Multi-source heterogeneous data storage method and system for wind power equipment

Abstract

The invention is suitable for the technical field of data storage, and provides a multi-source heterogeneous data storage method and system for wind power equipment, and the method comprises the following steps: building a four-stage topological structure of the wind power equipment, the four-stage topological structure comprises fan information, component information, part information and sensor information, and distributing a topological path code for each sensor; multi-source heterogeneous data uploaded by all sensors are processed respectively, spatial dimensionality reduction is carried out on numerical data, semantic compression is carried out on text data, background screening is carried out on image data, and representative feature retention is carried out on time sequence data; and binding corresponding timestamps and topological path codes for each piece of multi-source heterogeneous data, and storing the multi-source heterogeneous data. According to the method, the multi-source heterogeneous data can be respectively processed according to types, and the storage cost of the data is greatly reduced by using space dimension reduction, semantic compression, background screening and representative feature retention technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and in particular to a multi-source heterogeneous data storage method and system for wind power equipment. Background Art

[0002] As wind turbines become increasingly intelligent, the data generated during their operation is experiencing explosive growth. Wind turbines typically consist of complex components such as wind turbines, blades, gearboxes, and generators. Each component contains a large number of sensors (such as vibration sensors, temperature sensors, and image acquisition devices), forming multi-source heterogeneous data streams. Traditional data storage methods face the following technical challenges: low data structuring: fuzzy device hierarchical relationships and a lack of unified identification for sensor data lead to inefficient data retrieval; low heterogeneous data processing efficiency: direct mixed storage of numerical, textual, image, and time-series data, insufficient data compression, and the resulting large storage space consumption and slow query response; isolated anomaly detection: traditional methods only perform anomaly detection on a single data source, ignoring the correlation between multi-source data and making it difficult to locate complex faults. Therefore, it is necessary to provide a multi-source heterogeneous data storage method and system for wind turbines to address the aforementioned issues. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention aims to provide a multi-source heterogeneous data storage method and system for wind power equipment to solve the problems existing in the above-mentioned background technology.

[0004] The present invention is implemented as follows: a multi-source heterogeneous data storage method for wind power equipment, the method comprising the following steps: Establish a four-level topology structure for wind power equipment, which includes wind turbine information, component information, part information, and sensor information, and assign a topology path code to each sensor; The multi-source heterogeneous data uploaded by all sensors are processed separately, with spatial dimensionality reduction performed on numerical data, semantic compression performed on text data, background removal performed on image data, and representative feature retention performed on time series data. Bind the corresponding timestamp and topology path code to each multi-source heterogeneous data, and store the multi-source heterogeneous data; Identify abnormal information in multi-source heterogeneous data, determine abnormal data groups, and establish hyperlinks between abnormal data groups. The abnormal data groups contain multiple interrelated multi-source heterogeneous data.

[0005] As a further solution of the present invention: the step of performing spatial dimensionality reduction on numerical data specifically includes: Classify the numerical data according to the data items and determine the spatial area corresponding to each numerical data in each category; Determine whether the numerical data of different spatial regions are within the set threshold, merge the numerical data within the set threshold, and merge the corresponding spatial regions; Determine the component range covered by the spatial area corresponding to each numerical data.

[0006] As a further solution of the present invention: the step of semantically compressing text data specifically includes: Determine the corresponding extraction template according to the text type, and generate a semantic compression requirement statement based on the extraction template; Input text data and semantic compression requirement sentences into the language model to obtain compressed text data.

[0007] As a further solution of the present invention: the step of performing background removal on the image data specifically includes: Determine part information based on the topological path encoding of the sensor corresponding to the image data, and retrieve part features and key locations of the part information; Perform part contour recognition on image data based on part features to obtain the main body and key parts of the image; The image subject in image data is retained, the background is removed, and the local resolution of key parts is improved.

[0008] As a further solution of the present invention: the step of retaining representative features of time series data specifically includes: Determining a characteristic state of the time series data, where the characteristic state is one of a stable state, an abnormal state, and a noise state; For stable time series data, the RMS value is retained; for abnormal time series data, the full-band spectrum is retained; and noisy time series data is discarded.

[0009] As a further solution of the present invention: the step of determining abnormal data groups and establishing hyperlinks between abnormal data groups specifically includes: Determine the associated part information according to the abnormal type of the abnormal information and the corresponding topological path code; Determine mutually related multi-source heterogeneous data according to the part information and the abnormal time to obtain an abnormal data group; A hyperlink is established in the multi-source heterogeneous data with abnormal information, and the hyperlink can directly access the multi-source heterogeneous data in the abnormal data group.

[0010] Another object of the present invention is to provide a multi-source heterogeneous data storage system for wind power equipment, the system comprising: A topology building module is used to establish a four-level topology structure of wind power equipment. The four-level topology structure includes wind turbine information, component information, part information, and sensor information, and assigns a topology path code to each sensor. The multi-source data compression module is used to process the multi-source heterogeneous data uploaded by all sensors separately, perform spatial dimensionality reduction on numerical data, perform semantic compression on text data, remove background information from image data, and retain representative features for time series data; A heterogeneous data storage module is used to bind corresponding timestamps and topological path codes to each multi-source heterogeneous data and store the multi-source heterogeneous data; The abnormal data link module is used to identify abnormal information in multi-source heterogeneous data, determine abnormal data groups, and establish hyperlinks between abnormal data groups. The abnormal data groups contain multiple interrelated multi-source heterogeneous data.

[0011] As a further solution of the present invention: the multi-source data compression module includes: a spatial region determination unit, configured to classify the numerical data according to the data items and determine the spatial region corresponding to each numerical data in each category; The data space merging unit is used to determine whether the numerical data of different spatial regions are within a set threshold, and merge the numerical data within the set threshold, and also merge the corresponding spatial regions; The component range determination unit is used to determine the component range covered by the spatial area corresponding to each numerical data.

[0012] As a further solution of the present invention: the multi-source data compression module includes: A semantic compression requirement unit, configured to determine a corresponding extraction template according to a text type and generate a semantic compression requirement statement based on the extraction template; The compressed text data unit is used to input text data and semantic compression requirement statements into the language model to obtain compressed text data.

[0013] As a further solution of the present invention: the multi-source data compression module includes: A part information determination unit, configured to determine part information based on a topological path encoding of a sensor corresponding to the image data, and retrieve part features and key locations of the part information; The main part determination unit is used to perform part contour recognition on the image data based on part features to obtain the main body and key parts of the image; The image background removal unit is used to retain the image subject in image data, remove the background, and improve the local resolution of key parts.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention achieves precise mapping of data and physical devices through a four-level topological structure and topological path encoding, supports rapid data tracing and data access, and significantly improves subsequent retrieval efficiency. It can process multi-source heterogeneous data separately according to type, using spatial dimensionality reduction, semantic compression, background screening, and representative feature retention techniques to significantly reduce data storage costs. It also automatically identifies abnormal data groups, which contain multiple interrelated multi-source heterogeneous data. Hyperlinks are established between abnormal data groups, facilitating rapid access to the entire data set and confirming complex faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a flowchart of a multi-source heterogeneous data storage method for wind power equipment.

[0016] Figure 2 The present invention is a flowchart for performing spatial dimensionality reduction on numerical data in a multi-source heterogeneous data storage method for wind power equipment.

[0017] Figure 3 The present invention is a flowchart for semantic compression of text data in a multi-source heterogeneous data storage method for wind power equipment.

[0018] Figure 4 The present invention is a flowchart for background screening of image data in a multi-source heterogeneous data storage method for wind power equipment.

[0019] Figure 5 This is a flowchart for retaining representative features of time series data in a multi-source heterogeneous data storage method for wind power equipment.

[0020] Figure 6 The present invention is a flowchart of establishing hyperlinks between abnormal data groups in a multi-source heterogeneous data storage method for wind power equipment.

[0021] Figure 7 This is a structural diagram of a multi-source heterogeneous data storage system for wind power equipment. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0024] like Figure 1As shown, an embodiment of the present invention provides a multi-source heterogeneous data storage method for wind power equipment, the method comprising the following steps: S100, establishing a four-level topology structure of wind power equipment, the four-level topology structure including wind turbine information, component information, part information, and sensor information, and assigning a topology path code to each sensor; S200 processes the multi-source heterogeneous data uploaded by all sensors separately, performing spatial dimensionality reduction on numerical data, semantic compression on text data, background removal on image data, and representative feature retention on time series data. S300, binding a corresponding timestamp and topology path code to each multi-source heterogeneous data, and storing the multi-source heterogeneous data; S400 , identifying abnormal information in multi-source heterogeneous data, determining abnormal data groups, and establishing hyperlinks between abnormal data groups, wherein the abnormal data groups contain a plurality of interrelated multi-source heterogeneous data.

[0025] It's important to note that existing technologies lack a mapping between the device level and sensor data, making data tracing difficult. Hybrid storage of multi-source, heterogeneous data is not optimized for specific data types, and directly storing raw image data consumes significant bandwidth. Furthermore, traditional methods only flag anomalies in single data points and fail to identify complex anomaly patterns across multiple data sources (such as vibration, temperature, and image data), leading to missed fault detection or false alarms. The present invention aims to address these issues.

[0026] In an embodiment of the present invention, a four-level topology structure for wind turbine equipment must first be established. The four-level topology structure includes wind turbine information, component information, part information, and sensor information. A topology path code is assigned to each sensor. Preferably, the wind turbine information, component information, part information, and sensor information are all represented using codes to achieve precise mapping of data to physical equipment, supporting rapid data traceability and equipment health analysis. The multi-source heterogeneous data uploaded by all sensors is then efficiently compressed. Specifically, spatial dimensionality reduction is performed on numerical data. For example, if the values of multiple temperature sensors in different regions are the same, the temperature data of these spatial regions are fused. Semantic compression is performed on text data to extract key information and delete repeated descriptive text. Background filtering is performed on image data, and local resolution is enhanced for key components such as bearings and gearboxes. Representative features of time series data are retained, retaining key frequency bands that represent fault characteristics, while streamlining the remaining frequency bands. In this way, the multi-source heterogeneous data is significantly compressed. Each piece of multi-source heterogeneous data is then bound to a corresponding timestamp and topological path code. The timestamp is the sensor's acquisition time or period. The multi-source heterogeneous data is then stored, significantly improving retrieval efficiency. For example, if the search keywords are time and part information, the corresponding multi-source heterogeneous data can be quickly identified and quickly retrieved. Furthermore, abnormal information in multi-source heterogeneous data is identified. For example, if a value in multi-source heterogeneous data is outside the normal range, it is identified as an anomaly. The type of anomaly can be determined based on the trend and degree of deviation of the abnormal data. Furthermore, abnormal data groups that may cause or reflect the anomaly are identified. The abnormal data groups contain multiple interrelated multi-source heterogeneous data. Hyperlinks are established between abnormal data groups, facilitating quick access to the entire set of data and identifying complex abnormal patterns across data sources.

[0027] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of performing spatial dimensionality reduction on numerical data specifically includes: S201, classifying numerical data according to data items, and determining the spatial region corresponding to each numerical data in each category; S202, determining whether the numerical data of different spatial regions are within a set threshold, merging the numerical data within the set threshold, and merging the corresponding spatial regions; S203: Determine the component range covered by the spatial region corresponding to each numerical data.

[0028] In an embodiment of the present invention, for numerical data, the numerical data is first classified according to the data item (e.g., wind speed, temperature, humidity), and then each numerical data item within each category is analyzed separately. During the separate analysis, the spatial region corresponding to each numerical data item is first determined. Here, the spatial region corresponding to each sensor of the numerical data item must be determined in advance. Then, it is determined whether the numerical data in different spatial regions are within a set threshold. The numerical data within the set threshold is merged, and the corresponding spatial regions are also merged. For example, for temperature, if the set threshold is 0.2 degrees Celsius, and the temperature differences between spatial regions 02, 06, 12, and 17 are all within 0.2 degrees Celsius, the temperature data in spatial regions 02, 06, 12, and 17 are merged, and the final temperature value can be obtained by taking the average value. Finally, it is also necessary to determine the component range covered by the spatial area corresponding to each numerical data. For example, if the merged temperature data (merged from four temperature sensor data) covers ten component parts, the topological path encoding of the temperature data will reflect the four sensor information and ten component information. When the topological path encoding does not need to be accurate to the part information, the encoding of the part information part can be omitted.

[0029] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of semantically compressing text data specifically includes: S204, determining a corresponding extraction template according to the text type, and generating a semantic compression requirement statement based on the extraction template; S205: Input the text data and the semantic compression requirement statement into the language model to obtain compressed text data.

[0030] In an embodiment of the present invention, it is first necessary to determine an extraction template for each text type. For example, the extraction template for the device status log is: (time, device ID, parameter, value, threshold), the extraction template for the operation and maintenance log is: (time, personnel ID, action, object), and the extraction template for the abnormal event log is: (time, event type, key parameters). Then, a semantic compression requirement statement can be generated based on the extraction template. For example, the semantic compression requirement statement for the operation and maintenance log is: refine the description content of the operation and maintenance log, and identify the time, personnel ID, action, and object. Finally, the text data and the semantic compression requirement statement are input into the language model to automatically obtain compressed text data. The language model can use the GPT series, deepseek, Wenxin Yiyan, etc.

[0031] like Figure 4 As shown in FIG. 1 , as a preferred embodiment of the present invention, the step of performing background removal on image data specifically includes: S206, determining part information based on the topological path encoding of the sensor corresponding to the image data, and retrieving part features and key locations of the part information; S207, performing part contour recognition on the image data based on part features to obtain the image main body and key parts; S208: retain the main body of the image in the image data, remove the background, and improve the local resolution of key parts.

[0032] In this embodiment of the present invention, each piece of image data is collected by a sensor (e.g., a camera). The corresponding sensor's topological path encoding is retrieved to determine part information, which may include multiple parts. These parts are then entered into a pre-established part information library containing each part's features (outline, color, texture, etc.) and key locations. These features and key locations are then automatically output. The image data is then subjected to part outline recognition based on the part features to obtain the image's main body and key locations. The main body of the image data is then retained, while the background is removed, significantly compressing the data. The local resolution of key locations is also enhanced, improving data usability.

[0033] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of retaining representative features of time series data specifically includes: S209, determining a characteristic state of the time series data, where the characteristic state is one of a stable state, an abnormal state, and a noise state; S2010: retain the RMS value for time series data in a stable state, retain the full-band spectrum for time series data in an abnormal state, and discard time series data in a noisy state.

[0034] In an embodiment of the present invention, it is first necessary to determine the characteristic state of the time series data. For example, taking a vibration signal as an example, the frequency range of the stable state is 0-100Hz, the frequency range of the abnormal state is 100-3000Hz (characteristic frequency bands of faults such as bearing / gear damage), and the frequency range of the noise state is greater than 3000Hz (sensor electronic noise, environmental interference). Then, different characteristic states are processed differently. For example, the RMS value is retained for the time series data in the stable state. The RMS value reflects the average energy level of the signal in the time domain, and the storage efficiency is extremely optimized. The overall state of the device can be judged in seconds through the RMS trend chart. The full-band spectrum is retained for the time series data in the abnormal state, and the time series data in the noise state can be discarded.

[0035] like Figure 6As shown, as a preferred embodiment of the present invention, the step of determining abnormal data groups and establishing hyperlinks between abnormal data groups specifically includes: S401, determining associated part information according to the exception type of the exception information and the corresponding topological path code; S402, determining mutually related multi-source heterogeneous data based on the part information and the abnormal time to obtain an abnormal data group; S403: Establish a hyperlink in the multi-source heterogeneous data containing abnormal information.

[0036] In an embodiment of the present invention, the associated part information is determined based on the anomaly type and the topological path encoding of the anomaly data, thereby determining the anomaly type of the abnormal part. The abnormal part and the anomaly type are entered into an associated database, which needs to be established in advance. The associated database contains the multi-source heterogeneous data associated with all abnormal parts under each anomaly type. This allows for the rapid generation of an abnormal data set. A hyperlink is then established within the multi-source heterogeneous data containing the anomaly information, providing direct access to the multi-source heterogeneous data within the anomaly data set. This facilitates rapid access to the entire set of related data and facilitates in-depth analysis of the anomaly.

[0037] like Figure 7 As shown, an embodiment of the present invention further provides a multi-source heterogeneous data storage system for wind power equipment, the system comprising: A topology structure building module 100 is used to establish a four-level topology structure of a wind power device, which includes wind turbine information, component information, part information, and sensor information, and assigns a topology path code to each sensor; Multi-source data compression module 200, used to process the multi-source heterogeneous data uploaded by all sensors separately, perform spatial dimensionality reduction on numerical data, perform semantic compression on text data, perform background screening on image data, and retain representative features for time series data; The heterogeneous data storage module 300 is used to bind a corresponding timestamp and topological path code to each multi-source heterogeneous data and store the multi-source heterogeneous data; The abnormal data linking module 400 is used to identify abnormal information in multi-source heterogeneous data, determine abnormal data groups, and establish hyperlinks between abnormal data groups. The abnormal data groups contain multiple interrelated multi-source heterogeneous data.

[0038] As a preferred embodiment of the present invention, the multi-source data compression module 200 includes: a spatial region determination unit, configured to classify the numerical data according to the data items and determine the spatial region corresponding to each numerical data in each category; The data space merging unit is used to determine whether the numerical data of different spatial regions are within a set threshold, and merge the numerical data within the set threshold, and also merge the corresponding spatial regions; The component range determination unit is used to determine the component range covered by the spatial area corresponding to each numerical data.

[0039] As a preferred embodiment of the present invention, the multi-source data compression module 200 includes: A semantic compression requirement unit, configured to determine a corresponding extraction template according to a text type and generate a semantic compression requirement statement based on the extraction template; The compressed text data unit is used to input text data and semantic compression requirement statements into the language model to obtain compressed text data.

[0040] As a preferred embodiment of the present invention, the multi-source data compression module 200 includes: A part information determination unit, configured to determine part information based on a topological path encoding of a sensor corresponding to the image data, and retrieve part features and key locations of the part information; The main part determination unit is used to perform part contour recognition on the image data based on part features to obtain the main body and key parts of the image; The image background removal unit is used to retain the image subject in image data, remove the background, and improve the local resolution of key parts.

[0041] The above is only a detailed description of the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0042] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0043] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0044] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A multi-source heterogeneous data storage method for wind power equipment, characterized in that: The method comprises the following steps: Establish a four-level topology structure for wind power equipment, which includes wind turbine information, component information, part information, and sensor information, and assign a topology path code to each sensor; The multi-source heterogeneous data uploaded by all sensors are processed separately, with spatial dimensionality reduction performed on numerical data, semantic compression performed on text data, background removal performed on image data, and representative feature retention performed on time series data. Bind the corresponding timestamp and topology path code to each multi-source heterogeneous data, and store the multi-source heterogeneous data; Identify abnormal information in multi-source heterogeneous data, determine abnormal data groups, and establish hyperlinks between abnormal data groups. The abnormal data groups contain multiple interrelated multi-source heterogeneous data.

2. The multi-source heterogeneous data storage method for wind power equipment according to claim 1, characterized in that: The step of performing spatial dimensionality reduction on numerical data specifically includes: Classify the numerical data according to the data items and determine the spatial area corresponding to each numerical data in each category; Determine whether the numerical data of different spatial regions are within the set threshold, merge the numerical data within the set threshold, and merge the corresponding spatial regions; Determine the component range covered by the spatial area corresponding to each numerical data.

3. The multi-source heterogeneous data storage method for wind power equipment according to claim 1, characterized in that: The step of semantically compressing text data specifically includes: Determine the corresponding extraction template according to the text type, and generate a semantic compression requirement statement based on the extraction template; Input text data and semantic compression requirement sentences into the language model to obtain compressed text data.

4. The multi-source heterogeneous data storage method for wind power equipment according to claim 1, characterized in that: The step of performing background removal on the image data specifically includes: Determine part information based on the topological path encoding of the sensor corresponding to the image data, and retrieve part features and key locations of the part information; Perform part contour recognition on image data based on part features to obtain the main body and key parts of the image; The image subject in image data is retained, the background is removed, and the local resolution of key parts is improved.

5. The multi-source heterogeneous data storage method for wind power equipment according to claim 1, characterized in that: The step of retaining representative features of time series data specifically includes: Determining a characteristic state of the time series data, where the characteristic state is one of a stable state, an abnormal state, and a noise state; For stable time series data, the RMS value is retained; for abnormal time series data, the full-band spectrum is retained; and noisy time series data is discarded.

6. The multi-source heterogeneous data storage method for wind power equipment according to claim 1, characterized in that: The step of determining abnormal data groups and establishing hyperlinks between the abnormal data groups specifically includes: Determine the associated part information according to the abnormal type of the abnormal information and the corresponding topological path code; Determine mutually related multi-source heterogeneous data according to the part information and the abnormal time to obtain an abnormal data group; A hyperlink is established in the multi-source heterogeneous data with abnormal information, and the hyperlink can directly access the multi-source heterogeneous data in the abnormal data group.

7. A multi-source heterogeneous data storage system for wind power equipment, characterized in that: The multi-source heterogeneous data storage method for wind power equipment according to any one of claims 1 to 6 is implemented, wherein the system comprises: A topology building module is used to establish a four-level topology structure of wind power equipment. The four-level topology structure includes wind turbine information, component information, part information, and sensor information, and assigns a topology path code to each sensor. The multi-source data compression module is used to process the multi-source heterogeneous data uploaded by all sensors separately, perform spatial dimensionality reduction on numerical data, perform semantic compression on text data, remove background information from image data, and retain representative features for time series data; A heterogeneous data storage module is used to bind corresponding timestamps and topological path codes to each multi-source heterogeneous data and store the multi-source heterogeneous data; The abnormal data link module is used to identify abnormal information in multi-source heterogeneous data, determine abnormal data groups, and establish hyperlinks between abnormal data groups. The abnormal data groups contain multiple interrelated multi-source heterogeneous data.

8. The multi-source heterogeneous data storage system for wind power equipment according to claim 7, characterized in that: The multi-source data compression module includes: a spatial region determination unit, configured to classify the numerical data according to the data items and determine the spatial region corresponding to each numerical data in each category; The data space merging unit is used to determine whether the numerical data of different spatial regions are within a set threshold, and merge the numerical data within the set threshold, and also merge the corresponding spatial regions; The component range determination unit is used to determine the component range covered by the spatial area corresponding to each numerical data.

9. The multi-source heterogeneous data storage system for wind power equipment according to claim 7, characterized in that: The multi-source data compression module includes: A semantic compression requirement unit, configured to determine a corresponding extraction template according to a text type and generate a semantic compression requirement statement based on the extraction template; The compressed text data unit is used to input text data and semantic compression requirement statements into the language model to obtain compressed text data.

10. The multi-source heterogeneous data storage system for wind power equipment according to claim 7, characterized in that: The multi-source data compression module includes: A part information determination unit, configured to determine part information based on a topological path encoding of a sensor corresponding to the image data, and retrieve part features and key locations of the part information; The main part determination unit is used to perform part contour recognition on the image data based on part features to obtain the main body and key parts of the image; The image background removal unit is used to retain the image subject in image data, remove the background, and improve the local resolution of key parts.

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