Data quality evaluation method, device, equipment and storage medium

Abstract

The present application relates to the field of regional energy data governance technology, and discloses a data quality evaluation method, apparatus, device, and storage medium. The method includes: analyzing the received energy data to be evaluated, determining the current data type of the energy data to be evaluated; determining the remaining data types that are missing when performing quality evaluation based on the current data type, and obtaining target energy data corresponding to the remaining data types from historical energy data; generating simulated energy data corresponding to the remaining data types based on a preset energy data knowledge graph and target energy data. Since the present application can generate simulated energy data based on a preset energy data knowledge graph and target energy data, and perform quality evaluation of the energy data to be evaluated based on the simulated energy data, there is no need to wait for receiving energy data of all data types before performing batch evaluation, thereby ensuring the real-time nature of data quality evaluation.

Description

Technical Field

[0001] The present application relates to the field of regional energy data governance technology, and in particular to a data quality evaluation method, device, equipment and storage medium. Background Art

[0002] In the field of regional energy data processing, there are often multiple different types of energy data, with varying sources, formats, and characteristics. Because different energy data are interrelated, for example, the power generation of renewable energy sources like wind and solar energy can be closely correlated with meteorological data (such as wind speed and light intensity), data quality assessments must comprehensively consider these interdependencies to ensure data accuracy and reliability in practical applications.

[0003] Existing data quality assessments for energy data often involve correlations between different types of energy data. This typically requires batch evaluations to ensure accuracy after receiving all the related energy data types. However, this requires waiting for the device to receive all the data types before batch processing, resulting in poor real-time performance. Summary of the Invention

[0004] The main purpose of this application is to provide a data quality evaluation method, which aims to solve the technical problem that the existing technology performs batch processing after receiving all types of data, resulting in poor real-time performance.

[0005] To achieve the above objectives, this application proposes a data quality evaluation method, which includes:

[0006] Analyzing the received energy data to be evaluated to determine the current data type of the energy data to be evaluated;

[0007] Determining, based on the current data type, remaining data types that are missing during quality evaluation, and acquiring target energy data corresponding to the remaining data types from historical energy data;

[0008] generating simulated energy data corresponding to the remaining data types according to a preset energy data knowledge graph and the target energy data, wherein the preset energy data knowledge graph is generated by the historical energy data;

[0009] A quality evaluation is performed on the energy data to be evaluated based on the simulated energy data.

[0010] In one embodiment, the step of generating simulated energy data corresponding to the remaining data types based on the preset energy data knowledge graph and the target energy data includes:

[0011] Performing time series analysis on the target energy data to obtain time series characteristics corresponding to the target energy data;

[0012] Obtain data type associations between various data types based on a preset energy data knowledge graph;

[0013] The target energy data is classified according to the time series characteristics, and simulated energy data corresponding to the remaining data types are generated according to the data type association and the classified target energy data.

[0014] In one embodiment, before the step of analyzing the received energy data to be evaluated, the method further includes:

[0015] Performing entity analysis based on historical energy data to determine data entities corresponding to each data type, and performing association analysis based on the historical energy data to determine data type associations between the data types;

[0016] Generating data nodes according to the data entities, and generating data edges according to the data type associations;

[0017] A preset energy data knowledge graph is constructed based on the data nodes and the data edges.

[0018] In one embodiment, the step of constructing a preset energy data knowledge graph based on the data nodes and the data edges includes:

[0019] Constructing an initial energy knowledge graph based on the data nodes and the data edges;

[0020] Obtaining a mean square error and a mean absolute error based on the data in the initial energy knowledge graph, and performing a quality assessment on the initial energy knowledge graph based on the mean square error and the mean absolute error;

[0021] When the quality assessment result meets the preset quality requirements, the initial energy knowledge graph is used as the preset energy data knowledge graph.

[0022] In one embodiment, the step of performing entity analysis based on historical energy data to determine the data entities corresponding to each data type further includes:

[0023] Performing time series analysis based on historical energy data to obtain historical time series characteristics corresponding to the historical energy data;

[0024] generating a time series graph according to the historical time series characteristics and the historical energy data, and analyzing the time series graph;

[0025] When the analysis result shows that there are missing values, obtaining an average value of the historical energy data based on the historical energy data, and filling the historical energy data based on the average value of the historical energy data to obtain new historical energy data;

[0026] An entity analysis is performed based on the new historical energy data to determine data entities corresponding to each data type.

[0027] In one embodiment, the step of performing quality evaluation on the energy data to be evaluated based on the simulated energy data includes:

[0028] Determining whether the simulated energy data meets preset business rules;

[0029] When the simulated energy data does not satisfy the preset business rules, performing regression analysis on the simulated energy data based on the historical energy data;

[0030] The simulated energy data is corrected based on the regression analysis result, and quality evaluation of the energy data to be evaluated is performed based on the corrected simulated energy data.

[0031] In one embodiment, the step of determining, based on the current data type, the remaining data types that are missing for quality evaluation includes:

[0032] Determining a target evaluation rule corresponding to the current data type based on the current data type;

[0033] Determining the complete data type required by the target evaluation rule according to the target evaluation rule;

[0034] The remaining data types missing for quality evaluation are determined based on the complete data type and the current data type.

[0035] In addition, to achieve the above objectives, the present application also proposes a data quality evaluation device, which includes:

[0036] An analysis module, configured to analyze the received energy data to be evaluated and determine the current data type of the energy data to be evaluated;

[0037] A selection module is configured to determine, based on the current data type, remaining data types that are missing during quality evaluation, and obtain target energy data corresponding to the remaining data types from historical energy data;

[0038] a simulation module, configured to generate simulated energy data corresponding to the remaining data types based on a preset energy data knowledge graph and the target energy data, wherein the preset energy data knowledge graph is generated using the historical energy data;

[0039] An evaluation module is used to perform quality evaluation on the energy data to be evaluated based on the simulated energy data.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a data quality evaluation device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data quality evaluation method described above.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the data quality evaluation method described above are implemented.

[0042] The present application proposes a data quality evaluation method, device, equipment and storage medium, the method comprising: analyzing the received energy data to be evaluated, determining the current data type of the energy data to be evaluated; determining the remaining data types that are missing when performing quality evaluation based on the current data type, and obtaining the target energy data corresponding to the remaining data types from the historical energy data; generating simulated energy data corresponding to the remaining data types based on a preset energy data knowledge graph and the target energy data, the preset energy data knowledge graph being generated by the historical energy data; and performing quality evaluation on the energy data to be evaluated based on the simulated energy data. Since the present application can generate simulated energy data based on the preset energy data knowledge graph and the target energy data, and perform quality evaluation on the energy data to be evaluated based on the simulated energy data, there is no need to wait for receiving energy data of all data types before performing batch evaluation, thus ensuring the real-time nature of data quality evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the following briefly introduces the drawings required for use in the embodiment or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a flowchart of the first embodiment of the data quality assessment method proposed in the embodiments of the present application;

[0046] Figure 2 This is a flow chart of the second embodiment of the data quality assessment method proposed in the embodiments of the present application;

[0047] Figure 3 This is a periodic time series node matching diagram in the data quality evaluation method proposed in the embodiment of the present application;

[0048] Figure 4 This is a flowchart of the third embodiment of the data quality assessment method proposed in the embodiments of the present application;

[0049] Figure 5 A schematic diagram of a preset energy data knowledge graph in the data quality evaluation method proposed in an embodiment of the present application;

[0050] Figure 6 A diagram of a data quality evaluation device provided in this embodiment;

[0051] Figure 7 FIG. 4 is a schematic diagram of the structure of a data quality evaluation device suitable for implementing this embodiment.

[0052] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not intended to limit the present application.

[0054] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0055] It should be noted that all directional indications in this embodiment (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship and movement status of the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0056] Understandably, in the field of energy data processing, there are often multiple different types of energy data, with varying sources, formats, and characteristics. Because different energy data are interrelated, for example, the power generation of renewable energy sources like wind and solar energy can be closely correlated with meteorological data (such as wind speed and light intensity), data quality assessments must comprehensively consider these interdependencies to ensure data accuracy and reliability in practical applications.

[0057] Existing data quality assessments for energy data often involve correlations between different types of energy data. This typically requires batch evaluations to ensure accuracy after receiving all the related energy data types. However, this requires waiting for the device to receive all the data types before batch processing, resulting in poor real-time performance.

[0058] Therefore, in order to solve the technical problem of poor real-time performance caused by batch processing after receiving all types of data in the existing technology, this embodiment proposes a data quality evaluation method, which includes: analyzing the received energy data to be evaluated and determining the current data type of the energy data to be evaluated; determining the remaining data types that are missing when performing quality evaluation based on the current data type, and obtaining the target energy data corresponding to the remaining data types from the historical energy data; generating simulated energy data corresponding to the remaining data types based on the preset energy data knowledge graph and the target energy data, wherein the preset energy data knowledge graph is generated by the historical energy data; and performing quality evaluation on the energy data to be evaluated based on the simulated energy data. Since this embodiment can generate simulated energy data based on the preset energy data knowledge graph and the target energy data, and perform quality evaluation on the energy data to be evaluated based on the simulated energy data, there is no need to wait for receiving energy data of all data types before performing batch evaluation, thus ensuring the real-time performance of data quality evaluation.

[0059] For ease of understanding, the following Figures 1 to 7 The data quality evaluation method provided in this embodiment and the data quality evaluation method, apparatus, device and storage medium provided in the following embodiments are introduced in detail.

[0060] This embodiment provides a data quality evaluation method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the data quality assessment method proposed in the embodiments of the present application.

[0061] like Figure 1 As shown, the method includes:

[0062] Step S10: Analyze the received energy data to be evaluated to determine the current data type of the energy data to be evaluated.

[0063] It should be noted that the execution subject of this embodiment can be a multifunctional machine device with data quality evaluation, such as a data quality evaluation device, or a device capable of performing the above functions. This embodiment uses a data quality evaluation device (hereinafter referred to as the device) for illustration.

[0064] It should also be noted that the energy data to be evaluated may be energy data of a preset area received by the device, such as wind energy data, solar energy data, and electric energy data of a certain industrial park. The current data type may be the type of energy, environmental factors, and data source corresponding to the energy data to be evaluated. Examples include wind energy data type, electric power data type, solar energy data type, power generation equipment data type, meteorological data type, and the like. The device may analyze the received energy data through part-of-speech analysis, a preset mapping relationship table, and the like. This embodiment uses part-of-speech analysis for explanation, but does not impose specific limitations on this embodiment.

[0065] In a specific implementation, upon receiving energy data from a preset area, the device may perform part-of-speech analysis on the received energy data to distinguish nouns, quantifiers, and unit words in the energy data. Furthermore, the device may determine whether the nouns in the energy data conform to a preset data type. If so, the data type corresponding to the noun is used as the current data type. The preset data type may be a data type pre-set by the user based on the data types available in the preset area.

[0066] For ease of understanding, the following example is provided, but this embodiment is not specifically limited. Assume that the device receives a piece of energy data, "The wind speed at a certain wind farm is 10 m / s, and the power generation is 500 kilowatts." Natural language processing techniques, such as part-of-speech analysis, identify the term "wind speed" and compare it with a preset data type to determine that it belongs to the wind energy category.

[0067] Step S20: determining the remaining data types that are missing during quality evaluation based on the current data type, and acquiring target energy data corresponding to the remaining data types from historical energy data.

[0068] It should be noted that the aforementioned remaining data types may be other types of data required for quality evaluation of the energy data to be evaluated. For example, when analyzing the complementarity of wind energy and solar energy to optimize regional energy distribution, assume that the equipment in the regional energy system receives wind speed data from a certain industrial park, and through part-of-speech analysis, it is determined that the current data type is wind energy data. In this case, in order to conduct a comprehensive quality evaluation of the wind energy data, solar energy data is also required for comprehensive analysis. Therefore, the remaining data type is solar energy data. The aforementioned historical energy data may be energy data accumulated in a preset area during past operations. The aforementioned target energy data may be energy data corresponding to the remaining data types obtained from the historical energy data.

[0069] Furthermore, in order to determine the remaining data types, the step of determining the remaining data types that are missing when performing quality evaluation based on the current data type includes:

[0070] Step S21: determining a target evaluation rule corresponding to the current data type based on the current data type;

[0071] Step S22: determining the complete data type required by the target evaluation rule according to the target evaluation rule;

[0072] Step S23: Determine the remaining data types that are missing for quality evaluation based on the complete data type and the current data type.

[0073] It should be noted that the target evaluation rules may be preset rules that guide the device on how to use different data types for quality evaluation. The complete data types may be all data types required for quality evaluation to meet the requirements of the target evaluation rules.

[0074] In a specific implementation, after confirming the current data type of the energy data to be evaluated, the device can select a target evaluation rule corresponding to the current data type from a built-in preset evaluation rule library based on the current data type. The device can select the target evaluation rule by searching a preset mapping table based on the current data type, where data types and evaluation rules correspond to each other.

[0075] After determining the target evaluation rules, the above-mentioned device will analyze these rules, confirm the complete data type required for quality evaluation, and finally determine the remaining data types in the complete data type except the current data type based on the complete data type and the current data type.

[0076] For ease of understanding, the following examples are provided, but this embodiment is not specifically limited. Assume that the device receives wind speed data from a wind farm and determines that the current data type is wind speed data. Based on a preset evaluation rule library, the device identifies target rules for evaluating wind energy quality, which include two key indicators: wind speed and power generation. Analysis shows that the complete data type should include both wind energy data and power data. After comparison, it is found that no data of the power data type is received, so the power data type is determined to be the remaining data type.

[0077] Step S30: Generate simulated energy data corresponding to the remaining data types according to a preset energy data knowledge graph and the target energy data, wherein the preset energy data knowledge graph is generated by the historical energy data.

[0078] It should be noted that the preset energy data knowledge graph can be a knowledge graph generated from historical energy data that includes different data types and their relationships. The simulated energy data can be simulated data generated based on the knowledge graph and target data to supplement missing data types.

[0079] In a specific implementation, the above-mentioned device analyzes the part of the knowledge graph related to the target energy data, extracts the rules and parameters required to generate simulated energy data, and uses the above-mentioned rules and parameters, combined with the obtained target energy data, to generate simulated energy data corresponding to the remaining data types through preset algorithms and models (such as time series analysis or machine learning models).

[0080] For ease of understanding, the following description is given by way of examples, but does not specifically limit this embodiment.

[0081] Consider a scenario where a device has already received wind energy data but still requires solar energy data to complete a quality assessment. The device leverages a pre-set energy data knowledge graph, which contains historical relationships between wind and solar energy (e.g., their changing trends under specific weather conditions). By analyzing these relationships and combining them with current wind energy data, the device generates corresponding simulated solar energy data using time series analysis or machine learning models. Even without actual solar energy data, the device can perform a complete quality assessment using simulated energy data.

[0082] Step S40: performing quality evaluation on the energy data to be evaluated based on the simulated energy data.

[0083] In the specific implementation, after generating the simulated energy data corresponding to the remaining data types, the above-mentioned equipment will unify these simulated energy data and the energy data to be evaluated into a unified batch, and use the above-mentioned target evaluation rules to conduct a comprehensive quality evaluation of the energy data to be evaluated, and evaluate indicators such as data integrity, accuracy, consistency and timeliness.

[0084] Furthermore, in order to ensure the accuracy of the simulated data and thus improve the accuracy of the quality evaluation, the step of performing quality evaluation on the energy data to be evaluated based on the simulated energy data includes:

[0085] Step S41: determining whether the simulated energy data meets preset business rules;

[0086] It should be noted that the above-mentioned preset business rules can be rules preset by users according to business needs, which are used to verify the integrity and accuracy of the simulated energy data. In this embodiment, the ratio range between the simulated energy data and the limited value of the data can be used as the preset business rules. For example, in the electric field, assuming that the simulated data is the power generated by the generator, the simulated power is y, and the rated power is , preset business rules:

[0087] ;

[0088] When y and When the ratio between them is within the above range, it is determined that the simulated power meets the preset business rules; otherwise, it is determined that the simulated power does not meet the preset business rules.

[0089] Step S42: When the simulated energy data does not satisfy the preset business rules, performing regression analysis on the simulated energy data according to the historical energy data;

[0090] It should be noted that regression analysis is an operation that can construct a model that can best fit the observed data to analyze quantitative relationships.

[0091] Step S43: correcting the simulated energy data based on the regression analysis result, and performing quality evaluation on the energy data to be evaluated based on the corrected simulated energy data.

[0092] In a specific implementation, when evaluating the quality of the energy data to be evaluated based on simulated energy data, the device first determines whether the simulated energy data meets preset business rules. If the simulated energy data does not meet the preset business rules, the device performs a regression analysis on the simulated energy data using historical energy data. Based on the results of the regression analysis, the device corrects the simulated energy data. Finally, the device conducts a comprehensive quality evaluation of the energy data to be evaluated based on the corrected simulated energy data.

[0093] Furthermore, the step of performing regression analysis on the simulated energy data based on the historical energy data includes:

[0094] Fitting a preset regression model based on the historical energy data to obtain a target regression coefficient;

[0095] Regression analysis is performed on the simulated energy data according to the target regression coefficient and the preset regression model.

[0096] The step of correcting the simulated energy data based on the regression analysis result includes:

[0097] The simulated energy data and the target regression coefficient are input into the preset regression model to obtain the corrected simulated energy data.

[0098] It should be noted that the preset regression model may be a mathematical model pre-selected based on the characteristics of the energy data, and used to describe the relationship between the energy data, such as a linear regression model, a polynomial regression model, etc. The target regression coefficient may be a model parameter obtained through regression analysis, and used to quantify the relationship between different energy data.

[0099] In a specific implementation, the device uses a regression analysis method, such as linear regression, to establish a mathematical model that reflects the inherent relationships between energy data as a preset regression model. The preset regression model is then fitted to historical energy data to obtain a target regression coefficient. Using this target regression coefficient and the preset regression model, the device then performs regression analysis on the simulated energy data. Specifically, the simulated energy data and the target regression coefficient are input into the regression model to obtain corrected simulated energy data.

[0100] In addition, in this embodiment, in order to use historical energy data to fit the preset regression model, it is necessary to first analyze the preset regression model. This is explained as a preset regression model, but it does not limit this embodiment. Where y is the target variable (such as power generation), X is the feature matrix (such as wind speed, temperature, etc.), β is the regression coefficient to be calculated, is the error term. And the least square method is used to determine the target fitting formula: , by fitting the preset regression model with historical energy data and the target fitting formula, β is determined, and then the preset correction formula is used: Correct the simulated energy data, where is the average of historical energy data, is the simulated feature matrix, is the simulated energy data after correction, is the regression coefficient vector, which is the true regression coefficient An estimate of It is a correction parameter used to control the weight between the mean of historical energy data and simulation results. Its value range is usually between 0 and 1. = 0, the corrected results are based entirely on the simulation model; when =1, the corrected result is based entirely on the mean of historical data.

[0101] To facilitate understanding, the following examples are provided, but this embodiment is not intended to be limiting. Assume that the aforementioned device, when analyzing wind energy data, collects historical wind speed and power generation data. Using a linear regression model, the device fits the relationship between wind speed (the independent variable) and power generation (the dependent variable), obtaining the target regression coefficient: Power generation = 20 × wind speed + 50. This indicates that for every 1 m / s increase in wind speed, power generation is expected to increase by 20 kilowatts.

[0102] When the device receives new simulated energy data, such as wind speed data, it feeds this data into a pre-set regression model. The model then calculates the expected power generation based on the target regression coefficients. If the power generation predicted by the simulated data differs significantly from the model's calculations, the device identifies potential errors in the simulated data and makes adjustments accordingly to improve the accuracy and reliability of the data.

[0103] This embodiment generates simulated energy data based on a preset energy data knowledge graph and target energy data, and performs quality evaluation on the energy data to be evaluated based on the simulated energy data. There is no need to wait for receiving energy data of all data types before performing batch evaluation, thus ensuring the real-time nature of data quality evaluation.

[0104] Based on the first embodiment, in the second embodiment, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , Figure 2 This is a flowchart of the second embodiment of the data quality evaluation method proposed in the embodiment of the present application. Further, the step of generating simulated energy data corresponding to the remaining data types based on the preset energy data knowledge graph and the target energy data includes:

[0105] Step S24: performing time series analysis on the target energy data to obtain time series features corresponding to the target energy data;

[0106] It should be noted that time series analysis can be used as a means of analyzing the time series of the target energy data mentioned above. When conducting time series analysis, historical data must first be collected, ensuring that the data is arranged in chronological order, addressing missing values ​​and outliers, and then performing feature extraction on the processed data. Feature extraction can include trend analysis (identifying long-term trends in the data, such as linear growth or decay); seasonal analysis (detecting cyclical patterns in the data, such as daily, weekly, and monthly cyclical changes); and periodic analysis (identifying long-term cyclical changes in the data, such as annual cycles). Next, an appropriate time series model is selected based on the data's characteristics and applied to the historical data to learn the patterns within the data.

[0107] Furthermore, it should be noted that the aforementioned time series features can be statistical properties exhibited in time series data, such as trends, seasonality, and periodicity. In a specific implementation, after receiving the target energy data, the aforementioned device first preprocesses the data to remove noise and outliers. Next, the device uses statistical methods and visualization tools to identify long-term trends, short-term fluctuations, and periodic patterns in the data, and quantifies these characteristics into specific parameters, namely, time series features.

[0108] For ease of understanding, the following examples are given for illustration, but this embodiment is not specifically limited. Assume that the above-mentioned device is processing wind speed data. First, the device preprocesses the data, such as filling missing values ​​and removing noise. Then, the moving average method is used to smooth the data to make the trend more obvious. By drawing a time series graph, the device observed that the wind speed has obvious periodic changes in a day: the wind speed is higher during the day and lower at night. Further analysis found that this daily periodicity can be described by a 24-hour cycle. In addition, the device also identified seasonal changes, that is, the wind speed is generally higher in spring and autumn, while it is relatively lower in summer and winter. These time series features will be quantified and used for subsequent simulation energy data generation.

[0109] Step S25: Obtain data type associations between various data types based on a preset energy data knowledge graph.

[0110] It should be noted that the aforementioned data type associations can be relationships between different data types, such as the positive correlation between wind speed and power generation. In a specific implementation, the device first loads the graph and identifies data entities and relationship nodes. For example, in a scenario involving wind power and grid load, the device identifies entities such as wind speed and power generation. Next, by querying the associations defined in the graph, the device obtains data type associations, such as the positive correlation between wind speed and power generation.

[0111] Step S26: classifying the target energy data according to the time series characteristics, and generating simulated energy data corresponding to the remaining data types according to the data type association and the classified target energy data.

[0112] In a specific implementation, after obtaining time series features, the device classifies the data based on these features. For example, data with similar periodicity and trends may be grouped together. Combining the classified target energy data with the associated data types, the device generates simulated energy data corresponding to the remaining data types.

[0113] Furthermore, in order to generate simulated energy data adapted to the current energy data to be evaluated, the step of generating simulated energy data corresponding to the remaining data types based on the target energy data after the data type association and data classification includes:

[0114] Performing time series node analysis on the energy data to be evaluated to determine the current time series node of the energy data to be evaluated;

[0115] Filtering target time series data from the target energy data after data classification based on the current time series node, and selecting a target data type association between the target time series data and the energy data to be evaluated from the data type associations according to the target time series data and the energy data to be evaluated;

[0116] A target association expression is generated according to the target data type association, and simulated energy data corresponding to the remaining data types is generated according to the target time series data and the target association expression.

[0117] It should be noted that the current time series node can be the position of the time point corresponding to the energy data to be evaluated in the time series, for example, when the T / 2 position in the data with a period of T is the current time series node, or when the season of the energy data to be evaluated is spring, etc. The target time series data can be the target energy data that is at the same relative position as the current time series node in the historical period, season or trend. Figure 3 , Figure 3 This is the periodic time series node matching diagram in the data quality evaluation method proposed in the embodiment of the present application. For example, in the case of period T (i.e. Figure 3 In the periodic timing of T=24h), if the current timing node (i.e. Figure 3 The current timing node (T / 2) in the current cycle (i.e. Figure 3 The target time series data can be the target energy data in the past cycle (i.e. Figure 3 The position of T / 2 in historical cycle 1 and historical cycle 2 (i.e. Figure 3 The target energy data of the target time series data point (T / 2) in .

[0118] Furthermore, it should be noted that the aforementioned target data type association can be an association between the energy data to be evaluated and the target time series data, such as a linear association. The aforementioned target association expression can be an expression generated based on the association between the data and used for data conversion. Different target data type associations correspond to different target association expressions. For example, when the data type association between Y and X is a linear association, the corresponding association expression is Y = aX + b, where Y is the energy data to be evaluated, X is the target time series data, and a and b are the association coefficients.

[0119] In practice, when performing regional energy data management, the aforementioned device first performs a time series node analysis on the energy data to be evaluated. Based on the time series characteristics of the energy data, it identifies its location at a specific time node and determines its current time series node. For example, when analyzing power generation data from a regional wind farm, time series analysis can be used to determine whether the current electricity consumption period is peak or off-peak.

[0120] Next, based on the determined current time series node, the device filters out target time series data from the classified target energy data, and selects a target data type association from the data type associations by combining the target time series data with the energy data to be evaluated.

[0121] Finally, based on the selected target data type association, a target association expression is generated. Based on the target time series data and the target association expression, simulated energy data corresponding to the remaining data types is generated. For example, once the relationship between wind power, wind speed, and wind direction is known, this data and the association expression can be used to simulate the power generated under specific wind speed and direction conditions.

[0122] Based on the first and second embodiments, in the third embodiment, the same or similar contents as those in the first and second embodiments can be referred to above and will not be described in detail. Figure 4 , Figure 4 This is a flowchart of the third embodiment of the data quality evaluation method proposed in the embodiments of the present application. Furthermore, in order to obtain a preset energy data knowledge graph, before the step of analyzing the received energy data to be evaluated, the method further includes:

[0123] Step S01: performing entity analysis based on historical energy data to determine data entities corresponding to each data type, and performing association analysis based on the historical energy data to determine data type associations between the data types.

[0124] It should be noted that the above-mentioned entity analysis can be the process of identifying and extracting entities with clear boundaries from a large amount of data. For example, objects, people, places, organizations, events, etc. In this embodiment, the above-mentioned device can identify key data objects such as power generation equipment, substations, users, sensors, etc. from historical energy data through entity analysis. In a specific implementation, a series of rules can be formulated to identify entities based on domain knowledge and data characteristics. For example, in energy data, it is stipulated that all equipment numbers, geographic location information, etc. that conform to a specific format correspond to specific data entities. For example, a number starting with "wind turbine-" corresponds to a specific wind turbine device.

[0125] The aforementioned association analysis can be a process of studying the relationships between different entities, their strength, and their regularity. In this embodiment, the aforementioned device can use association analysis to identify associations between different data entities. Examples include correlation, dependency, or causal relationships. In a specific implementation, correlation coefficients, such as the Pearson correlation coefficient or the Spearman rank correlation coefficient, can be calculated between different data entities to determine whether there is a linear or nonlinear relationship between them.

[0126] Furthermore, it should be noted that the aforementioned data entities can be specific objects described by the data, such as power generation equipment, substations, and users. For example, in a power plant, each generator set, each transformer, and each transmission line can be a data entity. These are the fundamental carriers of energy data, carrying specific attributes and information. The aforementioned data type associations can be relationships between different data types, such as the positive correlation between wind speed and power generation.

[0127] In a specific implementation, the above-mentioned device can identify historical energy data through preset identification rules, determine the data entities corresponding to each data type in the historical energy data, and confirm the data type association between each data type by calculating the correlation coefficient between different data entities.

[0128] Furthermore, in order to ensure the accuracy of historical energy data, the step of performing entity analysis based on the historical energy data to determine the data entities corresponding to each data type further includes:

[0129] Step S011: performing time series analysis based on historical energy data to obtain historical time series features corresponding to the historical energy data;

[0130] Step S012: generating a time series graph according to the historical time series characteristics and the historical energy data, and analyzing the time series graph;

[0131] Step S013: when the analysis result shows that there are missing values, obtaining an average value of the historical energy data based on the historical energy data, and filling the historical energy data based on the average value of the historical energy data to obtain new historical energy data;

[0132] Step S014: performing entity analysis based on the new historical energy data to determine data entities corresponding to each data type.

[0133] It should be noted that the aforementioned time series graph may be a chart constructed based on time and data values ​​to display data trends and distribution. The aforementioned missing values ​​may be data points that are missing in the historical energy data. The aforementioned historical time series features may be data features extracted by analyzing the chronologically arranged time series of historical energy data, such as trend features, seasonality, cyclicity, volatility, and mutation features. The aforementioned historical energy data average may be the average value obtained by calculating the average of data of each data type.

[0134] In practice, the device performs time series analysis based on historical energy data to identify historical time series features. For example, when analyzing historical wind farm data, the device can identify trends in power generation over time and fluctuation patterns across different time periods.

[0135] The device then generates a time series graph, such as a line chart or scatter plot, based on the historical time series characteristics and historical energy data to visually demonstrate the data's changing trends and distribution. The graph is then analyzed to determine whether there are any anomalies, such as missing values. If missing values ​​are found, the device calculates an average value based on the historical energy data and uses this average value to fill in the missing data, generating new historical energy data.

[0136] Step S02: Generate data nodes according to the data entities, and generate data edges according to the data type associations.

[0137] Step S03: Constructing a preset energy data knowledge graph based on the data nodes and the data edges.

[0138] It should be noted that the aforementioned data nodes can be the basic units that constitute the knowledge graph, representing data entities in the knowledge graph. Each node represents a specific entity, such as a specific power generation equipment or a substation, and can contain relevant attributes and information about that entity. The aforementioned data edges can be the lines between nodes in the knowledge graph that represent the relationship between data entities. For example, in energy data, the power transmission relationship between a power generation equipment and a substation can be represented by an edge, and the type and attributes of the edge can reflect the nature and strength of the relationship.

[0139] refer to Figure 5 , Figure 5 This is a schematic diagram of the preset energy data knowledge graph in the data quality evaluation method proposed in the embodiment of this application. In a specific implementation, the above device generates data nodes (i.e. Figure 5 Wind turbine #001IDWT23, substation X capacity: 50MW, wind speed sensor #01, industrial zone A and meteorological database), and then generate data edges based on data type association (i.e. Figure 5 The above edges connect related nodes. For example, an edge exists between a power generation equipment node and a substation node, representing the power transmission relationship. Finally, these nodes and edges are used to construct a pre-defined energy data knowledge graph for these devices.

[0140] Furthermore, in order to ensure the accuracy of the preset energy data knowledge graph, the step of constructing the preset energy data knowledge graph based on the data nodes and the data edges includes:

[0141] Step S031: constructing an initial energy knowledge graph based on the data nodes and the data edges;

[0142] Step S032: obtaining a mean square error and a mean absolute error based on the data in the initial energy knowledge graph, and performing a quality assessment on the initial energy knowledge graph based on the mean square error and the mean absolute error;

[0143] Step S033: When the quality assessment result meets the preset quality requirements, the initial energy knowledge graph is used as the preset energy data knowledge graph.

[0144] It should be noted that the above-mentioned initial energy knowledge graph can be a knowledge graph constructed based on data nodes and edges, representing the entities in the energy system and their relationships. The above-mentioned mean square error can be the average value of the square difference between the predicted value and the true value, which is used to evaluate the prediction accuracy of the model. The above-mentioned mean absolute error can be the average value of the absolute difference between the predicted value and the true value, which intuitively reflects the size of the average error. The above-mentioned preset quality requirements can be pre-set quality standards in the process of constructing the energy data knowledge graph. In practical applications, the mean square error threshold, the mean absolute error threshold, and the degree of completeness of the knowledge graph can be used as preset quality requirements. In this embodiment, the preset mean square error threshold and the preset mean absolute error threshold are used as preset quality requirements for explanation, but no specific limitation is imposed on this embodiment.

[0145] In practice, the device first constructs an initial energy knowledge graph based on data nodes and edges. Data nodes represent entities in the energy system, such as power generation equipment, substations, and users; data edges represent relationships between entities, such as power transmission and data collection. For example, in a wind farm, the device identifies entities such as wind turbines, wind speed sensors, and power generation monitoring equipment and generates nodes. It then generates edges based on the relationships between these entities to construct the initial energy knowledge graph.

[0146] Next, the device calculates the mean squared error (MSE) and mean absolute error (MAE) based on the data in the initial energy knowledge graph. The MSE measures the average of the squared differences between the predicted value and the true value and is used to evaluate the model's prediction accuracy; the MAE measures the average of the absolute differences between the predicted value and the true value and intuitively reflects the magnitude of the average error. For example, the device can compare the difference between the power generation predicted based on the knowledge graph and the actual power generation to calculate the MSE and MAE. For example, the preset quality requirements are that the MSE is less than 0.1 and the MAE is less than 0.05.

[0147] If the quality assessment results of the initial energy knowledge graph meet the preset quality requirements, the device will determine the initial energy knowledge graph as the preset energy data knowledge graph. If the quality assessment results do not meet the requirements, the device will optimize and adjust the knowledge graph, such as correcting incorrect entity attributes or relationships, supplementing missing data, etc., and then re-evaluate the quality until the preset quality requirements are met.

[0148] This embodiment also provides a first embodiment of a data quality evaluation device, please refer to Figure 6 , Figure 6 This is a diagram of a data quality evaluation device provided in this embodiment, which includes:

[0149] An analysis module, configured to analyze the received energy data to be evaluated and determine the current data type of the energy data to be evaluated;

[0150] A selection module is configured to determine, based on the current data type, remaining data types that are missing during quality evaluation, and obtain target energy data corresponding to the remaining data types from historical energy data;

[0151] a simulation module, configured to generate simulated energy data corresponding to the remaining data types based on a preset energy data knowledge graph and the target energy data, wherein the preset energy data knowledge graph is generated using the historical energy data;

[0152] An evaluation module, configured to perform a quality evaluation on the energy data to be evaluated based on the simulated energy data;

[0153] The evaluation module is further configured to determine whether the simulated energy data satisfies preset business rules; if the simulated energy data does not satisfy the preset business rules, perform regression analysis on the simulated energy data based on the historical energy data; correct the simulated energy data based on the regression analysis results, and perform quality evaluation on the energy data to be evaluated based on the corrected simulated energy data;

[0154] The selection module is also used to determine the target evaluation rule corresponding to the current data type based on the current data type; determine the complete data type required by the target evaluation rule according to the target evaluation rule; and determine the remaining data types that are missing when performing quality evaluation based on the complete data type and the current data type.

[0155] With reference to the first embodiment of the data quality evaluation device, this embodiment also proposes a second embodiment of the data quality evaluation device. The same or similar contents as those of the first embodiment of the data quality evaluation device can be referred to the above introduction and will not be repeated later.

[0156] The selection module is further configured to perform a time series analysis on the target energy data to obtain time series features corresponding to the target energy data; obtain data type associations between various data types based on a preset energy data knowledge graph; classify the target energy data according to the time series features, and generate simulated energy data corresponding to the remaining data types based on the data type associations and the classified target energy data;

[0157] The selection module is further configured to perform time series node analysis on the energy data to be evaluated to determine the current time series node at which the energy data to be evaluated is located; filter out target time series data from the target energy data after data classification based on the current time series node, and select a target data type association between the target time series data and the energy data to be evaluated from the data type associations based on the target time series data and the energy data to be evaluated; generate a target association expression based on the target data type association, and generate simulated energy data corresponding to the remaining data types based on the target time series data and the target association expression.

[0158] With reference to the first embodiment of the data quality evaluation device and the second embodiment of the data quality evaluation device, this embodiment also proposes a third embodiment of the data quality evaluation device. The same or similar contents as those of the first embodiment of the data quality evaluation device and the second embodiment of the data quality evaluation device can be referred to the above introduction and will not be repeated later.

[0159] The analysis module is further configured to perform entity analysis based on historical energy data to determine data entities corresponding to each data type, and perform association analysis based on the historical energy data to determine data type associations between data types; generate data nodes based on the data entities, and generate data edges based on the data type associations; and construct a preset energy data knowledge graph based on the data nodes and the data edges;

[0160] The analysis module is further configured to construct an initial energy knowledge graph based on the data nodes and the data edges; obtain a mean square error and a mean absolute error based on the data in the initial energy knowledge graph, and perform a quality assessment on the initial energy knowledge graph based on the mean square error and the mean absolute error; and use the initial energy knowledge graph as a preset energy data knowledge graph when the quality assessment result meets a preset quality requirement;

[0161] The analysis module is further used to perform time series analysis based on historical energy data to obtain historical time series characteristics corresponding to the historical energy data; generate a time series graph based on the historical time series characteristics and the historical energy data, and analyze the time series graph; when the analysis result shows that there are missing values, obtain the average value of the historical energy data based on the historical energy data, and fill the historical energy data based on the average value of the historical energy data to obtain new historical energy data; perform entity analysis based on the new historical energy data to determine the data entities corresponding to each data type.

[0162] The data quality assessment device provided in this embodiment utilizes the data quality assessment method in the above-described embodiment, resolving the technical issue of prior art, which involves batch processing after receiving all types of data, resulting in poor real-time performance. Compared to prior art, the data quality assessment device provided in this embodiment achieves the same beneficial effects as the data quality assessment method provided in the above-described embodiment. Other technical features of the data quality assessment device are the same as those disclosed in the above-described embodiment and are not further elaborated here.

[0163] This embodiment provides a data quality assessment device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data quality assessment method in the above-mentioned embodiment one.

[0164] Reference below Figure 7 , Figure 7 FIG2 is a schematic diagram of a data quality assessment device suitable for implementing this embodiment. The data quality assessment device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The data quality evaluation device shown is only an example and should not bring any limitation to the function and scope of use of this embodiment.

[0165] like Figure 7As shown, the data quality assessment device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the data quality assessment device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input devices 1007, such as a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003, such as a magnetic tape or hard disk; and communication devices 1009. The communication device 1009 can allow the data quality assessment device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a data quality assessment device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or provided instead.

[0166] In particular, according to this embodiment, the process described above with reference to the flowchart can be implemented as a computer software program. For example, this embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in this embodiment are performed.

[0167] The data quality assessment device provided in this embodiment utilizes the data quality assessment method in the above-described embodiment, resolving the technical issue of prior art, which involves batch processing after receiving all types of data, resulting in poor real-time performance. Compared to prior art, the data quality assessment device provided in this embodiment has the same beneficial effects as the data quality assessment method provided in the above-described embodiment. Other technical features of this data quality assessment device are the same as those disclosed in the above-described embodiment and are not further elaborated here.

[0168] It should be understood that the various parts disclosed in this embodiment can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in an appropriate manner.

[0169] The above description is merely a specific implementation of this embodiment, but the scope of protection of this embodiment is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this embodiment should be included within the scope of protection of this embodiment. Therefore, the scope of protection of this embodiment should be based on the scope of protection of the claims.

[0170] This embodiment provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon. The computer-readable program instructions are used to execute the data quality evaluation method in the above embodiment.

[0171] The computer-readable storage medium provided in this embodiment may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0172] The computer-readable storage medium may be included in the data quality evaluation device; or may exist independently without being incorporated into the data quality evaluation device.

[0173] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the data quality evaluation device, the data quality evaluation device is enabled to: perform data quality evaluation.

[0174] The computer program code for performing the operations of the present embodiment can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0175] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present embodiment. In this regard, each box in the flow chart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for realizing the prescribed logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the prescribed function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0176] The modules described in this embodiment may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0177] The readable storage medium provided in this embodiment is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned data quality assessment method. This addresses the technical issue of prior art, which involves batch processing of all data types after receiving them, resulting in poor real-time performance. Compared to prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment are similar to those of the data quality assessment method provided in the aforementioned embodiment and are not further elaborated here.

[0178] The above descriptions are only some embodiments and do not limit the patent scope of this embodiment. All equivalent structural transformations made using the contents of the description and drawings of this application under the technical concept of this application, or direct / indirect application in other related technical fields are included in the patent protection scope of this application.

Claims

1. A data quality evaluation method, characterized in that: The method comprises: Analyzing the received energy data to be evaluated to determine the current data type of the energy data to be evaluated; Determining, based on the current data type, remaining data types that are missing for quality evaluation, and obtaining target energy data corresponding to the remaining data types from historical energy data, wherein the remaining data types are types of other data required for quality evaluation of the energy data to be evaluated; generating simulated energy data corresponding to the remaining data types according to a preset energy data knowledge graph and the target energy data, wherein the preset energy data knowledge graph is generated by the historical energy data, and the simulated energy data is data that supplements the missing data types; Performing a quality evaluation on the energy data to be evaluated based on the simulated energy data; The step of generating simulated energy data corresponding to the remaining data types according to the preset energy data knowledge graph and the target energy data includes: Performing time series analysis on the target energy data to obtain time series characteristics corresponding to the target energy data; Obtaining data type associations between various data types based on a preset energy data knowledge graph, wherein the data type associations are relationships between different data types; The target energy data is classified according to the time series characteristics, and simulated energy data corresponding to the remaining data types are generated according to the data type association and the classified target energy data.

2. The method according to claim 1, wherein Before the step of analyzing the received energy data to be evaluated, the method further includes: Performing entity analysis based on historical energy data to determine data entities corresponding to each data type, and performing association analysis based on the historical energy data to determine data type associations between the data types; Generating data nodes according to the data entities, and generating data edges according to the data type associations; A preset energy data knowledge graph is constructed based on the data nodes and the data edges.

3. The method according to claim 2, wherein The step of constructing a preset energy data knowledge graph based on the data nodes and the data edges includes: Constructing an initial energy knowledge graph based on the data nodes and the data edges; Obtaining a mean square error and a mean absolute error based on the data in the initial energy knowledge graph, and performing a quality assessment on the initial energy knowledge graph based on the mean square error and the mean absolute error; When the quality assessment result meets the preset quality requirements, the initial energy knowledge graph is used as the preset energy data knowledge graph.

4. The method according to claim 2, wherein The step of performing entity analysis based on historical energy data to determine data entities corresponding to each data type includes: Performing time series analysis based on historical energy data to obtain historical time series characteristics corresponding to the historical energy data; generating a time series graph according to the historical time series characteristics and the historical energy data, and analyzing the time series graph; When the analysis result shows that there are missing values, obtaining an average value of the historical energy data based on the historical energy data, and filling the historical energy data based on the average value of the historical energy data to obtain new historical energy data; An entity analysis is performed based on the new historical energy data to determine data entities corresponding to each data type.

5. The method according to claim 1, wherein The step of performing quality evaluation on the energy data to be evaluated based on the simulated energy data includes: Determining whether the simulated energy data meets preset business rules; When the simulated energy data does not satisfy the preset business rules, performing regression analysis on the simulated energy data based on the historical energy data; The simulated energy data is corrected based on the regression analysis result, and quality evaluation of the energy data to be evaluated is performed based on the corrected simulated energy data.

6. The method according to claim 1, wherein The step of determining the remaining data types that are missing when performing quality evaluation based on the current data type includes: Determining a target evaluation rule corresponding to the current data type based on the current data type; Determining the complete data type required by the target evaluation rule according to the target evaluation rule; The remaining data types missing for quality evaluation are determined based on the complete data type and the current data type.

7. A data quality evaluation device, characterized in that: The device comprises: An analysis module, configured to analyze the received energy data to be evaluated and determine the current data type of the energy data to be evaluated; a selection module configured to determine, based on the current data type, remaining data types that are missing for quality evaluation, and obtain target energy data corresponding to the remaining data types from historical energy data, wherein the remaining data types are types of other data required for quality evaluation of the energy data to be evaluated; a simulation module, configured to generate simulated energy data corresponding to the remaining data types based on a preset energy data knowledge graph and the target energy data, wherein the preset energy data knowledge graph is generated using the historical energy data, and the simulated energy data is data that supplements the missing data types; An evaluation module, configured to perform a quality evaluation on the energy data to be evaluated based on the simulated energy data; The simulation module is further used to perform time series analysis based on the target energy data to obtain time series characteristics corresponding to the target energy data; obtain data type associations between various data types based on a preset energy data knowledge graph, where the data type associations are relationships between different data types; classify the target energy data according to the time series characteristics, and generate simulated energy data corresponding to the remaining data types based on the data type associations and the target energy data after data classification.

8. A data quality evaluation device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data quality assessment method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data quality evaluation method according to any one of claims 1 to 6 are implemented.

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