Multi-dimensional data acquisition and intelligent analysis monitoring method and system for new energy station, and storage medium

Abstract

The invention discloses a multi-dimensional data acquisition and intelligent analysis monitoring method and system for a new energy station, and a storage medium, and relates to the field of new energy station monitoring. Constructing undirected isomerism graphs of the time sequence monitoring data and the operation state by utilizing the knowledge graph, wherein a plurality of undirected isomerism graphs form a knowledge graph intelligent analysis model; coding learning is performed on the undirected heterogeneous graph through the graph convolutional neural network, a coding learning result is subjected to feature extraction, a feature extraction result is analyzed, and an association relationship between the coding learning result and the feature extraction result is obtained; determining a data acquisition priority level of the new energy station according to the association relationship, and performing multi-dimensional data acquisition on the new energy station based on the data acquisition priority level to obtain a multi-dimensional data acquisition strategy; and collecting current data of the new energy station in real time according to the multi-dimensional data collection strategy, inputting the current data into the knowledge graph intelligent analysis model, and analyzing the influence of the current data on the operation state of the new energy station. According to the invention, accurate monitoring and scientific management of the new energy station are realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy station monitoring, and more specifically to a method, system and storage medium for multi-dimensional data collection and intelligent analysis monitoring of new energy stations. Background Art

[0002] As global demand for clean energy continues to grow, the scale and number of renewable energy power plants, such as solar and wind, are constantly increasing. Renewable energy generation is characterized by intermittent and fluctuating performance, and its efficiency is easily affected by a variety of complex factors, including light intensity, wind speed and direction, and temperature.

[0003] Traditional monitoring methods for renewable energy stations rely on single-dimensional data collection, such as focusing solely on power generation data, which provides limited insight into station operations. This one-sided data acquisition makes it difficult to accurately identify potential equipment failures and quickly locate the root cause when a failure occurs, significantly impacting the stability and reliability of renewable energy stations. Furthermore, the lack of efficient integration and intelligent analysis of massive amounts of dispersed data hinders operational decision-making, hindering the refined management and operational efficiency of renewable energy stations.

[0004] In terms of data processing and analysis, the amount of data generated by new energy stations is extremely large. A medium-sized wind farm can generate up to several terabytes of data every day, and the data sources are scattered, covering equipment from different manufacturers and a variety of sensors. However, existing systems are mostly simple data storage and lack efficient data integration and intelligent analysis capabilities. Massive amounts of data cannot be converted into valuable information to provide strong support for operational decisions such as daily operation and maintenance, equipment maintenance, and power generation forecasting at the station. For example, when arranging equipment maintenance plans, due to the lack of comprehensive analysis of historical equipment operation data and environmental factors, excessive maintenance or untimely maintenance often occurs, which not only increases operation and maintenance costs but also reduces equipment availability.

[0005] To sum up, in order to improve the operation and management level of new energy stations and ensure stable and reliable power supply, the development of a multi-dimensional data collection and intelligent analysis and monitoring method has become an urgent need for the development of the new energy industry. Summary of the Invention

[0006] In view of this, the present invention provides a multi-dimensional data collection and intelligent analysis monitoring method, system and storage medium for new energy stations. By comprehensively collecting various types of data and using advanced intelligent analysis technology, accurate monitoring and scientific management of new energy stations can be achieved, which is of great significance to promoting the high-quality development of the new energy industry.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for multi-dimensional data collection and intelligent analysis and monitoring of new energy stations, comprising the following steps:

[0009] Obtain historical time-series monitoring data and corresponding operating status of new energy stations, use knowledge graphs to construct undirected heterogeneous graphs of time-series monitoring data and operating status, and multiple undirected heterogeneous graphs form a knowledge graph intelligent analysis model;

[0010] Through graph convolutional neural networks, undirected heterogeneous graph encoding learning is carried out, feature extraction encoding learning results are extracted and analyzed, and the correlation between historical time series detection data and operating status is obtained;

[0011] The data collection priority of the new energy station is determined by the association relationship, and multi-dimensional data collection is performed on the new energy station based on the data collection priority to obtain a multi-dimensional data collection strategy for the new energy station;

[0012] According to the multi-dimensional data collection strategy, the current data of the new energy station is collected in real time, and the current data is input into the knowledge graph intelligent analysis model to analyze the impact of the current data on the operating status of the new energy station and display it on the control terminal.

[0013] Optionally, it also includes constructing an undirected heterogeneous graph of historical data control strategies and historical time-series monitoring data strategies in the knowledge graph intelligent analysis model; judging from the display terminal whether the current data is not conducive to the stable operation of the new energy station, and if so, inputting the current data into the knowledge graph intelligent analysis model, outputting the data control strategy, and updating the current knowledge graph intelligent analysis model.

[0014] Optionally, the knowledge graph is used to construct an undirected heterogeneous graph of the time series monitoring data and the operating status, specifically including: extracting the first key information from the time series monitoring data to construct a monitoring data layer; extracting the second key information from the operating status to construct an operating status layer; the first key information and the second key information constitute a relationship concept layer, clustering the relationship concept layer, simplifying the relationship concept layer according to the clustering results, so as to close the relationship between the first key information and the second key information, and at the same time completing the compression of the knowledge graph without affecting the accuracy.

[0015] Optionally, the data collection priority is also related to related indicators, where the related indicators include:

[0016] Grid impact: the proportion of station capacity in the regional grid and its contribution to voltage / frequency stability;

[0017] Real-time requirements: whether to participate in frequency modulation, peak load regulation, and other services that require a response within seconds;

[0018] Failure risk: equipment aging and historical failure frequency;

[0019] Economic value: electricity price benefits, subsidy dependence;

[0020] Data value density: the degree to which data supports prediction and operation and maintenance decisions.

[0021] Optionally, the data control strategy is to dynamically adjust the collection frequency and transmission path based on reinforcement learning to balance resource consumption and data value.

[0022] Optionally, the control terminal is connected to an Internet gateway to obtain real-time data of the current new energy station anytime and anywhere.

[0023] A multi-dimensional data acquisition and intelligent analysis and monitoring system for new energy stations, including:

[0024] Knowledge graph construction module: used to obtain historical time-series monitoring data of new energy stations and their corresponding operating status, and use the knowledge graph to construct an undirected heterogeneous graph of the time-series monitoring data and operating status. Multiple undirected heterogeneous graphs constitute the knowledge graph intelligent analysis model;

[0025] Operation status correlation factor analysis module: used to learn the encoding of undirected heterogeneous graphs through graph convolutional neural networks, extract the encoding learning results and analyze the feature extraction results to obtain the correlation between historical time series detection data and operation status;

[0026] Multi-dimensional data collection strategy determination module: used to determine the data collection priority of new energy stations based on the association relationship, perform multi-dimensional data collection on new energy stations based on the data collection priority, and obtain a multi-dimensional data collection strategy for the new energy stations;

[0027] Current operating status display module: used to collect the current data of new energy stations in real time according to the multi-dimensional data collection strategy, and input the current data into the knowledge graph intelligent analysis model, analyze the impact of the current data on the operating status of the new energy station, and display it on the control terminal.

[0028] Optionally, it also includes a control strategy output module: used to construct a strategy undirected heterogeneous graph of historical data control strategies and historical time-series monitoring data in the knowledge graph intelligent analysis model; determine from the display terminal whether the current data is not conducive to the stable operation of the new energy station. If so, input the current data into the knowledge graph intelligent analysis model, output the data control strategy, and update the current knowledge graph intelligent analysis model.

[0029] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods for multi-dimensional data collection and intelligent analysis and monitoring of a new energy station are implemented.

[0030] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a method, system and storage medium for multi-dimensional data collection and intelligent analysis and monitoring of new energy stations, which has the following beneficial effects:

[0031] 1. Utilizing the knowledge graph, an undirected heterogeneous graph is constructed to integrate historical time-series monitoring data and operational status of new energy stations. This allows for an intuitive graphical presentation of previously complex and dispersed data, making the relationships between different data points clear at a glance. This facilitates a comprehensive understanding of the station's operational history and lays a clear data foundation for subsequent analysis.

[0032] 2. Current data is collected in real time based on a multi-dimensional data collection strategy and fed into a knowledge graph intelligent analysis model. This enables the station to monitor its operating status in real time and promptly identify the impact of current data on operational status. Any abnormalities are quickly displayed on the control terminal, allowing managers to take immediate action to ensure stable station operation, reduce losses caused by failures, and improve the reliability and safety of new energy station operations.

[0033] 3. The entire solution provides managers with rich and accurate information through in-depth analysis of historical data, formulation of reasonable data collection strategies, and real-time operation status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0035] Figure 1 Schematic diagram of the method flow of the present invention;

[0036] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] The embodiment of the present invention discloses a method for multi-dimensional data collection and intelligent analysis and monitoring of new energy stations, such as Figure 1 As shown, the following steps are included:

[0039] Step 1: Obtain historical time-series monitoring data and corresponding operating status of new energy stations, and use the knowledge graph to construct an undirected heterogeneous graph of time-series monitoring data and operating status. Multiple undirected heterogeneous graphs constitute the knowledge graph intelligent analysis model;

[0040] Step 2: Use graph convolutional neural networks to learn the encoding of undirected heterogeneous graphs, extract features from the encoding learning results, and analyze the feature extraction results to obtain the historical temporal detection data and the correlation between the operating status;

[0041] Step 3: Determine the data collection priority of the new energy station based on the association relationship, perform multi-dimensional data collection on the new energy station based on the data collection priority, and obtain a multi-dimensional data collection strategy for the new energy station;

[0042] Step 4: Collect the current data of the new energy station in real time according to the multi-dimensional data collection strategy, input the current data into the knowledge graph intelligent analysis model, analyze the impact of the current data on the operating status of the new energy station, and display it on the control terminal.

[0043] Furthermore, this embodiment also includes constructing an undirected heterogeneous graph of historical data control strategies and historical time-series monitoring data strategies in the knowledge graph intelligent analysis model; judging from the display terminal whether the current data is not conducive to the stable operation of the new energy station, and if so, inputting the current data into the knowledge graph intelligent analysis model, outputting the data control strategy, and updating the current knowledge graph intelligent analysis model.

[0044] Furthermore, in step one, the knowledge graph is used to construct an undirected heterogeneous graph of the time series monitoring data and the operating status, specifically including: extracting the first key information from the time series monitoring data to construct a monitoring data layer; extracting the second key information from the operating status to construct an operating status layer; the first key information and the second key information constitute a relationship concept layer, clustering the relationship concept layer, and simplifying the relationship concept layer according to the clustering results to close the relationship between the first key information and the second key information, and at the same time, compressing the knowledge graph without affecting the accuracy.

[0045] Furthermore, in step 3, the data collection priority is also related to the associated indicators, where the associated indicators include:

[0046] Grid impact: the proportion of station capacity in the regional grid and its contribution to voltage / frequency stability;

[0047] Real-time requirements: whether to participate in frequency modulation, peak load regulation, and other services that require a response within seconds;

[0048] Failure risk: equipment aging and historical failure frequency;

[0049] Economic value: electricity price benefits, subsidy dependence;

[0050] Data value density: the degree to which data supports prediction and operation and maintenance decisions.

[0051] Furthermore, the priority levels are set as follows:

[0052] High-priority data: High-frequency data collection is used for critical equipment operating data, grid access parameters, and environmental data closely related to power generation efficiency. For example, electrical parameters such as inverter input and output voltage and current, as well as environmental data such as wind speed and light intensity, can be collected once per second or more to provide real-time information on equipment operating status and environmental changes, ensuring timely response to anomalies.

[0053] Medium-priority data: Equipment mechanical condition monitoring data, temperature data, and some non-critical equipment operating data can be collected at a lower frequency. For example, fan vibration data and generator temperature data can be collected every few minutes, which meets the need for monitoring equipment operating status without incurring excessive data storage and transmission pressure.

[0054] Low-priority data: Some auxiliary data, such as site geographic information and basic equipment configuration information, can be collected less frequently, for example, once a day or once a week. This data is relatively stable and does not require frequent updates.

[0055] Furthermore, the data control strategy is to dynamically adjust the collection frequency and transmission path based on reinforcement learning to balance resource consumption and data value.

[0056] Furthermore, the control terminal is connected to the Internet gateway to obtain real-time data of the current new energy station anytime and anywhere.

[0057] and Figure 1 Corresponding to the method shown, the present invention also discloses a new energy station multi-dimensional data acquisition and intelligent analysis monitoring system for Figure 1 The implementation of the method, the specific structure is as follows Figure 2 Shown, including:

[0058] Knowledge graph construction module: used to obtain historical time-series monitoring data of new energy stations and their corresponding operating status, and use the knowledge graph to construct an undirected heterogeneous graph of the time-series monitoring data and operating status. Multiple undirected heterogeneous graphs constitute the knowledge graph intelligent analysis model;

[0059] Operation status correlation factor analysis module: used to learn the encoding of undirected heterogeneous graphs through graph convolutional neural networks, extract the encoding learning results and analyze the feature extraction results to obtain the correlation between historical time series detection data and operation status;

[0060] Multi-dimensional data collection strategy determination module: used to determine the data collection priority of new energy stations based on the association relationship, perform multi-dimensional data collection on new energy stations based on the data collection priority, and obtain a multi-dimensional data collection strategy for the new energy stations;

[0061] Current operating status display module: used to collect the current data of new energy stations in real time according to the multi-dimensional data collection strategy, and input the current data into the knowledge graph intelligent analysis model, analyze the impact of the current data on the operating status of the new energy station, and display it on the control terminal.

[0062] Furthermore, in this embodiment, a control strategy output module is also included: it is used to construct a strategy undirected heterogeneous graph of historical data regulation strategies and historical time series monitoring data in the knowledge graph intelligent analysis model; it is determined from the display terminal whether the current data is not conducive to the stable operation of the new energy station. If so, the current data is input into the knowledge graph intelligent analysis model, the data regulation strategy is output, and the current knowledge graph intelligent analysis model is updated.

[0063] Finally, this embodiment discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods for multi-dimensional data collection and intelligent analysis and monitoring of new energy stations are implemented.

[0064] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0065] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for multi-dimensional data collection and intelligent analysis and monitoring of new energy stations, characterized in that: The following steps are involved: Obtain historical time-series monitoring data and corresponding operating status of new energy stations, use knowledge graphs to construct undirected heterogeneous graphs of time-series monitoring data and operating status, and multiple undirected heterogeneous graphs form a knowledge graph intelligent analysis model; Through graph convolutional neural networks, undirected heterogeneous graph encoding learning is carried out, feature extraction encoding learning results are extracted and analyzed, and the correlation between historical time series detection data and operating status is obtained; The data collection priority of the new energy station is determined by the association relationship, and multi-dimensional data collection is performed on the new energy station based on the data collection priority to obtain a multi-dimensional data collection strategy for the new energy station; According to the multi-dimensional data collection strategy, the current data of the new energy station is collected in real time, and the current data is input into the knowledge graph intelligent analysis model to analyze the impact of the current data on the operating status of the new energy station and display it on the control terminal.

2. A new energy station multi-dimensional data collection and intelligent analysis monitoring method according to claim 1, characterized in that: It also includes constructing an undirected heterogeneous graph of historical data control strategies and historical time-series monitoring data strategies in the knowledge graph intelligent analysis model; judging from the display terminal whether the current data is not conducive to the stable operation of the new energy station, and if so, inputting the current data into the knowledge graph intelligent analysis model, outputting the data control strategy, and updating the current knowledge graph intelligent analysis model.

3. The multi-dimensional data acquisition and intelligent analysis and monitoring method for a new energy station according to claim 1 is characterized in that: The knowledge graph is used to construct an undirected heterogeneous graph of time-series monitoring data and operating status, specifically including: extracting the first key information from the time-series monitoring data to construct a monitoring data layer; extracting the second key information from the operating status to construct an operating status layer; the first key information and the second key information constitute a relationship concept layer, clustering the relationship concept layer, and simplifying the relationship concept layer according to the clustering results to close the relationship between the first key information and the second key information. At the same time, the knowledge graph is compressed without affecting the accuracy.

4. The multi-dimensional data collection and intelligent analysis and monitoring method for a new energy station according to claim 1 is characterized in that: The data collection priority is also related to the associated indicators, including: Grid impact: the proportion of station capacity in the regional grid and its contribution to voltage / frequency stability; Real-time requirements: whether to participate in frequency modulation, peak load regulation, and other services that require a response within seconds; Failure risk: equipment aging and historical failure frequency; Economic value: electricity price benefits, subsidy dependence; Data value density: the degree to which data supports prediction and operation and maintenance decisions.

5. The multi-dimensional data collection and intelligent analysis and monitoring method for a new energy station according to claim 2 is characterized in that: The data control strategy is to dynamically adjust the collection frequency and transmission path based on reinforcement learning to balance resource consumption and data value.

6. The multi-dimensional data collection and intelligent analysis and monitoring method for a new energy station according to claim 1 is characterized in that: The control terminal is connected to the Internet gateway to obtain real-time data of the current new energy station anytime and anywhere.

7. A new energy station multi-dimensional data acquisition and intelligent analysis monitoring system, characterized by: include: Knowledge graph construction module: used to obtain historical time-series monitoring data of new energy stations and their corresponding operating status, and use the knowledge graph to construct an undirected heterogeneous graph of the time-series monitoring data and operating status. Multiple undirected heterogeneous graphs constitute the knowledge graph intelligent analysis model; Operation status correlation factor analysis module: used to learn the encoding of undirected heterogeneous graphs through graph convolutional neural networks, extract the encoding learning results and analyze the feature extraction results to obtain the correlation between historical time series detection data and operation status; Multi-dimensional data collection strategy determination module: used to determine the data collection priority of new energy stations based on the association relationship, perform multi-dimensional data collection on new energy stations based on the data collection priority, and obtain a multi-dimensional data collection strategy for the new energy stations; Current operating status display module: used to collect the current data of new energy stations in real time according to the multi-dimensional data collection strategy, and input the current data into the knowledge graph intelligent analysis model, analyze the impact of the current data on the operating status of the new energy station, and display it on the control terminal.

8. A new energy station multi-dimensional data acquisition and intelligent analysis monitoring system according to claim 7, characterized in that: It also includes a control strategy output module: used to construct a strategy undirected heterogeneous graph of historical data control strategies and historical time-series monitoring data in the knowledge graph intelligent analysis model; determine from the display terminal whether the current data is not conducive to the stable operation of the new energy station. If so, the current data is input into the knowledge graph intelligent analysis model, the data control strategy is output, and the current knowledge graph intelligent analysis model is updated.

9. A computer storage medium, characterized in that The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a multi-dimensional data collection and intelligent analysis and monitoring method for a new energy station as described in any one of claims 1 to 6.

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