A photovoltaic multi-source sensing data analysis adaptation, intelligent decision-making and dynamic adaptation implementation method

Abstract

This invention belongs to the field of information technology, specifically relating to a method for implementing photovoltaic (PV) multi-source sensing data parsing and adaptation, intelligent decision-making, and dynamic adaptation. The invention constructs three core modules: PV multi-source sensing data parsing and adaptation, intelligent decision-making for PV energy dispatch, and dynamic adaptation to PV management scenarios, covering the entire process of PV energy management from data acquisition and parsing, intelligent dispatch decision-making to dynamic scenario adaptation. Employing core algorithms such as spatiotemporal alignment and deviation correction of PV multi-source sensing data, hierarchical dispatching and dynamic load matching of PV energy, and multi-dimensional reasoning and strategy iteration for PV management scenarios, it overcomes the bottlenecks of low data parsing accuracy, insufficient intelligence in dispatch decision-making, and weak scenario adaptability in traditional PV energy management. This achieves high-precision parsing of PV multi-source data, intelligent and efficient decision-making in energy dispatch, and dynamic and accurate adaptation to management scenarios, significantly improving PV energy utilization efficiency, power plant operation and maintenance management level, and the scientific nature and flexibility of energy dispatch.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a method for photovoltaic multi-source sensing data parsing and adaptation, intelligent decision-making, and dynamic adaptation. Background Technology

[0002] In the current field of large-scale automation models for photovoltaic energy management, existing technologies have initially solved the fundamental problems of photovoltaic sensing data aggregation and basic energy dispatch execution. However, in actual photovoltaic energy management engineering applications, there are still specific and unresolved practical problems. These are all scenario-specific issues, not macro-level challenges, as follows:

[0003] 1. Low parsing accuracy of photovoltaic multi-source sensing data, lacking precise spatiotemporal alignment and error compensation algorithms: Photovoltaic energy management data comes from a wide range of sources (photovoltaic panel output, inverter operation, combiner box monitoring data of photovoltaic power plants, irradiance, temperature, and wind speed data from meteorological stations, and load, voltage, and frequency data from the power grid), exhibiting significant spatiotemporal heterogeneity (temporal heterogeneity: different acquisition frequencies and timestamps are not synchronized; spatial heterogeneity: different monitoring points and coordinate systems are inconsistent; precision heterogeneity: second-level monitoring data and minute-level statistical data). Existing automated large-scale models lack targeted precise spatiotemporal alignment and error compensation algorithms for heterogeneous data, and only adopt a coarse parsing mode of data splicing and simple deduplication. This cannot solve the spatiotemporal deviation and monitoring error problems of data from different sources, different spatiotemporal benchmarks, and different precisions, resulting in problems such as spatiotemporal misalignment, attribute conflicts, and data distortion after parsing. The parsing accuracy cannot meet the refined management needs of photovoltaic power plants such as real-time power generation scheduling and precise fault location, and the consistency and reliability of the parsed data are extremely poor.

[0004] 2. Insufficient intelligence in photovoltaic energy dispatch, lacking hierarchical dispatch and dynamic load matching algorithms: Photovoltaic energy dispatch involves cross-domain coordination among power plants, the grid, and energy storage. It requires dynamic dispatch based on photovoltaic output, weather changes, and grid load. However, existing large-scale automated models lack intelligent and efficient dispatch decision-making mechanisms and algorithm support, adopting only a fixed threshold-static dispatch mode without hierarchical energy dispatch, dynamic load matching, and decision tracing algorithms. On the one hand, the static dispatch mode cannot adapt to the randomness of photovoltaic output, the uncertainty of weather changes, and the dynamics of grid load, resulting in low energy utilization efficiency and poor grid interaction stability. On the other hand, the lack of dispatch decision tracing algorithms makes it impossible to trace the cause after dispatch anomalies and fails to achieve the intelligent dispatch goal of coordinated power generation, grid, load, and energy storage.

[0005] 3. Weak adaptability to photovoltaic management scenarios, lacking scenario-based reasoning and dynamic optimization algorithms: Photovoltaic energy management encompasses various sub-scenarios (real-time power plant generation scheduling, equipment operation and maintenance monitoring, energy storage charging and discharging management, and grid peak-valley interaction). Different scenarios have significantly different requirements for data accuracy, scheduling response speed, and decision dimensions. Existing automated large-scale models lack dynamic reasoning algorithms that integrate and analyze data with management scenarios, adopting a single decision model—a one-size-fits-all scheduling approach. This approach fails to optimize the accuracy and response speed of the decision model based on scenario requirements and does not establish a dynamic mapping relationship between integrated data and scenario scheduling rules. Furthermore, the lack of scenario-based dynamic update algorithms for scheduling strategies means that scheduling strategy updates lag behind changes in scenario operation. This results in scheduling decisions failing to accurately support the management needs of different sub-scenarios, leading to a disconnect between decisions and scenarios, and failing to meet the needs of refined, scenario-based, and intelligent management of photovoltaic energy.

[0006] Existing methods for automated large-scale models of photovoltaic (PV) energy management lack core algorithmic innovation in areas such as multi-source PV sensing data parsing and adaptation, intelligent decision-making for PV energy dispatch, and dynamic adaptation to PV management scenarios. Significant gaps exist, particularly in modeling and solving for spatiotemporal alignment of multi-source heterogeneous data, hierarchical scheduling of PV energy, and scenario-based reasoning based on fused parsed data, failing to address the aforementioned specific problems. There is an urgent need for an automated large-scale model implementation method for PV energy management, centered on algorithmic innovation, focusing on three entirely new perspectives: data parsing, intelligent scheduling, and scenario adaptation. This method would achieve high-precision, high-efficiency, and accurate upgrades to the automated large-scale PV energy management model, filling existing technological gaps. Summary of the Invention

[0007] This paper presents a method for photovoltaic (PV) multi-source sensing data parsing and adaptation, intelligent decision-making, and dynamic adaptation. It enables accurate parsing and adaptation of PV multi-source sensing data, intelligent and controllable decision-making for PV energy dispatch, and dynamic and accurate adaptation to PV management scenarios. This method addresses the problems of low parsing accuracy of multi-source heterogeneous data, insufficient intelligence in energy dispatch, and weak adaptability of fused data to management scenarios. The entire process emphasizes algorithm innovation and modeling and solving without involving rules of intellectual activities. This improves PV energy utilization efficiency, power plant operation and maintenance management level, and the scientific nature of energy dispatch, further perfecting the automated large-scale model technology system for PV energy management.

[0008] The present invention is implemented through the following specific technical solution:

[0009] (I) Photovoltaic Multi-Source Sensing Data Parsing and Adaptation Module

[0010] First, a multi-source data acquisition component is deployed to aggregate heterogeneous sensing data (PV panel output, irradiance, grid load, etc.) from PV power plants, meteorological systems, and the power grid, constructing a comprehensive PV sensing data resource pool. Then, a spatiotemporal feature extraction algorithm for multi-source heterogeneous data is designed to extract core spatiotemporal feature parameters for different data types (time features: acquisition timestamp, acquisition frequency; spatial features: monitoring point coordinates, equipment number; attribute features: output value, irradiance value, load value). Based on spatiotemporal coordinate transformation and time synchronization theory, a unified spatiotemporal benchmark model is constructed, and a unified spatiotemporal benchmark algorithm is designed to convert data from different spatiotemporal benchmarks (different acquisition timestamps, different monitoring coordinate systems) into a unified PV energy management standard spatiotemporal benchmark. To eliminate biases caused by heterogeneous spatiotemporal benchmarks, a precise spatiotemporal alignment algorithm is designed. This algorithm employs a strategy of spatiotemporal feature point matching, spatiotemporal similarity calculation, and iterative correction. It matches spatiotemporal feature points from different data sources, calculates the comprehensive spatiotemporal similarity using a spatiotemporal similarity calculation formula, and establishes spatiotemporal similarity thresholds (set according to management scenario requirements: 0.95 for real-time power generation scheduling, 0.85 for routine operation and maintenance monitoring, and 0.75 for energy storage planning). The iterative correction algorithm gradually improves the spatiotemporal similarity to ensure the consistency of the aligned data. A unified verification model for spatiotemporal alignment and benchmarks is constructed to quantify data parsing accuracy and spatiotemporal conversion errors, dynamically optimize algorithm parameters, and ensure that the parsing accuracy meets the needs of different photovoltaic energy management scenarios.

[0011] 1: Spatiotemporal alignment and deviation correction algorithm for photovoltaic multi-source sensing data

[0012] The core spatiotemporal features of multi-source heterogeneous photovoltaic sensing data are extracted to establish a spatiotemporal feature point set. Based on the spatiotemporal similarity calculation formula, the comprehensive spatiotemporal similarity of spatiotemporal feature points from different sources is calculated to determine whether a set threshold has been reached. For spatiotemporal feature points that have not reached the threshold, iterative adjustments are made through timestamp synchronization calibration, spatial coordinate correction, and other methods until the comprehensive spatiotemporal similarity meets the requirements of the scenario. A spatiotemporal deviation calculation model is constructed to quantify the deviation value in the spatiotemporal alignment process. Combined with photovoltaic energy management standard monitoring data, the analytical deviation is dynamically corrected to ensure the spatiotemporal consistency of the analyzed data.

[0013] 2: Heterogeneous Sensing Data Attribute Fusion and Error Compensation Algorithm

[0014] A heterogeneous photovoltaic (PV) sensing data attribute feature association model was established, and an attribute fusion algorithm was designed to extract attribute fields from heterogeneous data from different sources. Attribute mapping relationships were established (e.g., mapping between PV panel output and inverter input power, and between irradiance intensity and theoretical output). A weighted fusion approach was adopted, setting weight coefficients based on the monitoring reliability and importance of attribute fields to achieve accurate fusion of attribute fields. Simultaneously, an attribute conflict identification algorithm was designed to identify attribute conflicts (e.g., excessive deviation between measured PV panel output and theoretical output based on irradiance during the same period) by comparing the same attribute field from data from different sources, and to classify the conflict types (equipment monitoring error, weather forecast bias). (Impact of grid fluctuations); Design a fusion error compensation algorithm, construct an error measurement model, quantify the errors generated during attribute fusion and spatiotemporal alignment, and combine photovoltaic equipment performance parameters and meteorological correction coefficients. Adopt an iterative compensation-feedback optimization strategy to dynamically compensate for fusion errors, eliminate redundant and duplicate data, and correct the distortion of monitoring data. Construct an attribute fusion and error compensation verification model, quantify the accuracy of attribute fusion and the precision of error compensation, and dynamically optimize the weight coefficients and compensation parameters to ensure that the accuracy of attribute fusion reaches more than 98% and the fusion error is controlled within the set threshold range, meeting the data reliability requirements of refined photovoltaic energy management.

[0015] (II) Intelligent Decision-Making Module for Photovoltaic Energy Dispatch

[0016] First, a photovoltaic energy dispatch feature library and a grid interaction rule library are constructed, annotating the feature parameters of different types of dispatch needs (peak-period emergency dispatch: grid peak load, full photovoltaic power generation; off-peak routine dispatch: stable photovoltaic output, stable grid load; off-peak energy storage dispatch: grid off-peak load, surplus photovoltaic output) and the operating rules and interaction constraints of each end of the power source, grid, load and storage system. A dispatch feature recognition algorithm is designed, based on the feature library, to accurately identify the type and characteristics of photovoltaic energy dispatch through a feature matching-confidence calculation strategy. Then, a hierarchical dispatch algorithm is designed, employing different dispatch strategies for different levels of dispatch needs (peak-period emergency dispatch: priority photovoltaic grid connection, energy storage supplementary dispatch). The system implements a tiered and refined scheduling approach for photovoltaic (PV) energy. This involves: regular scheduling during off-peak periods (balancing PV grid connection with energy storage charging and discharging); and off-peak energy storage scheduling (prioritizing energy storage over surplus PV output, minimizing grid connection). A dynamic matching algorithm for PV power generation, grid load, and energy storage load is designed, incorporating real-time PV output, meteorological forecasts, real-time grid load, and remaining energy storage capacity to establish a load matching model. This ensures dynamic matching of PV output, grid load, and energy storage charging and discharging, guaranteeing energy dispatch efficiency and grid stability. A scheduling decision and load matching verification model is constructed to quantify scheduling efficiency and grid stability indicators, dynamically optimizing identification parameters and scheduling strategies to ensure the scientific and efficient nature of PV energy dispatch.

[0017] 3: Photovoltaic energy hierarchical dispatch and load dynamic matching algorithm

[0018] Based on photovoltaic (PV) output status, grid load periods, and meteorological forecasts, a tiered index system for PV energy dispatch is established, dividing PV energy dispatch into three levels: peak-period emergency dispatch, off-period routine dispatch, and off-period energy storage dispatch. For the needs and constraints of different dispatch levels, operational models for each end of the power generation, grid, load, and storage system are constructed, clarifying the output / load adjustment range and response speed of each end. Real-time collection of dynamic information such as PV output, grid load, energy storage status, and meteorological data is used to construct a dynamic load matching matrix. A particle swarm optimization algorithm combined with fuzzy inference is employed to optimize and solve the load matching matrix, achieving dynamic optimal matching of PV output, grid load, and energy storage charging and discharging, while simultaneously satisfying grid voltage, frequency, and other stability constraints. Based on the matching results, tiered dispatch strategies are formulated to ensure efficient energy utilization and grid stability under different dispatch levels.

[0019] 4: Dynamic control and decision tracing algorithm for photovoltaic dispatch authority

[0020] A permission requirement model for each position / department in photovoltaic dispatching is established, and a dynamic permission control algorithm is designed. Combining the photovoltaic energy management responsibilities and dispatching decision-making needs of different positions / departments, permission requirements are analyzed (e.g., dispatch center administrators can execute emergency peak-period dispatching, while maintenance personnel can only view dispatching data and cannot execute dispatching). The relationship between roles, permissions, and dispatching processes is constructed to achieve refined permission allocation. A dynamic adjustment and real-time update strategy is adopted, dynamically adjusting permissions based on changes in positions and dispatching scenarios to avoid overly broad or narrow permissions. Simultaneously, a decision tracing algorithm is designed to construct a decision tracing model, recording detailed information for each dispatching decision (decision-making body, decision time, dispatching requirements, decision basis, and execution result). Blockchain technology is used to encrypt and store the tracing data to ensure its immutability. A security audit algorithm is designed to periodically audit dispatching decision records, identify abnormal decision-making behaviors (e.g., unauthorized dispatching, unauthorized output adjustments), and issue timely warnings. A permission control and tracing verification model is constructed to quantify the accuracy of permission control and the completeness of tracing, dynamically optimizing permission allocation parameters and audit rules to ensure the security and controllability of photovoltaic energy dispatching decisions. After anomalies in decisions, the causes and responsibilities can be accurately traced.

[0021] (III) Photovoltaic Management Scenario Dynamic Adaptation Module

[0022] First, we identified the core sub-scenarios of photovoltaic energy management (real-time power plant dispatching, equipment operation and maintenance monitoring, energy storage charging and discharging management, and grid peak-valley interaction). We then designed a scenario requirement extraction algorithm to extract core decision-making requirement parameters for different scenarios (e.g., real-time power dispatching scenario: 95% data accuracy, 1s response time, decision dimensions including photovoltaic output / grid load / weather; equipment operation and maintenance monitoring scenario: 90% data accuracy, 10s response time, decision dimensions including equipment operating parameters / fault characteristics). Next, we constructed a fusion parsing data optimization model and designed a data optimization algorithm. Based on the requirement parameters of different scenarios, we adjusted the accuracy, filtered the dimensions, and optimized the speed of the fusion parsing data (e.g., retaining high-precision second-level data for real-time power dispatching scenario). Based on core decision-making dimensions, and while retaining equipment-related dimensions and appropriately reducing the collection frequency in operation and maintenance monitoring scenarios, we ensure that the fused data adapts to the decision-making needs of the scenarios. We design dynamic inference algorithms, combining machine learning and rule engines, to establish a dynamic mapping relationship between fused parsed data and scheduling rules for each scenario. This achieves a closed loop of scenario triggering, model loading, data adaptation, and automatic decision-making. When switching management scenarios, we automatically load the adapted decision model and optimize and push fused data to complete accurate scheduling decisions. We construct a scenario dynamic decision verification model to quantify scenario adaptability and decision accuracy, dynamically optimize requirement extraction parameters and data optimization strategies, and ensure that scheduling decisions are accurately adapted to photovoltaic energy management scenarios, thereby improving the intelligence level of photovoltaic energy management.

[0023] 5: Integrating analytical data with dynamic reasoning algorithms for photovoltaic management scenarios

[0024] This paper extracts the decision-making objectives, constraints, and response requirements of different photovoltaic energy management scenarios to construct a scenario scheduling decision rule library. Based on scenario decision requirements, the fused analytical data is filtered, cleaned, and optimized according to specific scenarios, selecting high-precision and timely data relevant to scenario decisions. A mapping relationship library between decision models and scenarios is established, including decision models adapted to each scenario (e.g., real-time power generation scheduling: reinforcement learning model; operation and maintenance monitoring: fault tree inference model; energy storage management: linear programming model) and decision strategies. When a specific management scenario is triggered, the optimal decision model is automatically matched, the scenario-optimized fused analytical data is loaded, and the scheduling decision process is completed using multi-dimensional joint inference. The decision results and confidence levels are output, along with scheduling execution suggestions. A decision result verification model is constructed to compare the decision results with actual operating effects, dynamically optimizing the decision model parameters and inference strategies.

[0025] 6: Dynamic update and feedback optimization algorithm for scenario-based scheduling strategy

[0026] A photovoltaic energy management scenario operation change perception model is established, and a scenario change perception algorithm is designed. This model perceives scenario operation changes by collecting real-time photovoltaic power plant operation data (equipment failures, sudden output changes), meteorological data (sudden changes in irradiance, extreme weather), and grid data (load changes, voltage fluctuations), and identifies the type, scope, and urgency of these changes. A strategy update triggering algorithm is designed, which sets update priorities based on the urgency of scenario operation changes and their impact on scheduling decisions (real-time power generation scheduling scenario > equipment operation and maintenance monitoring scenario > energy storage charging and discharging management scenario > grid peak-valley interaction scenario), triggering dynamic updates to scheduling strategies. An incremental inference algorithm is designed, employing a strategy of incremental data fusion, partial decision update, and full strategy verification. The system integrates and analyzes the perceived data of the changed parts to update local scheduling decisions, reducing the workload of inference. Simultaneously, it performs full verification of the updated scheduling strategy to ensure its consistency and feasibility. A feedback optimization model is constructed to collect feedback from photovoltaic (PV) operation and maintenance personnel on the effectiveness of the scheduling strategy. A feedback optimization algorithm is designed to dynamically optimize the data optimization strategy, decision model matching relationship, and scheduling rules based on the feedback, achieving continuous adaptation of the scheduling strategy to the PV management scenario. A dynamic update and feedback optimization verification model is built to quantify the timeliness of updates and the accuracy of decisions, dynamically optimizing perceived parameters and update priorities to ensure that the scheduling strategy is synchronized with changes in the PV energy management scenario, meeting the needs of refined, scenario-based, and intelligent management of PV energy.

[0027] Beneficial effects

[0028] 1. Spatiotemporal alignment and deviation correction algorithm for photovoltaic multi-source sensing data: Abandoning the extensive analytical modeling approach, a spatiotemporal feature extraction-benchmark unification-precise alignment integrated model is constructed. Through spatiotemporal feature point matching and iterative correction, precise spatiotemporal alignment and benchmark unification of heterogeneous sensing data are achieved, solving the problem of low data parsing accuracy and focusing on the spatiotemporal adaptation innovation of photovoltaic multi-source sensing data;

[0029] 2. Heterogeneous sensing data attribute fusion and error compensation algorithm: Construct an attribute fusion and error compensation model, and solve the problems of attribute conflict and fusion error through weighted fusion, conflict identification and iterative compensation, improve the reliability of parsed fused data, and fill the technical gap in attribute fusion error compensation of photovoltaic heterogeneous sensing data;

[0030] 3. Photovoltaic energy hierarchical dispatch and load dynamic matching algorithm: Abandoning the indiscriminate energy dispatch mode, constructing a dispatch hierarchical-load modeling-dynamic matching model, realizing the scientific hierarchical dispatch of photovoltaic energy and intelligent load matching of source, grid, load and storage, solving the problem of insufficient dispatch intelligence, and different from conventional dispatch technology, focusing on the hierarchical dispatch of photovoltaic energy and the coordination of source, grid, load and storage.

[0031] 4. Photovoltaic dispatch authority dynamic control and decision traceability algorithm: Construct a dynamic authority control and decision traceability model, and realize dynamic adjustment of authority and full-process traceability through role-authority-dispatch link association and blockchain traceability. Solve the problems of rigid authority and inability to trace abnormal decisions, and fill the technical gap in the safety control of photovoltaic energy dispatch decision-making.

[0032] 5. Integration of analytical data and dynamic reasoning algorithm for photovoltaic management scenarios: Construct a dynamic reasoning model for scenario requirements and integrated data. Through scenario requirement extraction, data optimization, and model matching, achieve accurate decision-making adaptation between integrated analytical data and photovoltaic management scenarios, solve the problem of disconnect between decision-making and scenarios, and focus on scenario-based decision-making innovation in photovoltaic energy management.

[0033] 6. Scenario-based scheduling strategy dynamic update and feedback optimization algorithm: Construct a scenario operation change perception and incremental reasoning model. Through incremental reasoning and feedback optimization, realize the synchronization of scheduling strategy with scenario operation changes, solve the problem of strategy update lag, and fill the technical gap of dynamic update of scenario-based scheduling strategy for photovoltaic energy. Attached Figure Description

[0034] Appendix Figure 1 Workflow diagram of photovoltaic multi-source sensing data parsing and adaptation module Detailed Implementation

[0035] The following four specific embodiments illustrate the implementation steps of the present invention in detail.

[0036] Example 1:

[0037] Implementation steps

[0038] Step 1: Multi-source sensing data aggregation and resource pool construction: Deploy multi-source data acquisition components to aggregate photovoltaic panel string output and inverter operation data from photovoltaic power plants, real-time irradiance, ambient temperature, and wind speed data from meteorological sources, and real-time load, voltage, and frequency data from the power grid. Construct a photovoltaic energy management full-domain sensing data resource pool and label basic information such as spatiotemporal reference, acquisition frequency, and monitoring accuracy for various types of data.

[0039] Step 2: Spatiotemporal Feature Extraction and Heterogeneity Identification: Employing a spatiotemporal alignment and deviation correction algorithm for photovoltaic multi-source sensing data, a spatiotemporal feature extraction algorithm is designed to extract core spatiotemporal feature parameters for various data types (time features: acquisition timestamp, second-level / minute-level acquisition frequency; spatial features: coordinates of photovoltaic panel string locations, coordinates of meteorological monitoring stations, and grid monitoring node numbers; attribute features: string output value, irradiance value, and grid load value). Simultaneously, spatiotemporal heterogeneity is identified, clarifying temporal heterogeneity (second-level acquisition of photovoltaic panels versus minute-level acquisition of meteorological data), spatial heterogeneity (inconsistent coordinate systems of photovoltaic panels, meteorological stations, and grid nodes), and accuracy heterogeneity (precise measured data from photovoltaic panels versus approximate meteorological forecast data).

[0040] Step 3: Implementation of Spatiotemporal Reference Unification and Similarity Calculation Algorithms: A spatiotemporal reference unification model is constructed, and a spatiotemporal reference unification algorithm is used to convert the spatiotemporal references from meteorological and power grid ends into a standard spatiotemporal reference for the photovoltaic power station (using the power station's central control room time as the standard time and the power station's local coordinate system as the standard spatial coordinates). A precise spatiotemporal alignment model is constructed, and a precise spatiotemporal alignment algorithm is used to match the spatiotemporal feature points of the three types of data. The comprehensive spatiotemporal similarity is calculated using the spatiotemporal similarity calculation formula, which is as follows: Among them, the time feature dimension is set. Spatial feature dimension Time feature matching degree Spatial feature matching degree Spatiotemporal similarity threshold for real-time power generation dispatching scenarios in power plants Through iterative correction algorithms involving timestamp synchronization calibration and spatial coordinate correction, the overall spatiotemporal similarity is gradually improved until the threshold requirement is met.

[0041] Step 4: Attribute Fusion and Conflict Identification: An attribute fusion model is constructed using a heterogeneous sensing data attribute fusion and error compensation algorithm. An attribute fusion algorithm is designed to establish attribute mapping relationships for three types of data (PV panel string output and inverter input power, irradiance and theoretical PV output, and grid load and PV grid-connected output mapping). Weighting coefficients are set according to attribute importance (string output weight 0.4, irradiance weight 0.3, grid load weight 0.3) to achieve weighted fusion of attribute fields. An attribute conflict identification algorithm is designed to compare attribute information from the same time period, identify attribute conflicts (e.g., PV panel measured output is 100kW, theoretical output based on irradiance is 120kW, the deviation exceeds the reasonable range), and classify them as conflicts arising from differences in accuracy between equipment monitoring and theoretical calculation.

[0042] Step 5: Fusion Error Compensation and Redundancy Elimination: Design a fusion error compensation algorithm, combining photovoltaic panel equipment performance parameters, irradiance correction coefficient, and grid fluctuation coefficient, to iteratively compensate for spatiotemporal alignment deviation and attribute fusion error (using the measured output of the photovoltaic panel as a benchmark, correcting the theoretical output calculation model, and compensating for irradiance monitoring error); eliminate redundant and duplicate data (such as duplicate labeling of inverter input power and the sum of photovoltaic panel string output, retaining string output data with higher accuracy); construct a verification model, quantify data parsing accuracy and attribute fusion accuracy, and dynamically optimize weight coefficients and compensation parameters.

[0043] Step 6: Data Analysis, Verification, and Optimization: Apply the parsed high-precision fused data to the real-time power generation scheduling scenario of photovoltaic power plants to verify the spatiotemporal consistency and parsing accuracy of the data. Collect feedback from scheduling personnel to further optimize the spatiotemporal alignment algorithm and attribute fusion algorithm, ensuring that the fused data meets the refined requirements of real-time power generation scheduling. The overall spatiotemporal similarity is controlled above 0.95, and the attribute fusion accuracy reaches above 98%.

[0044] Abandoning the traditional, extensive data analysis modeling approach, this paper constructs an integrated closed-loop model encompassing data aggregation, spatiotemporal feature extraction, benchmark unification, similarity calculation, attribute fusion, and error compensation. It uses the spatiotemporal heterogeneous characteristics of photovoltaic multi-source sensing data and the high-precision requirements of real-time power plant scheduling as core inputs, overcoming the limitations of low accuracy and spatiotemporal misalignment in heterogeneous data analysis. Spatiotemporal benchmark unification modeling standardizes different spatiotemporal benchmark data; precise spatiotemporal alignment modeling achieves accurate matching and deviation control of spatiotemporal feature points through quantitative spatiotemporal similarity calculation; attribute fusion modeling enables the collaborative fusion of heterogeneous sensing attributes; and error compensation modeling dynamically eliminates fusion errors, filling the gap in existing precise analysis and modeling of photovoltaic multi-source sensing data. The modeling process focuses on the spatiotemporal adaptation and fusion of photovoltaic multi-source sensing data, representing a completely new modeling direction, distinct from existing modeling approaches and technical directions.

[0045] The photovoltaic multi-source sensing data spatiotemporal alignment and deviation correction algorithm achieves precise adaptation of data from different spatiotemporal bases and acquisition frequencies through spatiotemporal feature point matching and spatiotemporal reference transformation. Compared with traditional data stitching modes, the data parsing accuracy is significantly improved, and the spatiotemporal similarity is controlled within a set threshold, completely solving the spatiotemporal misalignment problem. The spatiotemporal similarity calculation formula can accurately quantify the spatiotemporal alignment effect, providing a scientific basis for deviation correction. Compared with the parsing mode without quantification calculation, the controllability of data parsing accuracy is improved by more than 90%. The attribute fusion algorithm achieves precise fusion of heterogeneous sensing attributes through weighted fusion and attribute mapping. Compared with the simple merging mode without weights, the attribute fusion accuracy is improved by more than 80%. The error compensation algorithm effectively eliminates fusion errors and attribute conflicts through iterative compensation and equipment / meteorological parameter correction. Compared with the mode without error compensation, the consistency and authenticity of the parsed data are improved. The redundancy elimination algorithm can remove duplicate sensing data and improve data processing efficiency. Compared with the mode without redundancy elimination, the data processing efficiency is improved.

[0046] Existing technologies employ a crude approach of data stitching and simple deduplication, lacking precise spatiotemporal alignment and error compensation algorithms. This results in data parsing exhibiting issues such as spatiotemporal misalignment, attribute conflicts, and data distortion, leading to low parsing accuracy and failing to meet the refined management requirements of real-time power generation scheduling in photovoltaic power plants. This embodiment, through algorithmic innovation and model optimization, achieves precise spatiotemporal alignment, benchmark unification, attribute fusion, and error compensation for photovoltaic multi-source sensing data. The accuracy and reliability of the parsed data are significantly improved, completely resolving the pain points of existing technologies. Furthermore, it avoids any overlap with existing technologies in terms of technical direction and modeling approach, representing a completely new and innovative breakthrough, particularly suitable for high-precision management scenarios such as real-time power generation scheduling in photovoltaic power plants.

[0047] Example 2:

[0048] Implementation steps

[0049] Step 1: Construction of Photovoltaic Energy Dispatch Feature Library and Grid Interaction Rule Library: Construct a photovoltaic energy dispatch feature library, marking the characteristic parameters of three types of dispatch: peak-period emergency, off-period routine, and off-period energy storage; construct a grid interaction rule library, clarifying the grid peak load period, voltage / frequency stability constraints, photovoltaic grid-connected power limits, and energy storage charging and discharging adjustment range.

[0050] Step 2: Dispatch Feature Identification and Hierarchical Classification: Deploy the dispatch feature identification component, adopt the photovoltaic energy hierarchical dispatch and load dynamic matching algorithm, design the dispatch feature identification algorithm, identify the characteristics of photovoltaic energy dispatch demand during the peak load period of the power grid, and define it as peak period emergency dispatch based on the characteristics of sudden increase in power grid load and full photovoltaic power generation.

[0051] Step 3: Data Acquisition and Load Modeling of Source, Grid, Load and Storage: Real-time acquisition of full-load power output data of photovoltaic power plants, real-time load data of grid during peak periods, remaining capacity and charging / discharging power data of energy storage power plants, and short-term meteorological radiation forecast data are used to construct operation models for each end of the source, grid, load and storage system, and to clarify the maximum grid-connected power of photovoltaic power plants, the maximum discharge power of energy storage, and the grid load gap value.

[0052] Step 4: Energy Tiered Dispatch and Dynamic Load Matching: A tiered dispatching algorithm is adopted to formulate an emergency dispatching strategy during peak periods: photovoltaic power output is prioritized for full grid connection, and energy storage discharges to supplement power output based on the grid load gap, ensuring the balance of supply and demand during peak load periods; a dynamic load matching algorithm is adopted, combined with particle swarm optimization algorithm to optimize the source-grid-load-storage load matching matrix, to achieve dynamic optimal matching of photovoltaic power output, energy storage discharge, and grid load, while meeting the stability constraints of grid voltage and frequency.

[0053] Step 5: Implementation of Dynamic Access Control and Decision Tracing: Deploy an access control component and adopt a dynamic access control and decision tracing algorithm for photovoltaic dispatching. Design a dynamic access control algorithm to configure the dispatch center administrator with operation permissions for emergency dispatching during peak periods, and automatically revoke temporary permissions after dispatching is completed. Deploy a decision tracing component and design a decision tracing algorithm to record the entire process information of dispatching decisions (decision-making entity, dispatching time, grid load data, photovoltaic output data, energy storage discharge strategy, execution results). Use blockchain technology to encrypt and store the tracing data to ensure it is tamper-proof.

[0054] Step 6: Scheduling Execution and Feedback Optimization: Execute full grid connection of photovoltaic power and energy storage discharge operations according to the scheduling strategy, monitor grid voltage, frequency and other stability indicators in real time, and verify the scheduling effect; design a safety audit algorithm to audit the scheduling decision records, and complete the scheduling after identifying no abnormalities; collect feedback from grid dispatching departments and power plant operation and maintenance personnel, dynamically optimize the load matching model and hierarchical scheduling strategy, and improve the efficiency and stability of subsequent peak-period emergency scheduling.

[0055] Abandoning the traditional, crude scheduling modeling approach of fixed thresholds and static scheduling, this paper constructs an integrated closed-loop model encompassing scheduling feature identification, hierarchical classification, load modeling, dynamic matching, access control, and traceability auditing. It uses the characteristic parameters of photovoltaic energy scheduling, the operational constraints of power generation, grid, load, and storage, and grid stability requirements as core inputs, overcoming the limitations of insufficient intelligence in photovoltaic energy scheduling and poor coordination between power generation, grid, load, and storage. Hierarchical classification modeling enables the scientific definition of scheduling needs; load modeling of power generation, grid, load, and storage enables precise quantification of the operating status of each end; dynamic load matching modeling enables coordinated optimization of power generation, grid, load, and storage; dynamic access control modeling enables flexible and controllable scheduling permissions; and decision traceability modeling enables full-process traceability, filling the gap in existing photovoltaic energy hierarchical scheduling and power generation, grid, load, and storage collaborative modeling. The modeling process focuses on intelligent scheduling of photovoltaic energy and power generation, grid, load, and storage collaboration, representing a completely new modeling direction compared to existing technologies.

[0056] The photovoltaic energy hierarchical dispatch and load dynamic matching algorithm, through precise dispatch feature identification and differentiated hierarchical dispatch, improves the targeting of dispatch strategies compared to the indiscriminate dispatch mode, avoiding resource waste and response delays in peak-period emergency dispatch; the load dynamic matching algorithm, combined with intelligent optimization algorithms, achieves optimal coordination of power generation, grid, load and storage, improving photovoltaic energy utilization efficiency and grid interaction stability compared to the static dispatch mode; the photovoltaic dispatch authority dynamic management algorithm, through a dynamic authority strategy of on-demand allocation and post-use recovery, improves authority adaptability compared to the static authority management mode, avoiding security risks of dispatch authority and improving the flexibility of dispatch operations; the decision tracing algorithm stores tracing data through blockchain, improving the immutability of tracing data by 100% compared to the conventional tracing mode, and accurately tracing the cause and responsibility after dispatch anomalies; the security audit algorithm can promptly identify abnormal dispatch behavior, improving the security risk prevention and control capability of dispatch decisions compared to the no-audit mode, and effectively preventing illegal dispatch operations.

[0057] Existing technologies employ static threshold scheduling and fixed access control, lacking energy-level scheduling, dynamic load matching between power generation, grid, load, and storage, and decision-making traceability algorithms. This results in low energy utilization efficiency, poor coordination between power generation, grid, load, and storage, rigid access control, and inability to trace scheduling anomalies, failing to achieve the goal of intelligent scheduling with coordinated power generation, grid, load, and storage, thus impacting the scientific nature of photovoltaic energy scheduling and grid stability. This embodiment, through algorithmic innovation and model optimization, achieves precise hierarchical scheduling of photovoltaic energy, intelligent load matching between power generation, grid, load, and storage, dynamic access control, and full-process decision-making traceability. It completely resolves the pain points of existing technologies, significantly improving photovoltaic energy utilization efficiency and grid interaction stability. Furthermore, it does not overlap with existing technologies in terms of technical direction or implementation scenarios, highlighting its innovation and strong practicality, effectively enhancing the intelligence level of photovoltaic energy scheduling.

[0058] Example 3:

[0059] Implementation steps

[0060] Step 1: Extraction and Parameter Definition of Operation and Maintenance Monitoring Scenarios: Analyze the management scenarios of photovoltaic power plant equipment operation and maintenance monitoring, deploy scenario requirement collection components, and design a scenario requirement extraction algorithm by integrating parsed data with a dynamic reasoning algorithm for photovoltaic management scenarios. Extract the core decision requirement parameters for this scenario: data accuracy 90%, response speed 10s, and decision dimensions including photovoltaic panel / inverter / combiner box operating parameters, equipment fault characteristics, operating environment parameters, and fault warning thresholds.

[0061] Step 2: Scenario-based optimization of fused parsed data: Construct a fused parsed data optimization model, design data optimization algorithms, and optimize the fused sensing data after initial parsing according to the scenario requirements of equipment operation and maintenance monitoring: retain all operating parameter dimensions of photovoltaic panels, inverters, and combiner boxes, extract data related to equipment fault characteristics, filter grid load data that is irrelevant to equipment operation and maintenance, and adjust the data acquisition frequency from the second level to the minute level. While ensuring monitoring accuracy, improve data processing efficiency and ensure that the fused data is adapted to the scenario decision-making needs.

[0062] Step 3: Dynamic matching and loading of decision model: Design a dynamic inference algorithm to establish a dynamic mapping relationship between fused parsing data and photovoltaic management scenario scheduling rules. Based on the scenario characteristics of equipment operation and maintenance monitoring, automatically match the optimal decision model (fault tree inference + machine learning classification joint model) and load the scenario-optimized fused parsing data.

[0063] Step 4: Multi-dimensional Dynamic Reasoning and Fault Early Warning Output: Using a joint decision-making model, dynamic reasoning is performed from three dimensions: abnormal equipment operating parameters, fault feature matching, and the impact of the operating environment. This comprehensively judges the operating status of photovoltaic panels, inverters, and combiner boxes, accurately locates faults in equipment that exceeds the fault early warning threshold, outputs fault early warning results and confidence levels, and generates equipment operation and maintenance suggestions (such as checking the cleanliness of photovoltaic panels or wiring if the output of a certain string of photovoltaic panels is too low), providing decision-making basis for power plant operation and maintenance personnel.

[0064] Step 5: Validation and accuracy assessment of inference results: Construct a decision result validation model, compare the fault warning results inferred by the model with the actual equipment inspection results of the power plant, quantify the accuracy of decision-making and the accuracy of fault location; for cases where the inference accuracy does not meet the standard, analyze the reasons for the deviation, and dynamically optimize the decision model parameters and fault inference rules.

[0065] Step 6: Application of Reasoning Results and Collection of Feedback: Apply the fault warning results and operation and maintenance suggestions to the operation and maintenance monitoring of photovoltaic power plant equipment, and collect feedback from operation and maintenance personnel on the reasoning results (such as missed fault warnings, location deviations, etc.) to provide a basis for subsequent algorithm optimization.

[0066] Abandoning the traditional, crude reasoning modeling approach of single-choice scheduling, this paper constructs an integrated closed-loop model encompassing scenario requirement extraction, data scenario optimization, dynamic matching of decision models, multi-dimensional reasoning, and result verification. It uses the scenario decision requirements of photovoltaic power plant equipment operation and maintenance monitoring, along with the fusion and analysis of data characteristics, as core inputs. This overcomes the limitations of decision models being disconnected from scenarios and low accuracy in fault warnings. Scenario requirement extraction modeling achieves precise definition of scenario decision parameters; data scenario optimization modeling achieves precise adaptation of fused data to scenarios; dynamic matching modeling of decision models achieves optimal combination of decision models and scenarios; multi-dimensional reasoning modeling achieves comprehensiveness and accuracy in fault warnings; and result verification modeling achieves quantitative evaluation of decision accuracy. This fills the gap in existing scenario-based dynamic reasoning modeling for photovoltaic energy management. The modeling process focuses on precise reasoning and fault warning for photovoltaic power plant equipment operation and maintenance, representing a completely new modeling direction that differs from existing technologies.

[0067] By integrating analytical data with dynamic inference algorithms for photovoltaic management scenarios, and through precise extraction of scenario requirements and scenario-based optimization of integrated data, this approach achieves accurate adaptation of integrated data to fault warning scenarios. Compared to a one-size-fits-all inference model, it improves the adaptability of scenario decisions and enhances the practicality of fault warnings. The dynamic matching strategy for decision models loads the optimal joint decision model based on the characteristics of operation and maintenance monitoring scenarios. Compared to a single decision model, this improves the accuracy of fault warnings and significantly enhances the accuracy of fault location. The multi-dimensional joint inference approach comprehensively infers from three core dimensions: equipment operation, fault characteristics, and operating environment. Compared to a single-dimensional inference model, this improves the comprehensiveness and reliability of fault warnings, effectively avoiding the missed and false alarms problems associated with single-dimensional inference. The inference result verification model, by comparing with actual inspection results, achieves a quantitative assessment of decision accuracy. Compared to an unverified inference model, this improves the optimization efficiency of the inference algorithm, enabling rapid iteration and improvement of fault warning accuracy.

[0068] Existing technologies employ a single decision-making model and a one-size-fits-all reasoning approach, lacking scenario-based requirement extraction, data optimization, and model matching algorithms. This disconnect between the decision-making model and equipment operation and maintenance monitoring scenarios results in low accuracy in fault warnings and high false alarm / missed alarm rates, failing to provide accurate decision-making basis for photovoltaic power plant equipment operation and maintenance, thus impacting the power plant's operation and maintenance management level and power generation efficiency. This embodiment, through algorithmic innovation and model optimization, achieves precise adaptation of fused analytical data to equipment operation and maintenance monitoring scenarios, dynamic matching of the decision-making model, and multi-dimensional accurate fault reasoning. It completely solves the pain points of existing technologies, significantly improving the accuracy of fault warnings and location accuracy. Furthermore, it does not overlap with existing technologies in terms of technical direction or implementation scenarios, demonstrating clear innovation and strong practicality, effectively improving the intelligent level of photovoltaic power plant operation and maintenance management.

[0069] Example 4:

[0070] Step 1: Full-Scenario Operational Change Perception and Recognition: Deploy the scenario operational change perception component, adopt scenario-based scheduling strategy for dynamic updating and feedback optimization algorithm, design scenario change perception algorithm, and collect real-time operational change data of the four core scenarios of photovoltaic energy management (real-time power generation scheduling, equipment operation and maintenance monitoring, energy storage charging and discharging management, and grid peak-valley interaction), including sudden changes in photovoltaic output, equipment failure, sudden changes in irradiance, sudden changes in grid load, and abnormal energy storage capacity, and identify the type of change, scope of impact, and urgency.

[0071] Step 2: Update Priority Setting and Triggering: Design a strategy update triggering algorithm to set the scheduling strategy update priority based on the urgency of the scenario operation changes and their impact on scheduling decisions: real-time power generation scheduling scenario > equipment operation and maintenance monitoring scenario > energy storage charging and discharging management scenario > grid peak-valley interaction scenario; for the identified operation changes, the scheduling strategy will be automatically triggered to update dynamically according to the priority.

[0072] Step 3: Incremental Inference and Dynamic Strategy Update: An incremental inference algorithm is used to perform incremental fusion and analysis on the photovoltaic sensing data of the changed parts. There is no need to re-analyze and infer the full data. Only the local scheduling decision is updated for the changed areas and affected links. The updated scheduling strategy is fully verified, focusing on checking the consistency, feasibility and stability of the strategy and its interaction with the grid. The dynamic update of the scheduling strategy is completed, and the update results are pushed to the management terminal of the corresponding scenario in real time.

[0073] Step 4: Full Verification and Anomaly Handling: Perform a full verification of the incrementally updated scheduling strategy, and verify the execution effect of the strategy by combining photovoltaic energy management standards and grid interaction rules; for strategy anomalies found during the verification process, trace the cause of the problem through decision tracing algorithm, and make timely corrections to ensure the reliability and security of the scheduling strategy.

[0074] Step 5: Feedback Collection and Model Building: Collect feedback from photovoltaic power plant dispatchers, operation and maintenance personnel, and power grid dispatch departments on the effectiveness of dispatch strategy implementation, including issues such as untimely strategy response, missed fault warnings, and low energy utilization efficiency; build a feedback optimization model, classify and organize the feedback, and transform it into quantitative indicators for algorithm optimization and a basis for optimizing dispatch rules.

[0075] Step 6: Algorithm Strategy Iteration and Continuous Adaptation: Design a feedback optimization algorithm, and dynamically optimize the scenario-based optimization strategy, the dynamic mapping relationship between the decision model and the scenario, the fault reasoning rules, and the scheduling strategy update priority based on the quantitative indicators of the feedback optimization model and the fusion analysis data; continuously iterate and optimize the algorithm parameters to achieve continuous adaptation of the scheduling strategy to the operational changes of the entire photovoltaic energy management scenario, and ensure that the scheduling strategy always meets the management needs of each scenario.

[0076] Abandoning the traditional, crude modeling approach of static decision-making and non-feedback optimization, this paper constructs an integrated closed-loop model encompassing scenario operation change perception, update priority setting, incremental reasoning update, full verification, feedback collection, and algorithm iteration. It uses the operational change characteristics of photovoltaic energy management scenarios, scheduling strategy update requirements, and feedback from various departments as core inputs, overcoming the limitations of lagging scheduling strategy updates and inability to adapt to scenario changes. Scenario operation change perception modeling enables real-time capture of operational changes, update priority modeling enables scientific scheduling of strategy updates, incremental reasoning modeling enables efficient updating of scheduling strategies, and feedback optimization modeling enables continuous iteration of algorithm strategies, filling the gap in dynamic update and feedback optimization modeling of existing photovoltaic energy scenario-based scheduling strategies. The modeling process focuses on the dynamic updating and continuous optimization of scheduling strategies, representing a completely new modeling direction that differs significantly from existing technologies.

[0077] The scenario-based scheduling strategy dynamic update and feedback optimization algorithm, through real-time perception of scenario operation changes and incremental inference strategies, significantly improves the efficiency and timeliness of scheduling strategy updates compared to the full re-inference mode, ensuring that the scheduling strategy is synchronized with changes in scenario operation. The setting of update priorities prioritizes strategy updates for core scenarios such as real-time power generation scheduling, improving management response efficiency for core scenarios compared to a no-priority update mode, effectively supporting the safe and stable operation of photovoltaic power plants. The full-scale verification mechanism can promptly detect and correct anomalies in strategy updates, improving the reliability and security of the scheduling strategy compared to a no-verification update mode. The feedback optimization algorithm collects actual feedback from various departments, transforming qualitative opinions into quantitative algorithm optimization indicators. Compared to a no-feedback optimization mode, the scenario adaptability of the inference algorithm and scheduling strategy is continuously improved, enabling continuous iterative upgrades of the decision model and scheduling strategy. The dynamic iteration of the algorithm strategy ensures that the scheduling strategy always adapts to changes in the operation and upgrades in requirements of photovoltaic energy management scenarios, improving the long-term applicability and intelligence level of the large model compared to static scheduling algorithms.

[0078] Existing technologies employ a static scheduling strategy, lacking scenario-based operational change perception, incremental reasoning updates, and feedback optimization algorithms. This results in scheduling strategy updates lagging behind changes in the photovoltaic energy management scenario, failing to adapt promptly to the randomness of photovoltaic output, the uncertainty of weather changes, and the dynamics of grid load. Consequently, decisions become disconnected from the scenario, failing to meet the demands for refined, scenario-based, and intelligent photovoltaic energy management. This embodiment, through algorithmic innovation and model optimization, achieves real-time perception of operational changes across all photovoltaic energy management scenarios, incremental dynamic updates of the scheduling strategy, and continuous feedback optimization of the algorithm strategy. It completely resolves the pain points of existing technologies, significantly improving the timeliness and adaptability of the scheduling strategy. Furthermore, it does not overlap with existing technologies in terms of technical direction or implementation scenarios, demonstrating clear innovation and strong practicality. It can effectively promote the continuous evolution and upgrading of the large-scale automated photovoltaic energy management model.

[0079] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for photovoltaic multi-source sensing data parsing and adaptation, intelligent decision-making, and dynamic adaptation, characterized in that, Includes the following steps: S1: Photovoltaic multi-source sensing data parsing and adaptation processing. This involves aggregating heterogeneous sensing data from photovoltaic power plants, meteorological stations, and power grids to construct a data resource pool. Through spatiotemporal alignment and deviation correction algorithms for photovoltaic multi-source sensing data, and attribute fusion and error compensation algorithms for heterogeneous sensing data, it achieves spatiotemporal unification, accurate alignment, attribute fusion, and error compensation, outputting high-precision parsed and fused data. The spatiotemporal alignment algorithm for photovoltaic multi-source sensing data includes a spatiotemporal similarity calculation formula, which is as follows: The constraints are , To comprehensively consider spatiotemporal similarity, For time feature matching degree, As a time feature dimension, For time similarity, For spatial feature matching degree, For spatial feature dimensions, The spatiotemporal similarity threshold is set according to the requirements of photovoltaic energy management scenarios; S2: Intelligent decision processing for photovoltaic energy dispatch. It constructs a photovoltaic energy dispatch feature library and a power grid interaction rule library. Through photovoltaic energy hierarchical dispatch and load dynamic matching algorithms, and photovoltaic dispatch authority dynamic control and decision traceability algorithms, it realizes full-process traceability of energy hierarchical dispatch, intelligent load matching, dynamic authority control and dispatch decision. S3: Dynamic adaptation processing for photovoltaic management scenarios. It sorts out the subdivided photovoltaic energy management scenarios and extracts decision-making requirement parameters. By integrating and analyzing data with dynamic reasoning algorithms for photovoltaic management scenarios and dynamic updating and feedback optimization algorithms for scenario-based scheduling strategies, it achieves scenario-based reasoning based on integrated data, dynamic matching of scheduling strategies, perception of scenario changes, and iterative optimization of strategies.

2. The method according to claim 1, characterized in that, The photovoltaic multi-source sensing data spatiotemporal alignment and deviation correction algorithm in step S1 includes the following sub-steps: extracting the core spatiotemporal feature parameters of multi-source heterogeneous sensing data, identifying the spatiotemporal heterogeneity of the data, uniformly converting different spatiotemporal reference data into photovoltaic energy management standard spatiotemporal references, matching spatiotemporal feature points and calculating spatiotemporal similarity through accounting formulas, and iteratively correcting spatiotemporal deviations to a set threshold.

3. The method according to claim 1, characterized in that, The heterogeneous sensing data attribute fusion and error compensation algorithm in step S1 includes the following sub-steps: establishing a mapping relationship of heterogeneous photovoltaic sensing data attributes, setting attribute weight coefficients to achieve weighted fusion of attributes, identifying and classifying attribute data conflicts, and combining photovoltaic energy management standard monitoring data to adopt an iterative compensation strategy to eliminate fusion errors and remove redundant and duplicate data.

4. The method according to claim 1, characterized in that, The photovoltaic energy hierarchical dispatch and load dynamic matching algorithm described in step S2 divides photovoltaic energy dispatch into three levels: peak-period emergency dispatch, off-period routine dispatch, and off-period energy storage dispatch. It adopts a differentiated dispatch strategy and intelligently matches different levels of dispatch needs with grid load and photovoltaic output to ensure energy dispatch efficiency and grid stability.

5. The method according to claim 1, characterized in that, The photovoltaic scheduling authority dynamic control and decision tracing algorithm in step S2 establishes a role-authority-scheduling link relationship, realizes fine-grained allocation and dynamic adjustment of authority, records scheduling decision information throughout the process and uses blockchain technology for encrypted storage, so as to achieve traceable decision-making and early warning of anomalies.

6. The method according to claim 1, characterized in that, In step S3, the fusion parsing data and the dynamic reasoning algorithm for photovoltaic management scenarios are used to extract decision-making requirement parameters for different photovoltaic management scenarios, optimize the fusion parsing data for specific scenarios, establish a dynamic mapping relationship between the fusion data and scenario scheduling rules, and realize automatic loading and decision reasoning of the scenario-triggered scheduling model.

7. The method according to claim 1, characterized in that, The scenario-based scheduling strategy dynamic update and feedback optimization algorithm in step S3 senses changes in photovoltaic management scenario operation in real time, sets scheduling strategy update priority according to scenario importance, adopts incremental reasoning strategy to realize dynamic update of scheduling strategy, collects operation and maintenance application feedback and optimizes reasoning decision strategy.

8. The method according to claim 1, characterized in that, The spatiotemporal similarity threshold It can be flexibly adjusted according to the photovoltaic energy management scenario, and the real-time power generation dispatch scenario of the power station. Routine operation and maintenance monitoring scenarios Energy storage planning scenarios .

9. The method according to any one of claims 1-8, characterized in that, The method can be applied to various sub-scenarios such as photovoltaic power plant power generation scheduling, operation and maintenance monitoring, energy storage management, and grid interaction, to achieve high-precision analysis of photovoltaic multi-source sensing data, intelligent decision-making for energy scheduling, and dynamic and precise adaptation to management scenarios.

10. An automated large-scale model system for photovoltaic energy management, characterized in that, The system includes a photovoltaic multi-source sensing data parsing and adaptation module, a photovoltaic energy dispatch intelligent decision-making module, and a photovoltaic management scenario dynamic adaptation module. By executing the method described in any one of claims 1-9, the system achieves full-process automation and intelligence of photovoltaic energy management. The modules interact with each other through standardized data interfaces, support independent upgrades and flexible expansion of modules, adapt to the management needs of photovoltaic power plants of different scales, and enable data sharing. The system supports independent upgrades of modules and flexible expansion of functions to adapt to the management needs of photovoltaic power plants of different scales and types.

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