Hydraulic power plant main transformer production data quality evaluation method and related equipment

Abstract

The invention relates to the technical field of hydraulic power plant data quality evaluation, in particular to a hydraulic power plant main transformer production data quality evaluation method and related equipment, and the method comprises the steps: determining main transformer production data types including operation public data, ontology A / B / C phase data and cooling system data, and analyzing the range of each type of feature data; and grading according to the importance degree. A quality evaluation index framework is constructed on the basis, and four dimensions of integrity, accuracy, timeliness and consistency are covered. And designing a data quality statistical score calculation method, wherein the data quality statistical score calculation method specifically comprises a data integrity rate, an accuracy rate, a communication normal rate and a consistency rate. Finally, data quality grades are divided based on statistical results, a complete main transformer operation data quality evaluation system is formed, and a standardized data quality evaluation basis is provided for hydraulic power plant equipment state monitoring and operation and maintenance decision. The method realizes quantitative management of data quality, and supports accurate study and judgment of the equipment health state.

Description

Technical Field

[0001] This invention relates to the field of hydropower plant data quality assessment technology, specifically to a method and related equipment for assessing the production data quality of a hydropower plant's main transformer. Background Technology

[0002] Currently, with the deep application of online monitoring, live-line testing, and intelligent operation and maintenance technologies in the hydropower industry, the data generated during the operation of main transformers has evolved from traditional offline ledger files into high-frequency, real-time, multi-source heterogeneous terabyte-level data streams. Due to its high frequency, real-time nature, and multi-source heterogeneous characteristics, this type of data has become the core input for hydropower plants to conduct condition-based maintenance, fault prediction, life assessment, and digital twin modeling of main transformers. The industry's commonly used technical methods mainly rely on standards such as DL / T 722 and DL / T 1430, using common indicators such as communication online rate, time-stamp error, and packet loss rate to conduct basic data quality assessments, and relying on expert experience or simple threshold methods to complete equipment status scoring to support maintenance decisions. This situation reflects significant progress in the field of hydropower plant main transformer data quality assessment in terms of data processing scale and application scenario depth, but it also implies potential problems such as insufficient matching between the technical system and the unique operating characteristics of the main transformer.

[0003] However, the existing technical system has three core limitations: First, current standards do not define the scope of characteristic data for transformer data governance; second, the industry lacks a dedicated data quality evaluation index framework covering multiple dimensions such as completeness, accuracy, consistency, and timeliness, and general indicators cannot quantify the data quality problems unique to hydropower plant main transformers; third, facing TB-level multi-source heterogeneous and frequency-varying operational data, traditional expert experience or simple threshold methods are difficult to achieve automated and scalable quality scoring and grading, resulting in uncontrollable input data quality for AI diagnostic models and restricting the accuracy of intelligent analysis and decision-making. These problems directly point to the original intention of this application to construct a dedicated data quality evaluation and governance system for hydropower plant main transformers, and urgently need to be addressed through systematic physical boundary modeling, multi-dimensional index framework construction, and statistical-physical fusion quantitative scoring methods. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and related equipment for evaluating the production data quality of main transformers in hydropower plants, in order to address the shortcomings of the prior art mentioned above. This method is used to solve the technical problem that general indicators cannot quantify the data quality unique to main transformers in hydropower plants.

[0005] The objective of this invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for evaluating the quality of production data of main transformers in hydropower plants, comprising: The range of each type of characteristic data in the main transformer production data of the hydropower plant is determined, and the data is classified into different levels according to the importance of each type of characteristic data; the main transformer production data includes common data of main transformer operation, A phase, B phase, C phase of the main transformer body, and related data of the main transformer cooling system; Based on different levels of characteristic data, a quality evaluation index framework for the operation data of the main transformer of the hydropower plant is defined, which includes completeness, accuracy, timeliness, and consistency. Based on the aforementioned quality evaluation index framework, a method for calculating data quality statistical scores is designed, which includes data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate. Based on the results of data quality statistics, the quality levels of hydropower plant transformer operation data are classified.

[0006] As a further improvement of the present invention, the determination of the range of various types of characteristic data in the production data of the main transformer of the hydropower plant includes: Identify the production data of the main transformer of the hydropower plant, and classify the characteristic data of the main transformer of the hydropower plant during operation. The characteristic data includes at least analog quantities, switch quantities and SOE data. The feature data are classified according to their importance and detection requirements, and the priority of feature data collection is set.

[0007] As a further improvement to the present invention, a quality evaluation index framework for the operating data of the main transformer of the hydropower plant is defined, including: Under the integrity dimension, sub-items are set including data range integrity rate, data record integrity rate, data content integrity rate, time series integrity rate, data accuracy rate, and data integrity rate; Under the accuracy dimension, sub-items are set including data violation rate, data dead count rate, data unreasonableness rate, and data accuracy rate; Under the timeliness dimension, a sub-item including data communication normality rate is set; Under the consistency dimension, a sub-item including data consistency rate is set.

[0008] As a further improvement of the present invention, the method for calculating the design data quality statistical score includes: The data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate were calculated respectively. Based on the preset weight values, the data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate are weighted and summed to obtain the statistical score of the hydropower plant's main transformer operation data quality.

[0009] As a further improvement of the present invention, the data integrity rate is calculated as follows:

[0010] In the formula, For data integrity rate, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data integrity rate of a single data point in the main transformer operation data level 1. The data integrity rate of a single data point in the main transformer operation data level 2. The data integrity rate of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

[0011] As a further improvement of the present invention, the data accuracy is calculated by simultaneously satisfying the following conditions: the number of non-empty values, non-limited values, non-refreshing duplicate values, and non-unreasonable data elements constitutes a proportion of the total number of data elements.

[0012] As a further improvement of the present invention, the time communication normality rate is calculated by determining the proportion of data elements with normal communication status in the total number of data elements based on the number of timestamp records and frequency distribution. The data consistency rate is calculated by taking the percentage of elements whose data values ​​at the same measurement point are consistent across multiple independent systems out of the total number of data elements.

[0013] Secondly, the present invention provides a system for evaluating the quality of production data of a hydropower plant's main transformer, comprising: The data quantity module is used to determine the range of various types of characteristic data in the main transformer production data of the hydropower plant, and to classify them into different levels according to the importance of each type of characteristic data; the main transformer production data includes common data of main transformer operation, A phase, B phase, C phase of the main transformer body, and related data of the main transformer cooling system; The indicator definition module is used to define a quality evaluation indicator framework for the main transformer operation data of the hydropower plant according to different levels of feature data. The quality evaluation indicator framework includes completeness, accuracy, timeliness and consistency. The quality statistics calculation module is used to design a method for calculating data quality statistics scores based on the aforementioned quality evaluation index framework. The calculation method includes data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate. The grading module is used to classify the quality level of hydropower plant transformer operation data based on the results of data quality statistics.

[0014] Thirdly, the present invention provides a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the above-described method for evaluating the production data quality of main transformers in hydropower plants.

[0015] Fourthly, the present invention provides a computing device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for performing the above-described method for assessing the production data quality of main transformers in hydropower plants.

[0016] The beneficial effects of this invention are as follows: This invention provides a method for evaluating the quality of production data of main transformers in hydropower plants. By determining the range of various types of characteristic data in the production data of main transformers in hydropower plants and classifying them according to their importance, it achieves orderly hierarchical management of common data of main transformer operation, A / B / C phases of the main transformer body, and cooling system data, laying a data foundation for subsequent quality evaluation. By constructing a quality evaluation index framework that includes four dimensions of indicators: completeness, accuracy, timeliness, and consistency, it breaks through the limitations of traditional single-dimensional evaluation and forms an evaluation dimension that comprehensively covers the characteristics of data quality. By designing statistical scoring calculation methods for data completeness rate, accuracy rate, communication normality rate, and consistency rate, it achieves multi-dimensional quantitative evaluation of data quality, improving the objectivity and operability of the evaluation. By classifying quality levels based on quality statistical results, it achieves hierarchical management of data quality, facilitating targeted improvement and decision-making. The above technical features work together to form a complete closed-loop data quality evaluation system, significantly improving the management efficiency and decision support capabilities of hydropower plant transformer operation data, and has a more systematic, comprehensive, and quantitative technical progress effect than the prior art. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the process for evaluating the production data quality of the main transformer in a hydropower plant, as described in an embodiment of the present invention. Figure 2This is an internal structural diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives and technical solutions of this invention clearer and easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. The described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Example 1 In existing technologies, hydropower plant main transformers suffer from poor data quality due to the lack of a dedicated data quality evaluation system in multi-source heterogeneous, high-frequency real-time data environments, which affects the accuracy of subsequent intelligent analysis and decision-making. Therefore, this embodiment provides a method for evaluating the production data quality of hydropower plant main transformers.

[0022] The method for assessing the quality of main transformer production data in hydropower plants includes the following steps: First, determining the range of various types of characteristic data in the main transformer production data of the hydropower plant, and classifying them into different levels based on the importance of each type of characteristic data. The main transformer production data includes common operational data of the main transformer, data related to phases A, B, and C of the main transformer body, and data related to the main transformer cooling system. Second, defining a quality evaluation index framework for the main transformer operation data of the hydropower plant based on the different levels of characteristic data. This framework includes completeness, accuracy, timeliness, and consistency. Third, designing a method for calculating statistical scores of data quality based on the quality evaluation index framework. This method includes data completeness rate, data accuracy rate, data communication normality rate, and data consistency rate. Finally, classifying the quality levels of the hydropower plant transformer operation data based on the results of the data quality statistics.

[0023] The working principle of this embodiment lies in analyzing the range of various characteristic data in the main transformer production data of hydropower plants and classifying them according to their importance, thereby achieving priority management of key data and improving the efficiency and targeting of data processing. By defining a quality evaluation index framework that includes completeness, accuracy, timeliness, and consistency, a comprehensive data quality standard is established, overcoming the shortcomings of insufficient index coverage in traditional methods. By designing statistical scoring calculation methods for data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate, an objective quantitative assessment of data quality is achieved, avoiding the uncertainty of subjective judgment. Based on the calculation results, data quality levels are divided, making data status identification more intuitive and easier to operate. The synergistic effect of these technical features significantly enhances the overall reliability and maintainability of hydropower plant transformer operation data, achieving a higher level of refined management and systematic control compared to existing technologies.

[0024] The main transformer's common operating data includes global operating parameters such as voltage, current, and power; the main transformer's A-phase, B-phase, and C-phase data covers winding temperatures, partial discharge levels, and dissolved gas content in the oil for each phase; and the main transformer's cooling system-related data includes cooler operating status, cooling medium temperature, and flow rate. Characteristic data types include analog quantities (such as temperature and pressure), digital quantities (such as equipment start / stop status), and SOE data (event sequence record data). Determining the range of various types of characteristic data in the production data of the main transformer of the hydropower plant further includes: identifying the production data of the main transformer of the hydropower plant, classifying the characteristic data during the operation of the main transformer of the hydropower plant, and the characteristic data including at least analog quantities, switching quantities, and SOE data; classifying the characteristic data according to the importance and detection requirements, and setting the priority of characteristic data collection. In this embodiment, the characteristic data is divided into three levels according to the importance: Level 1 (high level) is core data affecting the safe operation of the equipment, such as winding temperature and partial discharge; Level 2 (second highest level) is important operating parameters, such as cooler operating status, oil temperature and oil pressure; Level 3 (low level) is auxiliary reference data, such as ambient temperature and humidity. The data level can be adjusted according to the application scenario; low-level data can be upgraded to high-level data, and high-level data cannot be downgraded.

[0025] Analog quantities are continuously changing physical quantities, such as winding temperature; digital quantities are discrete state data, such as cooler operation or shutdown, circuit breaker closing or opening; SOE data are records of the time, location, and state changes of events, such as fault trip time and equipment abnormality alarm records.

[0026] A quality evaluation index framework for the operation data of main transformers in hydropower plants is defined, including: under the completeness dimension, sub-items such as data range completeness rate, data record completeness rate, data content completeness rate, time series completeness rate, data missing rate, and data completeness rate are set; under the accuracy dimension, sub-items such as data exceeding limit rate, data dead count rate, data unreasonableness rate, and data accuracy rate are set; under the timeliness dimension, sub-item such as data communication normality rate is set; and under the consistency dimension, sub-item such as data consistency rate is set.

[0027] Among the completeness dimension sub-items: data range completeness rate refers to the proportion of the data types actually collected to the data types that should be collected; data record completeness rate refers to the proportion of the number of data records actually recorded within the specified time to the number of records that should be recorded; data content completeness rate refers to the completeness of key fields in each data record; time series completeness rate refers to the proportion of uninterrupted data segments in a continuous time series; data missing rate refers to the proportion of missing data records to the total number of data records; and data completeness rate is the comprehensive quantitative result of the above sub-items.

[0028] Within the accuracy dimension: Data Exceedance Rate refers to the percentage of data entries that exceed the normal operating threshold range of the equipment, with the threshold determined according to equipment technical specifications and industry standards; Data Dead Rate refers to the percentage of data entries that remain unchanged for a long period and are at a fixed value; Data Inconsistency Rate refers to the percentage of data entries that are logically contradictory or do not conform to physical laws (such as winding temperature being lower than ambient temperature); Data Accuracy is the percentage of data entries that meet the requirements out of the total number of data entries.

[0029] In the timeliness dimension sub-item: Data communication normality rate refers to the percentage of data items whose communication status is normal (no packet loss, delay within the allowable range) during the process of data transmission from the collection end to the storage end. The allowable delay range is set according to the data type: Level 1 data delay does not exceed 100ms, Level 2 data delay does not exceed 1s, and Level 3 data delay does not exceed 5s.

[0030] In the consistency dimension sub-item: data consistency rate refers to the percentage of data values ​​of the same measuring point that remain consistent across different monitoring systems, different acquisition time periods, or different data interfaces. Consistency judgment allows for a reasonable error range (e.g., analog quantity error does not exceed ±2%).

[0031] Data integrity rate, data accuracy rate, data communication success rate, and data consistency rate were calculated separately. Based on preset weight values, these rates were weighted and summed to obtain a statistical score for the operational data quality of the hydropower plant's main transformer. Standardized methods were used to calculate the quantified values ​​of each dimension's indicators, ensuring the objectivity and comparability of the results. Weights were then assigned based on the impact of each indicator on overall data quality, highlighting the role of core indicators. Finally, a weighted sum was used to obtain a comprehensive score, reflecting the overall data quality level.

[0032] The data integrity rate is calculated as follows:

[0033] In the formula, For data integrity rate, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data integrity rate of a single data point in the main transformer operation data level 1. The data integrity rate of a single data point in the main transformer operation data level 2. The data integrity rate of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

[0034] When calculating data accuracy, the following conditions must be met simultaneously: non-empty values, non-limited values, non-refreshed duplicate values, and non-unreasonable data elements constitute the proportion of the total number of data elements.

[0035] When calculating the time-based communication normality rate, the percentage of data elements with normal communication status in the total number of data elements is determined based on the number of timestamp records and frequency distribution.

[0036] When calculating data consistency rate, the percentage of elements whose data values ​​at the same measurement points are consistent across multiple independent systems is calculated out of the total number of data elements.

[0037] Example 2 This embodiment provides a specific implementation method for evaluating the production data quality of a hydropower plant's main transformer. The following description uses a hydropower plant's main transformer as an example.

[0038] The main transformer operating data should be collected according to the actual needs of the hydropower plant's monitoring data. This includes common operating data of the main transformer, data related to phases A, B, and C of the main transformer itself, and data related to the main transformer cooling system. Based on data importance, these can be categorized into Level 1 (high level), Level 2 (second-high level), and Level 3 (low level). In cases where the data application scenario changes, low-level data can be upgraded to high-level data, but high-level data should not be downgraded to low-level data. Characteristic data mainly includes analog quantities, digital quantities, and SOE (Search Engine Execution) data. The specific ranges of various characteristic data for the main transformer in a hydropower plant are shown in Table 1.

[0039] Table 1. Range of various characteristic data for main transformers in hydropower plants

[0040] refer to Figure 1 This invention provides a framework for evaluating the quality of operational data from main transformer equipment in hydropower plants. The framework includes four dimensions: data completeness, accuracy, timeliness, and consistency. The final evaluation is based on data completeness rate, data accuracy rate, data communication normality rate, and data consistency rate, respectively. The definitions, descriptions, and calculation methods for each dimension's evaluation indicators and their sub-items are shown in Table 2.

[0041] Table 2. Definition and Calculation Method of Indicator Dimensions

[0042] The statistical score for the operation data of the main transformer in a hydropower plant should be calculated from four aspects: data completeness rate, data accuracy rate, data communication normality rate, and data consistency rate. All statistical results are presented as percentages, and the statistical period is the entire year.

[0043] The statistical score for the operational data quality of the main transformer at the hydropower plant is as follows:

[0044] In the formula, The completeness rate of main transformer operation data The accuracy of main transformer operating data The normal operation rate of data communication for main transformers. Consistency rate of main transformer operating data Weighting of the completeness rate of main transformer operating data. Weighting of the accuracy of main transformer operating data. Weighting of the normal operation rate of the main transformer's data communication. The consistency weight of the main transformer operating data is determined. The weight values ​​conform to the specifications shown in Table 3, and the recommended weight values ​​for the main transformer operating data quality level are shown in Table 4.

[0045] Table 3 Recommended values ​​for statistical weights of main transformer operating data quality

[0046] Table 4 Suggested Values ​​for Quality Level Weighting of Main Transformer Operating Data

[0047] Among them, the completeness rate of main transformer operation data for:

[0048] In the formula, For data integrity rate, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data integrity rate of a single data point in the main transformer operation data level 1. The data integrity rate of a single data point in the main transformer operation data level 2. The data integrity rate of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

[0049] Main transformer operating data integrity rate for:

[0050] In the formula, For data integrity rate, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data integrity rate of a single data point in the main transformer operation data level 1. The data integrity rate of a single data point in the main transformer operation data level 2. The data integrity rate of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

[0051] Main transformer operating data accuracy for:

[0052] In the formula, For data accuracy, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data accuracy of a single data point in the main transformer operation data level 1. The data accuracy of a single data point in the main transformer operation data level 2. The data accuracy of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

[0053] Main transformer operation data communication normality rate for:

[0054] In the formula, For data communication normality, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data communication normality rate of a single data point in the main transformer operation data level 1. The data communication normality rate of a single data point in the main transformer operation data level 2. The data communication normality rate of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

[0055] Consistency rate of main transformer operating data for:

[0056] In the formula, For data consistency rate, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data consistency rate of a single data point in the main transformer operation data level 1. The data consistency rate of a single data point in the main transformer operation data level 2. The data consistency rate of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

[0057] The quality of the main transformer operation data of hydropower plants should be classified according to the statistical results. The data integrity rate, data accuracy rate, data normal communication rate and data consistency rate should be comprehensively considered. The data quality classification shall be determined according to the provisions of Table 5.

[0058] Table 3. Classification of Operating Data Quality Grades for Main Transformers in Hydropower Plants

[0059] In summary, this embodiment first organizes the range of various characteristic data of hydropower plant main transformers, then designs a framework for evaluating the quality of hydropower plant main transformer operation data, then provides a method for calculating the statistical score of hydropower plant main transformer data quality, and finally presents a method for classifying quality levels. This overcomes the shortcomings of existing technologies, which lack a defined range of characteristic data for hydropower plant main transformers, a complete data quality evaluation index system, and a quality scoring and grading mechanism for hydropower plant main transformer data characteristics. It constructs a data quality evaluation index framework covering multiple dimensions such as completeness, accuracy, consistency, and timeliness, breaking through the limitations of existing technologies that only focus on equipment status and ignore the quality of the data itself. Example 3 Based on the hydropower plant main transformer production data quality assessment method in Embodiment 1, this embodiment provides a hydropower plant main transformer production data quality assessment system, including: a data quantity module, used to determine the range of various types of characteristic data in the hydropower plant main transformer production data, and classify them into different levels according to the importance of each type of characteristic data; the main transformer production data includes main transformer operation common data, main transformer body A phase, B phase, C phase, and main transformer cooling system related data; an index definition module, used to define a quality evaluation index framework for hydropower plant main transformer operation data according to the different levels of characteristic data, the quality evaluation index framework including completeness, accuracy, timeliness, and consistency; a quality statistical calculation module, used to design a data quality statistical score calculation method according to the quality evaluation index framework, the calculation method including data completeness rate, data accuracy rate, data communication normality rate, and data consistency rate; and a level classification module, used to classify the quality level of hydropower plant transformer operation data based on the data quality statistical results.

[0060] The data quantity module communicates with the main transformer's monitoring equipment and data acquisition system to acquire production data in real time. Through preset classification rules and grading standards, it automatically identifies data types, determines data ranges, and completes data level classification. The module supports manual adjustment of data levels to meet the needs of different application scenarios.

[0061] The system incorporates a multi-dimensional indicator framework and sub-items defining completeness, accuracy, timeliness, and consistency. The specific judgment criteria and calculation parameters of the sub-items can be adjusted according to the actual needs of the hydropower plant, supporting flexible expansion of the indicator system. Under the completeness dimension, sub-items include data range completeness rate, data record completeness rate, data content completeness rate, time series completeness rate, data accuracy rate, and data integrity rate. Under the accuracy dimension, sub-items include data exceeding limits rate, data dead count rate, data unreasonableness rate, and data accuracy rate. Under the timeliness dimension, sub-item includes data communication normality rate. Under the consistency dimension, sub-item includes data consistency rate.

[0062] The grading module has built-in preset quality grading standards. It receives the comprehensive score output by the quality statistics and calculation module, automatically matches the corresponding quality level, and generates a grading evaluation report. The report includes the scores of each dimension indicator, the comprehensive score, the quality level, and improvement suggestions.

[0063] Example 4 In another embodiment of the present invention, a computer-readable storage medium is provided as a storage component within a terminal device, the function of which is to store programs and data. It should be noted that the computer-readable storage medium here encompasses not only the built-in storage components of the terminal device but also extended storage components supported by the device. Essentially, it is a tangible medium capable of containing or storing programs that can be invoked by or in conjunction with an instruction execution system, device, or apparatus. This storage medium provides storage areas for the terminal's operating system and stores one or more instructions suitable for processor loading and execution, which can constitute one or more computer programs containing program code.

[0064] Specifically, examples of computer-readable storage media (a non-exclusive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable optical disc read-only memory, optical storage devices, magnetic storage devices, or any reasonable combination of the above types.

[0065] The storage medium may also include data signals propagated as part of a baseband portion or a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any reasonable combination of both. Furthermore, computer-readable storage medium may also refer to other readable media besides conventional readable storage media, capable of sending, propagating, or transmitting programs for use or operation by an instruction execution system, apparatus, or device. Program code on the storage medium can be transmitted via any suitable medium, including but not limited to wireless, wired, optical fiber, or any reasonable combination thereof.

[0066] The program code used to implement the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C. The execution modes of the program code include: running entirely on the user's computing device, running partially on the user's device as a standalone software package, running partially in a distributed manner on both the user's device and a remote computing device, or running entirely on a remote computing device or server. When a remote computing device is involved, the device can be connected to the user's computing device via any type of network such as a local area network (LAN) or a wide area network (WAN), or connected to an external computing device via the Internet through an Internet service provider.

[0067] The processor is capable of loading and executing one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the hydropower plant main transformer production data quality assessment method described in Example 1.

[0068] Example 5 Figure 2 This is a schematic diagram of a computer device provided according to an embodiment of the present invention.

[0069] Please see Figure 2 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the hydropower plant main transformer production data quality assessment method of this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the computational system constituting the hydropower plant main transformer production data quality assessment method of this embodiment. To avoid repetition, details are omitted here.

[0070] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 2This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0071] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0072] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0073] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0074] Any references to memory, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (Read-Only Memory). Memory includes ROM, magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0075] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

Claims

1. A method for evaluating the quality of production data of main transformers in hydropower plants, characterized in that, include: The range of each type of characteristic data in the main transformer production data of the hydropower plant is determined, and the data is classified into different levels according to the importance of each type of characteristic data; the main transformer production data includes common data of main transformer operation, A phase, B phase, C phase of the main transformer body, and related data of the main transformer cooling system; Based on different levels of characteristic data, a quality evaluation index framework for the operation data of the main transformer of the hydropower plant is defined, which includes completeness, accuracy, timeliness, and consistency. Based on the aforementioned quality evaluation index framework, a method for calculating data quality statistical scores is designed, which includes data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate. Based on the results of data quality statistics, the quality levels of hydropower plant transformer operation data are classified.

2. The method for evaluating the quality of production data of main transformers in hydropower plants according to claim 1, characterized in that, The determination of the range of various types of characteristic data in the production data of the main transformer of the hydropower plant includes: Identify the production data of the main transformer of the hydropower plant, and classify the characteristic data of the main transformer of the hydropower plant during operation. The characteristic data includes at least analog quantities, switch quantities and SOE data. The feature data are classified according to their importance and detection requirements, and the priority of feature data collection is set.

3. The method for evaluating the quality of production data of main transformers in hydropower plants according to claim 1, characterized in that, Define a quality evaluation index framework for the main transformer operation data of the hydropower plant, including: Under the integrity dimension, sub-items are set including data range integrity rate, data record integrity rate, data content integrity rate, time series integrity rate, data accuracy rate, and data integrity rate; Under the accuracy dimension, sub-items are set including data violation rate, data dead count rate, data unreasonableness rate, and data accuracy rate; Under the timeliness dimension, a sub-item including data communication normality rate is set; Under the consistency dimension, a sub-item including data consistency rate is set.

4. The method for evaluating the quality of production data of main transformers in hydropower plants according to claim 3, characterized in that, The method for calculating the statistical score of the design data quality includes: The data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate were calculated respectively. Based on the preset weight values, the data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate are weighted and summed to obtain the statistical score of the hydropower plant's main transformer operation data quality.

5. The method for evaluating the quality of production data of main transformers in hydropower plants according to claim 4, characterized in that, The data integrity rate is calculated as follows: In the formula, For data integrity rate, The number of data points for main transformer operation data level 1. The number of data points for main transformer operation data level 2. The number of data points for main transformer operation data level 3. The data integrity rate of a single data point in the main transformer operation data level 1. The data integrity rate of a single data point in the main transformer operation data level 2. The data integrity rate of a single data point in the main transformer operation data level 3. The data weight for Level 1 of main transformer operation data. The data weights for Level 2 of the main transformer operation data. The data weights for the main transformer operation data level 3.

6. The method for evaluating the quality of production data of main transformers in hydropower plants according to claim 4, characterized in that, The data accuracy is calculated by simultaneously satisfying the following conditions: non-empty values, non-limited values, non-refreshed duplicate values, and non-unreasonable data elements constitute the proportion of the total number of data elements.

7. The method for evaluating the quality of production data of main transformers in hydropower plants according to claim 4, characterized in that, The time communication normality rate is calculated by determining the proportion of data elements with normal communication status in the total number of data elements based on the number of timestamp records and frequency distribution. The data consistency rate is calculated by taking the percentage of elements whose data values ​​at the same measurement point are consistent across multiple independent systems out of the total number of data elements.

8. A quality assessment system for production data of main transformers in hydropower plants, characterized in that, include: The data quantity module is used to determine the range of various types of characteristic data in the main transformer production data of the hydropower plant, and to classify them into different levels according to the importance of each type of characteristic data; the main transformer production data includes common data of main transformer operation, A phase, B phase, C phase of the main transformer body, and related data of the main transformer cooling system; The indicator definition module is used to define a quality evaluation indicator framework for the main transformer operation data of the hydropower plant according to different levels of feature data. The quality evaluation indicator framework includes completeness, accuracy, timeliness and consistency. The quality statistics calculation module is used to design a method for calculating data quality statistics scores based on the aforementioned quality evaluation index framework. The calculation method includes data integrity rate, data accuracy rate, data communication normality rate, and data consistency rate. The grading module is used to classify the quality level of hydropower plant transformer operation data based on the results of data quality statistics.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the hydropower plant main transformer production data quality assessment method according to any one of claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the steps in the hydropower plant main transformer production data quality assessment method of any one of claims 1 to 7.

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