Power station health state abnormity AI monitoring method based on large model data analysis

Abstract

The invention relates to the technical field of power station intelligent monitoring, and discloses a power station health state abnormity AI monitoring method based on large model data analysis. The method comprises the following steps: synchronously acquiring multi-modal operation data and environment background information of a power station, constructing a power station global operation situation topological graph reflecting a dynamic association relationship between equipment, and extracting multi-granularity abnormal symptoms from the topological graph; and analyzing historical fault cases by using the large model, and constructing and dynamically updating an exception knowledge base containing fault evolution modes and strategies. And performing matching reasoning on the real-time abnormal symptom and the knowledge base mode to generate a multi-layer abnormal decision flow. The decision flow is fused and optimized through a collaborative analysis engine, a monitoring conclusion is formed, a power station health state portrait and a corresponding operation and maintenance decision cluster are output, a complete number sequence physical model is intelligently constructed for the space of an intelligent power station, and accurate perception and intelligent diagnosis of overall and relevance abnormity of the power station are achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for power plants, specifically to an AI-based monitoring method for abnormal health status of power plants based on large-scale model data analysis. Background Technology

[0002] Current power plant anomaly monitoring generally relies on threshold alarms and isolated analysis of discrete equipment operating parameters. Conventional technical solutions set static alarm thresholds at each monitoring point and judge whether individual data such as current, voltage, and power of inverters and strings exceed limits. This type of method treats the power plant as a simple collection of independent monitoring points, and can only capture the explicit faults of the equipment itself, failing to depict the mutual influence and implicit correlations between equipment due to electrical connections and environmental coupling. When anomalies are propagated through the power plant's internal network or caused by the combined effect of minor degradation of multiple devices, isolated analysis is difficult to locate the root cause and is prone to generating a large number of false alarms, failing to reflect the overall operational health of the power plant.

[0003] Existing fault diagnosis methods largely rely on pre-set rule bases or classification models based on historical data. Their knowledge bases are typically composed of simple "symptom-fault" correspondences summarized manually, making updates and maintenance difficult. These methods have a simplistic and linear decision-making logic, lacking the ability to analyze complex fault evolution processes and the coupling effects of multiple factors. Faced with novel faults or complex scenarios involving multiple intertwined abnormal symptoms, static rule bases struggle to match the data, resulting in often one-sided diagnostic conclusions that fail to generate optimal operation and maintenance decision sequences that comprehensively consider fault probability, evolutionary risk, and handling costs. Power plant operation and maintenance still require secondary assessment based on human experience, resulting in bottlenecks in efficiency and accuracy. Summary of the Invention

[0004] The purpose of this invention is to provide an AI-based monitoring method for abnormal power plant health status based on large model data analysis, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides an AI-based monitoring method for abnormal power plant health status based on large-scale model data analysis, the method comprising: Simultaneously acquire multimodal operating data and associated environmental background information generated by equipment within the power plant during operation; The multimodal operation data is used to construct a topology map of the power plant's overall operation status that reflects the dynamic relationships of the power plant. Based on the power plant's overall operational status topology map and the environmental background information, multi-granularity abnormal signs of power plant operation with different semantic levels are extracted. By using large models to conduct in-depth analysis of historical failure cases, a power plant anomaly knowledge base containing failure evolution patterns and response strategies is constructed and dynamically updated. The power plant's multi-granularity abnormal symptoms are matched and inferred in real time with the fault evolution patterns in the power plant's anomaly knowledge base. Based on the matching and reasoning results, a multi-layered anomaly decision flow is generated for the current abnormal situation; Launch a collaborative analysis engine to fuse and optimize the multi-layered anomaly decision flow and generate the final monitoring conclusions; Based on the monitoring results, a power plant health status profile representing the overall health status of the power plant and the corresponding operation and maintenance decision cluster are output.

[0006] Preferably, the multimodal operation data refers to a heterogeneous data set that is collected by different types of sensors and monitoring equipment during the operation of the power plant, covering electrical parameters, equipment status, operation logs and communication messages.

[0007] Preferably, constructing a power plant global operational status topology map that reflects the global dynamic correlation of the power plant using the multimodal operational data specifically includes: The multimodal operation data is cleaned and aligned to eliminate misalignment in time and space. The relationships between various physical devices, electrical connections, and logic control units within the power plant were identified from the cleaned and aligned data. Based on the aforementioned relationships, the dynamic influence weights between each associated node are calculated; Based on the dynamic influence weights and the associated relationships, a topology map of the entire power plant's operational status, consisting of nodes and edges with dynamically variable weights, is generated.

[0008] Preferably, based on the power plant's overall operational status topology map and the environmental background information, extracting multi-granularity anomalies in power plant operation with different semantic levels specifically includes: Traverse the nodes and edges in the topology diagram of the power plant's overall operation status, and identify abnormal nodes and abnormal connection edges whose operating parameters deviate from the predetermined benchmark range; The environmental background information is analyzed to determine its inducing or aggravating effect on the abnormal nodes and abnormal connecting edges, thus forming an abnormal environmental coupling feature. Combining the abnormal nodes, abnormal connection edges, and environmental coupling anomaly characteristics, multi-granularity anomaly symptoms of power plant operation are mined from three dimensions: equipment level, branch level, and system level, respectively, to represent local anomalies, related anomalies, and system-level risks.

[0009] Preferably, a power plant anomaly knowledge base containing fault evolution patterns and response strategies is constructed and dynamically updated by using a large model to conduct in-depth analysis of historical fault cases. Specifically, this includes: Collect historical fault case reports, maintenance records, and corresponding complete operational data sequences of the power plant; The historical failure case reports and the complete operational data sequence are jointly analyzed using a large model to extract the initial causes, development paths, key turning points and final handling strategies of the failures. The extracted information is stored in a structured manner to form a power plant anomaly knowledge base indexed by fault modes, containing fault development chains and response operation sequences. Once a new fault case has been processed and confirmed, the update process is automatically initiated, integrating the analysis results of the new case into the power plant anomaly knowledge base.

[0010] Preferably, the real-time matching and reasoning of the multi-granularity abnormal symptoms of the power plant operation with the fault evolution patterns in the power plant anomaly knowledge base specifically includes: The multi-granularity abnormal symptoms of the power plant operation are used as query inputs, and a multi-dimensional search is performed in the power plant anomaly knowledge base. Calculate the similarity between the multi-granularity abnormal symptoms of the power plant operation and each fault evolution mode in the power plant anomaly knowledge base in terms of symptom manifestation, development sequence and environmental context; A set of candidate fault evolution patterns with high confidence is selected based on the similarity. The evolutionary patterns of the candidate faults are extrapolated using a large model to predict the most likely development path and potential risk points of the current anomaly.

[0011] Preferably, based on the matching and inference results, a multi-layered anomaly decision flow is generated for the current abnormal situation, specifically including: Based on the matched fault evolution patterns and their predicted development paths, a sequence of preliminary handling recommendations for different levels of urgency is generated; Analyze the power plant's overall operational status topology map and assess the potential cascading effects of implementing the preliminary handling recommendations on the non-faulty parts of the power plant; By integrating the preliminary treatment recommendation sequence with the assessment results of the chain reaction, a multi-layered abnormal decision flow is generated, which includes execution steps, expected effects, risk warnings, and backup plans.

[0012] Preferably, a collaborative analysis engine is launched to fuse and optimize the multi-layered anomaly decision flow, generating the final monitoring conclusions, specifically including: The collaborative analysis engine simulates the power plant's operating rules and safety constraints, and performs logical consistency verification and conflict detection on each operation in the multi-layered abnormal decision flow. Under the premise of satisfying all operating rules and safety constraints, conflicting or redundant operation steps are merged, sorted and optimized. By combining the real-time updated multimodal operational data, the optimized decision flow is pre-evaluated. Output the final monitoring conclusions after verification, optimization, and pre-evaluation.

[0013] Preferably, based on the monitoring conclusions, the output of a power plant health status profile characterizing the overall health status of the power plant specifically includes: Based on the monitoring findings, determine the overall impact level of the current anomaly on the power plant's power generation efficiency, equipment safety, and system stability. The current health indicators of the power plant are quantified from the dimensions of equipment availability, system reliability, and power loss. By integrating the comprehensive impact level with the health index, a visual health status profile of the power plant is formed, which intuitively displays the overall health status, weak links, and risk distribution of the power plant.

[0014] Preferably, based on the monitoring conclusions, the corresponding operation and maintenance decision clusters output specifically include: The operation instructions in the monitoring conclusions are classified according to the executing entity, execution priority, and execution time window; Configure detailed operating parameters, execution conditions, and acceptance criteria for each type of operation instruction; All categorized operation instructions with detailed configurations are packaged into a structured operation and maintenance decision cluster that can be directly parsed and scheduled by the operation and maintenance system.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By integrating multimodal operational data to construct a dynamically weighted topology map of the entire power plant's operational status, the monitoring object is upgraded from discrete equipment nodes to a visualized, dynamically interconnected network. The weights of the edges in the graph are driven by real-time electrical and thermal data, which can quantify the propagation path and impact intensity of anomalies in the physical topology. This technology enables the capture of global, latent anomalies caused by the deterioration of correlations, such as accurately locating the root node of a cascading decline in system efficiency caused by a fault in a certain branch, thus overcoming the limitation of traditional point-based alarms that cannot reveal the transmission patterns of anomalies.

[0016] This approach leverages large-scale models to perform in-depth analysis of unstructured historical cases, extracting multi-stage fault evolution patterns that incorporate temporal and causal relationships, and constructing a dynamically growing knowledge graph. Real-time extracted multi-granularity anomaly symptoms are then semantically matched with this graph, generating multiple candidate diagnostic decision flows based on confidence levels and evidence chains. These flows are then fused and optimized using a collaborative analysis engine. This scheme simulates the reasoning process of expert consultation, deconstructing complex and coupled anomalies and outputting an optimal decision sequence that integrates diagnostic accuracy, urgency of action, and operational economy. This enables the system to generalize and evolve in handling unknown combined faults. Attached Figure Description

[0017] Figure 1This is a flowchart illustrating the workflow of the AI-based monitoring method for abnormal power plant health status based on large model data analysis as described in this invention. Figure 2 A flowchart for constructing a topology diagram of the overall operation status of the power plant; Figure 3 A flowchart for building and dynamically updating a power plant anomaly knowledge base; Figure 4 A diagram illustrating the impact of power grid assessment risks on power generation losses; Figure 5 A comparative chart for quantitative analysis of power plant health indicators. Detailed Implementation

[0018] 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.

[0019] Please see Figure 1 This invention provides an AI-based monitoring method for abnormal power plant health status based on large-scale model data analysis. The method includes: synchronously acquiring multimodal operational data generated by equipment within the power plant during operation, along with associated environmental background information. The multimodal operational data covers electrical parameters, equipment status, operation logs, and communication messages. The environmental background information includes light intensity, ambient temperature, and meteorological data. Using the multimodal operational data, a power plant-wide operational status topology map reflecting the dynamic relationships within the power plant is constructed. This topology map represents the relationships between various physical devices, electrical connections, and logical control units within the power plant in the form of nodes and edges, with the edge weights representing dynamic influence weights. Based on the power plant-wide operational status topology map and the environmental background information, multi-granularity abnormal symptoms of power plant operation with different semantic levels are extracted from three dimensions: equipment level, branch level, and system level.

[0020] A large-scale model is used to conduct in-depth analysis of historical fault case reports and corresponding complete operational data sequences, extracting the initial causes, development paths, key turning points, and final handling strategies of the faults. This leads to the construction and dynamic updating of a power plant anomaly knowledge base indexed by fault modes and containing fault evolution patterns and response strategies. Real-time matching and reasoning are performed between the extracted multi-granularity anomaly symptoms of the power plant operation and the fault evolution patterns in the power plant anomaly knowledge base. The similarity in symptom manifestation, development sequence, and environmental context is calculated to filter candidate patterns and predict development paths. Based on the matching and reasoning results, a multi-layered anomaly decision flow is generated, including execution steps, expected effects, risk warnings, and backup plans. A collaborative analysis engine is launched to perform logical consistency verification, conflict detection, operational optimization, and effect pre-evaluation on the multi-layered anomaly decision flow, generating the final monitoring conclusions. Based on the monitoring conclusions, the comprehensive impact level is determined from the dimensions of power generation efficiency, equipment safety, and system stability, and equipment availability, system reliability, and power loss are quantified and generated, and integrated to form a visualized power plant health status profile; at the same time, the operation instructions in the monitoring conclusions are classified according to the execution subject, priority, and time window, and after configuring detailed parameters, they are packaged to generate a structured operation and maintenance decision cluster.

[0021] Example 1: The multimodal operation data refers to a heterogeneous data set collected by different types of sensors and monitoring equipment during the operation of the power station, encompassing electrical parameters, equipment status, operation logs, and communication messages. Electrical parameter data includes the DC-side voltage and current, AC-side voltage, current, and power of the inverter, as well as the electrical quantities of the transformer substation and connecting lines. Equipment status data includes the internal temperature, fan status, switch opening and closing position signals, and protection device status of equipment such as inverters, combiner boxes, and transformer substations. The operation log records key events and operation sequences within the equipment controller. Communication messages capture the communication interaction data between the station's monitoring system and various intelligent devices, as well as between devices themselves. All of this multimodal operation data constitutes the data foundation for anomaly monitoring and analysis.

[0022] In practice, multimodal operation data is synchronously acquired from the power plant's monitoring system. This data encompasses electrical parameters, equipment status, operation logs, and communication messages, collected in real time by sensors and monitoring equipment deployed throughout the power plant. Electrical parameter data includes the inverter's DC-side voltage and current, AC-side voltage and current, active and reactive power, as well as the voltage, current, power factor, and frequency of the box-type transformer and the connecting lines. Equipment status data includes the temperature of the inverter's internal insulated gate bipolar transistor (IGBT) modules, the opening and closing positions of the combiner box's DC circuit breakers, the monitored temperature of the box-type transformer windings, the cooling fan speed signal, and alarm and trip signals from protection devices. The operation log records inverter start-up events, inverter stop-up events, maximum power point tracking (MPPT) algorithm mode switching records, and the grid-connected contactor closing and opening sequences. The communication message data capture includes command and response frames between the monitoring host and the inverter based on the Modbus TCP protocol, polling and reporting messages between the combiner box and the data acquisition unit based on the RS485 bus, and manufacturing and specification messages between the box-type transformer protection device and the station control layer based on the IEC61850 standard. In some embodiments, specific examples of electrical parameter data include: at 12:00 noon, a group of inverters has a DC input voltage of 800 volts, a DC input current of 200 amps, an AC output voltage of 400 volts, an AC output current of 150 amps, and an output active power of 95 kilowatts; compared to the same group of inverters at 6:00 pm, the same group has a DC input voltage of 600 volts, a DC input current of 50 amps, an AC output voltage of 400 volts, an AC output current of 20 amps, and an output active power of 8 kilowatts.

[0023] In some embodiments, specific examples of device status data include: inverter A001 having an internal temperature sensor reading of 65 degrees Celsius, a cooling fan speed sensor reading of 3000 rpm, and a DC-side circuit breaker position signal in a closed state; compared to inverter A002 having an internal temperature sensor reading of 80 degrees Celsius, a cooling fan speed sensor reading of 4000 rpm, and a DC-side circuit breaker position signal in a closed state. Specific examples of operation log data include: inverter B001 executing an automatic restart sequence at timestamp 10:00:00 and completing grid synchronization at timestamp 10:00:05; compared to another entry in the operation log recording inverter B001 triggering a derating command due to an over-temperature alarm at timestamp 14:30:20. A specific example of the communication message data shows that the monitoring host (Internet Protocol address 192.168.1.10) sends a read holding register command with function code 0x03 to the inverter (Internet Protocol address 192.168.1.20), with a starting address of 0x0001 and a register count of 0x0002. The inverter's reply message data field contains a DC voltage register value of 800 and a DC current register value of 200. In contrast, the monitoring host (Internet Protocol address 192.168.1.10) sends a read input register command with function code 0x04 to the combiner box (Internet Protocol address 192.168.1.30), with a starting address of 0x1000 and a register count of 0x0006. The combiner box's reply message data field contains six string current values ​​of 5.1 amps, 5.2 amps, 5.0 amps, 5.3 amps, 0.1 amps, and 5.1 amps, respectively.

[0024] Optionally, the sampling frequency for electrical parameter data in the multimodal operation data is set to once per second, the sampling frequency for equipment status data is set to once every 5 seconds, the operation log data is appended to the log file in real time at the time of event occurrence, and the communication message data is captured by a network packet capture tool and appended with a timestamp in milliseconds. Optionally, the storage format of the multimodal operation data is as follows: Electrical parameter data and equipment status data are stored in a time-series database, operation log data is stored in a relational database, and communication message data is stored in binary files. In specific implementation, the multimodal operation data is preprocessed using a data integration formula, expressed as: ,in: This represents the aggregated multimodal running dataset, where K represents the data category index and belongs to the set. E represents electrical parameter data category, S represents equipment status data category, L represents operation log data category, C represents communication message data category, and T represents a unified timeline sequence. This represents the original data set under category K. This function represents the time alignment and format conversion of data in category K. Different categories of data are mapped to a unified timestamp and structured records. It can be understood that the heterogeneity of multimodal operation data is reflected in the differences in data value types: electrical parameter data is mainly floating-point values, equipment status data is mainly Boolean or integer enumeration values, operation log data is mainly string text, and communication message data is mainly hexadecimal byte stream.

[0025] Example 2: See Figure 2 The multimodal operation data is cleaned and aligned to eliminate misalignments in time and space, including uniformly calibrating timescales, filling in short-term data gaps caused by communication interruptions, and removing outliers that significantly exceed physical ranges. From the cleaned and aligned data, the relationships between various physical devices, electrical connections, and logic control units within the power station are identified. For example, the physical connections between all strings under the same combiner box, all combiner boxes under the same inverter, and all inverters under the same transformer substation are identified, as well as the logical control and communication relationships between the monitoring host and each lower-level device. Based on these relationships, the dynamic influence weights between each associated node are calculated. These dynamic influence weights are quantified based on the tightness of electrical coupling between nodes, the frequency of data flow interaction, and the dependency of control logic. Based on the dynamic influence weights and the relationships, a dynamically variable topology map of the entire power station's operation status, composed of nodes and edges, is generated. By traversing the nodes and edges in the topology diagram of the entire power plant's operation status, abnormal nodes and abnormal connection edges whose operating parameters deviate from a predetermined benchmark range are identified. The predetermined benchmark range is set based on equipment rated parameters, historical normal operating data, and environmental conditions. The inducing or aggravating effect of the environmental background information on the abnormal nodes and abnormal connection edges is analyzed to form environmental coupling anomaly characteristics. For example, the contribution of high-temperature environments to equipment temperature anomaly nodes is analyzed, or the rationality of power anomaly edges under low irradiance conditions is analyzed. Combining the abnormal nodes, abnormal connection edges, and environmental coupling anomaly characteristics, multi-granularity anomaly symptoms representing local anomalies, correlated anomalies, and system-level risks are mined from three dimensions: equipment level, branch level, and system level. Equipment-level symptoms include overheating of a single inverter module; branch-level symptoms include current inconsistency imbalance in multiple string currents under a combiner box; and system-level symptoms include systematic deviations between the overall power curve and theoretical values.

[0026] In practice, the multimodal operating data is cleaned and aligned to eliminate misalignments in time and space. This multimodal operating data includes electrical parameter data, equipment status data, operating logs, and communication messages. Specifically, the time alignment operation addresses clock discrepancies between different monitoring devices. For example, the environmental background information timestamp collected by the weather station is 10:00:00, while the power data timestamp collected by inverter A01 is 10:00:03. The alignment process uses the main clock of the power plant monitoring system as a reference, correcting the timestamps of all data to a unified 10:00:00. For cases where irradiance data at 10:00:00 from the weather station is missing, linear interpolation is performed using data from nearby times 10:00:05 and 09:59:55 to generate an estimated irradiance value at 10:00:00. Spatial alignment addresses inconsistencies in data source identifiers. For example, the operation log records "Inverter A01 fault," while the device address in the communication message is identified as "INV-01." The alignment process establishes a device alias mapping table, associating "Inverter A01" and "INV-01" with a unified device identifier "INV_A_01." Outlier removal identifies and processes data points exceeding physical ranges. For instance, a series of current sensors reports a current value of 125 amps at 10:00:00, while the rated maximum current for this model of string is 15 amps. The cleaning process marks this 125-amp data point as invalid and removes it from the data sequence. In some embodiments, a comparison of the cleaned and aligned data is as follows: the original data shows the inverter's DC voltage at 800 volts at 10:00:00, while the same inverter's DC voltage at 10:00:01 is recorded as 0.8 volts. The alignment and cleaning logic determines that 0.8 volts is a dimensional recording error and corrects it to 800 volts based on historical data patterns. In some embodiments, the relationships between physical devices, electrical connections, and logic control units within the power station are identified from the cleaned and aligned data. The identification process is based on the power station design drawings and configuration tables, establishing physical connection relationships. For example, it is identified that string numbers PV_String_001, PV_String_002 to PV_String_016 belong to combiner box number CJB_01, the output of combiner box number CJB_01 is connected to the DC input terminal of inverter number INV_A_01, and the AC output of inverter number INV_A_01 is connected to the low-voltage side of box-type transformer number T_01. Logic control relationships are parsed from communication messages. For example, the start / stop control command messages sent by the monitoring host to inverter number INV_A_01 are parsed to establish the logical control relationship between the monitoring host and inverter number INV_A_01. Similarly, the messages from the data acquisition unit periodically polling the currents within combiner box number CJB_01 are parsed to establish the data acquisition association between the data acquisition unit and combiner box number CJB_01.

[0027] Based on the correlation relationships, the dynamic influence weights between each associated node are calculated. Associated nodes refer to physical devices or logical units. The dynamic influence weights are quantified based on the tightness of electrical coupling between nodes, the frequency of data flow interaction, and the dependencies of control logic. The tightness of electrical coupling is calculated through the correlation of electrical parameters; for example, calculating the Pearson correlation coefficient between the string current of each circuit under combiner box number CJB_01 and the total output current of combiner box number CJB_01. The frequency of data flow interaction is determined by statistically analyzing the number and type of communication messages per unit time; for example, statistically analyzing the number of heartbeat messages and power control commands exchanged between the monitoring host and inverter number INV_A_01 per minute. Control logic dependencies are determined by analyzing the order of event triggering and conditional judgment logic in the operation log. The calculation of dynamic influence weights is achieved through an integrated formula, expressed as: ,in: This represents the dynamic influence weight between node m and node n. This represents the absolute value of the correlation coefficient between node m and node n within a selected time window, indicating the correlation between their key electrical parameter sequences. This represents the frequency of communication between node m and node n per unit time. This represents the maximum communication frequency between all node pairs across the entire site, used for normalization. This represents the control dependency of node m on node n. Its value is assigned between 0 and 1 based on the priority of the control command and whether it is a critical control path. α, β, and γ are the weighting coefficients for electrical coupling, communication interaction, and control dependency, respectively, and satisfy the following conditions: Optionally, based on dynamic influence weights and relationships, a topology diagram of the entire power plant's operational status, consisting of nodes and edges with dynamically variable weights, is generated. Nodes represent physical devices or logical units, and edges represent the relationships between nodes. The weights of the edges are determined by the dynamic influence weights. The decision is made. In the example, the topology graph contains nodes "PV_String_001", "CJB_01", and the edge connecting these two nodes. The weight of this edge is calculated based on the correlation coefficient between the string current and the total current of the combiner box, and the data reporting frequency between the string and the combiner box. In specific implementation, the nodes and edges in the topology graph of the entire power plant's operation status are traversed to identify abnormal nodes and abnormal connection edges whose operating parameters deviate from the predetermined benchmark range. The predetermined benchmark range is set based on the rated parameters of the equipment, historical normal operating data, and environmental conditions. For example, the predetermined benchmark range for the internal temperature of inverter number INV_A_01 is set to 0 degrees Celsius to 70 degrees Celsius. Under historical normal operating conditions, the internal temperature of this inverter is 65 degrees Celsius at an ambient temperature of 25 degrees Celsius and an output power of 80 kW. If the internal temperature of inverter number INV_A_01 is detected to reach 85 degrees Celsius, then node "INV_A_01" is determined to be an abnormal node. For connection edges, the predetermined reference range can be set as the normal difference range between associated parameters. For example, the difference in current of each string under combiner box number CJB_01 under uniform illumination should be less than 10% of the average value. If the current of string number PV_String_005 is detected to be 1 ampere, while the current of the other 15 strings is 5 amperes, then the edge connecting "PV_String_005" and "CJB_01" is determined to be an abnormal connection edge. The influence of environmental background information on the induction or aggravation of abnormal nodes and abnormal connection edges is analyzed to form environmental coupling anomaly characteristics. For example, when an abnormal temperature node is identified inside inverter number INV_A_01, and the environmental temperature data in the environmental background information is read as 40 degrees Celsius, the environmental coupling anomaly characteristic can be quantified as an "environmental high temperature contribution factor." This factor is based on the equipment thermal model to calculate the theoretical contribution ratio of the current environmental temperature to the equipment temperature rise. The equipment thermal model first determines the reference thermal balance state of the equipment under standard environmental conditions based on the equipment's design specifications and historical normal operation data, including the equipment's own heat generation, heat dissipation capacity, and heat exchange characteristics with the surrounding environment. When abnormal equipment temperature is detected, the model incorporates the current ambient temperature data in real time. By comparing the actual temperature value with the expected temperature value under the baseline thermal equilibrium state, the driving effect of environmental factors on equipment temperature rise is analyzed. Optionally, by combining the abnormal nodes, abnormal connection edges, and environmental coupling anomaly characteristics, multi-granularity anomaly symptoms in power plant operation are identified from three dimensions: equipment level, branch level, and system level. Equipment-level anomaly symptoms represent local anomalies, such as "the internal temperature of inverter INV_A_01 exceeds the safety threshold, and the environmental high temperature contribution factor is 0.3." Branch-level anomaly symptoms represent correlated anomalies, such as "the current of string number PV_String_005 under combiner box number CJB_01 is severely low, only 20% of the branch average current, and there is no record of shadow occlusion at the location of this string."System-level anomalies characterize system-level risks. For example, "the total output power of the power plant shows a continuous downward trend during periods of sustained high midday irradiance, deviating from the theoretical power curve by more than 15%, and the temperature of multiple inverter nodes shows an upward trend." It can be understood that the extraction of multi-granularity anomalies in power plant operation is based on cleaned and aligned data, dynamic topological correlations, and environmental coupling analysis.

[0028] Example 3: See Figure 3 Historical fault case reports, maintenance records, and corresponding complete operational data sequences of the power plant are collected, covering the entire process before, during, and after a fault. A large-scale model is used to jointly analyze the historical fault case reports and the complete operational data sequences. The model parses the textual descriptions in the fault reports and maps them to the operational data change trends of the corresponding time periods, thereby extracting the initial causes, development paths, key turning points, and final handling strategies of the fault. The extracted information is structured and stored to form a power plant anomaly knowledge base indexed by fault modes, containing fault development chains and response operation sequences. Each knowledge record includes fault phenomena, evolutionary stage characteristics, related environmental factors, handling measures, and effects. When a new fault case is processed and confirmed, an automatic update process is initiated, integrating the analysis results of the new case into the power plant anomaly knowledge base to achieve knowledge accumulation and evolution. Real-time extracted multi-granularity anomaly symptoms of power plant operation are used as query input for multi-dimensional retrieval in the power plant anomaly knowledge base. Search dimensions include anomaly parameter type, equipment level at which the anomaly occurred, and related environmental background. The similarity between the multi-granularity anomaly symptoms of the power plant operation and various fault evolution patterns in the power plant anomaly knowledge base is calculated in terms of symptom manifestation, development sequence, and environmental context. This similarity calculation integrates feature matching degree, time series morphological distance, and contextual relevance. Based on the similarity, a set of high-confidence candidate fault evolution patterns is selected. A large model is used to extrapolate the candidate fault evolution pattern set, and combined with the structure and state of the current power plant's overall operational topology, the most likely development path and potential risk points of the current anomaly are predicted.

[0029] In practice, historical fault case reports, maintenance records, and corresponding complete operational data sequences of the power plant are collected. The complete operational data sequences cover the entire process before, during, and after a fault occurs. Historical fault case reports are stored in text file format, recording the fault phenomenon description, occurrence time, personnel involved, cause analysis, and final handling measures. Maintenance records are stored in database tables, including faulty equipment identification, maintenance operation steps, information on replaced parts, and post-maintenance test results. The corresponding complete operational data sequences are extracted from the power plant's historical monitoring database, containing multimodal operational data from several hours to several days before and after the fault event. Example 1's historical fault case report describes that "Inverter INV_C_03 tripped due to AC side overcurrent protection at 14:20 on July 15, 2023. On-site inspection revealed that the cooling fan was stuck and the internal insulated gate bipolar transistor module was overheating. After handling, the fan was replaced and the unit was restarted to restore operation." The corresponding complete operating data sequence includes minute-by-minute internal temperature data, output current data, fan speed data, and event records in the operation log for inverter INV_C_03 from 12:00 to 16:00 on July 15. Example 2's historical fault case report describes that "The output current of multiple strings under combiner box CJB_12 remained consistently low. Inspection revealed a blown DC fuse and corrosion at the connection points." The corresponding complete operating data sequence includes daily current curves of each string under combiner box CJB_12 for the week prior to the fault, ambient temperature and humidity data, and comparative current data of other normal branches within the same combiner box. In some embodiments, a large model is used to jointly analyze historical fault case reports and complete operational data sequences. The large model parses the text descriptions in the fault reports, for example, extracting the key entities "cooling fan" and "insulated gate bipolar transistor module" and the states "stuck" and "overheating" from the report text "cooling fan stuck, internal insulated gate bipolar transistor module temperature too high". Simultaneously, the large model correlates the complete operational data sequence for the corresponding time period, identifying a trend where the internal temperature of inverter INV_C_03 continuously increased from 65 degrees Celsius to 90 degrees Celsius in the two hours before the trip, the fan speed data decreased from 3000 revolutions per minute to 0 revolutions per minute in the half hour before the fault, and the output current spiked instantaneously before the trip. By jointly analyzing the text semantics and data temporal changes, the large model extracts the initial cause of the fault as "cooling fan stopped," the development path as "fan stopped leading to cooling failure, insulated gate bipolar transistor module temperature continuously rising, overheating triggering overcurrent protection," the key turning point as "fan speed dropping to zero," and the final handling strategy as "replacing the cooling fan and restarting the inverter".In some embodiments, the extracted information is stored in a structured manner to form a power plant anomaly knowledge base indexed by fault modes, containing fault development chains and response operation sequences. Each knowledge record includes a fault mode name, fault phenomenon description, evolution stage characteristics, associated environmental factors, response measure sequence, and effect feedback. For example, a knowledge record is constructed for the above example, with the fault mode index being "Inverter heat dissipation fault mode A". The fault phenomenon description field includes "AC side overcurrent protection trip" and "Inverter internal temperature too high". The evolution stage characteristics field includes "Fan speed gradually decreasing stage", "Temperature linearly increasing stage", and "Protection triggering trip stage". The associated environmental factors field includes "Ambient temperature higher than 35 degrees Celsius". The response measure sequence field includes "1. Remotely confirm fan fault alarm; 2. Dispatch on-site inspection work order; 3. Replace the faulty fan on-site; 4. Clear fault record and remotely restart the inverter". The effect feedback field includes "After replacement, the inverter temperature returned to normal, and grid connection was successful after restarting".

[0030] In practice, once a new fault case is resolved and confirmed by on-site maintenance personnel, the update process is automatically initiated. The update process is triggered when the maintenance record status field is updated to "closed loop" and more than 24 hours have passed since the fault occurred. The automatic update process invokes the large-scale model analysis service, inputting the new fault case report and complete operational data sequence. The large-scale model outputs new knowledge entries. The knowledge management system compares and integrates these new entries with existing entries in the power plant anomaly knowledge base. If it's a completely new pattern, an index is added; if it's a supplementary case to an existing pattern, the evolution characteristics or handling measures of the original record are updated. Optionally, real-time extracted multi-granularity anomaly symptoms of power plant operation can be used as query input for multi-dimensional retrieval in the power plant anomaly knowledge base. Retrieval dimensions include anomaly parameter type, equipment level at which the anomaly occurred, and associated environmental background. For example, real-time extracted multi-granularity anomaly symptoms of power plant operation include "inverter number INV_D_05 internal temperature exceeds 75 degrees Celsius, fan speed is 2800 rpm, ambient temperature is 38 degrees Celsius." The retrieval process matches all fault modes containing the keywords "overheating" and "fan" in the power plant anomaly knowledge base, and further filters records with the equipment level of "inverter" and environmental factors including "high temperature." In specific implementation, the similarity between the multi-granularity anomaly symptoms of power plant operation and various fault evolution modes in the power plant anomaly knowledge base in terms of symptom manifestation, development sequence, and environmental context is calculated. Symptom manifestation similarity compares the closeness of the specific parameters of the current anomaly symptom to the parameters of historical fault phenomena recorded in the knowledge base. Development sequence similarity compares the matching degree of the changing trend of the current anomaly symptom parameters with the time series pattern of historical fault development stages recorded in the knowledge base. Environmental context similarity compares the consistency of the environmental background information when the current anomaly occurs with the environmental factors associated with historical faults recorded in the knowledge base. The similarity calculation is achieved through a multi-dimensional feature distance integration formula, expressed as: Where: S represents the overall similarity score. This represents the Euclidean distance between the current abnormal symptom feature vector X and the historical symptom feature vector Y of a certain fault mode in the knowledge base. The feature vector contains normalized values ​​of specific parameters such as temperature and speed. Indicates the curve of current abnormal parameter changes Compared with the fault development time-series curves recorded in the knowledge base The dynamic time-normalized distance between them Represents the current environment context vector Context vectors of historical failure cases Cosine similarity distance between them The weighting coefficients are for three dimensions: symptom manifestation, development sequence, and environmental context, and satisfy the following conditions: This can be understood as filtering out a set of candidate fault evolution patterns with high confidence based on similarity, and setting a similarity score threshold, for example, setting a threshold. =0.8, all fault modes with an S value greater than 0.8 are selected as the candidate fault evolution mode set. Optionally, a large model is used to extrapolate the candidate fault evolution mode set to predict the most likely development path and potential risk points of the current anomaly. The input of the large model includes the historical development chain of each mode in the candidate fault evolution mode set, the structure and status of the current power plant's overall operating topology map, and the latest multimodal operating data. For example, if the candidate set includes "inverter heat dissipation fault mode A" and "DC-side insulation fault mode B", the large model analyzes the position of inverter number INV_D_05 in the circuit and the status of its adjacent equipment based on the topology map. Combined with the current continuous high temperature conditions, it extrapolates that if it develops according to "inverter heat dissipation fault mode A", inverter number INV_D_05 may trigger over-temperature derating within 2 hours, affecting the power generation of its branch; if there are early characteristics of "DC-side insulation fault mode B", it may develop into a short circuit to ground risk. The large model output prediction result is "the most likely development path is inverter heat dissipation failure mode A, and the potential risk point is that over-temperature protection will be triggered within 2 hours, resulting in power loss".

[0031] Example 4: Based on the matched fault evolution pattern and its predicted development path, preliminary handling suggestion sequences of different urgency levels are generated. These sequences are derived from the corresponding fault mode response operation sequences in the power plant anomaly knowledge base. The power plant's overall operational topology is analyzed to assess the potential cascading effects of executing the preliminary handling suggestion sequences on non-faulty parts of the power plant. For example, the impact of disconnecting a faulty branch on the power quality and system stability at the grid connection point is assessed, or the impact of derated operation of a certain device on overall power generation is assessed. The preliminary handling suggestion sequences and the cascading impact assessment results are integrated to generate a multi-layered anomaly decision flow containing execution steps, expected effects, risk warnings, and backup plans. The decision flow clearly defines the operation objects, operation instructions, execution conditions, and expected results for each step. A collaborative analysis engine is activated. This engine simulates the power plant's operating rules and safety constraints, performing logical consistency checks and conflict detection on each operation in the multi-layered anomaly decision flow. For example, it detects whether mutually exclusive instructions are issued to the same device simultaneously, or whether the operation sequence violates electrical operation safety regulations. Under the premise of satisfying all operational rules and safety constraints, conflicting or redundant operational steps are merged, sorted, and optimized to form a logically consistent and efficient operational sequence. Combined with the real-time updated multimodal operational data, the optimized decision flow is pre-evaluated to simulate possible changes in the power plant's operational status after execution. The collaborative analysis engine outputs the final monitoring conclusions after verification, optimization, and pre-evaluation.

[0032] In practice, based on the matched fault evolution mode and its predicted development path, a sequence of preliminary handling suggestions of different urgency levels is generated. The fault evolution mode is derived from records in the power plant anomaly knowledge base. For example, if the matched fault mode is "inverter heat dissipation fault mode A" and its predicted development path is "over-temperature derating may be triggered within 2 hours," then the generated sequence of preliminary handling suggestions includes: high urgency "immediately remotely reduce the output power of inverter INV_D_05 to 80% of the rated value," medium urgency "dispatch an inspection work order to check the cooling fan of inverter INV_D_05," and low urgency "arrange for nighttime shutdown to clean the cooling system." The sequence of preliminary handling suggestions is stored in the form of a structured list, and each suggestion contains the operation object, operation instruction, and urgency level identifier. In some embodiments, the topology map of the entire power plant's operational status is analyzed to assess the potential cascading effects of implementing the initial response recommendations on non-faulty parts of the power plant. The assessment process is based on the dynamic impact weights and electrical connections between nodes in the topology map. For example, assessing the operation of remotely reducing the output power of inverter INV_D_05, which is the largest capacity inverter under box-type transformer T_02, may lead to a decrease in the load rate of box-type transformer T_02, thereby affecting the power output plan required by the grid dispatch. The cascading impact assessment result is quantified as "the total output power of box-type transformer T_02 is expected to decrease by 50 kilowatts, which may trigger grid assessment." In some embodiments, the initial response recommendations and the cascading impact assessment results are integrated to generate a multi-layered anomaly decision flow that includes execution steps, expected effects, risk warnings, and backup plans. The multi-layered anomaly decision flow adopts a hierarchical structure, with the top layer being the strategic objective layer, the middle layer being the tactical operation layer, and the bottom layer being the specific execution layer. The strategic objective layer defines the overall handling objective, such as "preventing equipment damage while minimizing power generation losses." The tactical operation layer prioritizes and assigns dependencies to operational steps, while the specific execution layer refines the parameters and execution conditions for each step. The fusion process associates each suggestion in the initial handling recommendation sequence with the corresponding cascading impact assessment results and adds backup plans for high-risk operations. For example, for the step of "remotely reducing the output power of inverter INV_D_05," a risk warning is added: "This may cause the output of box-type transformer T_02 to fail to meet standards," and a backup plan is set: "If the grid allows, prioritize adjusting the power of other branches for compensation." See Table 1, Multi-level Anomaly Decision Flow Table.

[0033] Table 1: Multi-level Anomaly Decision Flow Table Decision flow hierarchy Operation steps description Expected results Risk Warning Alternative Plan Strategic Target Level Balancing equipment safety and power generation efficiency Avoid overheating failures and keep losses under control. Power fluctuations may affect the power grid none Tactical Operations Layer Step 1: Derating the inverter INV_D_05 Reduce the rate of temperature rise The total output of transformer T_02 decreased. Step 1a: Adjust the power of inverter INV_D_04 Tactical Operations Layer Step 2: Distribute fan inspection work orders Identify and fix the root cause. Risk persists during inspection Step 2a: Prepare a spare fan Execution layer Set the power limit for INV_D_05 to 80%. Temperature stabilized below 70℃ Real-time power data monitoring Perform step 1a In practical implementation, a collaborative analysis engine is activated. This engine simulates the power plant's operating rules and safety constraints. Operating rules include electrical operation safety procedures, equipment operating limits, and grid dispatch protocols. Safety constraints include insulation safety distances, maximum permissible loads, and protection setting ranges. The collaborative analysis engine performs logical consistency checks and conflict detection on each operation in the multi-layered abnormal decision flow. Logical consistency checks verify whether the operation sequence conforms to the operational logic; for example, it checks for the reverse order of "disconnecting the combiner box circuit breaker" before "confirming the combiner box current is zero." Conflict detection identifies the mutual exclusion between operation commands; for example, it detects that two operations, "closing the inverter AC contactor" and "performing an inverter insulation resistance test," exist simultaneously in the multi-layered abnormal decision flow, while safety rules require that the AC side connection must be disconnected during the insulation test. Conflict detection quantifies the degree of conflict between operation steps using a conflict score formula, expressed as:

[0034] in: Indicates operation steps Operating steps Conflict scores between them This indicates the total number of operating rules and safety constraints for the power plant. This represents the Kth rule or constraint. It is an indicator function, when the operation and Simultaneous violation or potential violation of the rules The function value is 1 when the condition is met, and 0 otherwise. For example, rule The instruction is "AC side must be disconnected during insulation testing". For "closed inverter AC contactor", operation To perform an inverter insulation resistance test, then =1. Optionally, under the premise of satisfying all operating rules and safety constraints, conflicting or redundant operation steps are merged, sorted, and optimized. The merging operation integrates multiple operations targeting the same device into a composite operation, such as merging "reduce power to 80%" and "enable backup cooling mode" into "set power limit to 80% and enable backup cooling". The sorting operation rearranges the order of steps according to operation dependencies and urgency. The optimization operation removes redundant steps, such as removing duplicate "confirm equipment status" steps. The optimization process aims to minimize the conflict score and maximize execution efficiency. Optionally, the optimized decision flow is pre-evaluated by combining real-time updated multimodal operating data. The pre-evaluation simulates the execution of each step in the optimized decision flow and predicts changes in the power plant status based on real-time data. For example, after simulating the execution of "set the power limit of inverter number INV_D_05 to 80%", real-time irradiance data and inverter efficiency curves are read to predict the change in the total power generation of the power plant in the next hour and the change trend of the internal temperature of inverter number INV_D_05. The preliminary effect assessment outputs quantitative indicators such as expected power loss and probability of risk reduction. It can be understood that the collaborative analysis engine outputs the final monitoring conclusions after verification, optimization, and preliminary assessment. These final monitoring conclusions are a comprehensive statement, including confirmed anomaly types, recommended action sequences, expected results, and a description of remaining risks.

[0035] See Figure 4 In the assessment of the cascading impact of reducing the output power of inverter INV_D_05, the impact on grid assessment risk and power generation loss is quantified using multi-dimensional indicators. Specifically, the analysis uses "Power Quality," "Power Output," "Voltage Stability," "Frequency Fluctuation," and "Grid Connection Compliance" as impact categories, corresponding to quantified grid assessment risk scores (0-100 points) and power generation loss percentages (%). Grid assessment risk (purple bars) is represented by scores from 0-100, reaching its highest level (approximately 74 points) under the "Power Output" category, indicating the most significant impact of this operation on grid assessment in the power output dimension. The lowest risk (approximately 5 points) is under the "Frequency Fluctuation" category, reflecting a smaller disturbance to frequency stability. Power generation loss (orange line) quantifies the loss magnitude as a percentage, with the highest loss (approximately 8%) under the "Power Output" category, consistent with the peak dimension of grid assessment risk. The lowest loss (approximately 0.3%) is under the "Frequency Fluctuation" category, corresponding to the risk distribution. In the process of quantifying indicators, the power grid assessment risk is calculated based on the dynamic influence weight of nodes in the topology graph and the threshold of the power grid dispatch protocol. The power generation loss is derived by combining the equipment capacity ratio and real-time irradiance data. The correlation distribution of the two intuitively presents the differences in the impact of operations on different dimensions of the power plant system.

[0036] Example 5: Based on the monitoring conclusions, determine the comprehensive impact level of the current anomaly on the power plant's power generation efficiency, equipment safety, and system stability. This comprehensive impact level is categorized according to the potential power loss, equipment damage risk, and system outage probability. Quantify the power plant's current health indicators from multiple dimensions, including equipment availability, system reliability, and power loss. Equipment availability is calculated based on the proportion of unavailable equipment capacity; system reliability is assessed based on the frequency and severity of recent anomaly events; and power loss is calculated based on the difference between the current actual power and the ideal power. Integrate the comprehensive impact level with the health indicators to form a visually appealing power plant health status profile, intuitively displaying the overall health status, weaknesses, and risk distribution. This profile is displayed through a graphical interface, using different colors and legends to identify the health status and risk level of each area. Classify the operational instructions from the monitoring conclusions according to the executing entity, execution priority, and execution time window. The executing entities include the station's automatic control system, robotic inspection system, remote monitoring center, or on-site maintenance personnel. For each type of operation instruction, configure detailed operation parameters, execution conditions, and acceptance criteria. Operation parameters include setting power limits and target temperature thresholds; execution conditions include meeting specific environmental conditions or equipment status; and acceptance criteria include the range to which key parameters should be restored after the operation. Package all categorized operation instructions with detailed configurations into a structured operation and maintenance decision cluster that can be directly parsed and scheduled by the operation and maintenance system. This decision cluster can be distributed to the corresponding execution system or personnel.

[0037] In practice, the comprehensive impact level of the current anomaly on the power plant's power generation efficiency, equipment safety, and system stability is determined based on monitoring conclusions. These conclusions originate from the final monitoring results output by the collaborative analysis engine. For example, a monitoring conclusion might state that "Inverter INV_D_05 has poor heat dissipation; it is recommended to reduce its operating rate and schedule inspections. This is expected to cause a 20% decrease in the power generation of its branch, posing a risk of overheating damage, but has no direct impact on the overall system stability." Based on this conclusion, the comprehensive impact level is determined according to preset quantitative rules. The impact level on power generation efficiency is categorized based on the power loss ratio; for example, a loss of less than 5% is considered "low impact," a loss between 5% and 20% is "medium impact," and a loss greater than 20% is "high impact." In this example, a 20% power loss corresponds to a "medium impact" level. The impact level on equipment safety is categorized based on the risk and consequences of equipment failure; for example, "risk of overheating damage" corresponds to a "medium-high risk" level. The impact level on system stability is categorized based on whether it triggers grid fluctuations or protection actions; in this example, "no direct impact on the overall system stability" corresponds to a "no impact" level. Combining the above three dimensions, a decision matrix maps "Medium impact on power generation efficiency," "Medium-high risk on equipment safety," and "No impact on system stability" to a comprehensive impact level of "Level 2 Alarm." In some embodiments, the current health index of the power plant is quantified from equipment availability, system reliability, and power loss. The equipment availability index calculates the percentage of currently unavailable equipment capacity relative to the total installed capacity of the power plant. For example, if the total installed capacity of the power plant is 10 MW, and the effective capacity of inverter INV_D_05 is reduced from 1 MW to 0.8 MW due to derating, it is considered that 0.2 MW of capacity is unavailable. Assuming that no other equipment in the plant is down, the equipment availability rate = (10-0.2) / 10*100% = 98%. The system reliability index is calculated based on the frequency and severity of recent abnormal events. For example, it counts the number of alarm events of different levels that occurred in the past 24 hours and performs a weighted sum, expressed by the formula:

[0038] Where: R represents the system reliability index score range of 0-100, and N represents the total number of alarm event types that occurred within the statistical period. This represents the number of times the i-th type of alarm event occurred within the statistical period. This represents the severity weighting coefficient for the i-th type of alarm event. The weighting coefficient is predefined based on the severity of the alarm's impact; for example, a "Level 1 alarm" has a weight of 5, a "Level 2 alarm" has a weight of 3, and a "Level 3 alarm" has a weight of 1. The power loss index calculates the percentage difference between the current actual total power generation and the theoretical maximum power generation under current environmental conditions. For example, if the current actual total power generation is 8.5 MW, and the theoretical maximum power generation calculated based on real-time irradiance and temperature is 9.0 MW, then the power loss = (9.0 - 8.5) / 9.0 * 100% ≈ 5.56%. Optionally, the comprehensive impact level and health index can be integrated to form a visually appealing profile of the power plant's overall health, weaknesses, and risk distribution. The power plant health status profile is presented through a graphical interface. The center of the interface features a simplified topology diagram of the power plant's electrical system. Different device icons in the topology diagram represent their status through color and shape. For example, inverters in normal operation are displayed as green squares, inverter number INV_D_05 in derating operation is displayed as an orange square flashing, and devices in a fault-prone offline state are displayed as red squares. One side of the interface displays real-time values ​​for health indicators such as device availability (98%), system reliability score (85), and power loss (5.56%) in a dashboard format. The other side of the interface lists currently active alarms and their comprehensive impact levels, such as "Inverter INV_D_05 heat dissipation abnormality, comprehensive impact level: Level 2 alarm". The color intensity of device icons in the topology diagram also reflects the historical alarm frequency of that device; darker colors indicate more frequent recent alarms, thus identifying weak points. Risk distribution is represented by red semi-transparent areas of varying transparency overlaid on relevant locations in the topology diagram. The size and color transparency of the areas indicate the breadth and severity of the risk impact.

[0039] In practical implementation, based on monitoring conclusions, corresponding operation and maintenance decision clusters are output, including recommended operation sequences. The operation instructions in the monitoring conclusions are categorized according to the executing entity, execution priority, and execution time window. The executing entity refers to the recipient and executor of the operation instructions, and categories include on-site automatic control systems, robotic inspection systems, remote monitoring centers, and on-site maintenance personnel. Execution priority is categorized according to the urgency of the operation, such as "execute immediately," "execute within 1 hour," "execute today," and "planned execution." The execution time window specifies the specific or relative time range within which the operation is allowed to be executed. For example, for the operation "remotely reduce the output power of inverter INV_D_05 to 80%" in the above monitoring conclusions, its executing entity is categorized as "on-site automatic control system," its execution priority is categorized as "execute immediately," and its execution time window is categorized as "immediately after the temperature continuously exceeds the threshold." The operation "dispatch an inspection work order to check the cooling fan of inverter INV_D_05" is categorized as "on-site maintenance personnel," its execution priority is categorized as "execute today," and its execution time window is categorized as "today's daytime working hours." In some embodiments, detailed operating parameters, execution conditions, and acceptance criteria are configured for each type of operation instruction. For the instruction "Remotely reduce the output power of inverter INV_D_05 to 80%", the configured operating parameter is "Power limit ratio: 80%", the execution condition is "The internal temperature of inverter INV_D_05 remains above 75 degrees Celsius for 5 minutes and the fan speed is below the rated value", and the acceptance criterion is "Within 2 minutes after the instruction is issued, monitoring data shows that the output power of inverter INV_D_05 drops to 80% ± 2% of the rated value, and the internal temperature stops rising or begins to decrease". For the instruction "Dispatch an inspection work order to check the cooling fan of inverter INV_D_05", the configured operating parameters are "Work order type: Equipment inspection; Target equipment: INV_D_05; Inspection items: Cooling fan operation status, fan inlet and outlet air ducts", the execution condition is "The work order is received by the dispatch system and assigned to available maintenance personnel", and the acceptance criterion is "The maintenance personnel provide feedback on the inspection results via mobile terminal, including photos and descriptions, and close the work order". It is understandable that all categorized operation instructions with detailed configurations are packaged into a structured operation and maintenance decision cluster that can be directly parsed and scheduled by the operation and maintenance system. The operation and maintenance decision cluster adopts a standardized data exchange format, such as JSON or XML, and defines the root node as "operation and maintenance decision cluster", which contains multiple "operation instruction" sub-nodes. Each "operation instruction" sub-node contains "execution subject", "execution priority", "execution time window", "operation parameters", "execution conditions" and "acceptance criteria".Optionally, after parsing the operation and maintenance decision cluster, the operation and maintenance system routes the instructions to the corresponding automated execution module or generates a manual work order to be dispatched to the corresponding personnel based on the "execution subject" field. It arranges the execution queue according to the "execution priority" and "execution time window". During the execution, it issues control commands based on the "operation parameters", determines whether to trigger execution based on the "execution conditions", and verifies the execution effect based on the "acceptance criteria" after execution.

[0040] See Figure 5 In the quantitative analysis of power plant health indicators, a multi-dimensional bar chart was used to present a comparison of normal values, current values, and alarm thresholds for three core health indicators: equipment availability, system reliability score, and power loss. Specifically, in the equipment availability dimension, both the normal and current values ​​are close to 100% and higher than the alarm threshold; in the system reliability score dimension, the normal value is 100, the current value is approximately 85, and the alarm threshold is 80; in the power loss dimension, the current value is approximately 5%, and the alarm threshold is approximately 10%. This quantitative comparison of indicators intuitively reflects the current operating status of the power plant: equipment availability is at a good level, the system reliability score is close to the alarm threshold requiring attention, and power loss is within a controllable range. This chart can quickly pinpoint weak points in the power plant's health status, providing data support for operation and maintenance decisions.

[0041] 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.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power plant health state anomaly AI monitoring method based on large model data analysis, characterized in that, The following process is performed: Synchronously acquire multi-modal operation data generated by equipment in the power station during operation and associated environmental background information; Use the multi-modal operation data to construct a power station global operation situation topology graph that can reflect the global dynamic correlation of the power station; Based on the power station global operation situation topology graph and the environmental background information, extract power station operation multi-granularity abnormal signs with different semantic levels; Use a large model to deeply analyze historical fault cases, construct and dynamically update a power station abnormal knowledge base containing fault evolution patterns and coping strategies; Real-time match and reason the power station operation multi-granularity abnormal signs with the fault evolution patterns in the power station abnormal knowledge base; According to the matching and reasoning results, generate a multi-layer abnormal decision flow for the current abnormal situation; Start a collaborative analysis engine to fuse and optimize the multi-layer abnormal decision flow to generate the final monitoring conclusion; Based on the monitoring conclusion, output a power station health status portrait representing the overall health status of the power station and the corresponding operation and maintenance decision cluster.

2. The power plant health state anomaly AI monitoring method based on large model data analysis according to claim 1, characterized in that, The multi-modal operation data refers to a heterogeneous data set collected by different types of sensors and monitoring equipment during the operation of the power station, covering electrical parameters, equipment status, operation logs, and communication messages. 3.The power plant health state anomaly AI monitoring method based on large model data analysis of claim 1, wherein, Using the multi-modal operation data to construct a power station global operation situation topology graph that can reflect the global dynamic correlation of the power station specifically includes: Clean and align the multi-modal operation data to eliminate data misalignment in time and space dimensions; Identify the correlation between each physical device, electrical connection, and logical control unit in the power station from the cleaned and aligned data; Based on the correlation, calculate the dynamic influence weight between each correlation node; According to the dynamic influence weight and the correlation, generate a power station global operation situation topology graph with dynamic variable weights composed of nodes and edges.

4. The power plant health state anomaly AI monitoring method based on large model data analysis according to claim 1, characterized in that, Based on the power station global operation situation topology graph and the environmental background information, extract power station operation multi-granularity abnormal signs with different semantic levels specifically includes: Traverse the nodes and edges in the power station global operation situation topology graph to identify abnormal nodes and abnormal connection edges whose operation parameters deviate from the predetermined reference range; Analyze the inducing or aggravating effect of the environmental background information on the abnormal nodes and the abnormal connection edges to form environmental coupling abnormal characteristics; Combine the abnormal nodes, the abnormal connection edges, and the environmental coupling abnormal characteristics to mine power station operation multi-granularity abnormal signs representing local abnormalities, correlation abnormalities, and system-level risks from three dimensions: device level, branch level, and system level. 5.The power plant health state anomaly AI monitoring method based on large model data analysis of claim 1, wherein, Use a large model to deeply analyze historical fault cases, construct and dynamically update a power station abnormal knowledge base containing fault evolution patterns and coping strategies specifically includes: Collect historical fault case reports, maintenance records, and corresponding complete operation data sequences of the power station; Use a large model to jointly analyze the historical fault case reports and the complete operation data sequences to extract the initial cause, development path, key turning point, and final disposal strategy of the fault; The extracted information is structured and stored to form a power plant anomaly knowledge base indexed by failure modes, containing failure development chains and response operation sequences; When a new failure case is processed and confirmed, an update process is automatically started to integrate the analysis results of the new case into the power plant anomaly knowledge base.

6. The power plant health state anomaly AI monitoring method based on large model data analysis according to claim 1, characterized in that, Real-time matching and reasoning of the power plant operation multi-granularity abnormal signs with the failure evolution modes in the power plant anomaly knowledge base specifically includes: The power plant operation multi-granularity abnormal signs are input as queries for multi-dimensional search in the power plant anomaly knowledge base; The similarity of the power plant operation multi-granularity abnormal signs with each failure evolution mode in the power plant anomaly knowledge base in terms of sign performance, development timing, and environmental context is calculated; According to the similarity, a high-confidence candidate failure evolution mode set is screened out; Using a large model, the candidate failure evolution mode set is deduced to predict the most likely development path and potential risk points of the current anomaly. 7.The power plant health state anomaly AI monitoring method based on large model data analysis of claim 1, wherein, According to the matching and reasoning results, a multi-layer anomaly decision flow for the current abnormal situation is generated, which specifically includes: According to the matched failure evolution modes and their predicted development paths, a preliminary disposal suggestion sequence of different emergency levels is generated; The power plant global operation situation topology is analyzed to evaluate the possible chain effects on the non-failure parts of the power plant caused by executing the preliminary disposal suggestion sequence; The preliminary disposal suggestion sequence and the evaluation results of the chain effects are fused to generate a multi-layer anomaly decision flow containing execution steps, expected effects, risk prompts, and backup solutions. 8.The power plant health state anomaly AI monitoring method based on large model data analysis of claim 1, wherein, A collaborative analysis engine is started to fuse and optimize the multi-layer anomaly decision flow to generate the final monitoring conclusion, which specifically includes: The collaborative analysis engine simulates power plant operation rules and safety constraints to perform logical consistency verification and conflict detection on each operation in the multi-layer anomaly decision flow; On the premise of meeting all operation rules and safety constraints, operation steps with conflicts or redundancies are merged, sorted, and optimized; Combined with the real-time updated multi-modal operation data, the optimized decision flow is pre-evaluated for effectiveness; The final monitoring conclusion after verification, optimization, and pre-evaluation is output. 9.The power plant health state anomaly AI monitoring method based on large model data analysis of claim 1, wherein, Based on the monitoring conclusion, a power plant health status portrait representing the overall health status of the power plant is output, which specifically includes: According to the monitoring conclusion, the comprehensive impact level of the current anomaly on power plant generation efficiency, equipment safety, and system stability is determined; From the dimensions of equipment availability, system reliability, and power loss, the current health degree index of the power plant is quantitatively generated; The comprehensive impact level and the health degree index are fused to form a visual power plant health status portrait that directly displays the overall health status, weak links, and risk distribution of the power plant. 10.The power plant health state anomaly AI monitoring method based on large model data analysis of claim 1, wherein, Based on the monitoring conclusion, an operation and maintenance decision cluster corresponding to it is output, which specifically includes: The operation instructions in the monitoring conclusion are classified according to execution subject, execution priority, and execution time window; Detailed operation parameters, execution conditions, and acceptance standards are configured for each type of operation instruction; All classified operation instructions with detailed configurations are packaged to form a structured operation and maintenance decision cluster that can be directly parsed and scheduled by the operation and maintenance system.

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