratio of the path node's buffer queue, and the number of abnormal communication events. Simultaneously, the adjacent measurement time intervals within the current collection period are recorded. A principal component collaborative enhancement algorithm is used to collaboratively enhance the number of data frames forwarded by each path segment in each period, the resource occupancy ratio, and the number of abnormal communication events, constructing a principal component model based on collaborative change trends. The perturbation value is calculated by determining the change trajectory of the principal component perturbation value within the current period. The main driving load factor of the path segment is extracted, and the main driving load factor is expanded within a unit time to obtain the load change value of the path segment. Field feature data is collected, including: the original field data received by the main channel, the field value sequence of the data frame received by the main channel and the redundant channel, and the field value set of each frame of data. Hash digest calculation is performed on the field value set of each frame of data in the main channel and the redundant channel. The field values ​​are combined and arranged into a structured sequence according to the field order. Then, an independent hash operation is performed on each field, and the results are concatenated in order to form the field hash code sequence of the corresponding data frame.

[0012] Furthermore, the specific steps for preprocessing the collected link structure data and field feature data to construct a standardized communication transmission dataset are as follows: Preprocessing the collected link structure data and field feature data to achieve unified standardization of time-related data, including receive timestamps and send timestamps, and eliminating time sequence conflicts caused by inverter communication interruptions and frame skipping; For path distance and the number of communication segments, combined with the actual network topology of the photovoltaic inverter deployment, compressing and mapping hop segment mutations and path redundancy records to restore the true transmission structure skeleton; before processing the number of forwarded data frames, buffer occupancy ratio, and the number of abnormal communication events... By introducing a link status sliding window monitoring mechanism, the difference of communication fluctuations within a local period is checked, and abnormal uplink rate segments during debugging are dynamically marked. Before hash calculation, the field value set is matched with the inverter protocol template to automatically perform field name unification, content deduplication, and null value filling. At the same time, in order to deal with data conflicts caused by concurrent uploads from multiple inverters in photovoltaic scenarios, redundant field merging rules are introduced in the preprocessing stage. Through the main channel priority reconstruction and high-frequency field priority retention strategy, the bandwidth occupation of highly repetitive fields is compressed. The link structure data and field feature data after preprocessing are normalized to build a standardized communication transmission dataset.

[0013] Furthermore, the specific steps for analyzing the transmission delay of communication nodes at each level in the link based on the standardized communication transmission dataset are as follows: divide the difference between the receiving timestamp and the sending timestamp of each node by the communication distance, multiply it by the ratio between the load change value of the path and the adjacent measurement time interval, and take the absolute value to obtain the response delay load value; calculate the response delay load value of all communication segments, and add the response delay load values ​​of the communication segments in the entire transmission path and divide by the number of communication segments to obtain the path delay evaluation value.

[0014] Furthermore, the specific steps for dynamically adjusting the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results are as follows: Real-time comparison of the current path delay assessment value with the path delay threshold: When the path delay assessment value is less than or equal to the path delay threshold, it is determined that the communication path from the inverter to the cloud platform is operating stably in the current cycle. The existing dual-mode uplink channel priority and data frame encapsulation parameters remain unchanged. The current field mapping structure and standard communication protocol format are continued to be used for data conversion and reporting. At the same time, the path delay distribution results of this cycle are recorded, and the link performance benchmark is updated. When the path delay assessment value is greater than the path delay threshold, it is immediately determined that there is communication path congestion and intermediate node response lag. The path switching logic is triggered. Based on the current link quality perception results, the data output channel is temporarily switched from cloud communication mode to grid acquisition mode. The data frame format is re-encapsulated to match the local Modbus interface. The bidirectional protocol conversion compression strategy is enabled simultaneously to reduce the field mapping length and timestamp density, reduce the instantaneous data load, and send back the abnormal jump point number and delay peak to the communication diagnostic module.

[0015] Furthermore, the specific steps for assessing the structural mismatch risk in the main channel and redundant channel based on the standardized communication transmission dataset are as follows: Calculate the difference vector norm between the field value sequences of the data frames received by the main channel and redundant channel; add the difference vector norm between the field value sequences to the difference vector norm of the field hash code sequences of the data frames in the main channel and redundant channel, and then divide by the sum of the field value sequence vector norms of the data frames received by the main channel and the redundant channel to obtain the structural loss ratio; take the logarithm of the structural loss ratio and multiply it by the corresponding redundant channel adjustment factor to obtain the redundancy compensation value; extract the original field data received by the main channel, subtract the redundancy compensation value from the original field data, and obtain the redundancy correction output value.

[0016] Furthermore, the specific steps of the dynamic path switching control and structural reorganization process based on the evaluation results are as follows: For fluctuations in the redundancy correction value, the hierarchical control strategy is adjusted in real time to achieve dynamic adaptation and anomaly defense of the link structure: When the redundancy correction value remains unchanged or continuously decreases compared to the previous period: the field compression upload strategy is activated, high-frequency fields are merged into structural segments, the real-time alignment process of the backup channel is suspended, and the redundant bandwidth usage is reduced; When the redundancy correction value fluctuates upwards for two consecutive periods: the field-level interpolation fusion mechanism is activated, the corresponding fields are extracted from the backup channel for bias repair, the field consistency verification process is activated, dual-path synchronization confirmation is forcibly enabled for marked fields, and the fields are temporarily switched to hash index priority upload to improve verification efficiency; When the redundancy correction value changes drastically within a short period, and anomalies such as segment sequence number disorder, field overlap, and hash mismatch occur: the field rearrangement parsing process is activated, the field and segment index mapping is reconstructed, the segment synchronization command is forcibly issued to the main channel inverter, the local data frame structure is refreshed, the channel is marked as structural reorganization state, and the main / backup channel switching and upload path buffering strategies are linked to prevent abnormal data from entering the database.

[0017] Further, the specific steps for comprehensively analyzing the current link transmission status using transmission delay analysis results and mismatch risk assessment results as input are as follows: A continuous response delay sequence is constructed by directly collecting the request sending time and response receiving time of the data frames sent by the inverter during communication. The continuous response delay sequence is dynamically smoothed using a sliding window weighted average algorithm. The stable delay center value within the sliding window is extracted and converted using the minimum delay offset compression rule to obtain the median delay offset value. The path delay evaluation value is obtained. The absolute value of the path delay evaluation value minus the median delay offset value is then incremented by one. The logarithm of the absolute value of the path delay evaluation value minus the median delay offset value plus one is then incremented by one to obtain the differential compression value. One is subtracted from the redundancy correction output value and then divided by the differential compression value to obtain the basic compression ratio. The redundancy correction output value is multiplied by the corresponding link reconfiguration excitation factor and then incremented by one to obtain the control gain value. The basic compression ratio is multiplied by the control gain value to obtain the link adaptive control value.

[0018] Furthermore, the specific steps for dynamically adjusting the rhythm distribution structure based on the analysis results are as follows: Real-time comparison of the current link adaptive control value with the path control threshold, where the path control threshold includes a first control threshold and a second control threshold; when the link adaptive control value is less than or equal to the second control threshold, maintaining the existing dual-mode communication channel priority, segmented encapsulation structure, and protocol conversion parameter configuration, and simultaneously enabling the micro-frame response tracking mechanism; periodically sampling and recording the encapsulation redundancy and acknowledgment response interval of each level of forwarding nodes; when the link adaptive control value is greater than the second control threshold and less than or equal to the first control threshold, immediately adjusting the main channel encapsulation structure, reconstructing the data frame assembly order according to field aggregation priority, and linking the compression controller between multiple levels of nodes. Inserting a field-level skip list simplifies redundant field forwarding paths, dynamically compresses time stamp precision, and reduces the impact of intermediate path layers on response timing. Simultaneously, it records the link latency increment distribution and redundant field jump frequency for the current period, constructing a cross-node encapsulation compression behavior trajectory. When the link adaptive control value exceeds the first control threshold, the cloud platform channel is retained as a response distribution channel, the collected link is converted to a local power grid compensation path, and the field hierarchy in the highly nested encapsulation structure is split and reorganized, stripping additional redundant fields and injecting synchronization flags. Simultaneously, the high-frequency uplink rhythm is divided into a two-level sub-rhythm structure, respectively adapting to latency-sensitive segments and content-redundant segments, initiating the link compression flow control recording program, and archiving all field forwarding layer numbers and path structure jump nodes.

[0019] The second aspect of this invention provides a high-efficiency photovoltaic inverter communication monitoring and conversion system, comprising: a multi-level encapsulation structure analysis module, which collects link structure data and field feature data, and preprocesses the collected link structure data and field feature data to construct a standardized communication transmission dataset; a communication path delay tracking module, which analyzes the transmission delay of each level of communication nodes in the link based on the standardized communication transmission dataset, and dynamically adjusts the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results; a transmission consistency verification and redundancy reassembly module, which assesses the structural mismatch risk in the primary and redundant channels based on the standardized communication transmission dataset, and dynamically triggers path switching control and structural reassembly processes based on the assessment results; and an end-to-end link collaborative control module, which comprehensively analyzes the current link transmission status using the transmission delay analysis results and mismatch risk assessment results as input, and dynamically adjusts the rhythm distribution structure based on the analysis results.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) The high-efficiency photovoltaic inverter communication monitoring and conversion method and system improves the structural integration efficiency and path fault tolerance in the data transmission process by constructing a redundancy mechanism for main and backup channels and a field compression strategy.

[0023] (2) The efficient photovoltaic inverter communication monitoring and conversion method and system introduces link response tracking and dynamic field mapping technology to achieve fine-grained monitoring and adjustment of encapsulation redundancy and response rhythm in the communication path.

[0024] (3) The high-efficiency photovoltaic inverter communication monitoring and conversion method and system enhances the system's adaptability to communication formats of multiple types of inverters and its cross-protocol interaction performance by integrating multi-channel protocol conversion and data frame structure reconstruction processes.

[0025] (4) This efficient photovoltaic inverter communication monitoring and conversion method and system enhances the integrity verification capability and structural anomaly identification accuracy of uploaded data frames by integrating field-level hash verification and structural consistency detection process.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] Figure 1 This is a flowchart of a high-efficiency photovoltaic inverter communication monitoring and conversion method according to the present invention;

[0028] Figure 2 This is a structural diagram of a high-efficiency photovoltaic inverter communication monitoring and conversion system according to the present invention;

[0029] Figure 3 This is a line graph of the link adaptive control value involved in this invention;

[0030] Figure 4 This is a segmented data channel scheduling diagram involved in the present invention. Detailed Implementation

[0031] 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, and 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.

[0032] Please see Figures 1-4This invention provides a technical solution: a high-efficiency photovoltaic inverter communication monitoring and conversion method and system, comprising: S1, collecting link structure data and field feature data, and preprocessing the collected link structure data and field feature data to construct a standardized communication transmission dataset; S2, based on the standardized communication transmission dataset, analyzing the transmission delay of each level of communication nodes in the link, and dynamically adjusting the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results; S3, based on the standardized communication transmission dataset, assessing the structural mismatch risk in the primary and redundant channels, and dynamically triggering path switching control and structural reorganization processes based on the assessment results; S4, using the transmission delay analysis results and mismatch risk assessment results as input, comprehensively analyzing the current link transmission status, and dynamically adjusting the rhythm distribution structure based on the analysis results.

[0033] Specifically, the steps for collecting link structure data and field feature data are as follows: Structured parsing of the encapsulation content at each level is performed; based on field boundary features, intra-frame nesting rules, and encapsulation redundancy indicators, the logical structure and physical forwarding path in the communication data are restored layer by layer to construct a complete encapsulation structure mapping chain; link structure data is collected, including: the number of communication segments, the receive timestamp and send timestamp of each node, the communication distance of each path segment, the number of data frames forwarded by each path segment in each period, the resource occupancy ratio of the path node's buffer queue, and the number of abnormal communication events. Simultaneously, the adjacent measurement time intervals within the current collection period are recorded to support the assessment of path timing continuity and flow control stability.

[0034] A principal component co-enhancement algorithm is employed to co-enhance the number of data frames forwarded, the resource occupancy ratio, and the number of abnormal communication events in each cycle of a path segment. This algorithm integrates the co-variable trends, compresses information dimensions, and improves the sensitivity of disturbance identification, constructing principal component disturbance values ​​based on these co-variable trends. Subsequently, by calculating the trajectory of the principal component disturbance values ​​in the current cycle, the algorithm captures the evolution trend of path segment state fluctuations, extracts the main driving load factor of the path segment, and expands the main driving load factor within a unit of time to construct a sequence of load change values ​​for the path segment. This sequence is then used for subsequent load transfer strategies and path control priority determination.

[0035] Collect field feature data, which includes: the original field data received by the main channel, the field value sequence of the data frames received by the main channel and the redundant channel, and the set of field values ​​extracted from each frame of data according to the field distribution, which constitute the basic input for field-level structure analysis.

[0036] Hash digest calculations are performed on the field value sets of each frame of data in the main channel and redundant channel. First, the field value sets are arranged into a structured sequence according to the field order to ensure sequential consistency and controllability of comparison. Then, an independent hash operation is performed on each field to extract the unique fingerprint identifier of the field. All hash results are concatenated in order to form the field hash code sequence of the corresponding data frame, which serves as the core basis for field layer consistency verification and synchronization offset detection.

[0037] This implementation scheme achieves full-process structured analysis and dynamic load assessment of photovoltaic inverter communication data, constructs a basic data framework for link behavior modeling and field consistency monitoring, and supports adaptive control strategies for subsequent communication path regulation, primary / backup channel switching, and field-level anomaly identification. By restoring multi-level encapsulated content through structured analysis, the communication paths and data distribution logic of each segment in the link are clarified, providing a structural basis for path status monitoring. Structured data including node timestamps, path lengths, and data frame loads are extracted, and key load disturbance factors are identified using a principal component analysis algorithm, enabling quantitative modeling of path segment load changes. A field feature mapping between the primary and redundant channels is constructed, and a hash digest mechanism is used to achieve compressed expression of field order consistency and value domain integrity, providing index support for subsequent field mismatch, redundancy jumps, and content difference correction. Using the primary drive load factor and field hash code sequence as input, a dual basis for link status identification and field consistency judgment is formed, supporting real-time calculation and linkage response of link adaptive control values ​​and redundancy correction output values.

[0038] Specifically, the preprocessing of the collected link structure data and field feature data to construct a standardized communication transmission dataset involves the following steps: First, the collected link structure data and field feature data undergo unified standardization, including both received and transmitted timestamps. The time formats recorded by different communication nodes are uniformly converted to a high-precision timestamp format to ensure time alignment accuracy across devices. Simultaneously, time sequence conflicts caused by inverter communication interruptions and frame skipping in the original data are scanned and eliminated to reconstruct the temporal continuity of the link data. Then, considering the path distance and the number of communication segments, and combining the actual network topology of the photovoltaic inverter deployment, a topology identification algorithm automatically identifies forwarding hops and duplicate path segments. Jump segment mutations and path redundancy records are compressed and mapped to restore the true segmented transmission structure skeleton, eliminating path distortion caused by dynamic node reallocation.

[0039] Before processing metrics such as the number of data frames forwarded within a path segment, buffer usage ratio, and number of abnormal communication events, a link status sliding window monitoring mechanism is introduced in the preprocessing flow. This mechanism tracks the changes in communication parameters in real time using a fixed-width sliding window, performs difference checks on communication fluctuation trends within local periods, and dynamically identifies abnormal segments where uplink rates experience sudden changes or lags during debugging, serving as the basis for subsequent control responses. Before entering the hash calculation process, the field value set undergoes field structure matching based on the inverter communication protocol template. This automatically standardizes field names, eliminates redundancy in nested field content, and fills in missing values ​​for missing fields, ensuring structural consistency and data integrity.

[0040] Meanwhile, to effectively address field conflicts and redundancy congestion caused by concurrent uploads from multiple inverters in photovoltaic scenarios, redundant field merging rules are further introduced in the preprocessing stage. Based on the main channel priority reconstruction mechanism and field access frequency sorting logic, highly repetitive fields are dynamically identified and merged / replaced, prioritizing the retention of core field content and compressing its bandwidth proportion in the data frame. Finally, the link structure data and field feature data processed through multiple rounds are uniformly normalized to construct a stable and comparable standardized communication transmission dataset, providing a unified data foundation for subsequent path control calculations, redundancy correction analysis, and channel switching strategies.

[0041] This implementation plan comprehensively improves the transmission efficiency and structural consistency of photovoltaic inverter communication data through in-depth preprocessing of link structure data and field feature data. When processing time-related data, standardized receiving and sending timestamps are used, and time sequence conflicts caused by communication interruptions and frame skipping are eliminated to ensure the continuity and reliability of time-series data. Regarding path structure processing, the transmission skeleton is accurately restored using path compression mapping based on the actual network topology of the photovoltaic inverter deployment, effectively avoiding structural misjudgments caused by segment redundancy. For load characteristics such as the number of forwarded data frames, buffer occupancy ratio, and the number of abnormal communication events, a link status sliding window mechanism is used to dynamically mark abnormal uplink rate segments, achieving high-precision perception of local communication fluctuations. In terms of field features, field names are unified, null values ​​are filled, and content redundancy is removed using the inverter protocol template, improving the consistency and accuracy of hash calculations. Simultaneously, redundant field merging rules are introduced, along with a main channel priority reconstruction and high-frequency field priority retention strategy, effectively alleviating the link conflict pressure caused by concurrent uploading from multiple inverters. Finally, a standardized communication transmission dataset is constructed through normalization processing, providing high-quality, structurally clear basic data support for subsequent path control analysis and redundancy verification mechanisms.

[0042] Specifically, based on a standardized communication transmission dataset, the analysis of transmission delay at each level of communication nodes in the link involves the following steps: dividing the difference between the receiving timestamp and the sending timestamp of each node by the communication distance, multiplying it by the ratio between the path load change value and the adjacent measurement time interval, and taking the absolute value to obtain the response delay load value; calculating the response delay load value of all communication segments, summing the response delay load values ​​of all communication segments in the entire transmission path, and then dividing by the number of communication segments to obtain the path delay evaluation value.

[0043] The formula for calculating the path delay assessment value is:

[0044]

[0045] In the formula, N represents the number of communication segments, which is used to identify the number of link hops that can be fully counted within this evaluation period. It is the basis for segment calculation and comes from the path node division information between edge devices and the cloud. This represents the receiving timestamp of the i-th node, used to record the actual time when the data frame arrives at the node. It is a key time reference point for calculating communication delay and is derived from the receiving event logs of each intermediate communication node. This represents the transmission timestamp of the i-th node, used to record the start time of data frame forwarding and response by this node. It is a key indicator for analyzing the source of delay and link congestion, and originates from the transmission event records at the node end; L i Represents the communication distance of the i-th path segment, used to characterize the communication hop distance between nodes, and is the basic unit for delay normalization calculation, derived from the network topology and routing path configuration table; A i Δt represents the load change value of the i-th path segment per unit time. It is used to measure the communication load change trend of the path segment per unit time and is a core indicator reflecting the stability of the link structure and the change of node scheduling pressure. It is derived from the main driving load factor extracted based on the principal component perturbation value trajectory analysis and the dynamic load expression sequence obtained by expansion calculation within the periodic sliding window. Δt represents the time interval between adjacent measurements and is used to normalize the time span of the load change rate. It is a basic dimension parameter for time domain calculation and is derived from the periodic sampling time window.

[0046] In this implementation plan, the coupling relationship between the latency difference and load impact of data transmission in a multi-node link is quantified. Key parameters such as the difference between the node's receiving time and sending time, communication distance, communication load per unit time and its rate of change are comprehensively considered to achieve a coordinated evaluation of the link's transmission timeliness and load balance, supporting the formulation of dynamic monitoring and optimization control strategies for the communication path status of photovoltaic inverters.

[0047] Specifically, the steps for dynamically adjusting the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results are as follows: Real-time comparison of the current path latency assessment value with the path latency threshold; while maintaining dynamic awareness of the path status; and combining the node response records and forwarding timing characteristics of the communication link in the previous cycle to achieve phased judgment and strategy linkage switching of link timeliness stability.

[0048] When the path delay assessment value is less than or equal to the path delay threshold, it is determined that the communication path from the inverter to the cloud platform is operating stably in the current cycle. The existing data path topology remains unchanged, the existing dual-mode uplink channel priority and data frame encapsulation parameters remain unchanged, and the current field mapping structure and DL / T698.45 protocol format are continued to be used for data conversion and reporting. At the same time, the forwarding time records, response interval distribution and buffer usage fluctuations of the involved path segments are archived and statistically analyzed to generate the path delay distribution results for this cycle, which are written into the control reference table as link performance benchmark update items.

[0049] When the path delay assessment value exceeds the path delay threshold, it is immediately determined that there is communication path congestion and intermediate node response lag, triggering path switching logic. Based on the current link quality perception results, the data output channel is temporarily switched from cloud communication mode to power grid acquisition mode, and the data frame format is repackaged to match the local Modbus interface protocol requirements. Simultaneously, a bidirectional protocol conversion compression strategy is enabled, dynamically reducing the mapping length and standardizing the field hash index structure during the field packaging stage. In time information processing, the timestamp density and redundant flag bits are compressed to reduce the buffering pressure of instantaneous data load on intermediate nodes. In addition, the abnormal jump point number, the delay peak position in the path sequence, and the node cache response value are sent back to the communication diagnostic module.

[0050] This implementation scheme enables periodic determination of the communication link's operating status and dynamic switching of path strategies. By comparing the path delay assessment value with the path delay threshold in real time, the stability level of the current communication path from the inverter to the cloud platform is identified. When the path is stable, the existing channel priority, field encapsulation structure, and protocol format remain unchanged to ensure communication efficiency and protocol consistency. When the path experiences abnormal fluctuations, the system quickly switches to the local acquisition channel based on the link quality perception results, and coordinates protocol conversion and data compression strategies to reduce load pressure and ensure data continuity. At the same time, abnormal path information is fed back to the diagnostic module to provide a reference for subsequent link structure correction and stability recovery.

[0051] Specifically, based on a standardized communication transmission dataset, the assessment of structural mismatch risk in the main channel and redundant channels involves the following steps: Calculate the norm of the difference vector between the field value sequences of the data frames received by the main channel and redundant channels; add the norm of the difference vector between the field value sequences to the norm of the difference vector between the field hash code sequences of the data frames in the main channel and redundant channels; then divide by the sum of the norms of the field value sequences of the data frames received by the main channel and the redundant channels to obtain the structural loss ratio; take the logarithm of the structural loss ratio and multiply it by the corresponding redundant channel adjustment factor to obtain the redundancy compensation value; extract the original field data received by the main channel and subtract the redundancy compensation value from the original field data to obtain the redundancy correction output value.

[0052] The formula for calculating the redundancy correction output value is:

[0053]

[0054] In the formula, B represents the original field data received by the main channel, which is used as the basic data frame content for reassembly reference and comes from the buffer of the uplink main link receiver; F1 represents the field value sequence of the main channel and the received data frame, and F2 represents the field value sequence of the data frame received by the redundant channel. The field value sequence is used for field-level numerical difference comparison and comes from the corresponding field set in the multi-path acquisition record; H1 represents the field hash code sequence of the data frame in the main channel, and H2 represents the field hash code sequence of the data frame in the redundant channel. The field hash code sequence is used for structural consistency verification and is a lightweight index value to assist in judging the integrity of the content. It comes from the real-time hash calculation of the field content; λ represents the redundant channel adjustment factor, which ranges from 0.5 to 2 and is used to adjust the influence weight of the structural consistency difference between the main channel and the redundant channel in the overall evaluation. It comes from the joint fluctuation level of the multi-dimensional indicators of field synchronization integrity rate, structural offset frequency and conflict field ratio exhibited by the redundant channel in the operation cycle. In the specific calculation, firstly, the synchronization markers and reconstruction power of each data frame field in the redundant channel within the continuous sampling window are extracted to establish a field-level synchronization matching sequence. Then, the temporal position offset of the field value, the redundancy of the redundant segment, and the frequency of field overlap are combined to construct the structural offset trend trajectory. Next, the above features are aligned with the structural stability features of the main channel within the same time window, and the field matching stability coefficient and structural mismatch compression ratio are calculated. Finally, based on the channel's load contribution rate and fluctuation recovery capability in the current task scenario, the above results are weighted and fused to output the redundant channel adjustment factor. When the redundant channel has poor stability, frequent mismatches, and high field redundancy interference, the redundant channel adjustment factor is increased to reduce its influence weight in the structural consistency calculation; conversely, when the redundant channel has high structural integrity and stable compensation behavior, the redundant channel adjustment factor is appropriately reduced to strengthen its balancing compensation effect on the fluctuation of the main channel.

[0055] In this implementation scheme, the redundancy correction output value is calculated to evaluate the consistency of the data frame content received by the main channel and the redundant channel at the structural level. By measuring the difference between the joint field value sequence and the field hash code sequence, the integrity degradation and conflict distribution of the data structure during transmission are dynamically reflected, providing key judgment basis for subsequent data field compensation correction, protocol conversion compression and channel weight adjustment.

[0056] Specifically, the process of dynamically triggering path switching control and structural reorganization based on the evaluation results involves the following steps: Adjusting the hierarchical control strategy in real time to address fluctuations in redundancy correction values, thereby achieving dynamic adaptation and anomaly defense of the link structure.

[0057] When the redundancy correction value remains unchanged or decreases continuously compared with the previous period, it is determined that the data content difference between the current main channel and the redundant channel is gradually converging and the field structure is stabilizing. The field compression upload strategy is enabled, and the frequently occurring repeated fields are merged into segment block format according to the structural position. The field mapping density is optimized, and the real-time synchronization alignment process of the backup channel is suspended to reduce the redundant bandwidth resource occupation in the multi-path concurrency process and alleviate the instantaneous transmission pressure caused by channel conflicts.

[0058] When the redundancy correction value fluctuates and rises for two consecutive periods, and the field matching relationship drifts slightly, the field-level interpolation fusion mechanism is activated. The field values ​​corresponding to the main channel are extracted from the backup channel for bias repair. At the same time, the field consistency verification process is activated. The dual-path synchronous confirmation strategy is forced to be enabled for the content marked as deviation fields, and the fields are temporarily switched to the hash index priority upload channel to ensure that the field hash digest matching efficiency is prioritized, thereby improving the overall accuracy of data repair and processing response speed.

[0059] When the redundancy correction value changes drastically within a short period and structural anomalies such as segment number disorder, field overlap, and hash mismatch are detected, the field rearrangement and parsing process is immediately initiated. This process reconstructs the mapping relationship between fields and segment indexes in the main channel data and forces a segment synchronization command to be sent to the main channel inverter to refresh the data frame structure layout in its local cache. At the same time, the current communication channel is marked as being in a structural reconstruction state, and the switching process between the main and backup channels and the data upload path buffering strategy are coordinated to prevent data errors from being entered into the database and communication feedback anomalies during the period of structural disorder, thus ensuring the data stability and redundancy fault tolerance of the entire link.

[0060] In this implementation plan, data upload and channel control strategies are dynamically adjusted based on the fluctuation trend of redundancy correction values ​​to achieve adaptive regulation of communication link structure stability and ensure data consistency under abnormal scenarios. By identifying the stable, gradually increasing, and drastic states of redundancy correction values, field compression upload, interpolation fusion repair, and structure reconstruction mechanisms are triggered respectively, thereby dynamically switching field mapping methods, channel priorities, and upload path selection, effectively improving data integration efficiency, link transmission resilience, and structural fault tolerance in multi-channel high-frequency transmission scenarios.

[0061] Specifically, using transmission delay analysis results and mismatch risk assessment results as input, the comprehensive analysis of the current link transmission status involves the following steps: A continuous response delay sequence is constructed by directly collecting the request sending time and response receiving time of the data frames sent by the inverter during communication. The continuous response delay sequence is dynamically smoothed using a sliding window weighted average algorithm. The stable delay center value within the sliding window is extracted and converted using the minimum delay offset compression rule to obtain the median delay offset value. The path delay evaluation value is obtained. The absolute value of the path delay evaluation value minus the median delay offset value is then incremented by one. The logarithm of the absolute value of the path delay evaluation value minus the median delay offset value plus one is then incremented by one to obtain the differential compression value. One is subtracted from the redundancy correction output value and then divided by the differential compression value to obtain the base compression rate. The redundancy correction output value is multiplied by the corresponding link reconfiguration excitation factor and then incremented by one to obtain the control gain value. The base compression rate is multiplied by the control gain value to obtain the link adaptive control value.

[0062] The formula for calculating the link adaptive control value is:

[0063]

[0064] In the formula, R represents the redundancy correction output value, which measures the degree of difference between primary and backup channel data and is a core indicator reflecting the fluctuation trend of field consistency; D represents the path delay assessment value, which measures the communication delay status of the current end-to-end path and is a basic variable for identifying channel response timeliness; θ represents the median delay offset value, which is directly collected by the request sending time and response receiving time of the data frame sent by the inverter during the communication process to construct a continuous response delay sequence; then, the continuous response delay sequence is dynamically smoothed by the sliding window weighted average algorithm to extract the stable delay center value within the window, and combined with the minimum delay offset compression rule to obtain the median delay offset value; η represents the link reconfiguration excitation factor, which ranges from 0.5 to 2.0 and is used to regulate the response intensity of primary and backup channel weight adjustment and dynamic channel structure reconfiguration under the background of abnormal path delay. It is derived from the comprehensive deviation of the link performance fluctuation amplitude, path hop frequency and field consistency correction value within the current sampling period. In the specific calculation, firstly, the path delay change rate, field mapping mismatch count, and path hop interval sequence in the current communication channel are extracted, and their severity and duration are evaluated based on a sliding window mechanism. Then, a link fluctuation benchmark model is constructed by combining the stable statistical characteristics of various link indicators under historical steady-state cycles. Next, the current indicator offset value is fitted with the benchmark model using polynomial difference to calculate its change amplitude and superposition trend. Finally, based on the mapping function, the hop frequency factor, field mismatch penalty factor, and path disturbance equilibrium factor are aggregated to form the link reconfiguration incentive factor. When link anomalies continue to worsen and the critical path channel mapping failure rate increases, the link reconfiguration incentive factor increases accordingly to enhance the driving force of link reconfiguration and channel switching. Conversely, when the link state tends to stabilize and path indicators recover to a stable range, the link reconfiguration incentive factor value automatically decreases to suppress the resource overhead caused by excessive reconfiguration, thereby achieving robust adaptation of the link structure under complex fluctuation scenarios and incentive adjustment of recovery strategies.

[0065] In this implementation example, the redundancy correction output value of Example 1 is set to 0.523, the path delay evaluation value is 0.684, the median delay offset value is 0.071, the link reconfiguration excitation factor is 0.906, and the link adaptive control value is 0.476.

[0066] In Example 2, the redundancy correction output value is set to 0.719, the path delay evaluation value is 0.623, the median delay offset value is 0.225, and the link reconfiguration excitation factor is 1.524.

[0067] In Example 3, the redundancy correction output value is set to 0.591, the path delay evaluation value is 0.919, the median delay offset value is 0.175, and the link reconfiguration excitation factor is 0.989.

[0068] In Example 4, the redundancy correction output value is set to 0.688, the path delay evaluation value is 0.761, the median delay offset value is 0.107, and the link reconfiguration excitation factor is 1.876.

[0069] In Example 5, the redundancy correction output value is set to 0.436, the path delay evaluation value is 0.937, the median delay offset value is 0.254, and the link reconfiguration excitation factor is 0.745.

[0070] In Example 6, the redundancy correction output value is set to 0.803, the path delay evaluation value is 1.368, the median delay offset value is 0.251, and the link reconfiguration excitation factor is 1.345.

[0071] In Example 7, the redundancy correction output value is set to 0.270, the path delay evaluation value is 0.894, the median delay offset is 0.112, and the link reconfiguration excitation factor is 1.962. The link adaptive control values ​​for each example are calculated as shown in Table 1.

[0072] Table 1 Link Adaptive Control Value Data Table

[0073]

[0074] like Figure 3 As shown in Table 1, the link adaptive control value provided in this application example is a line graph. Figure 3 As can be seen, Instance 7 has the highest link adaptive control value, reaching 0.663, indicating that it exhibits strong dynamic adaptation characteristics in terms of redundancy correction output, path delay control, and link excitation response. It possesses even higher adaptive adjustment capabilities, especially under structurally unstable conditions, making it suitable for prioritizing primary / backup path adjustment and segment-level compression strategies to achieve anomaly avoidance and ensure smooth data flow. Conversely, Instance 6 has the lowest link adaptive control value, at only 0.386. Although its redundancy correction output value is relatively high, its overall link adjustment potential is limited by the coupling effect of path delay offset and excitation factor fluctuations. It is suitable to retain it as a buffer path during stable transmission phases, reducing the frequency of high-frequency control interventions. The link adaptive control value line graph clearly shows the distribution of structural adjustment potential for different instances within the current communication cycle, helping to identify key control channels and adaptive bottleneck nodes under high load conditions, providing a foundation for subsequent link structure reconstruction and hierarchical control strategies.

[0075] Specifically, the steps for dynamically adjusting the rhythm distribution structure based on the analysis results are as follows: Real-time comparison of the current link adaptive control value with the path control threshold. The path control threshold includes a first control threshold and a second control threshold, used to classify the link's operating state and trigger corresponding control strategies.

[0076] When the link adaptive control value is less than or equal to the second control threshold, it is determined to be a smooth link phase. The existing dual-mode communication channel priority, segmented encapsulation structure and protocol conversion parameter configuration remain unchanged to avoid interference with the stable link structure. Simultaneously, the micro-frame response tracking mechanism is enabled to periodically collect the encapsulation redundancy, acknowledgment response interval and data frame response rate of each level of forwarding nodes, and extract the stable feature interval within the continuous period.

[0077] When the link adaptive control value is greater than the second control threshold and less than or equal to the first control threshold, it is determined to be a link load fluctuation stage. The main channel encapsulation structure is immediately adjusted, and the data frame assembly order is reconstructed according to the field aggregation priority to shorten the high-frequency field path forwarding chain. The linkage compression controller inserts a field-level skip list between multi-level nodes to compress and map the inter-field relationship, further simplifying the relay process of redundant fields. The time stamp accuracy is compressed synchronously to reduce the proportion of latency feature fields occupied in the intermediate layer. The distribution of link latency increment and the frequency of redundant field jumps within the period are recorded, and the location of frequently occurring abnormal jump segments is marked. Finally, a cross-node encapsulation compression behavior trajectory is constructed to help determine the load evolution mode of the current encapsulation structure.

[0078] When the link adaptive control value exceeds the first control threshold, it is determined to be a link structure instability stage. The dual-channel decoupling mechanism is immediately activated, reserving the channel as a response distribution channel, converting the acquisition link into a local power grid compensation path, and constructing a lightweight data path deviating from the backbone network. The segmented data channel scheduling diagram is regenerated, and the field hierarchy in the current highly nested encapsulation structure is split and reorganized, removing additional redundant fields and injecting synchronization flag bits to ensure the integrity of the transmission structure. At the same time, the current high-frequency uplink rhythm is divided into a two-level sub-rhythm structure to adapt to the rhythm distribution characteristics of the delay-sensitive segment and the content-redundant segment, respectively. The link compression flow control recording program is activated to archive the forwarding layer number, response time curve, and path structure jump nodes of all fields in this stage, providing traceability and evolution samples for link structure reconstruction and strategy optimization.

[0079] like Figure 3The diagram shown illustrates the segmented data channel scheduling provided in this application, describing the complete scheduling process from the acquisition terminal to the data distribution channel. The specific process is as follows: First, data is sent from the acquisition terminal, encapsulated through a highly nested structure, and then enters scheduling node 1 for initial parsing. Subsequently, the data undergoes a field stripping step, decomposing the nested fields, and enters scheduling node 2 for reconstruction and structural organization. After reassembly, a synchronization marker is inserted as the anchor point for subsequent channel selection. Next, the data enters the path distributor, dynamically distributed to different downstream channels based on the synchronization marker information: one is the cloud platform channel, undertaking uplink response tasks; the other is the local compensation channel, used to supplement critical data and ensure integrity and stability. The segmented data channel scheduling diagram clearly demonstrates the collaborative distribution mechanism and scheduling logic of complex encapsulated data between primary and backup channels.

[0080] In this implementation scheme, based on the real-time evaluation results of the link adaptive control value, the stability stage of the current communication path is dynamically determined, and the corresponding link control mechanism is triggered accordingly to achieve precise adjustment of the communication channel structure, encapsulation strategy, and forwarding rhythm. By setting dual threshold demarcation points, three stages—link smoothness, fluctuation, and instability—are clearly defined, effectively controlling the processing logic under different states: maintaining the original structural stability in the link smoothness stage, performing structural compression and rhythm reconstruction in the link fluctuation stage, and executing channel decoupling and path reconstruction in the link instability stage. This step significantly improves the link adaptability, control accuracy, and data transmission robustness of the photovoltaic inverter in complex communication environments.

[0081] A second aspect of the present invention provides a high-efficiency photovoltaic inverter communication monitoring and conversion system, comprising:

[0082] The multi-level encapsulation structure parsing module collects link structure data and field feature data. The link structure data includes the number of encapsulation layers between communication nodes, the number of path hops, and the node response latency. The field feature data includes field hash digest values, field sequence positions, and redundancy mapping numbers. The module also preprocesses the collected data, performing operations such as unified field naming, null value filling, and time tag alignment, to construct a standardized communication transmission dataset that includes path structure, field mapping, and transmission order.

[0083] The communication path delay tracking module, based on a standardized communication transmission dataset, deconstructs the transmission delay of each level of communication nodes in the link step by step, identifies the location of stable and transitional segments, and dynamically adjusts the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results. This includes adjusting the field encapsulation granularity, compressing the time precision of delay-dense fields, increasing the relay priority of nodes with delayed response, and constructing a dynamic forwarding structure that adapts to different path load states.

[0084] The transmission consistency verification and redundancy reassembly module, based on a standardized communication transmission dataset, performs multi-source field comparison assessment of structural mismatch risks in the main channel and redundant channels, including field hash matching offset, segment-level structure jump frequency and field overlap redundancy, identifies potential data structure misalignment problems, and dynamically triggers path switching control and structural reassembly processes based on the assessment results.

[0085] The end-to-end link collaborative control module takes the transmission delay analysis results and mismatch risk assessment results as inputs to conduct a comprehensive analysis of the current link transmission status. It combines the path compression ratio, rhythm mismatch degree and node interference index to construct a link status score, and dynamically adjusts the rhythm distribution structure based on the score results to divide the data rhythm levels of the main channel and redundant channels.

[0086] In this implementation plan, the multi-level encapsulation structure parsing module performs structured extraction and standardization processing on the structured transmission data and field hierarchy information collected in the link, ensuring that the field mapping relationship is clear and the node hop count and encapsulation depth are traceable in the subsequent analysis process, providing a unified data foundation for path delay modeling and consistency verification;

[0087] The communication path delay tracking module identifies the transmission delay distribution between nodes in the link, locates high-latency nodes and sudden transition sections, and dynamically adjusts the data encapsulation structure and the scheduling strategy of the primary and backup channels accordingly, thereby reducing the interference of path lag on the overall communication rhythm.

[0088] The transmission consistency verification and redundancy reassembly module evaluates the consistency status of the main channel and the redundant channel in terms of field structure, order and content, detects abnormal structures such as field misalignment, segment disorder and redundancy overlap, and triggers the structure reassembly logic after identifying the deviation to realize the rapid reconstruction and data alignment of abnormal path structures.

[0089] The end-to-end link coordination and control module comprehensively analyzes the path delay status and structural mismatch risk, and dynamically adjusts the data distribution rhythm and channel division strategy according to the indicators of transmission stability and node rhythm consistency, so as to realize cross-channel coordination and link adaptive adjustment, and ensure the continuity and steady-state transmission capability of the link in complex communication scenarios.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0091] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A high efficiency photovoltaic inverter communication monitoring conversion method, characterized by: include: S1. Collect link structure data and field feature data, and preprocess the collected link structure data and field feature data to construct a standardized communication transmission dataset; S2, based on a standardized communication transmission dataset, analyzes the transmission delay of communication nodes at all levels in the link, and dynamically adjusts the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results; S3, based on a standardized communication transmission dataset, assesses the structural mismatch risk in the main channel and redundant channels, and dynamically triggers path switching control and structural reorganization processes based on the assessment results. S4 takes the transmission delay analysis results and mismatch risk assessment results as input, performs a comprehensive analysis of the current link transmission status, and dynamically adjusts the rhythm distribution structure based on the analysis results.

2. The method of claim 1, wherein the method further comprises: The specific steps for collecting the link structure data and field feature data are as follows: The encapsulation content at each level is structured and parsed to collect link structure data. At the same time, the adjacent measurement time intervals within the current collection period are recorded. The link structure data includes: the number of communication segments, the receive timestamp and send timestamp of each node, the communication distance of each path segment, the number of data frames forwarded by each path segment in each period, the resource occupancy ratio of the path node buffer queue, and the number of abnormal communication events. The principal component collaborative enhancement algorithm is used to collaboratively enhance the number of data frames forwarded, the resource occupancy ratio, and the number of abnormal communication events of the path segment in each period. The principal component perturbation value based on the collaborative change trend is constructed. By calculating the change trajectory of the principal component perturbation value in the current period, the main driving load factor of the path segment is extracted, and the main driving load factor is expanded in a unit time to obtain the load change value of the path segment. Collect field feature data, which includes: the raw field data received by the main channel, the sequence of field values ​​of data frames received by the main channel and the redundant channel, and the set of field values ​​for each frame of data; A hash digest is calculated for the set of field values ​​of each frame of data in the main channel and the redundant channel. The set of field values ​​is then arranged into a structured sequence according to the field order. Subsequently, an independent hash operation is performed on each field, and the results are concatenated in order to form the field hash code sequence of the corresponding data frame.

3. The method of claim 2, wherein the method further comprises: The specific steps for preprocessing the collected link structure data and field feature data to construct a standardized communication transmission dataset are as follows: The collected link structure data and field feature data are preprocessed to standardize time-related data, including receiving timestamps and sending timestamps, and to eliminate time sequence conflicts caused by inverter communication interruptions and frame skipping. Based on the path distance and the number of communication segments, and combined with the actual network topology of the photovoltaic inverter deployment, the jump segment mutations and path redundancy records are compressed and mapped to restore the true transmission structure skeleton. Before processing the number of forwarded data frames, cache occupancy ratio, and number of abnormal communication events, a link status sliding window monitoring mechanism is introduced to perform differential verification on communication fluctuations within a local period and dynamically mark abnormal uplink rate segments during debugging; before hash calculation, the field value set is matched with the inverter protocol template to automatically perform field name unification, content deduplication, and null value filling. Meanwhile, to address data conflicts caused by concurrent uploads from multiple inverters in photovoltaic scenarios, redundant field merging rules are introduced in the preprocessing stage. By prioritizing the reconstruction of the main channel and retaining high-frequency fields, the bandwidth occupied by highly repetitive fields is compressed. Furthermore, the link structure data and field feature data after preprocessing are normalized to construct a standardized communication transmission dataset.

4. The method of claim 3, wherein the method further comprises: The specific steps for analyzing the transmission delay of communication nodes at each level in the link based on the standardized communication transmission dataset are as follows: Divide the difference between the receive timestamp and send timestamp of each node by the communication distance, multiply it by the ratio of the load change value of the path and the adjacent measurement time interval, and take the absolute value to obtain the response delay load value. Calculate the response delay load value of all communication segments, add up the response delay load values ​​of all communication segments in the entire transmission path, and then divide by the number of communication segments to obtain the path delay evaluation value.

5. The method of claim 4, wherein the method further comprises: The specific steps for dynamically adjusting the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results are as follows: Real-time comparison of the current path latency assessment value with the path latency threshold: When the path delay assessment value is less than or equal to the path delay threshold, it is determined that the communication path from the inverter to the cloud platform is operating stably in the current cycle. The existing dual-mode uplink channel priority and data frame encapsulation parameters remain unchanged. The current field mapping structure and standard communication protocol format are continued to be used for data conversion and reporting. At the same time, the path delay distribution results for this cycle are recorded, and the link performance benchmark is updated. When the path delay assessment value exceeds the path delay threshold, it is immediately determined that there is communication path congestion and intermediate node response lag, triggering path switching logic. Based on the current link quality perception result, the data output channel is temporarily switched from cloud communication mode to power grid acquisition mode, and the data frame format is re-encapsulated to match the local Modbus interface. At the same time, a bidirectional protocol conversion compression strategy is enabled to reduce field mapping length and timestamp density, reduce instantaneous data load, and send back the abnormal jump point number and delay peak to the communication diagnostic module.

6. The method of claim 2, wherein the method further comprises: The specific steps for assessing the structural mismatch risk in the main channel and redundant channels based on the standardized communication transmission dataset are as follows: Calculate the difference vector norm between the field value sequences of the data frames received by the main channel and the redundant channel. Add the difference vector norm between the field value sequences to the difference vector norm of the field hash code sequences of the data frames in the main channel and the redundant channel. Then divide by the sum of the difference vector norm of the field value sequences of the data frames received by the main channel and the redundant channel to obtain the structural loss ratio. The redundancy compensation value is obtained by multiplying the logarithm of the structural loss ratio by the corresponding redundancy channel adjustment factor. Extract the raw field data received from the main channel, subtract the redundancy compensation value from the raw field data, and obtain the redundancy correction output value.

7. The method of claim 6, wherein the method further comprises: The specific steps of the process for dynamically triggering path switching control and structural reorganization based on the evaluation results are as follows: To address fluctuations in redundancy correction values, the hierarchical control strategy is adjusted in real time to achieve dynamic adaptation of the link structure and anomaly defense. When the redundancy correction value remains unchanged or decreases continuously compared to the previous period: enable the field compression upload strategy, merge high-frequency fields into structural blocks, suspend the real-time alignment process of the backup channel, and reduce the redundancy bandwidth usage. When the redundancy correction value fluctuates and rises for two consecutive cycles: the field-level interpolation fusion mechanism is activated, the corresponding field is extracted from the backup channel for bias repair, the field consistency verification process is activated, dual-path synchronous confirmation is forced to be enabled for the marked field, and the field is temporarily switched to hash index for priority upload to improve verification efficiency. When the redundancy correction value changes drastically within a short period of time, and anomalies such as segment sequence number disorder, field overlap, and hash mismatch occur: the field rearrangement parsing process is initiated, the field and segment index mapping is reconstructed, the segment synchronization command is forcibly issued to the main channel inverter, the local data frame structure is refreshed, the channel is marked as a structure reconstruction state, and the main and backup channel switching and upload path buffering strategies are linked to prevent abnormal data from entering the database.

8. The method of claim 6, wherein the method further comprises: The specific steps for comprehensively analyzing the current link transmission status using transmission delay analysis results and mismatch risk assessment results as input are as follows: A continuous response delay sequence is constructed by directly collecting the request sending time and response receiving time of the data frames sent by the inverter during the communication process. The continuous response delay sequence is dynamically smoothed by the sliding window weighted average algorithm. The stable delay center value within the sliding window is extracted and combined with the minimum delay offset compression rule to obtain the median delay offset value. Obtain the path delay assessment value, subtract the median delay offset from the path delay assessment value, take the absolute value and add one, take the logarithm of the absolute value of the path delay assessment value minus the median delay offset and add one, and obtain the difference compression value. Subtract the redundant correction output value from 1 and then divide by the difference compression value to obtain the base compression ratio. Multiply the redundancy correction output value by the corresponding link reconstruction excitation factor and then add one to obtain the control gain value; Multiplying the base compression rate by the control gain value yields the link adaptive control value.

9. The method of claim 8, wherein the method further comprises: The specific steps for dynamically adjusting the rhythm distribution structure based on the analysis results are as follows: Real-time comparison of the current link adaptive control value with the path control threshold, which includes a first control threshold and a second control threshold: When the link adaptive control value is less than or equal to the second control threshold, the existing dual-mode communication channel priority, segmented encapsulation structure and protocol conversion parameter configuration are maintained, and the micro-frame response tracking mechanism is enabled simultaneously: the encapsulation redundancy and acknowledgment response interval of each level of forwarding node are periodically sampled and recorded. When the link adaptive control value is greater than the second control threshold and less than or equal to the first control threshold, the main channel encapsulation structure is immediately adjusted, the data frame assembly order is reconstructed according to the field aggregation priority, and the linkage compression controller inserts field hierarchical skip lists between multi-level nodes to simplify redundant field forwarding paths, dynamically compress time stamp accuracy, and reduce the intensity of the path intermediate layer on response timing; at the same time, the link delay increment distribution and redundant field skip frequency in this period are recorded to construct the cross-node encapsulation compression behavior trajectory; When the link adaptive control value is greater than the first control threshold, the cloud platform channel is reserved as the response distribution channel, the acquisition link is converted to the local compensation path of the power grid, the field hierarchy in the highly nested encapsulation structure is split and reorganized, additional redundant fields are stripped and a synchronization flag bit is injected; at the same time, the high-frequency uplink rhythm is divided into a two-level sub-rhythm structure to adapt to the delay-sensitive segment and the content-redundant segment respectively, the link compression flow control recording program is started, and all field forwarding layer numbers and path structure jump nodes are archived.

10. A high-efficiency photovoltaic inverter communication monitoring and conversion system, characterized in that, include: The multi-level encapsulation structure parsing module collects link structure data and field feature data, and preprocesses the collected link structure data and field feature data to construct a standardized communication transmission dataset. The communication path delay tracking module analyzes the transmission delay of communication nodes at all levels in the link based on a standardized communication transmission dataset, and dynamically adjusts the data encapsulation structure and primary / backup channel forwarding strategy based on the analysis results. The transmission consistency verification and redundancy reassembly module assesses the structural mismatch risk in the main channel and redundant channels based on a standardized communication transmission dataset, and dynamically triggers path switching control and structural reassembly processes based on the assessment results. The end-to-end link coordination and control module takes the transmission delay analysis results and mismatch risk assessment results as input, performs a comprehensive analysis of the current link transmission status, and dynamically adjusts the rhythm distribution structure based on the analysis results.

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