Hydroelectric generating set data asset package construction method and device and storage medium

Abstract

The invention discloses a hydroelectric generating set data asset package construction method and device and a storage medium, and the method comprises the steps: classifying measuring points according to business purposes, and collecting the measuring points of each class to form a business data set of a specific class; configuring a processing strategy of the data set, and outputting a time label table of the data; filtering the original business data set based on the time label table to form a preliminary data set; perfecting the attribute information of the data set, and desensitizing the attribute information to form a final data set which can be independently shared and used.

Description

Technical Field

[0001] The present invention relates to hydroelectric power generation equipment technology, and in particular to a method, device and storage medium for constructing a data asset package of a hydroelectric generator set. Background Art

[0002] Currently, most of the data asset management methods on the market include data asset catalogs, metadata management, and data model models. There are few descriptions of how data sets are formed and constructed, especially how industrial time series data form data assets.

[0003] However, power companies have a huge amount of equipment operation sequence data, and these raw data usually make it difficult for business personnel to intuitively and quickly understand the data source, data link, and the relationship between data and business, and they cannot quickly find, understand, and use data. Therefore, it is necessary to build a data set with business meaning for business personnel and provide corresponding data services. Summary of the invention

[0004] The present invention provides a method, device and storage medium for constructing a data asset package of a hydropower unit.

[0005] In a first aspect, a method for constructing a data asset package of a hydropower unit is provided, the method comprising: Use the deep neural network model to classify the measurement points, and aggregate the measurement points of each category to form a business data set of a specific category; Configure the processing strategy for the data set and output the time label table of the data; Filter the original business data set based on the time tag table to form a preliminary data set; Improve the attribute information of the data set and desensitize the attribute information to form a final data set that can be shared and used independently.

[0006] Furthermore, the deep neural network adopts a convolutional neural network or a long short-term memory network.

[0007] Furthermore, the processing strategy of the configuration data set and the time label table of the output data include: If at a certain point in time If the difference between the maximum power value and the minimum power value of the unit power in the set time window is less than the set fluctuation amplitude, then the time point is judged to be a steady-state time point, where the time window Push forward in time The time range of seconds, i.e. , maximum power value and minimum power value Specifically: ; ; ; in, To set the power fluctuation amplitude threshold.

[0008] The steady-state time points are accumulated into time periods. When the time period is greater than the shortest running time, the time period is judged to be steady-state. When performing steady-state calculations, power data below the minimum steady-state threshold are eliminated; When the interval between two steady-state time periods is less than a set time interval threshold, the two time periods are merged.

[0009] Furthermore, the aggregating the measurement points of each category to form a business data set of a specific category includes: The vibration and swing data of the upper guide, lower guide, water guide, upper frame, stator frame, lower frame and top cover are formed into a vibration swing business data set.

[0010] Furthermore, the attribute information of the improved data set includes: the name, description, and category of the improved data set.

[0011] Furthermore, the set time window is Seconds: A time range that is pushed forward a certain number of seconds from the time point.

[0012] In a second aspect, the present invention provides a device for constructing a data asset package of a hydropower unit, the device comprising: Get the dataset module, used for: Classify the measurement points according to business purposes, and aggregate the measurement points of each category to form a business data set of a specific category; Get the time label table module, used for: Configure the processing strategy for the data set and output the time label table of the data; Data asset package formation module, used to: Filter the original business data set based on the time tag table to form a preliminary data set; Improve the attribute information of the data set and desensitize the attribute information to form a final data set that can be shared and used independently.

[0013] Furthermore, the module for obtaining the time label table includes: Configuration processing submodule, used to: If at a certain time point, the difference between the maximum power value and the minimum power value of the unit power in the set time window is less than the set fluctuation range, then the time point is judged to be a steady-state time point; The steady-state time points are accumulated into time periods. When the time period is greater than the shortest running time, the time period is judged to be steady-state. When performing steady-state calculations, power data below the minimum steady-state threshold are eliminated; When the interval between two steady-state time periods is less than a set time interval threshold, the two time periods are merged.

[0014] Furthermore, the device comprises: The dataset attribute module is used to improve the name, description, and category of the dataset.

[0015] In a third aspect, the present invention provides a device for constructing a data asset package of a hydropower unit, the device comprising: Memory, used to store programs; The processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the above method.

[0016] In a fourth aspect, the present invention provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, causes the one or more processors to execute the above method.

[0017] Compared with the prior art, the present invention has the following advantages: It overcomes the shortcomings of the existing technology and proposes a targeted solution for the construction of time-series data assets of hydropower generators that are unique to the hydropower industry. The data asset package is constructed by combining the business characteristics of hydropower, the characteristics of data steady state, and the data desensitization mechanism; the formed data asset package can be shared independently and used for data analysis and model training of similar units in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a method for constructing a hydropower unit data asset package provided by an embodiment of the present invention; Figure 2 A schematic diagram of the composition of a device for constructing a data asset package for a hydropower unit provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] Figure 1 A schematic diagram of a method for constructing a hydropower unit data asset package provided by an embodiment of the present invention, the method comprising: Use the deep neural network model to classify the measurement points, and aggregate the measurement points of each category to form a business data set of a specific category; Configure the processing strategy for the data set and output the time label table of the data; Filter the original business data set based on the time tag table to form a preliminary data set; Improve the attribute information of the data set and desensitize the attribute information to form a final data set that can be shared and used independently.

[0021] In one embodiment, the deep neural network adopts a convolutional neural network or a long short-term memory network.

[0022] In one embodiment, the processing strategy of configuring the data set and outputting the time tag table of the data include: If at a certain point in time If the difference between the maximum power value and the minimum power value of the unit power in the set time window is less than the set fluctuation amplitude, then the time point is judged to be a steady-state time point, where the time window Push forward in time The time range of seconds, i.e. , maximum power value and minimum power value Specifically: ; ; ; in, To set the power fluctuation amplitude threshold.

[0023] The steady-state time points are accumulated into time periods. When the time period is greater than the shortest running time, the time period is judged to be steady-state. When performing steady-state calculations, power data below the minimum steady-state threshold are eliminated; When the interval between two steady-state time periods is less than a set time interval threshold, the two time periods are merged.

[0024] Configure the processing strategy for the data set and output the time label table of the data, including: a. At a certain moment, the unit power is within the forward "600 seconds" time window, and the difference between the maximum value and the minimum value of the data is less than 20mw. This time point is judged to be a steady-state time point.

[0025] b. Steady-state time points are accumulated into time periods. When a time period is greater than "1800 seconds", the time period is considered to be steady-state.

[0026] c. The above calculation needs to exclude power data below "100mw" and does not participate in the steady-state calculation.

[0027] d. Time period merging: When the interval between two steady-state time periods is less than "300 seconds", the two time periods are merged.

[0028] In one embodiment, the step of aggregating the measurement points of each category to form a business data set of a specific category includes: The vibration and swing data of the upper guide, lower guide, water guide, upper frame, stator frame, lower frame and top cover are formed into a vibration swing business data set.

[0029] In one embodiment, a "vibration swing steady-state health data asset package" is constructed for a mixed flow unit in a power station, and the vibration and swing data of the upper guide, lower guide, water guide, upper frame, stator frame, lower frame, and top cover are selected to form a vibration swing business data set.

[0030] In one embodiment, the attribute information of the improved data set includes: the name, description, and category of the improved data set.

[0031] An embodiment of the present invention provides a device for constructing a data asset package of a hydropower unit, the device comprising: Get the dataset module, used for: Classify the measurement points according to business purposes, and aggregate the measurement points of each category to form a business data set of a specific category; Get the time label table module, used for: Configure the processing strategy for the data set and output the time label table of the data; Data asset package formation module, used to: Filter the original business data set based on the time tag table to form a preliminary data set; Improve the attribute information of the data set and desensitize the attribute information to form a final data set that can be shared and used independently.

[0032] Furthermore, the module for obtaining the time label table includes: Configuration processing submodule, used to: If at a certain time point, the difference between the maximum power value and the minimum power value of the unit power in the set time window is less than the set fluctuation range, then the time point is judged to be a steady-state time point; The steady-state time points are accumulated into time periods. When the time period is greater than the shortest running time, the time period is judged to be steady-state. When performing steady-state calculations, power data below the minimum steady-state threshold are eliminated; When the interval between two steady-state time periods is less than a set time interval threshold, the two time periods are merged.

[0033] Furthermore, the device comprises: The dataset attribute module is used to improve the name, description, and category of the dataset.

[0034] Figure 2 A schematic diagram of a device for constructing a data asset package for a hydropower unit provided in an embodiment of the present invention; the device comprises: Memory, used to store programs; The processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the above method.

[0035] In addition, an embodiment of the present invention provides a storage medium storing computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the above method.

[0036] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a hydropower unit data asset package, characterized in that: The method comprises: Use the deep neural network model to classify the measurement points, and aggregate the measurement points of each category to form a business data set of a specific category; Configure the processing strategy for the data set and output the time label table of the data; Filter the original business data set based on the time tag table to form a preliminary data set; Improve the attribute information of the data set and desensitize the attribute information to form a final data set that can be shared and used independently.

2. The method for constructing a hydropower unit data asset package according to claim 1, characterized in that: The deep neural network adopts a convolutional neural network or a long short-term memory network.

3. The method for constructing a hydropower unit data asset package according to claim 1, characterized in that: The processing strategy of the configuration data set and the time label table of the output data include: If at a certain point in time If the difference between the maximum power value and the minimum power value of the unit power in the set time window is less than the set fluctuation amplitude, then the time point is judged to be a steady-state time point, where the time window Push forward in time The time range of seconds, i.e. , maximum power value and minimum power value Specifically: ； ； ； in, To set a power fluctuation amplitude threshold; The steady-state time points are accumulated into time periods. When the time period is greater than the shortest running time, the time period is judged to be steady-state. When performing steady-state calculations, power data below the minimum steady-state threshold are eliminated; When the interval between two steady-state time periods is less than a set time interval threshold, the two time periods are merged.

4. The method for constructing a hydropower unit data asset package according to claim 1, characterized in that: The process of aggregating the measurement points of each category to form a business data set of a specific category includes: The vibration and swing data of the upper guide, lower guide, water guide, upper frame, stator frame, lower frame and top cover are formed into a vibration swing business data set.

5. The method for constructing a hydropower unit data asset package according to claim 1, characterized in that: The attribute information of the improved data set includes: the name, description, and category of the improved data set.

6. A device for constructing a data asset package for a hydropower unit, characterized in that: The device comprises: Get the dataset module, used for: Classify the measurement points according to business purposes, and aggregate the measurement points of each category to form a business data set of a specific category; Get the time label table module, used for: Configure the processing strategy for the data set and output the time label table of the data; Data asset package formation module, used to: Filter the original business data set based on the time tag table to form a preliminary data set; Improve the attribute information of the data set and desensitize the attribute information to form a final data set that can be shared and used independently.

7. The device for constructing a data asset package of a hydropower unit according to claim 6, characterized in that: The module for obtaining the time label table includes: Configuration processing submodule, used to: If at a certain time point, the difference between the maximum power value and the minimum power value of the unit power in the set time window is less than the set fluctuation range, then the time point is judged to be a steady-state time point; The steady-state time points are accumulated into time periods. When the time period is greater than the shortest running time, the time period is judged to be steady-state. When performing steady-state calculations, power data below the minimum steady-state threshold are eliminated; When the interval between two steady-state time periods is less than a set time interval threshold, the two time periods are merged.

8. The device for constructing a data asset package of a hydropower unit according to claim 6, characterized in that: The device comprises: The dataset attribute module is used to improve the name, description, and category of the dataset.

9. A device for constructing a data asset package for a hydropower unit, characterized in that: The device comprises: Memory, used to store programs; A processor, configured to execute the program stored in the memory; when the program stored in the memory is executed, the processor is configured to execute the steps of the method as claimed in any one of claims 1 to 5.

10. A storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors are caused to perform the steps of the method according to any one of claims 1 to 5.

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