CN117033920A - Data quality assessment method and device for distributed energy storage system and computer equipment - Google Patents
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Abstract
The invention relates to the technical field of distributed energy storage, in particular to a data quality evaluation method and device of a distributed energy storage system and computer equipment. The method comprises the following steps: acquiring data to be evaluated of a distributed energy storage system; determining an integrity evaluation result according to the data missing condition of the data to be evaluated; determining a consistency evaluation result according to the time relation and the numerical relation of different data sources in the data to be evaluated; determining an accuracy evaluation result according to the data change condition of the data to be evaluated; and determining a timeliness evaluation result according to the time information of the data to be evaluated. By implementing the method, the system and the device for evaluating the data to be evaluated of the distributed energy storage system, the data to be evaluated of the distributed energy storage system are evaluated in four dimensions of integrity, consistency, accuracy and timeliness, the quality of the multidimensional data of the distributed energy storage system is evaluated, and comprehensive prediction and energy coordination management of the distributed energy storage system are facilitated.
Description
Technical Field
The invention relates to the technical field of distributed energy storage, in particular to a data quality evaluation method and device of a distributed energy storage system and computer equipment.
Background
The distributed energy storage has the characteristics of quick power control and flexible energy throughput, can be used as a flexible resource to participate in voltage regulation, frequency modulation and demand response of a power grid, and promote the consumption of renewable energy sources, and is one of important means for solving the problem of unbalanced source load. On the other hand, the distributed energy storage equipment has various types and scattered positions, and the multi-source heterogeneous data characteristics of the distributed energy storage equipment bring challenges to the data quality management of the distributed energy storage system.
The existing data quality evaluation mainly comprises two types of evaluation methods: the method is more general and has strong universality, but the method has less utilization on field information; the other type is an evaluation method in a specific field, and the evaluation index has uniqueness. The monitoring means and the quality control in the prior energy storage technical field focus on a battery level, and the energy storage management units such as battery monomers, battery clusters and the like are monitored in real time through a battery energy management system (Battery Management System, BMS), but the prior method lacks in evaluating the quality of multidimensional data.
Disclosure of Invention
In view of the above, the invention provides a data quality evaluation method, a data quality evaluation device and computer equipment of a distributed energy storage system, so as to solve the problem that the prior energy storage technology field lacks in evaluating the quality of multidimensional data.
In a first aspect, the present invention provides a data quality assessment method for a distributed energy storage system, the method comprising: acquiring data to be evaluated of a distributed energy storage system; determining an integrity evaluation result according to the data missing condition of the data to be evaluated; determining a consistency evaluation result according to the time relation and the numerical relation of different data sources in the data to be evaluated; determining an accuracy evaluation result according to the data change condition of the data to be evaluated; and determining a timeliness evaluation result according to the time information of the data to be evaluated.
According to the data quality evaluation method for the distributed energy storage system, the data to be evaluated of the distributed energy storage system is evaluated in four dimensions of integrity, consistency, accuracy and timeliness, so that the evaluation of the multi-dimensional data quality of the distributed energy storage system is realized, and comprehensive prediction and energy coordination management of the distributed energy storage system are facilitated.
In an alternative embodiment, the data to be evaluated includes device parameter information and monitoring data of the distributed energy storage system, and after obtaining the data to be evaluated of the distributed energy storage system, the method further includes: preprocessing data to be evaluated; before acquiring the data to be evaluated of the distributed energy storage system, the method further comprises: an evaluation parameter is determined.
In this embodiment, when the data to be evaluated is obtained, not only the monitoring data but also the device parameter information of the system are obtained, so that the problem that the integration and fusion of the data are affected because the energy storage system supporting devices such as a converter are not incorporated into a unified data quality management framework in the related art, and the comprehensive prediction and energy coordination management of the distributed energy storage system are not facilitated is solved.
In an alternative embodiment, determining the integrity assessment result according to the data absence of the data to be assessed includes: and calculating the data loss rate according to the actual acquisition point number and the theoretical acquisition point number obtained by acquiring the data to be evaluated through the sliding window, and determining an integrity evaluation result.
In the embodiment, the number of actual acquisition points is obtained by adopting a sliding window mode, and the data loss rate is calculated together with the number of theoretical acquisition points, so that the data integrity evaluation is realized.
In an alternative embodiment, the different data sources are divided according to the control instruction, the consistency evaluation result is determined according to the time relation and the numerical relation of the different data sources in the data to be evaluated, and the different data sources are divided according to the control instruction, including: taking the change of the state quantity in the data to be evaluated as a signal of a control instruction, and dividing different monitoring data tables with the same state quantity information in the data to be evaluated into different data sources; determining time consistency according to the time relation of the acquisition points in each data source and the time relation of different data sources; determining a numerical consistency according to correlation analysis between different data sources; and determining a consistency evaluation result according to the time consistency and the numerical consistency.
In this embodiment, when the consistency evaluation is performed, the consistency evaluation is performed from two aspects of time and numerical value of the data, so as to improve the comprehensiveness of the consistency evaluation.
In an alternative embodiment, determining the temporal consistency from the temporal relationship of acquisition points within each data source and the temporal relationship of different data sources includes: determining a mean value of the acquisition time intervals of each data source based on the time differences of adjacent acquisition points in each data source; calculating the average value of the acquisition time intervals of all the data sources based on the time differences of the adjacent acquisition points in all the data sources; calculating the square sum of errors among groups according to the number of acquisition points of different data sources, the average value of the acquisition time interval of each data source and the average value of the acquisition time intervals of all data sources; calculating the total error square sum according to the time interval of each acquisition point of each data source and the average value of the acquisition time intervals of all the data sources; and determining the time consistency according to the ratio of the sum of squares of errors among the groups to the sum of squares of total errors.
In an alternative embodiment, the evaluation parameters comprise a preset interval, a preset distribution model and a confidence interval, and the accuracy evaluation result comprises a point abnormality rate and/or a sequence abnormality rate; the point anomaly rate is determined in the following manner: determining a point abnormality rate according to the relation between the difference value of the monitoring data of the adjacent acquisition points and a preset interval; the sequence anomaly rate is determined in the following manner: cutting the monitoring data to obtain a plurality of fragments; calculating the variation of the monitoring data of adjacent acquisition points in each segment and the average value of the variation to obtain the parameters of the samples to be compared; and determining whether each segment is abnormal or not according to the relation between the sample parameters to be compared and the confidence interval based on a preset distribution model, and obtaining the sequence abnormality rate.
In the embodiment, the accuracy evaluation is performed on the data through two aspects of the point anomaly rate and the sequence anomaly rate, so that the comprehensiveness of the accuracy evaluation is improved.
In an alternative embodiment, the evaluation parameter includes a preset sampling period, and determining the time-lapse evaluation result according to the time information of the data to be evaluated includes: calculating the time difference of the data recording time and the data generating time of the monitoring data; and determining the data delay rate according to the average value of the time difference, the number of the monitoring data in each period and the preset sampling period, and determining the timeliness evaluation result.
In an alternative embodiment, the method further comprises: obtaining a quality analysis report based on the integrity evaluation result, the consistency evaluation result, the accuracy evaluation result and the timeliness evaluation result; and updating the evaluation parameters according to the operation parameters of the distributed energy storage system.
In this embodiment, by generating a quality analysis report, the evaluation result of the data can be comprehensively displayed; by updating the evaluation parameters, the accuracy of subsequent evaluation is further improved.
In a second aspect, the present invention provides a data quality assessment apparatus for a distributed energy storage system, the apparatus comprising: the data acquisition module is used for acquiring data to be evaluated of the distributed energy storage system; the integrity evaluation module is used for determining an integrity evaluation result according to the data missing condition of the data to be evaluated; the consistency evaluation module is used for determining a consistency evaluation result according to the time relationship and the numerical relationship of different data sources in the data to be evaluated; the accuracy evaluation module is used for determining an accuracy evaluation result according to the data change condition of the data to be evaluated; and the timeliness evaluation module is used for determining a timeliness evaluation result according to the time information of the data to be evaluated.
In an alternative embodiment, the data to be evaluated includes device parameter information and monitoring data of the distributed energy storage system, and the apparatus further includes: the preprocessing module is used for preprocessing the data to be evaluated; and the parameter determining module is used for determining the evaluation parameter.
In an optional implementation manner, the integrity evaluation module is specifically configured to calculate a data loss rate according to the actual number of acquisition points and the theoretical number of acquisition points obtained by acquiring the data to be evaluated in a sliding window, and determine an integrity evaluation result.
In an alternative embodiment, the consistency assessment module includes: the data source dividing module is used for dividing different monitoring data tables with the same state quantity information in the data to be evaluated into different data sources by taking the change of the state quantity in the data to be evaluated as a signal of a control instruction; the time consistency evaluation module is used for determining time consistency according to the time relation of the acquisition points in each data source and the time relation of different data sources; the numerical value consistency evaluation module is used for determining numerical value consistency according to correlation analysis among different data sources; and the evaluation sub-module is used for determining a consistency evaluation result according to the time consistency and the numerical consistency.
In an alternative embodiment, the different data sources are divided according to the control instruction, and the time consistency evaluation module is specifically configured to: determining a mean value of the acquisition time intervals of each data source based on the time differences of adjacent acquisition points in each data source; calculating the average value of the acquisition time intervals of all the data sources based on the time differences of the adjacent acquisition points in all the data sources; calculating the square sum of errors among groups according to the number of acquisition points of different data sources, the average value of the acquisition time interval of each data source and the average value of the acquisition time intervals of all data sources; calculating the total error square sum according to the time interval of each acquisition point of each data source and the average value of the acquisition time intervals of all the data sources; and determining the time consistency according to the ratio of the sum of squares of errors among the groups to the sum of squares of total errors.
In an alternative embodiment, the evaluation parameters comprise a preset interval, a preset distribution model and a confidence interval, and the accuracy evaluation result comprises a point abnormality rate and/or a sequence abnormality rate; the point anomaly rate is determined in the following manner: determining a point abnormality rate according to the relation between the difference value of the monitoring data of the adjacent acquisition points and a preset interval; the sequence anomaly rate is determined in the following manner: cutting the monitoring data to obtain a plurality of fragments; calculating the variation of the monitoring data of adjacent acquisition points in each segment and the average value of the variation to obtain the parameters of the samples to be compared; and determining whether each segment is abnormal or not according to the relation between the sample parameters to be compared and the confidence interval based on a preset distribution model, and obtaining the sequence abnormality rate.
In an alternative embodiment, the evaluation parameter includes a preset sampling period, and the timeliness evaluation module is specifically configured to: calculating the time difference of the data recording time and the data generating time of the monitoring data; and determining the data delay rate according to the average value of the time difference, the number of the monitoring data in each period and the preset sampling period, and determining the timeliness evaluation result.
In an alternative embodiment, the apparatus further comprises: the report determining module is used for obtaining a quality analysis report based on the integrity evaluation result, the consistency evaluation result, the accuracy evaluation result and the timeliness evaluation result; and the updating module is used for updating the evaluation parameters according to the operation parameters of the distributed energy storage system.
In a third aspect, the present invention provides a computer device comprising: the data quality evaluation method of the distributed energy storage system according to the first aspect or any one of the corresponding embodiments of the first aspect is implemented by the processor, and the memory and the processor are communicatively connected with each other, and the memory stores computer instructions.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the data quality assessment method of the distributed energy storage system of the first aspect or any of its corresponding embodiments.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for evaluating data quality of a distributed energy storage system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for data quality assessment of yet another distributed energy storage system according to an embodiment of the present invention;
FIG. 3 is a block diagram of a data quality assessment device of a distributed energy storage system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In accordance with an embodiment of the present invention, there is provided a data quality assessment method embodiment for a distributed energy storage system, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
In this embodiment, a data quality evaluation method of a distributed energy storage system is provided, which may be used in an electronic device, such as a computer, a mobile phone, a tablet computer, etc., fig. 1 is a flowchart of a data quality evaluation method of a distributed energy storage system according to an embodiment of the present invention, and as shown in fig. 1, the flowchart includes the following steps:
step S101, obtaining data to be evaluated of a distributed energy storage system.
Specifically, the operation modes of the distributed energy storage system include an operation mode (off-grid and grid-connected) of the PCS (Power Conversion System, converter), a charging and discharging mode (constant current, constant voltage and constant power) of the energy storage battery, a connection mode between the energy storage battery cluster and the PCS, and the like. The to-be-evaluated data specifically comprises equipment parameter information and monitoring data of the distributed energy storage system, wherein equipment in the distributed energy storage system comprises energy storage management units, converters, distribution transformers and other auxiliary equipment in various forms such as battery modules, battery clusters and the like in the distributed energy storage system, and the equipment parameter information comprises equipment types and performance parameters such as conventional parameters and input and output parameters of PCS (personal communications System), nominal, structural and electrical parameters of an energy storage battery and the like. The monitoring data can be monitoring data acquired in real time, and specifically comprises analog quantities such as current, voltage, temperature, state Of Charge (SOC), state Of Health (SOH) and the like acquired by a battery management system, and State quantities such as a battery system on-off State and a communication State; the energy storage converter collects DC side voltage, current and power, AC side three-phase voltage, three-phase current, input and output power and other analog quantities and state quantities such as a switch state, a charge and discharge state and the like; three-phase wiring temperature, three-phase current and the like collected by the distribution transformer. It should be noted that, the obtained data to be evaluated may be stored in time sequence, that is, each time stamp and the corresponding data to be evaluated are stored together, so as to facilitate subsequent evaluation.
Step S102, determining an integrity evaluation result according to the data missing condition of the data to be evaluated.
Specifically, the integrity evaluation is specifically implemented on the acquired missing condition of the data to be evaluated, such as the missing rate of the calculated data.
Step S103, determining a consistency evaluation result according to the time relation and the numerical relation of different data sources in the data to be evaluated.
The control instruction refers to instruction information issued to a battery energy management system (BMS) by a converter (PCS), and issued to a battery cluster, a battery module or a battery cell by the BMS, and the instruction controls the charge and discharge states of the battery by changing the switch state of the battery system. Specifically, according to the issued control instruction, different data tables with the same state quantity information in the data to be evaluated are divided into a plurality of data sources. And then, consistency assessment of the data to be assessed is realized based on the time relation and the numerical relation among the plurality of data sources.
Step S104, determining an accuracy evaluation result according to the data change condition of the data to be evaluated.
Specifically, the change condition of the data includes the change condition between adjacent data to be evaluated, and may also include the change condition between a plurality of fragments after slicing the data to be evaluated, and the accuracy evaluation result is determined according to the change condition.
Step S105, determining a timeliness evaluation result according to the time information of the data to be evaluated.
The time information of the data to be evaluated specifically comprises a relation between data generation and recording time, and timeliness evaluation is achieved based on the relation.
According to the data quality evaluation method for the distributed energy storage system, the data to be evaluated of the distributed energy storage system is evaluated in four dimensions of integrity, consistency, accuracy and timeliness, so that the evaluation of the multi-dimensional data quality of the distributed energy storage system is realized, and comprehensive prediction and energy coordination management of the distributed energy storage system are facilitated.
In an alternative embodiment, after acquiring the data to be evaluated of the distributed energy storage system, the method further comprises: preprocessing data to be evaluated; before acquiring the data to be evaluated of the distributed energy storage system, the method further comprises: an evaluation parameter is determined.
The preprocessing of the data can specifically implement different preprocessing processes according to the evaluation of different dimensions, such as data sampling, cleaning, transformation, encoding and the like, and the specific preprocessing mode is specifically described in the subsequent specific evaluation. The evaluation parameters are specific to the relevant parameters adopted in the process of performing the evaluation of different dimensions, and can refer to the subsequent specific evaluation process, which is not described herein.
In this embodiment, a data quality evaluation method of a distributed energy storage system is provided, where the process includes the following steps:
step S201, obtaining data to be evaluated of the distributed energy storage system.
Step S202, determining an integrity evaluation result according to the data missing condition of the data to be evaluated.
Specifically, the step S202 includes:
step S2021, calculating the data missing rate according to the actual collection point number and the theoretical collection point number obtained by collecting the data to be evaluated through the sliding window, and determining the integrity evaluation result.
When the deletion rate is calculated, firstly fixing a sliding window T, and then randomly selecting k sliding windows without coincidence; calculating the number r of actual acquisition points in the window after the duplication elimination; calculating the number t of theoretical acquisition points in a sliding window according to the equipment acquisition period; calculating the data missing rate missing_rate according to the actual collection point number and the theoretical collection point number, wherein the formula is as follows:
step S203, determining a consistency evaluation result according to the time relation and the numerical relation of different data sources in the data to be evaluated, wherein the different data sources are divided according to the control instruction;
specifically, the step S203 includes:
step S2031, dividing different monitoring data tables with the same state quantity information in the data to be evaluated into different data sources by using the change of the state quantity in the data to be evaluated as a signal of a control instruction; specifically, the physical devices at the same level under the same control instruction collect information as the same data source, where the physical devices at the same level may be a plurality of battery clusters controlled by the same PCS, or a plurality of battery modules connected in parallel in one battery cluster, or a group of unit batteries connected in series. Wherein, when the control command is changed, the collected monitoring data is changed to a great extent, thereby dividing the monitoring data table with the same state information into different data sources based on the control command. The control instruction information may be obtained from the device state quantity information in the data to be evaluated. The monitoring data table area stores physical equipment acquisition information, information acquired by single physical equipment of the same level under the same control instruction is stored in one data table, and then the data table corresponding to a plurality of physical equipment of the same level under the same control instruction is divided into one monitoring data table area, so that a plurality of monitoring data table areas can be obtained.
Step S2032, determining the time consistency according to the time relationship of the acquisition points in each data source and the time relationship of different data sources.
Specifically, the step S2032 includes:
step a1, determining the average value of the acquisition time interval of each data source based on the time difference of the adjacent acquisition points in each data source; specifically, the time difference between adjacent acquisition points in each data source is determined according to the time stamp of the acquired data to be evaluated, namely the previous time point is subtracted from the next time point to obtain an acquisition time interval, and then all the acquisition time intervals deltas in each data source are averaged to obtain the average value of the acquisition time intervals
Step a2, calculating the time of all data sources based on the time difference of the adjacent acquisition points in all data sourcesThe mean value of the interval; specifically, the time differences of adjacent acquisition points in all data sources are added and divided by the number of the time differences to obtain the average value of the acquisition time intervals of all the data sources
And a3, calculating the square sum of errors among groups according to the number of acquisition points of different data sources, the average value of the acquisition time interval of each data source and the average value of the acquisition time intervals of all the data sources. Specifically, the sum of squares of the errors between the groups is determined using the following formula:
Wherein n is i Represents the number of collection points in the ith data source, and k represents the total number of data sources.
Step a4, calculating the total error square sum according to the time interval of each acquisition point of each data source and the average value of the acquisition time intervals of all the data sources; specifically, the total error sum of squares is determined using the following formula:
wherein Deltas ij The time difference between the jth acquisition point of the ith data source is represented, and is specifically the time difference between the acquisition point and the previous (jth-1) acquisition point in the data source.
And a5, determining the time consistency according to the ratio of the sum of squares of errors among the groups to the sum of squares of total errors. Specifically, the time coherence timer_rate is determined using the following formula:
step S2033, determining the numerical consistency according to correlation analysis among different data sources; the method comprises the steps of firstly carrying out preprocessing such as de-duplication, filling missing records, removing abnormal values and the like on data before carrying out numerical consistency evaluation, and then carrying out pairwise correlation analysis on all data sources to obtain a correlation coefficient matrix; according to the number k of the data sources and the correlation coefficient matrix, calculating the numerical value consistency rate numerc_rate of different data sources, wherein the formula is as follows:
wherein r is ij Is the correlation coefficient between data source i and data source j. The correlation coefficient can be selected according to the characteristics of the data by a method such as a Pierson correlation coefficient, an included angle cosine formula, a Spilot class correlation coefficient and the like.
Step S2034, determining a consistency evaluation result according to the time consistency and the numerical consistency. Specifically, the time consistency and the numerical consistency together constitute a consistency evaluation result.
Step S204, determining an accuracy evaluation result according to the data change condition of the data to be evaluated. Wherein, before the accuracy evaluation, the data is subjected to a pretreatment of de-duplication. The evaluation parameters comprise a preset interval, a preset distribution model and a confidence interval. The accuracy evaluation result comprises a point abnormality rate and/or a sequence abnormality rate.
Specifically, the point abnormality rate is determined as follows: step S2041, determining a point abnormality rate according to the relation between the difference value of the monitoring data of the adjacent acquisition points and a preset interval; specifically, the difference value of the data to be evaluated of the adjacent acquisition points can be determined by subtracting the analog quantity of the last acquisition point from the analog quantity to be evaluated obtained by each acquisition point, comparing the difference value with a preset interval, and when the difference value exceeds the preset interval, regarding the analog quantity of the corresponding acquisition point as a point abnormality, thereby determining the point abnormality rate by adopting the following formula:
where m represents the total number of analog quantities in the data to be evaluated, and count (poiab) represents the number of point anomalies.
The sequence anomaly rate is determined in the following manner:
step S2042, cutting the monitoring data to obtain a plurality of fragments; specifically, in performing the accuracy evaluation, the sequence abnormality rate may be calculated in addition to the dot abnormality rate. When calculating the sequence anomaly rate, firstly cutting the analog quantity in the monitoring data, for example, cutting the analog quantity by adopting the charge-discharge state quantity to obtain a plurality of charge (or discharge) time sequence fragments. Wherein the state quantity is classified as 1 or 0 according to the charge state and the discharge state. And then, a certain segment of state quantity which is all 0 or all 1 is selected, the starting time and the ending time of the state quantity are obtained, and the analog quantity data segment is selected as a time sequence segment according to the starting time and the ending time.
Step S2043, calculating the variation of the monitoring data of adjacent acquisition points in each segment and the average value of the variation, and obtaining sample parameters to be compared; specifically, the variation of the monitoring data of the adjacent acquisition points (the difference value of the monitoring data of the adjacent two acquisition points) in each segment is calculated, then the average value of all the variation in each segment is calculated based on the variation, and the average value of the variation is used as the sample parameter to be compared. The preset distribution model can obtain a distribution rule of the mean value through observation of historical data and combination of a histogram, kernel density estimation, a QQ (Quantile-Quantile plot) or other nonparametric statistical methods. In general, the mean distribution approximates a normal distribution x-N (μ, σ) according to the central limit law 2 )。
Step S2044, based on a preset distribution model, determining whether each segment is abnormal according to the relation between the sample parameters to be compared and the confidence interval, and obtaining the sequence abnormality rate. Specifically, it is determined whether the sample parameters to be compared of each segment exceed the confidence interval [ m ] of the preset distribution model min ,m max ]If the sequence is exceeded, determining the corresponding fragment as an abnormal sequence. The sequence anomaly rate is thus expressed by the following formula:
in the formula, q represents the total number of fragments, and sum (seqab) represents the number of abnormal sequences.
It should be noted that, the above steps do not consider a specific distributed energy storage charging and discharging mode, but only provide a general form, and in other embodiments, if the distributed energy storage system is in a fixed charging mode, it may be selected to perform point anomaly rate evaluation on a fixed analog quantity, and perform a method of combining point anomaly rate and sequence anomaly rate on a changed analog quantity. For example, in the constant current charging mode, the point abnormality rate of the current value of the single battery, the point abnormality rate and the sequence abnormality rate of the voltage value can be evaluated.
Step S205, determining a timeliness evaluation result according to time information of the data to be evaluated. Before timeliness evaluation, determining a sampling period in an evaluation parameter to obtain a preset sampling period. And meanwhile, the data is de-duplicated and error data is deleted, and the missing precision is filled according to the theoretical acquisition period and precision, so that the data is preprocessed.
Specifically, the step S205 includes:
step S2051, calculating the time difference between the data recording time and the data generating time of the monitoring data; specifically, the data will generate a corresponding timestamp in the process of acquisition, and the timestamp is the data generation time. Meanwhile, the collected data is transmitted through a network, received and recorded by a database, and a time stamp is generated, which is called as data recording time. Both times may be recorded in a data table, identified by different value fields. Therefore, the data recording time and the data generating time corresponding to each data can be obtained from the data table, and the time difference corresponding to each data is obtained.
Step S2055, determining a data delay rate according to the average value of the time difference, the number of the monitoring data in each period and a preset sampling period, and determining a timeliness evaluation result. Specifically, the data delay rate is determined using the following formula:
wherein T represents a preset sampling period, n represents the number of acquisition points,data recording time indicating the ith data, < +.>The data generation time of the i-th data is represented.
In this embodiment, a data quality evaluation method of a distributed energy storage system is provided, and the method includes the following steps:
Step S301, obtaining data to be evaluated of a distributed energy storage system; please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S302, determining an integrity evaluation result according to the data missing condition of the data to be evaluated; please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S303, determining a consistency evaluation result according to the time relation and the numerical relation of different data sources in the data to be evaluated; please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S304, determining an accuracy evaluation result according to the data change condition of the data to be evaluated. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S305, determining a timeliness evaluation result according to time information of the data to be evaluated. Please refer to step S105 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S306, obtaining a quality analysis report based on the integrity evaluation result, the consistency evaluation result, the accuracy evaluation result and the timeliness evaluation result; specifically, the quality analysis report of the distributed energy storage system can be obtained by dynamically tracking the evaluation results of the four dimensions of the distributed energy storage system, then acquiring the evaluation results of the distributed energy storage system equipment in each dimension according to the cycles of week, month, season, year and the like, and reflecting the quality fluctuation and variation trend of the data in the forms of a line graph, a box graph and the like.
Step S307, the evaluation parameters are updated according to the operation parameters of the distributed energy storage system. The method specifically comprises the steps that when parameters are updated, the operation of the converter and the operation monitoring data characteristics of the energy storage system can be realized according to the operation time length of the distributed energy storage system, the circulation times of the energy storage battery, the discharge depth and the aging degree.
As a specific application embodiment of the present invention, as shown in fig. 2, the data quality evaluation method of the distributed energy storage system is implemented by adopting the following flow:
and 1, acquiring equipment parameter information and real-time monitoring data of the distributed energy storage system as data to be evaluated.
2, defining four dimensions including integrity, consistency, accuracy and timeliness, and quantitatively evaluating the data in the four dimensions.
And 3, dynamically tracking data quality evaluation results of four dimensions of the distributed energy storage system, and outputting a quality analysis report.
And 4, updating the evaluation parameters.
The embodiment also provides a data quality evaluation device of the distributed energy storage system, which is used for implementing the above embodiment and the preferred implementation manner, and the description is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The embodiment provides a data quality evaluation device of a distributed energy storage system, as shown in fig. 3, including:
a data acquisition module 31, configured to acquire data to be evaluated of the distributed energy storage system;
an integrity evaluation module 32, configured to determine an integrity evaluation result according to a data missing condition of the data to be evaluated;
a consistency evaluation module 33, configured to determine a consistency evaluation result according to the time relationship and the numerical relationship of different data sources in the data to be evaluated;
an accuracy evaluation module 34, configured to determine an accuracy evaluation result according to a data change condition of the data to be evaluated;
the timeliness evaluation module 35 is configured to determine a timeliness evaluation result according to time information of the data to be evaluated.
In an alternative embodiment, the data to be evaluated includes device parameter information and monitoring data of the distributed energy storage system, and the apparatus further includes: the preprocessing module is used for preprocessing the data to be evaluated; and the parameter determining module is used for determining the evaluation parameter.
In an optional implementation manner, the integrity evaluation module is specifically configured to calculate a data loss rate according to the actual number of acquisition points and the theoretical number of acquisition points obtained by acquiring the data to be evaluated in a sliding window, and determine an integrity evaluation result.
In an alternative embodiment, the different data sources are partitioned according to the control instructions, and the consistency assessment module includes: the data source dividing module is used for dividing different monitoring data tables with the same state quantity information in the data to be evaluated into different data sources by taking the change of the state quantity in the data to be evaluated as a signal of a control instruction; the time consistency evaluation module is used for determining time consistency according to the time relation of the acquisition points in each data source and the time relation of different data sources; the numerical value consistency evaluation module is used for determining numerical value consistency according to correlation analysis among different data sources; and the evaluation sub-module is used for determining a consistency evaluation result according to the time consistency and the numerical consistency.
In an alternative embodiment, the time consistency assessment module is specifically configured to: determining a mean value of the acquisition time intervals of each data source based on the time differences of adjacent acquisition points in each data source; calculating the average value of the acquisition time intervals of all the data sources based on the time differences of the adjacent acquisition points in all the data sources; calculating the square sum of errors among groups according to the number of acquisition points of different data sources, the average value of the acquisition time interval of each data source and the average value of the acquisition time intervals of all data sources; calculating the total error square sum according to the time interval of each acquisition point of each data source and the average value of the acquisition time intervals of all the data sources; and determining the time consistency according to the ratio of the sum of squares of errors among the groups to the sum of squares of total errors.
In an alternative embodiment, the evaluation parameters comprise a preset interval, a preset distribution model and a confidence interval, and the accuracy evaluation result comprises a point abnormality rate and/or a sequence abnormality rate; the point anomaly rate is determined in the following manner: determining a point abnormality rate according to the relation between the difference value of the monitoring data of the adjacent acquisition points and a preset interval; the sequence anomaly rate is determined in the following manner: cutting the monitoring data to obtain a plurality of fragments; calculating the variation of the monitoring data of adjacent acquisition points in each segment and the average value of the variation to obtain the parameters of the samples to be compared; and determining whether each segment is abnormal or not according to the relation between the sample parameters to be compared and the confidence interval based on a preset distribution model, and obtaining the sequence abnormality rate.
In an alternative embodiment, the evaluation parameter includes a preset sampling period, and the timeliness evaluation module is specifically configured to: calculating the time difference of the data recording time and the data generating time of the monitoring data; and determining the data delay rate according to the average value of the time difference, the number of the monitoring data in each period and the preset sampling period, and determining the timeliness evaluation result.
In an alternative embodiment, the apparatus further comprises: the report determining module is used for obtaining a quality analysis report based on the integrity evaluation result, the consistency evaluation result, the accuracy evaluation result and the timeliness evaluation result; and the updating module is used for updating the evaluation parameters according to the operation parameters of the distributed energy storage system.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The embodiment of the invention also provides computer equipment, which is provided with the data quality evaluation device of the distributed energy storage system shown in the figure 3.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 4, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 4.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created from the use of the computer device of the presentation of a sort of applet landing page, and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (18)
1. A method for evaluating data quality of a distributed energy storage system, the method comprising:
acquiring data to be evaluated of a distributed energy storage system;
determining an integrity evaluation result according to the data missing condition of the data to be evaluated;
determining a consistency evaluation result according to the time relation and the numerical relation of different data sources in the data to be evaluated;
determining an accuracy evaluation result according to the data change condition of the data to be evaluated;
and determining a timeliness evaluation result according to the time information of the data to be evaluated.
2. The method of claim 1, wherein the data to be evaluated includes device parameter information and monitoring data of the distributed energy storage system;
after acquiring the data to be evaluated of the distributed energy storage system, the method further comprises:
preprocessing data to be evaluated;
before acquiring the data to be evaluated of the distributed energy storage system, the method further comprises:
An evaluation parameter is determined.
3. The method of claim 2, wherein determining the integrity assessment result based on the absence of data from the data to be assessed comprises:
and calculating the data loss rate according to the actual acquisition point number and the theoretical acquisition point number obtained by acquiring the data to be evaluated through the sliding window, and determining an integrity evaluation result.
4. The method of claim 2, wherein the different data sources are partitioned according to the control instructions, and wherein determining the consistency assessment result according to the time relationship and the numerical relationship of the different data sources in the data to be assessed comprises:
taking the change of the state quantity in the data to be evaluated as a signal of a control instruction, and dividing different monitoring data tables with the same state quantity information in the data to be evaluated into different data sources;
determining time consistency according to the time relation of the acquisition points in each data source and the time relation of different data sources;
determining a numerical consistency according to correlation analysis between different data sources;
and determining a consistency evaluation result according to the time consistency and the numerical consistency.
5. The method of claim 4, wherein determining the temporal consistency based on the temporal relationship of the collection points within each data source and the temporal relationship of the different data sources comprises:
Determining a mean value of the acquisition time intervals of each data source based on the time differences of adjacent acquisition points in each data source;
calculating the average value of the acquisition time intervals of all the data sources based on the time differences of the adjacent acquisition points in all the data sources;
calculating the square sum of errors among groups according to the number of acquisition points of different data sources, the average value of the acquisition time interval of each data source and the average value of the acquisition time intervals of all data sources;
calculating the total error square sum according to the time interval of each acquisition point of each data source and the average value of the acquisition time intervals of all the data sources;
and determining the time consistency according to the ratio of the sum of squares of errors among the groups to the sum of squares of total errors.
6. The method according to claim 2, wherein the evaluation parameters include a preset interval, a preset distribution model and a confidence interval, and the accuracy evaluation result includes a point abnormality rate and/or a sequence abnormality rate;
the point anomaly rate is determined in the following manner:
determining a point abnormality rate according to the relation between the difference value of the monitoring data of the adjacent acquisition points and a preset interval;
the sequence anomaly rate is determined in the following manner:
cutting the monitoring data to obtain a plurality of fragments;
calculating the variation of the monitoring data of adjacent acquisition points in each segment and the average value of the variation to obtain the parameters of the samples to be compared;
And determining whether each segment is abnormal or not according to the relation between the sample parameters to be compared and the confidence interval based on a preset distribution model, and obtaining the sequence abnormality rate.
7. The method of claim 2, wherein the evaluation parameters include a preset sampling period, and determining the time-dependent evaluation result based on time information of the data to be evaluated includes:
calculating the time difference of the data recording time and the data generating time of the monitoring data;
and determining the data delay rate according to the average value of the time difference, the number of the monitoring data in each period and the preset sampling period, and determining the timeliness evaluation result.
8. The method according to any one of claims 2-7, further comprising:
obtaining a quality analysis report based on the integrity evaluation result, the consistency evaluation result, the accuracy evaluation result and the timeliness evaluation result;
and updating the evaluation parameters according to the operation parameters of the distributed energy storage system.
9. A data quality assessment device for a distributed energy storage system, the device comprising:
the data acquisition module is used for acquiring data to be evaluated of the distributed energy storage system;
the integrity evaluation module is used for determining an integrity evaluation result according to the data missing condition of the data to be evaluated;
The consistency evaluation module is used for determining a consistency evaluation result according to the time relationship and the numerical relationship of different data sources in the data to be evaluated;
the accuracy evaluation module is used for determining an accuracy evaluation result according to the data change condition of the data to be evaluated;
and the timeliness evaluation module is used for determining a timeliness evaluation result according to the time information of the data to be evaluated.
10. The apparatus of claim 9, wherein the data to be evaluated comprises device parameter information and monitoring data of the distributed energy storage system, the apparatus further comprising: the preprocessing module is used for preprocessing the data to be evaluated; and the parameter determining module is used for determining the evaluation parameter.
11. The device according to claim 10, wherein the integrity evaluation module is specifically configured to calculate the data missing rate according to the actual number of collection points and the theoretical number of collection points obtained by collecting the data to be evaluated in the sliding window, and determine the integrity evaluation result.
12. The apparatus of claim 10, wherein the different data sources are partitioned according to control instructions, and wherein the consistency assessment module comprises: the data source dividing module is used for dividing different monitoring data tables with the same state quantity information in the data to be evaluated into different data sources by taking the change of the state quantity in the data to be evaluated as a signal of a control instruction; the time consistency evaluation module is used for determining time consistency according to the time relation of the acquisition points in each data source and the time relation of different data sources; the numerical value consistency evaluation module is used for determining numerical value consistency according to correlation analysis among different data sources; and the evaluation sub-module is used for determining a consistency evaluation result according to the time consistency and the numerical consistency.
13. The apparatus of claim 12, wherein the time consistency assessment module is specifically configured to: determining a mean value of the acquisition time intervals of each data source based on the time differences of adjacent acquisition points in each data source; calculating the average value of the acquisition time intervals of all the data sources based on the time differences of the adjacent acquisition points in all the data sources; calculating the square sum of errors among groups according to the number of acquisition points of different data sources, the average value of the acquisition time interval of each data source and the average value of the acquisition time intervals of all data sources; calculating the total error square sum according to the time interval of each acquisition point of each data source and the average value of the acquisition time intervals of all the data sources; and determining the time consistency according to the ratio of the sum of squares of errors among the groups to the sum of squares of total errors.
14. The apparatus of claim 10, wherein the evaluation parameters include a preset interval, a preset distribution model, and a confidence interval, and the accuracy evaluation result includes a point abnormality rate and/or a sequence abnormality rate; the point anomaly rate is determined in the following manner: determining a point abnormality rate according to the relation between the difference value of the monitoring data of the adjacent acquisition points and a preset interval; the sequence anomaly rate is determined in the following manner: cutting the monitoring data to obtain a plurality of fragments; calculating the variation of the monitoring data of adjacent acquisition points in each segment and the average value of the variation to obtain the parameters of the samples to be compared; and determining whether each segment is abnormal or not according to the relation between the sample parameters to be compared and the confidence interval based on a preset distribution model, and obtaining the sequence abnormality rate.
15. The apparatus of claim 10, wherein the evaluation parameter comprises a preset sampling period, and wherein the timeliness evaluation module is configured to: calculating the time difference of the data recording time and the data generating time of the monitoring data; and determining the data delay rate according to the average value of the time difference, the number of the monitoring data in each period and the preset sampling period, and determining the timeliness evaluation result.
16. The apparatus according to any one of claims 10-15, wherein the apparatus further comprises: the report determining module is used for obtaining a quality analysis report based on the integrity evaluation result, the consistency evaluation result, the accuracy evaluation result and the timeliness evaluation result; and the updating module is used for updating the evaluation parameters according to the operation parameters of the distributed energy storage system.
17. A computer device, comprising:
a memory and a processor in communication with each other, the memory having stored therein computer instructions that, upon execution, perform the method of data quality assessment of a distributed energy storage system of any one of claims 1 to 8.
18. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the data quality assessment method of a distributed energy storage system according to any one of claims 1 to 8.
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