CN112925950A - Data quality control method and system for continuous star catalogue data - Google Patents
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Abstract
The invention relates to a data quality control method and a system for continuous star catalogue data, which comprises the following steps: dynamically generating a smooth light change curve; identifying a difference between the true light variation curve and the smooth light variation curve; and constructing a filtering strategy to discriminate the difference and realize quality control. The invention aims at continuous star catalogue data, can quantify data quality from different dimensions under the condition of not providing a celestial body data quality reference template, and can well distinguish scientific events and noise interference.
Description
Technical Field
The invention relates to the technical field of astronomy data processing, in particular to a data quality control method and system for continuous star catalogue data.
Background
In time domain astronomy, astronomical big data is generated by taking a picture of massive astronomical bodies through an optical telescope in real time, and high-definition images extract all astronomical information in the picture through technologies such as point source extraction and the like to form a star catalogue. For example, if a high-definition image captured by an optical telescope includes n celestial bodies, a star catalogue including n rows of data can be formed by a point source extraction technology and the like, each row of data represents information collected by one celestial body, such as brightness values, coordinate values and the like, and therefore conversion from unstructured image data to structured star catalogue data is achieved. When the optical telescope continuously shoots the same area, continuous star catalogue data can be formed. The same celestial body in the continuous star catalogue data can be recorded by different star catalogues, the data are extracted to form a time sequence of the change of each celestial body, and the abnormal change of the celestial body can be identified by analyzing the time sequences in real time to find valuable scientific events. However, due to various reasons, such as the clarity of instruments and equipment, cloud and fog shielding, or other light source pollution, etc., data will be interfered, and the accuracy of scientific event discovery will be affected, so it is very necessary to perform data quality control, and currently, the existing data quality control methods mainly include: (1) image noise reduction; (2) and controlling the quality of the star catalogue based on the template.
And (5) reducing the noise of the image. High-definition images shot by an optical telescope often need an image noise reduction algorithm to remove noise points. The currently used image denoising algorithms can be mainly classified into two categories, namely a spatial pixel characteristic denoising algorithm and a transform domain denoising algorithm. The former is processing directly in image space, and the latter is processing indirectly in the image transform domain.
The method based on spatial pixel characteristics is a method for acquiring a new central pixel value by analyzing direct relation between a central pixel and other adjacent pixels in a gray scale space in a window with a certain size. By means of a mathematical transformation, the signal is separated from the noise in the transform domain, and the noise is then filtered out, leaving the signal. Although noise can be eliminated by the image noise reduction method, the interference of noise sources to celestial body data cannot be filtered.
And controlling the quality of the star catalogue based on the template. In order to identify the interference of noise sources to celestial body data, image data can only be converted into star catalogue data, and the star catalogue data is analyzed by taking celestial body data as a basic unit. The current method is a template-based star catalogue quality control method. Firstly, the high-quality observation of a known celestial body can know the mean value and the variance of the fluctuation of the characteristic value of the celestial body, and a celestial body data quality reference template can be constructed by recording the information. When observed in real time, if the change of the acquired celestial feature value exceeds the range defined by the template, the data quality can be considered to be interfered. However, this method is not effective for newly observed unknown celestial bodies. In addition, when a certain scientific event occurs in the antenna, the collected characteristic value may exceed the normal value range in the template, so that the method is difficult to distinguish the difference between the scientific event and the noise interference.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for controlling data quality of continuous star catalogue data, which can quantify data quality from different dimensions without providing a reference template for celestial body data quality for continuous star catalogue data, and can well distinguish scientific events from noise interference.
In order to achieve the purpose, the invention adopts the following technical scheme: a data quality control method for continuous star catalogue data comprises the following steps: step 1, dynamically generating a smooth light variation curve; step 2, identifying the difference between the real light variation curve and the smooth light variation curve; and 3, constructing a filtering strategy to discriminate the difference and realize quality control.
Further, in step 1, the method for dynamically generating the smooth light change curve includes: optical variable curve by discrete wavelet transform DWT method
Real-time estimation is carried out to obtain the light variation curve
Real-time estimate of
From the real-time estimate
A smooth light change curve is constructed.
Further, the estimation method comprises the following steps:
wherein the content of the first and second substances,
as a curve of light variation
At the t ∈ [ t ]k,tp]The value at the time point, wavelet _ func, is the wavelet function, and decomp _ level is the decomposition level.
Further, in step 2, the method for identifying the difference between the real light variation curve and the smooth light variation curve includes:
step 21, acquiring the error between each point of the real light variation curve and each corresponding point on the smooth light variation curve;
step 22, obtaining the mean square error of the whole optical variation curve according to the error of each point on the optical variation curve
Further, in step 22, the mean square error
Comprises the following steps:
wherein the content of the first and second substances,
is the length of the light change curve, and e (t) is the error at each point on the light change curve.
Further, in the step 3, the filtering strategy is constructed by using a dispersion quantization method and an outlier quantization method.
Further, the construction method of the filtering strategy adopting the diffusivity quantification comprises the following steps:
311, dividing the observation area into a plurality of sub-partitions according to a uniform grid partition mode;
step 312, further dividing the mag into different brightness intervals in each sub-partition, and dividing similar brightness into the same brightness interval;
313, calculating the median of all K mags in each mag interval of each sub-partition, and taking the median as the true dispersion of the K celestial bodies;
and step 314, determining a filtering threshold value to obtain a filtering rule.
Further, the step 314 includes the following steps:
step 3141, selecting the historical data of a certain day as the reference data, and calculating the data of each celestial body in the reference data in real time
A sequence;
step 3142, aggregate all of the same mag interval
Value and solve for
As a dispersion filter threshold for the mag interval;
step 3143, obtaining a filtering rule according to the filtering threshold value: if it is not
If the filtering threshold value is larger than the filtering threshold value, filtering to a point
Further, the construction method of the filtering strategy adopting the outlier quantization comprises the following steps:
step 321, dividing the observation area into a plurality of sub-partitions according to a uniform grid partition mode;
322, further dividing the mag into different brightness intervals in each sub-partition, and dividing similar brightness into the same brightness interval;
step 323, assuming the noise is random noise, in each mag interval in each sub-partition
Obeying a normal distribution N(μR,σR) In which μRIs a mean value, σRFiltering outliers using 3-Sigma principle for standard deviation;
step 324, calculate μRIs estimated by
σRIs estimated by
Determining an outlier;
step 325, obtaining a filtering rule according to the degree of outlier: if it is not
Then filter out points
A continuous star catalogue data oriented data quality control system comprising: the device comprises a smooth curve generation module, a difference identification module and a filtering discrimination module; the smooth curve generating module is used for dynamically generating a smooth light variation curve; the difference identification module is used for identifying the difference between the real light variation curve and the smooth light variation curve; the filtering discrimination module is used for constructing filtering strategy discrimination differences and realizing quality control.
Due to the adoption of the technical scheme, the invention has the following advantages: 1. the invention does not need to provide a celestial body data quality reference template, can save a large amount of cost for manufacturing the template, and is effective for newly observed unknown celestial bodies. 2. The invention provides two data quality control methods for respectively identifying the dispersion and the outlier of star catalogue data, and can identify different data quality interference conditions caused by noise sources. 3. The two data quality control methods adopted by the invention can well distinguish the difference between the scientific event and the noise interference while identifying the data interference.
Drawings
FIG. 1 is a schematic diagram of a diffusion point in an embodiment of the present invention;
FIG. 2 is a schematic diagram of outliers in an embodiment of the present invention;
FIG. 3 is a schematic diagram of scientific events in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
The invention provides a data quality control method for continuous star catalogue data. Suppose that the time range of data acquisition of the astronomical telescope is [ t ]k,tp]Wherein t iskIs the start time, tpIs the end time, the acquisition period is t', then at tk,tp]Can be collected (t)p-tk) T' star tables. Further assuming that n celestial data are included in each star table and the characteristic attribute is mag (star, etc., i.e., brightness), then for any celestial object i, at tk,tp]Connecting its mags together in time sequence, called light variation curve
For the star table data, many factors contribute to the degradation of the optical change curve, and the present invention distinguishes them into two different types: (1) dispersion of light variation curves caused by external factors; (2) outliers in the light change curve. As shown in fig. 1-3, extreme case diagrams of the dispersion points and outliers of the real light variation curve are given, both of which may be misidentified as scientific events. The present invention proposes two methods, a dispersion quantization method and an outlier quantization method. The two methods can generate two different quality sequences in real time, and the dispersivity and the outlier corresponding to the mag value are respectively marked, so that the quality control is realized. In combination with the following embodimentsThe present invention will be described in detail.
In a first embodiment of the present invention, a method for controlling data quality of continuous star catalogue data is provided, which includes the following steps:
step 1, dynamically generating a smooth light variation curve;
step 2, identifying the difference between the real light variation curve and the smooth light variation curve;
and 3, constructing a filtering strategy to discriminate the difference in the step 2 and realize quality control.
Preferably, in step 1, the method for dynamically generating the smooth light change curve includes: optical variable curve by discrete wavelet transform DWT method
Real-time estimation is carried out to obtain the light variation curve
Real-time estimate of
From the real-time estimate
Forming a smooth light variation curve; the DWT method is used for signal processing, and can decompose a base signal into a low-frequency part and a high-frequency part by selecting a proper wavelet function, wherein the low-frequency part can be regarded as a low-frequency part in the embodiment
The high frequency part may be considered as a noise signal.
The estimation method comprises the following steps:
wherein the content of the first and second substances,
as a curve of light variation
At the t ∈ [ t ]k,tp]The value at the time point, wavelet _ func, is the wavelet function, and decomp _ level is the decomposition level.
The decomposition level decomp _ level mainly influences the noise reduction effect; through multiple experiments, it is verified that in the present embodiment, the level _ func and the decomp _ level are selected to be sym6 and 4, respectively, so that the information can be obtained in real time
Preferably, in step 2, the method for identifying the difference between the real light variation curve and the smooth light variation curve is as follows:
step 21, acquiring the error between each point of the real light variation curve and each corresponding point on the smooth light variation curve;
because the light change curve is compact and smooth under ideal conditions, the light change curve becomes distorted due to dispersion caused by noise interference; i.e. the curve of variation of light
Namely the real light change curve. If this distortion can be measured and a dispersion threshold is used to filter out points with high dispersion, a high quality optical variation curve can be obtained. Therefore, the invention describes the dispersion degree of the light change curve by using the error distribution.
Obtaining a light variation curve
T time point of (1)
Value corresponding to the point in a noise-free environment
The error between e' (t) is:
will be as in formula (1)
From real-time estimates
Instead, the error e (t) for each point on the light variation curve can thus be obtained in real time:
step 22, obtaining the mean square error of the whole optical variation curve according to the error of each point on the optical variation curve
Wherein the content of the first and second substances,
is the length of the optical variation curve. To ensure that the mean square error is constantly changing in real-time updates, the length of the optical change curve should have a sufficient length, preferably 64 points in this embodiment.
Preferably, in step 3, the filtering strategy is constructed by using both a dispersion quantization method and an outlier quantization method.
(1) Method for quantifying degree of dispersion
For each of the celestial bodies, for example,
the physical significance of (a) is primarily a measure of the difference between the true light change curve and the expected smooth trend. If the light change curve of a celestial body is significantly distorted,
will become larger. But the occurrence of a scientific event is also essentially a partial value that deviates significantly from the expected smooth trend, and therefore only uses
Diffusion and scientific events remain indistinguishable. As shown in fig. 1-3, scientific events and dispersions can be very similar from the point of view of the light change curve itself. In fact, the occurrence of a scientific event is a small probability event, and it is unlikely that all celestial bodies in a certain region will occur together. The noise is likely to cause interference to all celestial bodies in the same region at the same time, such as cloud occlusion. Based on the prior knowledge, all celestial bodies in the same area can be measured
And distinguishing noise interference and possible scientific events according to the trend change degree. Therefore, the following filtering strategy metrics are used in this embodiment
The degree of change of the trend is the same.
311, dividing the observation area into a plurality of sub-partitions according to a uniform grid partition mode;
for example, into 49 sub-partitions. The dividing aims are mainly that the interference observation area of the noise is possibly uneven, the divided sub-partitions can isolate the unevenness, and the noise interference on the brightness of the celestial body in the same sub-partition is approximately even;
in step 312, the celestial bodies with different brightness have different noise immunity, and obviously, the brighter celestial body has stronger noise immunity. Based on this, the mag is further divided into different luminance sections in each sub-partition (for example, assuming that the luminance sections have [6,7] and [7.8], and the mag set in a certain sub-partition is {6.7,7.8,6.5,7.9}, the mag set can be divided into two mag sets, {6.7,6.5} and {7.8,7.9}, and the definition of the luminance sections is set according to the observation capability of the observation device.), and the similar luminances are divided into the same luminance section;
313, calculating the median of all K mags in each brightness interval of each sub-partition, and taking the median as the true dispersion of the K celestial bodies;
the median solving formula is:
in the formula (I), the compound is shown in the specification,
the degree of the same trend change in the mag interval is not greatly affected by the individual extreme value. The real noise must be caused
Become large without the scientific incident of the individual celestial body causing
Significant variations in the temperature of the sample.
Step 314, determining a filtering threshold value to further obtain a filtering rule;
when in use
After being able to solve in real time, the next key issue is how to determine the threshold for filtering. The method comprises the following steps:
step 3141, selecting the historical data of a certain day as the reference data, and calculating the data of each celestial body in the reference data in real time
A sequence;
among the criteria for selecting the baseline data are low noise interference during observation, e.g. no clouds, good weather and normal instrument operation. This information can be easily determined by looking at the history log.
Step 3142, aggregate all of the same mag interval
Value and solve for
(three quarters) as a threshold for the dispersivity filter for the mag interval;
if the noise interference of the reference data is too strong, the method can also be adopted
(median) as the threshold for the dispersion filter for this mag interval.
Step 3143, obtaining a filtering rule according to the filtering threshold value: if it is not
If the filtering threshold value is larger than the filtering threshold value, filtering to a point
(2) Method for quantifying degree of outlier
The same as the dispersion quantization, the space sub-partition and the mag interval are divided, and the dividing method is consistent with the dividing method in the dispersion quantization. Comprises the following steps
Step 321, dividing the observation area into a plurality of sub-partitions according to a uniform grid partition mode;
322, further dividing the mag into different brightness intervals in each sub-partition, and dividing similar brightness into the same brightness interval;
step 323, assume the noise is random noise, so in each mag interval in each sub-partition
Should obey a normal distribution N (μ)R,σR) In which μRIs a mean value, σRIs a standard deviation ofOutliers were filtered using 3-Sigma principles.
According to the principle of 3-Sigma,
fall in (mu)R-3σR,μR+3σR) The probability of inner is 0.9974; if it is not
Does not fall into (mu)R-3σR,μR+3σR) Is regarded as an outlier.
Step 324, calculate μRIs estimated by
σRIs estimated by
Determining an outlier;
to avoid the effects of outliers, use is made of
The mean of the front and rear 80% data points was taken as
Sample standard deviations of all data points were used as
Then degree of outlier
Is defined as:
step 325, obtaining a filtering rule according to the degree of outlier: if it is not
Then filter out points
In a second embodiment of the invention, a continuous star catalogue data oriented data quality control system is provided, which comprises a smooth curve generation module, a difference identification module and a filtering discrimination module;
the smooth curve generating module is used for dynamically generating a smooth light variation curve;
the difference identification module is used for identifying the difference between the real light variation curve and the smooth light variation curve;
the filtering discrimination module is used for constructing filtering strategy discrimination differences and realizing quality control.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (10)
1. A data quality control method for continuous star catalogue data is characterized by comprising the following steps:
step 1, dynamically generating a smooth light variation curve;
step 2, identifying the difference between the real light variation curve and the smooth light variation curve;
and 3, constructing a filtering strategy to discriminate the difference and realize quality control.
2. The data quality control method according to claim 1, wherein in the step 1, the method for dynamically generating the smooth light variation curve comprises: optical variable curve by discrete wavelet transform DWT method
Real-time estimation is carried out to obtain the light variation curve
Real-time estimate of
From the real-time estimate
A smooth light change curve is constructed.
3. The data quality control method of claim 2, wherein the estimating method is:
wherein the content of the first and second substances,
as a curve of light variation
At the t ∈ [ t ]k,tp]The value at the time point, wavelet _ func, is the wavelet function, and decomp _ level is the decomposition level.
4. The data quality control method according to claim 1, wherein in the step 2, the method for identifying the difference between the real light variation curve and the smooth light variation curve comprises:
step 21, acquiring the error between each point of the real light variation curve and each corresponding point on the smooth light variation curve;
step 22, obtaining the mean square error of the whole optical variation curve according to the error of each point on the optical variation curve
5. The data quality control method according to claim 4, wherein in the step 22, the mean square error
Comprises the following steps:
wherein the content of the first and second substances,
is the length of the light change curve, and e (t) is the error at each point on the light change curve.
6. The data quality control method according to claim 1, wherein in the step 3, the filtering strategy is constructed by using both a dispersion quantization method and an outlier quantization method.
7. The data quality control method according to claim 6, wherein the filtering strategy using the diffusivity quantization is constructed by:
311, dividing the observation area into a plurality of sub-partitions according to a uniform grid partition mode;
step 312, further dividing the mag into different brightness intervals in each sub-partition, and dividing similar brightness into the same brightness interval;
313, calculating the median of all K mags in each mag interval of each sub-partition, and taking the median as the true dispersion of the K celestial bodies;
and step 314, determining a filtering threshold value to obtain a filtering rule.
8. The data quality control method according to claim 7, wherein the step 314 comprises the steps of:
step 3141, selecting the historical data of a certain day as the reference data, and calculating the data of each celestial body in the reference data in real time
A sequence;
step 3142, aggregate all of the same mag interval
Value and solve for
As a dispersion filter threshold for the mag interval;
step 3143, obtaining a filtering rule according to the filtering threshold value: if it is not
If the filtering threshold value is larger than the filtering threshold value, filtering to a point
9. The data quality control method of claim 6, wherein the filtering strategy using the outlier quantization is constructed by:
step 321, dividing the observation area into a plurality of sub-partitions according to a uniform grid partition mode;
322, further dividing the mag into different brightness intervals in each sub-partition, and dividing similar brightness into the same brightness interval;
step 323, assuming the noise is random noise, in each mag interval in each sub-partition
Obeying a normal distribution N (μ)R,σR) In which μRIs a mean value, σRFiltering outliers using 3-Sigma principle for standard deviation;
step 324, calculate μRIs estimated by
σRIs estimated by
Determining an outlier;
step 325, obtaining a filtering rule according to the degree of outlier: if it is not
Then filter out points
10. A data quality control system for continuous star catalogue data, comprising: the device comprises a smooth curve generation module, a difference identification module and a filtering discrimination module;
the smooth curve generating module is used for dynamically generating a smooth light variation curve;
the difference identification module is used for identifying the difference between the real light variation curve and the smooth light variation curve;
the filtering discrimination module is used for constructing filtering strategy discrimination differences and realizing quality control.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105005978A (en) * | 2015-07-15 | 2015-10-28 | 天津大学 | Spectrum real-time filtering method based on Savitzky-Golay filter parameter optimization |
| CN106777084A (en) * | 2016-12-13 | 2017-05-31 | 清华大学 | For light curve on-line analysis and the method and system of abnormal alarm |
| CN110427996A (en) * | 2019-07-24 | 2019-11-08 | 清华大学 | The recognition methods of time series abnormal patterns and device based on fuzzy matching |
| CN111726341A (en) * | 2020-06-02 | 2020-09-29 | 五八有限公司 | Data detection method and device, electronic equipment and storage medium |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105005978A (en) * | 2015-07-15 | 2015-10-28 | 天津大学 | Spectrum real-time filtering method based on Savitzky-Golay filter parameter optimization |
| CN106777084A (en) * | 2016-12-13 | 2017-05-31 | 清华大学 | For light curve on-line analysis and the method and system of abnormal alarm |
| CN110427996A (en) * | 2019-07-24 | 2019-11-08 | 清华大学 | The recognition methods of time series abnormal patterns and device based on fuzzy matching |
| CN111726341A (en) * | 2020-06-02 | 2020-09-29 | 五八有限公司 | Data detection method and device, electronic equipment and storage medium |
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