CN114154122B - Flow measurement data quality evaluation method, device and application - Google Patents
Flow measurement data quality evaluation method, device and application Download PDFInfo
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- CN114154122B CN114154122B CN202210119994.3A CN202210119994A CN114154122B CN 114154122 B CN114154122 B CN 114154122B CN 202210119994 A CN202210119994 A CN 202210119994A CN 114154122 B CN114154122 B CN 114154122B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/66—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters
- G01F1/667—Arrangements of transducers for ultrasonic flowmeters; Circuits for operating ultrasonic flowmeters
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
- G01F23/22—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water
- G01F23/28—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water by measuring the variations of parameters of electromagnetic or acoustic waves applied directly to the liquid or fluent solid material
- G01F23/296—Acoustic waves
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
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Abstract
The application provides a flow measurement data quality assessment method, a flow measurement data quality assessment device and application, wherein the flow measurement hierarchical data of at least one water layer to be measured of a river channel, which is obtained by monitoring a flow meter, is subjected to quality assessment, and the method comprises the following steps: acquiring river channel data, flow meter installation data and at least one flow measuring hierarchical data corresponding to at least one water layer; judging whether the flow measurement hierarchical data of the water layer to be measured is effective or not based on the flow meter installation data and the river channel data, and if so, executing the step S3; and calculating to obtain a hierarchical self-similarity quality factor, a hierarchical mutual similarity quality factor, a hierarchical echo intensity quality factor, a hierarchical noise quality factor, a water level relevance quality factor and a periodic quality factor of the water layer to be measured based on the flow measurement hierarchical data, evaluating the quality of the flow measurement data based on the quality factors, and comprehensively considering external influence factors and the data characteristics of the flow measurement hierarchical data, wherein the evaluation of the whole flow measurement hierarchical data covers the data source and the process.
Description
Technical Field
The application relates to the field of water flow monitoring, in particular to a flow measurement data quality evaluation method, a flow measurement data quality evaluation device and application.
Background
River channel monitoring can provide a basis for river channel regulation and development, and can verify the action, benefit and influence of the built engineering, so that the river channel flow measurement data needs to be detected with high precision in the water conservancy field. At present, a common method is to test the water flow condition of a specific river channel through a flow meter and obtain corresponding flow measurement data, the accuracy of the flow measurement data directly affects the accuracy of river channel monitoring, and the common flow meter comprises an acoustic doppler profile current meter.
However, in a natural river channel, due to the influence of irregularity of the cross section of the river channel, different roughness of the river bed and other natural environmental conditions, the flow field distribution is not uniform in cross section, and meanwhile, under the influence of time-varying factors such as rainfall, upstream incoming water, tidal induction and the like, the flow velocity of the river channel also fluctuates, and uncontrollable natural factors cause deviation between the current measuring data and the actual value acquired by the current measuring instrument. In addition to the above natural factors, the accuracy of the flow meter is also limited by the conditions of the river itself and the installation conditions.
At present, the technical scheme of quality verification of the flow measurement data does not exist in the hydrology field, so that the quality of the flow measurement data is difficult to control, and the accuracy of river channel monitoring is further influenced.
Disclosure of Invention
The embodiment of the application provides a flow measurement data quality evaluation method, a flow measurement data quality evaluation device and application.
In a first aspect, an embodiment of the present application provides a method for evaluating quality of flow measurement data, where quality evaluation is performed on flow measurement hierarchical data of at least one water layer to be measured of a river channel, where the data is obtained by monitoring a flow meter, and the method includes:
step S1, acquiring river channel data, flow meter installation data and at least one flow measurement hierarchical data corresponding to at least one water layer, wherein the flow measurement hierarchical data at least comprises a flow measurement time sequence, a flow measurement flow velocity sequence, a flow measurement water level sequence and echo intensity corresponding to the water layer;
step S2, judging whether the flow measurement hierarchical data of the water layer to be measured is effective or not based on the flow meter installation data and the river channel data, and if so, executing the step S3;
step S3, calculating to obtain a layering self-similarity quality factor, a layering mutual similarity quality factor, a layering echo intensity quality factor, a layering noise quality factor, a water level correlation quality factor and a periodic quality factor of the water layer to be detected based on the flow measurement layering data;
step S4: and summarizing the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor and the periodic quality factor to obtain a comprehensive quality factor, wherein the comprehensive quality factor evaluation is in inverse proportion to the quality of flow measurement hierarchical data.
In a second aspect, an embodiment of the present application provides a method and an apparatus for evaluating quality of flow measurement data, including:
the data acquisition unit is used for acquiring at least one flow measurement hierarchical data, wherein the flow measurement hierarchical data at least comprises a flow measurement time sequence, a flow measurement flow velocity sequence, a flow measurement water level sequence and echo intensity corresponding to the water layer;
the external factor evaluation unit is used for judging whether the flow measurement hierarchical data of the water layer to be measured is effective or not based on the flow meter installation data and the river channel data;
the quality factor unit is used for calculating and obtaining a layering self-similarity quality factor, a layering mutual similarity quality factor, a layering echo intensity quality factor, a layering noise quality factor, a water level correlation quality factor and a periodic quality factor of the water layer to be measured based on the flow measurement layering data;
and the evaluation unit is used for summarizing the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo strength quality factor, the hierarchical noise quality factor, the water level correlation quality factor and the periodic quality factor to obtain a comprehensive quality factor, wherein the comprehensive quality factor evaluation is in inverse proportion to the quality of the flow measurement hierarchical data.
In a third aspect, an embodiment of the present application provides an electronic apparatus, including a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the computer program to perform the method for evaluating quality of lateral flow data.
The main contributions and innovation points of the invention are as follows:
the embodiment of the application designs a set of quality assessment scheme suitable for flow measurement hierarchical data of a flow meter under an actual working condition environment, external influence factors and the data characteristics of the flow measurement hierarchical data are comprehensively considered, the assessment of the whole flow measurement hierarchical data covers a data source and a process, the self-similarity of the hierarchy, the mutual similarity of the hierarchy, the echo intensity quality factor of the hierarchy, the noise quality factor of the hierarchy, the water level relevance and the periodicity factor are comprehensively considered, and the blank that the flow measurement data of the current flow meter is difficult to assess is filled.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method for flow measurement data quality assessment according to an embodiment of the present application;
FIG. 2 is a logic diagram of a method for flow measurement data quality assessment according to an embodiment of the present application;
FIG. 3 is a block diagram of a device for a method for evaluating the quality of flow measurement data according to an embodiment of the present application;
fig. 4 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of one or more embodiments of the specification, as detailed in the claims which follow.
It should be noted that: in other embodiments, the steps of the corresponding methods are not necessarily performed in the order shown and described herein. In some other embodiments, the method may include more or fewer steps than those described herein. Moreover, a single step described in this specification may be broken down into multiple steps for description in other embodiments; multiple steps described in this specification may be combined into a single step in other embodiments.
Example one
The scheme aims at providing a flow measurement data quality evaluation method, a flow measurement data quality evaluation device and application, and aims at integrating flow measurement instrument installation data, flow measurement hierarchical data and river channel data of flow measurement data obtained by a flow measurement instrument in a river channel to carry out quality verification on the flow measurement data so as to distinguish high-quality flow measurement data from low-quality flow measurement data and improve the river channel monitoring accuracy at a data end.
Referring to fig. 1 and fig. 2, an embodiment of the present application provides a method for evaluating quality of flow measurement data, where quality of flow measurement hierarchical data of at least one water layer to be measured of a river channel, which is monitored by a flow meter, is evaluated, and the method includes:
step S1, acquiring river channel data, flow meter installation data and at least one flow measurement hierarchical data corresponding to at least one water layer, wherein the flow measurement hierarchical data at least comprises a flow measurement time sequence, a flow measurement flow velocity sequence, a flow measurement water level sequence and echo intensity corresponding to the water layer;
step S2, judging whether the flow measurement hierarchical data of the water layer to be measured is effective or not based on the flow meter installation data and the river channel data, and if so, executing the step S3;
step S3, calculating to obtain a layering self-similarity quality factor, a layering mutual similarity quality factor, a layering echo intensity quality factor, a layering noise quality factor, a water level correlation quality factor and a periodic quality factor of the water layer to be detected based on the flow measurement layering data;
step S4: and summarizing the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor and the periodic quality factor to obtain a comprehensive quality factor, wherein the comprehensive quality factor evaluation is in inverse proportion to the quality of flow measurement hierarchical data.
It is worth mentioning that the scheme firstly analyzes external influence data such as the installation data of the flow meter and the river data, so as to evaluate the quality of the flow measurement hierarchical data based on the angle of the external influence factor, and the scheme can analyze the flow measurement hierarchical data under the condition that the external influence factor is in compliance, thereby not only fully considering the influence of the external influence factor on the flow measurement hierarchical data, but also reducing the analysis workload of the flow measurement hierarchical data; in addition, the scheme fully considers the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor and the periodic quality factor of the hierarchy to be tested, and comprehensively judges the quality of the flow measurement hierarchical data by utilizing multiple quality factors.
In the step of determining whether the current measurement hierarchical data of the water layer to be measured is valid based on the current meter installation data and the river channel data, determining whether the current measurement hierarchical data acquired by the current meter is valid by determining external influence factors such as the current meter installation posture, the installation position, the river channel basic condition and the like, where "valid" refers to whether the source of the current measurement hierarchical data meets the regulations, that is, when the external influence factors indicate that the source of the current measurement hierarchical data meets the regulations, the current measurement hierarchical data is valid data.
Specifically, the current meter installation data that this scheme obtained include at least that the current meter is gone into depth of water and current meter installation gesture, and wherein current meter installation gesture accessible is installed the attitude sensor on the current meter and is acquireed. Correspondingly, in the step of judging whether the flow measurement hierarchical data of the water layer to be detected is valid or not based on the flow meter installation data and the river channel data, if the water depth of the flow meter is smaller than a set depth threshold value, the flow measurement hierarchical data is invalid; and if any one of the rolling and pitching of the installation posture of the flow meter is larger than a set posture threshold value, the flow meter hierarchical data is invalid.
Illustratively, the depth threshold is set to be 1 meter, the inflow depth of the flow meter is obtained at this time, if the inflow depth of the flow meter is greater than 1 meter, the corresponding flow measurement hierarchical data is valid, and if the inflow depth of the flow meter is less than 1 meter, the corresponding flow measurement hierarchical data is invalid.
Setting the attitude threshold as follows: the roll attitude threshold value is 3 degrees, the pitch attitude threshold value is 2 degrees, if the actual roll and pitch of the installation state of the flow meter do not exceed 3 degrees and 2 degrees respectively, the flow meter hierarchical data is valid, and if the actual roll or pitch of the installation state of the flow meter exceeds 3 degrees or 2 degrees, the flow meter hierarchical data is invalid.
The river course data that this scheme obtained include river course section width at least, and what correspond is "based on current meter installation data and river course data, judge that awaits measuring the water layer whether current measurement layer data is effective", if the water layer distance of the water layer that awaits measuring is greater than river course section width, then current measurement layer data is invalid.
For example, if the width of the cross section of the river is 60 meters, if the distance corresponding to the water layer to be measured is less than 60 meters, the flow measurement hierarchical data is valid, and if the distance corresponding to the water layer to be measured is greater than 60 meters, the flow measurement hierarchical data is invalid.
It is worth mentioning that the measurement stratification data is set valid only in case all the above-mentioned external influencing factors meet the regulations.
Step S3, calculating to obtain a layering self-similarity quality factor, a layering mutual similarity quality factor, a layering echo intensity quality factor, a water level correlation quality factor and a periodic quality factor of the water layer to be measured based on the flow measurement layering data;
and on the premise that the flow measurement hierarchical data is effective, evaluating the quality of the flow measurement hierarchical data by using the quality factor set by the scheme. The hierarchical self-similarity quality factor of the scheme represents the similarity of adjacent flow velocity sequences, and if the similarity is higher, the quality of original flow measurement data is better; the hierarchical mutual similarity quality factor represents the similarity between all flow velocity sequences with the same time length, and if the similarity is higher, the quality of original flow measurement data is better; the quality factor of the layered echo intensity represents the echo intensity condition of the water layer, and if the echo intensity condition is larger, the quality of the original flow measurement data is better; the water level relevance quality factor represents the similarity between the flow rate sequence and the water level sequence or between the flow rate sequence and the water level sequence, and the higher the similarity is, the more the flow measurement data conforms to the natural law of water level and flow rate change; the periodic quality factor represents the similarity between the first flow velocity sequences of the adjacent periods, and the higher the similarity is, the more the flow measurement data conforms to the natural laws of tide and the like.
In other words, the scheme uses the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor and the hierarchical echo intensity quality factor to represent the quality of the original flow measurement data, and uses the water level correlation quality factor and the periodic quality factor to represent the conformity of the flow measurement data with the natural law.
Specifically, the hierarchical self-similarity quality factor is the similarity of two adjacent flow velocity sequences with the same time length in the water layer to be detected. The calculation mode of the hierarchical self-similarity quality factor is as follows: acquiring a first flow rate sequence and a second flow rate sequence corresponding to adjacent time sequences of a water layer to be detected, wherein the time sequences of the first flow rate sequence and the second flow rate sequence are the same in length, and calculating the similarity of the first flow rate sequence and the second flow rate sequence.
In the step of calculating the similarity between the first flow rate sequence and the second flow rate sequence, the first flow rate sequence is processed in a standardized manner to obtain a first flow rate standard sequence, the second flow rate sequence is processed in a standardized manner to obtain a second flow rate standard sequence, and the Euclidean distance average value of the first flow rate standard sequence and the second flow rate standard sequence obtains the similarity. The Euclidean distance average value is inversely proportional to the similarity, and if the Euclidean distance average value is larger, the similarity is smaller.
In order to reduce the influence of translation and scaling on the similarity, the flow rate sequence is subjected to normalization processing by adopting z-score normalization, specifically, the average value is subtracted from each data point of the first flow rate sequence and then divided by the standard deviation to obtain a first flow rate standard sequence, and the average value is subtracted from each data point of the second flow rate sequence and then divided by the standard deviation to obtain a second flow rate standard sequence.
The formula is as follows: (x- μ)/σ, where μ represents the mean of the flow rate series and σ represents the standard deviation of the flow rate series. After this treatment, the average value of each flow rate sequence became 0 and the standard deviation was 1.
According to the scheme, the Euclidean distance average value of the first flow rate standard sequence and the second flow rate standard sequence is calculated to obtain the similarity, specifically, the Euclidean distance of each pair of data points of the first flow rate standard sequence and the second flow rate standard sequence is taken, the average value of all the Euclidean distances is taken, and the Euclidean distance average value is obtained.
Specifically, the quality factor of the layered mutual similarity is the similarity between flow rate sequences of the water layer to be detected in the same time length. The calculation mode of the hierarchical mutual similarity quality factor is as follows: obtaining a plurality of third flow rate sequences corresponding to time sequences with the same length of a water layer to be detected, calculating an average value sequence of all the third flow rate sequences, and calculating the similarity of a fourth flow rate sequence to be compared and the average value sequence, wherein the time sequences of the fourth flow rate sequence and the third flow rate sequence have the same length.
In this embodiment, the "calculating the average value sequence of all the third flow rate sequences" specifically includes: and taking the average value of all data points corresponding to the same time sequence position of the third flow rate sequence to form an average value sequence. The following are exemplary: there are two third flow rate sequences: { x1, x2, x3, x4}, { y1, y2, y3, y4}, then z1= (x1+ y1)/2, z2= (x2+ y2)/2, z3= (x3+ y3)/2, z4= (x4+ y4)/2 are calculated, resulting in an average value sequence of { z1, z2, z3, z4 }.
In the step of calculating the similarity between the fourth flow rate sequence to be compared and the average value sequence, the fourth flow rate sequence is processed in a standardized manner to obtain a fourth flow rate standard sequence, the average value sequence is processed in a standardized manner to obtain an average value standard sequence, and the similarity is obtained by the Euclidean distance average value of the fourth flow rate standard sequence and the average value standard sequence. The Euclidean distance average value is inversely proportional to the similarity, and if the Euclidean distance average value is larger, the similarity is smaller.
In order to reduce the influence of translation and scaling on the similarity, the flow rate sequence is normalized by adopting a normalization processing means of z-score normalization, specifically, the average value is subtracted from each data point of the fourth flow rate sequence and then divided by the standard deviation to obtain a fourth flow rate standard sequence, and the average value is subtracted from each data point of the average value sequence and then divided by the standard deviation to obtain a standard sequence of the average value.
The formula is as follows: (x- μ)/σ, where μ represents the mean of the flow rate series and σ represents the standard deviation of the flow rate series. After each flow rate sequence was thus processed, the average value became 0 with a standard deviation of 1
According to the scheme, the Euclidean distance average value of the fourth flow speed standard sequence and the average value standard sequence is calculated to obtain the similarity, specifically, the Euclidean distance of each pair of data points of the fourth flow speed standard sequence and the average value standard sequence is taken, the average value of all the Euclidean distances is taken, and the Euclidean distance average value is obtained.
Specifically, the quality factor of the layered echo intensity is a difference value between 1 and a quotient of the echo intensity of the water layer to be detected and an echo intensity threshold. If the difference is smaller than 0, the values are unified to 0.
Specifically, the hierarchical noise quality factor is a standard deviation of a flow rate sequence of a water layer to be measured for a certain time length. If the maximum absolute value of the flow velocity sequence is larger than 1, normalizing the flow velocity sequence to obtain a normalized sequence, and then calculating the standard deviation of the normalized sequence; and if the maximum absolute value of the flow velocity sequence is not more than 1, directly calculating the standard deviation of the flow velocity sequence, and avoiding the noise of the amplified flow measurement.
Specifically, the water level correlation quality factor represents the similarity between the flow rate sequence and the water level sequence of the water layer to be measured. The calculation mode of the water level relevance quality factor is as follows: acquiring a fifth flow rate sequence of a water layer to be detected and a corresponding water level sequence, taking a negative value for the water level sequence to obtain a negative water level sequence, wherein the sequence lengths of the fifth flow rate sequence, the water level sequence and a time sequence corresponding to the negative water level sequence are the same, and acquiring a smaller value of the similarity of the fifth flow rate sequence, the water level sequence and the negative water level sequence.
In the step of calculating the similarity between the fifth flow rate sequence and the water level sequence, the fifth flow rate sequence is processed in a standardized manner to obtain a fifth flow rate standard sequence, the water level sequence is processed in a standardized manner to obtain a water level standard sequence, the water level standard sequence is subjected to negative value taking to obtain a negative water level standard sequence, and the similarity is calculated as the small value of the Euclidean distance average value of the fifth flow rate standard sequence, the water level standard sequence and the negative water level standard sequence. The Euclidean distance average value is inversely proportional to the similarity, and if the Euclidean distance average value is larger, the similarity is smaller.
In the mountain stream river channel environment, the higher the water level is, the faster the flow speed is generally; when some river channels are injected into the inlets of the lakes, the accepting capacity is high and the flow speed is high when the water level of the lakes is low, and the accepting capacity is low and the flow speed is low when the water level of the lakes is high; therefore, the similarity of the fifth flow rate standard sequence, the water level standard sequence and the negative water level standard sequence needs to be calculated respectively so as to be compatible with different natural environment working conditions.
In order to reduce the influence of translation and scaling on the similarity, the method adopts a normalization processing means of z-score normalization, specifically, a fifth flow rate standard sequence is obtained by subtracting the average value from each data point of the fifth flow rate sequence and dividing the average value by the standard deviation, and a water level standard sequence is obtained by subtracting the average value from each data point of the water level sequence and dividing the average value by the standard deviation.
The formula is as follows: (x- μ)/σ, where μ represents an average value of the flow rate series or the water level series, and σ represents a standard deviation of the flow rate series or the water level series. After each flow rate sequence or water level sequence is processed in this way, the average value becomes 0 and the standard deviation becomes 1.
In addition, according to the scheme, a water level standard sequence is obtained after the water level sequence is standardized, a negative water level standard sequence is obtained by taking a negative value for each value in the water level standard sequence, the Euclidean distance average value of the fifth flow rate standard sequence and the water level standard sequence and the Euclidean distance average value of the fifth flow rate standard sequence and the negative water level standard sequence are respectively calculated, the smaller value of the two Euclidean distance average values is similarity, specifically, the Euclidean distance of each pair of data points of the fifth flow rate standard sequence and the water level standard sequence or the negative water level standard sequence is taken, the average value of all the Euclidean distances is taken, and the Euclidean distance average value is obtained.
Specifically, the periodic quality factor represents the similarity between periodic flow rate sequences of the water layer to be detected. Specifically, the calculation formula of the periodic quality factor is as follows: and acquiring a sixth flow rate sequence of the first period and a seventh flow rate sequence of the second period of the water layer to be detected, wherein the period lengths of the first period and the second period are the same, and calculating the similarity of the sixth flow rate sequence and the seventh flow rate sequence. The cycle length in this scenario is 24 hours, characterizing the periodic effect of the tide on the river flow rate.
In the step of "calculating the similarity between the sixth flow rate sequence and the seventh flow rate sequence", the sixth flow rate sequence is normalized to obtain a sixth flow rate standard sequence, the seventh flow rate sequence is normalized to obtain a seventh flow rate standard sequence, and the euclidean distance between the sixth flow rate standard sequence and the seventh flow rate standard sequence is averaged to obtain the similarity. The Euclidean distance average value is inversely proportional to the similarity, and if the Euclidean distance average value is larger, the similarity is smaller.
In order to reduce the influence of translation and scaling on the similarity, the method adopts a normalization processing means of z-score normalization, specifically, a sixth flow rate standard sequence is obtained by subtracting the average value from each data point of the sixth flow rate sequence and dividing the average value by the standard deviation, and a seventh flow rate standard sequence is obtained by subtracting the average value from each data point of the seventh flow rate sequence and dividing the average value by the standard deviation.
The formula is as follows: (x- μ)/σ, where μ represents the mean of the flow rate series and σ represents the standard deviation of the flow rate series. After this treatment, the average value of each flow rate sequence became 0 and the standard deviation was 1.
According to the scheme, the Euclidean distance average values of the sixth flow velocity standard sequence and the seventh flow velocity standard sequence are calculated to obtain the similarity, specifically, the Euclidean distance of each pair of data points of the sixth flow velocity standard sequence and the seventh flow velocity standard sequence is taken, the average value of all the Euclidean distances is taken, and the Euclidean distance average value is obtained.
According to the scheme, after the layering self-similarity quality factor, the layering mutual similarity quality factor, the layering echo intensity quality factor, the layering noise quality factor, the water level correlation quality factor and the periodic quality factor of the water layer to be detected are obtained through calculation, the layering self-similarity quality factor, the layering mutual similarity quality factor, the layering echo intensity quality factor, the layering noise quality factor, the water level correlation quality factor and the periodic quality factor can be sorted into a two-dimensional matrix to obtain a quality factor matrix table, wherein the quality factor matrix table is shown in the following table I:
table-quality factor matrix table
| Water layer to be measured 1 | Water layer i to be measured | Water layer N to be measured | |
| Hierarchical self-similarity quality factor | [0,2]Value within interval | [0,2]Value within the interval | [0,2]Value within the interval |
| Hierarchical mutual similarity quality factor | [0,2]Value within the interval | [0,2]Value within the interval | [0,2]Value within interval |
| Layered echo intensity quality factor | [0,1]Value within the interval | [0,1]Value within the interval | [0,1]Value within the interval |
| Layered noise quality factor | [0,1]Value within the interval | [0,1]Value within the interval | [0,1]Value within the interval |
| Water level correlation quality factor | [0,2]Value within the interval | [0,2]Value within the interval | [0,2]Value within the interval |
| Periodic quality factor | [0,2]Value within the interval | [0,2]Value within the interval | [0,2]Value within the interval |
| Integrated quality factor | [0,10]Value within the interval | [0,10]Value within the interval | [0,10]Value within the interval |
In "the values of the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor, and the periodicity quality factor are summed to obtain a composite quality factor", the values of the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor, and the periodicity quality factor are summed to obtain the composite quality factor.
Illustratively, if the collected multi-layer flow measurement hierarchical data is: where time 1-20 corresponds to a time series and the flow rate corresponds to a flow rate series.
| Time of day | Flow Rate of layer 1 (m/s) | Stratification 2 flow velocity (m/s) | Laminar 3 flow (m/s) | Average flow velocity in layers (m/s) |
| 1 | 1.04 | 1.03 | 1.02 | 1.03 |
| 2 | 1.03 | 0.98 | 1.00 | 1.00 |
| 3 | 0.97 | 1.00 | 1.00 | 0.99 |
| 4 | 0.99 | 0.99 | 0.98 | 0.98 |
| 5 | 0.95 | 0.95 | 0.93 | 0.94 |
| 6 | 0.90 | 0.94 | 0.92 | 0.92 |
| 7 | 0.89 | 0.92 | 0.90 | 0.90 |
| 8 | 0.89 | 0.88 | 0.89 | 0.89 |
| 9 | 0.89 | 0.87 | 0.88 | 0.88 |
| 10 | 0.87 | 0.83 | 0.86 | 0.85 |
| 11 | 0.81 | 0.84 | 0.81 | 0.82 |
| 12 | 0.83 | 0.78 | 0.81 | 0.81 |
| 13 | 0.81 | 0.77 | 0.79 | 0.79 |
| 14 | 0.76 | 0.74 | 0.75 | 0.75 |
| 15 | 0.76 | 0.72 | 0.73 | 0.74 |
| 16 | 0.71 | 0.74 | 0.72 | 0.72 |
| 17 | 0.70 | 0.71 | 0.73 | 0.71 |
| 18 | 0.71 | 0.68 | 0.68 | 0.69 |
| 19 | 0.68 | 0.69 | 0.67 | 0.68 |
| 20 | 0.67 | 0.62 | 0.63 | 0.64 |
The manner of calculating the hierarchical self-similarity quality factor is as follows:
selecting a layer 1 as a layer to be tested, and calculating self-similarity between a time sequence at the moment 1-10 and a time sequence at the moment 11-20, wherein a first flow rate standard sequence obtained by standardizing a flow rate sequence at the moment 1-10 is as follows:
| time of day | Flow velocity (m/s) |
| 1 | 1.72 |
| 2 | 1.46 |
| 3 | 0.45 |
| 4 | 0.78 |
| 5 | 0.20 |
| 6 | -0.60 |
| 7 | -0.79 |
| 8 | -0.90 |
| 9 | -0.89 |
| 10 | -1.22 |
The second flow rate standard sequence obtained by normalizing the time sequence of the time 11-20 is as follows:
| time of day | Flow velocity (m/s) |
| 11 | 1.23 |
| 12 | 1.60 |
| 13 | 1.23 |
| 14 | 0.44 |
| 15 | 0.36 |
| 16 | -0.60 |
| 17 | -0.70 |
| 18 | -0.62 |
| 19 | -1.09 |
| 20 | -1.30 |
And calculating the Euclidean distance average value of the first flow rate standard sequence and the second flow rate standard sequence to be 0.26, and then the hierarchical self-similarity quality factor is 0.26.
The way to calculate the quality factor of the hierarchical mutual similarity is as follows:
the flow velocity sequence corresponding to the layered average flow velocity is an average value sequence, and the third flow velocity standard sequence corresponding to the time sequence of the time 1-10 is as follows:
| time of day | Flow rate (m/s) |
| 1 | 1.72 |
| 2 | 1.46 |
| 3 | 0.45 |
| 4 | 0.78 |
| 5 | 0.20 |
| 6 | -0.60 |
| 7 | -0.79 |
| 8 | -0.90 |
| 9 | -0.89 |
| 10 | -1.22 |
The average standard sequence of the time 1-10 is as follows:
| time of day | Flow velocity (m/s) |
| 1 | 1.61 |
| 2 | 1.09 |
| 3 | 0.86 |
| 4 | 0.77 |
| 5 | 0.09 |
| 6 | -0.31 |
| 7 | -0.62 |
| 8 | -0.94 |
| 9 | -1.07 |
| 10 | -1.55 |
And calculating the Euclidean distance average value of the third flow rate standard sequence and the average value standard sequence to be 0.2, and then, the quality factor of the layering mutual similarity is 0.2.
For example, the echo intensity threshold is set to 200, and the echo intensities of the layer to be measured 1, the layer to be measured 2, and the layer to be measured 3 are 240, 180, and 100, respectively, so that the corresponding echo intensity quality factors are 0, 0.1, and 0.5, respectively.
Illustratively, if a certain layer of collected flow measurement hierarchical data is: where time 1-10 corresponds to a time series and the flow rate corresponds to a flow rate series.
| Time of day | Flow Rate of layer 1 (m/s) |
| 1 | 1.24 |
| 2 | 1.13 |
| 3 | 0.97 |
| 4 | 1.19 |
| 5 | 0.95 |
| 6 | 0.90 |
| 7 | 1.29 |
| 8 | 1.12 |
| 9 | 1.09 |
| 10 | 0.98 |
The normalized flow velocity sequence was calculated as:
| time of day | Flow rate (m/s) |
| 1 | 1.00 |
| 2 | 0.91 |
| 3 | 0.78 |
| 4 | 0.96 |
| 5 | 0.77 |
| 6 | 0.73 |
| 7 | 1.04 |
| 8 | 0.90 |
| 9 | 0.88 |
| 10 | 0.79 |
The normalized sequence is calculated to have a standard deviation of 0.1, and the hierarchical noise quality factor is 0.1.
It is worth to be noted that the flow measurement data quality evaluation method provided by the scheme can be suitable for flow measurement data analysis of various flow measurement instruments, and whether the flow measurement data is selected or not is determined based on the evaluation result of the flow measurement data.
Illustratively, if the collected multi-layer flow measurement hierarchical data is: the flow velocity sequence corresponds to the layering flow velocity sequence from 1 day 0 to 2 days 23 and the corresponding water level sequence.
| Time | Flow Rate of layer 1 (m/s) | Water level (m) |
| 1 day 0 point | -0.65 | 9.19 |
| 1 st day | -0.67 | 9.28 |
| 1 day 2 o' clock | -0.48 | 9.21 |
| 1 day 3 o' clock | -0.01 | 8.92 |
| 1 day 4 points | 0.35 | 8.68 |
| 1 day 5 o' clock | 0.43 | 8.46 |
| 1 day 6 points | 0.55 | 8.24 |
| 7 o' clock on 1 day | 0.54 | 8.02 |
| 8 o' clock on 1 day | 0.51 | 7.80 |
| 9 o' clock on day 1 | 0.65 | 7.61 |
| 1 day 10 o' clock | 0.65 | 7.45 |
| 11 o' clock on day 1 | 0.46 | 7.50 |
| 12 o' clock on 1 day | 0.12 | 7.96 |
| 13 o' clock on day 1 | -0.03 | 8.19 |
| 14 o' clock on 1 day | -0.24 | 8.36 |
| 1 day 15 o' clock | -0.26 | 8.48 |
| 16 points on 1 day | -0.32 | 8.49 |
| 17 o' clock on day 1 | -0.10 | 8.28 |
| 18 o' clock on day 1 | 0.19 | 8.09 |
| 19 o' clock on day 1 | 0.28 | 7.96 |
| 20 o' clock on 1 day | 0.35 | 7.89 |
| 21 o' clock on 1 day | 0.11 | 8.16 |
| 22 o' clock on 1 day | -0.10 | 8.52 |
| 23 o' clock on day 1 | -0.42 | 8.84 |
| Day 20 point | -0.79 | 9.12 |
| 2 days 1 o' clock | -0.82 | 9.28 |
| 2 nd day 2 o' clock | -0.58 | 9.30 |
| 2 d 3 points | -0.26 | 9.09 |
| 2 days 4 points | 0.13 | 8.82 |
| 2 d 5 o' clock | 0.48 | 8.59 |
| 2 days 6 o' clock | 0.63 | 8.38 |
| 2 days and 7 o' clock | 0.68 | 8.16 |
| 2 d 8 o' clock | 0.47 | 7.93 |
| 2 days and 9 o' clock | 0.64 | 7.73 |
| 2 d 10 o' clock | 0.67 | 7.55 |
| Day 2, 11 o' clock | 0.66 | 7.42 |
| 12 o' clock on 2 days | 0.32 | 7.69 |
| Day 2, 13 o' clock | 0.09 | 8.07 |
| 14 o' clock on 2 days | -0.15 | 8.26 |
| 2 days 15 o' clock | -0.18 | 8.41 |
| 16 points on 2 days | -0.32 | 8.49 |
| Day 2, 17 o' clock | -0.18 | 8.41 |
| 18 o' clock on 2 days | 0.06 | 8.16 |
| 19 o' clock on 2 days | 0.20 | 8.00 |
| 20 o' clock 2 day | 0.24 | 7.88 |
| Day 2, 21 o' clock | 0.27 | 7.83 |
| 22 o' clock on 2 days | 0.06 | 8.18 |
| 23 o' clock on 2 days | -0.15 | 8.52 |
The water level relevance factor is calculated at this time as follows: and standardizing the flow rate sequences from 0 to 23 days on day 1 to obtain a fifth flow rate standard sequence as follows:
| Time | standard flow Rate of layer 1 (m/s) |
| 1 day 0 point | -1.83 |
| 1 st day | -1.87 |
| 1 day 2 o' clock | -1.39 |
| 1 day 3 o' clock | -0.23 |
| 1 day 4 points | 0.68 |
| 1 day 5 o' clock | 0.87 |
| 1 day 6 points | 1.16 |
| 7 o' clock on 1 day | 1.15 |
| 8 o' clock on 1 day | 1.08 |
| 9 o' clock on day 1 | 1.42 |
| 1 day 10 o' clock | 1.42 |
| 11 o' clock on day 1 | 0.95 |
| 12 o' clock on 1 day | 0.10 |
| 13 o' clock on day 1 | -0.27 |
| 14 o' clock on 1 day | -0.80 |
| 1 day 15 o' clock | -0.86 |
| 16 o' clock in 1 day | -1.00 |
| 17 o' clock on day 1 | -0.45 |
| 18 o' clock on day 1 | 0.26 |
| 19 o' clock on day 1 | 0.49 |
| 20 o' clock on 1 day | 0.68 |
| 21 o' clock in 1 day | 0.07 |
| 22 o' clock on 1 day | -0.45 |
| 23 o' clock on day 1 | -1.24 |
The sequence after the water level sequence standardization at 0-23 days of 1 day and the water level sequence standardization negation are as follows:
| time of day | Water level standard sequence | Water level standard sequence negation |
| 1 day 0 point | 1.72 | -1.72 |
| 1 st day | 1.89 | -1.89 |
| 1 day 2 o' clock | 1.76 | -1.76 |
| 1 day 3 o' clock | 1.17 | -1.17 |
| 1 day 4 points | 0.71 | -0.71 |
| 1 day 5 o' clock | 0.28 | -0.28 |
| 1 day 6 points | -0.15 | 0.15 |
| 7 o' clock on 1 day | -0.60 | 0.60 |
| 8 o' clock on 1 day | -1.02 | 1.02 |
| 9 o' clock in 1 day | -1.40 | 1.40 |
| 1 day 10 o' clock | -1.71 | 1.71 |
| 1 day11 point | -1.62 | 1.62 |
| 12 o' clock on 1 day | -0.71 | 0.71 |
| 13 o' clock on day 1 | -0.25 | 0.25 |
| 14 o' clock on 1 day | 0.07 | -0.07 |
| 1 day 15 o' clock | 0.31 | -0.31 |
| 16 points on 1 day | 0.34 | -0.34 |
| 17 o' clock on day 1 | -0.07 | 0.07 |
| 18 o' clock on day 1 | -0.44 | 0.44 |
| 19 o' clock on day 1 | -0.70 | 0.70 |
| 20 o' clock on 1 day | -0.85 | 0.85 |
| 21 o' clock in 1 day | -0.31 | 0.31 |
| 22 o' clock on 1 day | 0.39 | -0.39 |
| 23 o' clock on 1 day | 1.02 | -1.02 |
Calculating the Euclidean distance average value of the fifth flow rate standard sequence and the water level sequence corresponding to the layer 1 to be 1.57; calculating the Euclidean distance average value of the fifth flow rate standard sequence corresponding to the layer 1 and the water level sequence taking the negative value to be 0.47; taking the mature one of the materials, namely, the flow rate and water level correlation quality factor of the stratification 1 is 0.47.
The periodic quality factor is obtained as follows:
and (3) standardizing the flow rate sequences from 0 to 23 days on day 1 to obtain a sixth flow rate standard sequence as follows:
| Time | standard flow Rate of layer 1 (m/s) |
| 1 day 0 point | -1.83 |
| 1 st day | -1.87 |
| 1 day 2 o' clock | -1.39 |
| 1 day 3 o' clock | -0.23 |
| 1 day 4 points | 0.68 |
| 1 day 5 o' clock | 0.87 |
| 6 o' clock a day 1 | 1.16 |
| 7 o' clock on 1 day | 1.15 |
| 8 o' clock on 1 day | 1.08 |
| 9 o' clock on day 1 | 1.42 |
| 1 day 10 o' clock | 1.42 |
| 11 o' clock on day 1 | 0.95 |
| 12 o' clock on 1 day | 0.10 |
| 13 o' clock on day 1 | -0.27 |
| 14 o' clock on 1 day | -0.80 |
| 1 day 15 o' clock | -0.86 |
| 16 points on 1 day | -1.00 |
| 17 o' clock on day 1 | -0.45 |
| 18 o' clock a day 1 | 0.26 |
| 19 o' clock on day 1 | 0.49 |
| 20 o' clock on 1 day | 0.68 |
| 21 o' clock on 1 day | 0.07 |
| 22 o' clock on 1 day | -0.45 |
| 23 o' clock on day 1 | -1.24 |
And standardizing the flow rate sequences from 0 to 23 days on 2 days to obtain a seventh flow rate standard sequence as follows:
| Time | standard flow Rate of layer 1 (m/s) |
| Day 20 point | -2.00 |
| 2 days 1 o' clock | -2.07 |
| 2 nd day 2 o' clock | -1.52 |
| 2 d 3 points | -0.80 |
| 2 days 4 points | 0.09 |
| 2 d 5 o' clock | 0.89 |
| 2 days 6 o' clock | 1.24 |
| 2 days and 7 o' clock | 1.34 |
| 2 d 8 o' clock | 0.86 |
| 2 days and 9 o' clock | 1.24 |
| 2 d 10 o' clock | 1.33 |
| Day 2, 11 o' clock | 1.30 |
| 12 o' clock on 2 days | 0.53 |
| Day 2, 13 o' clock | -0.01 |
| 14 o' clock on 2 days | -0.56 |
| 2 days 15 o' clock | -0.61 |
| 16 points on 2 days | -0.93 |
| Day 2, 17 o' clock | -0.63 |
| 18 o' clock on 2 days | -0.08 |
| 19 o' clock on 2 days | 0.24 |
| 20 o' clock 2 day | 0.34 |
| Day 2, 21 o' clock | 0.42 |
| 22 o' clock on 2 days | -0.06 |
| 23 o' clock on 2 days | -0.54 |
The euclidean distance average of the sixth flow rate standard sequence and the seventh flow rate standard sequence is calculated to be 0.27, and the periodic quality factor is calculated to be 0.27.
Example two
Based on the same concept, referring to fig. 3, the present application further provides a flow measurement data quality evaluation apparatus, including:
a data obtaining unit 301, configured to obtain river channel data, flow meter installation data, and at least one flow measurement hierarchical data corresponding to at least one water layer, where the flow measurement hierarchical data at least includes a flow measurement time sequence, a flow measurement flow rate sequence, a flow measurement water level sequence, and echo intensity corresponding to the water layer;
an external factor evaluation unit 302, configured to determine whether the flow measurement hierarchical data of the water layer to be measured is valid based on the flow meter installation data and the river channel data;
a quality factor unit 303, configured to calculate a hierarchical self-similarity quality factor, a hierarchical mutual similarity quality factor, a hierarchical echo intensity quality factor, a hierarchical noise quality factor, a water level correlation quality factor, and a periodic quality factor of the water layer to be measured based on the flow measurement hierarchical data;
an evaluating unit 304, configured to sum up the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor, and the periodic quality factor to obtain a comprehensive quality factor, where the evaluation of the comprehensive quality factor is inversely proportional to the quality of the flow measurement hierarchical data.
The evaluation scheme of the flow measurement data quality evaluation device is the same as that of the first embodiment, wherein repeated technical features are not redundantly described here.
EXAMPLE III
The present embodiment further provides an electronic apparatus, referring to fig. 4, including a memory 404 and a processor 402, where the memory 404 stores a computer program, and the processor 402 is configured to execute the computer program to perform the steps in any of the embodiments of the method for evaluating quality of lateral flow data.
Specifically, the processor 402 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
The processor 402 may read and execute the computer program instructions stored in the memory 404 to implement any of the above-described methods for estimating the quality of the lateral flow data.
Optionally, the electronic apparatus may further include a transmission device 406 and an input/output device 408, where the transmission device 406 is connected to the processor 402, and the input/output device 408 is connected to the processor 402.
The transmitting device 406 may be used to receive or transmit data via a network. Specific examples of the network described above may include wired or wireless networks provided by communication providers of the electronic devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmitting device 406 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
The input and output devices 408 are used to input or output information. In this embodiment, the input information may be river data, flow meter installation data, flow measurement hierarchical data, and the like, and the output information may be various quality factors, and the like.
Optionally, in this embodiment, the processor 402 may be configured to execute the following steps by a computer program:
step S1, acquiring river channel data, flow meter installation data and at least one flow measurement hierarchical data corresponding to at least one water layer, wherein the flow measurement hierarchical data at least comprises a flow measurement time sequence, a flow measurement flow velocity sequence, a flow measurement water level sequence and echo intensity corresponding to the water layer;
step S2, judging whether the flow measurement hierarchical data of the water layer to be measured is effective or not based on the flow meter installation data and the river channel data, and if so, executing the step S3;
step S3, calculating to obtain a layering self-similarity quality factor, a layering mutual similarity quality factor, a layering echo intensity quality factor, a layering noise quality factor, a water level correlation quality factor and a periodic quality factor of the water layer to be detected based on the flow measurement layering data;
step S4: and summarizing the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor and the periodic quality factor to obtain a comprehensive quality factor, wherein the comprehensive quality factor evaluation is in inverse proportion to the quality of the flow measurement hierarchical data.
It should be noted that, for specific examples in this embodiment, reference may be made to examples described in the foregoing embodiments and optional implementations, and details of this embodiment are not described herein again.
In general, the various embodiments may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the invention is not limited thereto. While various aspects of the invention may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
Embodiments of the invention may be implemented by computer software executable by a data processor of the mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and/or macros can be stored in any device-readable data storage medium and they include program instructions for performing particular tasks. The computer program product may comprise one or more computer-executable components configured to perform embodiments when the program is run. The one or more computer-executable components may be at least one software code or a portion thereof. Further in this regard it should be noted that any block of the logic flow as in the figures may represent a program step, or an interconnected logic circuit, block and function, or a combination of a program step and a logic circuit, block and function. The software may be stored on physical media such as memory chips or memory blocks implemented within the processor, magnetic media such as hard or floppy disks, and optical media such as, for example, DVDs and data variants thereof, CDs. The physical medium is a non-transitory medium.
It should be understood by those skilled in the art that various features of the above embodiments can be combined arbitrarily, and for the sake of brevity, all possible combinations of the features in the above embodiments are not described, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the features.
The above examples are merely illustrative of several embodiments of the present application, and the description is more specific and detailed, but not to be construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (8)
1. A flow measurement data quality assessment method is used for carrying out quality assessment on flow measurement layered data of at least one water layer to be tested of a river channel, wherein the flow measurement layered data are obtained by monitoring of a flow meter, and the method is characterized by comprising the following steps:
step S1, acquiring river channel data, flow meter installation data and at least one flow measurement hierarchical data corresponding to at least one water layer, wherein the flow measurement hierarchical data at least comprises a flow measurement time sequence, a flow measurement flow velocity sequence, a flow measurement water level sequence and echo intensity corresponding to the water layer;
step S2, judging whether the flow measurement hierarchical data of the water layer to be measured is effective or not based on the flow meter installation data and the river channel data, and if so, executing the step S3;
step S3, calculating to obtain a layering self-similarity quality factor, a layering mutual similarity quality factor, a layering echo intensity quality factor, a layering noise quality factor, a water level correlation quality factor and a periodic quality factor of the water layer to be detected based on the flow measurement layering data; wherein the quality factor of the layered self-similarity is the similarity of two adjacent flow velocity sequences with the same time length of the water layer to be detected; the layered mutual similarity quality factor is the similarity between flow rate sequences of the water layer to be detected in the same time length; the quality factor of the layered echo intensity represents the echo intensity condition of the water layer to be detected; the quality factor of the layered noise is the standard deviation of a flow velocity sequence of a water layer to be detected in a certain time length; the water level correlation quality factor represents the similarity between the flow rate sequence and the water level sequence of the water layer to be detected; the periodic quality factor represents the similarity between periodic flow rate sequences of the water layer to be detected; the calculation mode of the hierarchical mutual similarity quality factor is as follows: acquiring a plurality of third flow rate sequences corresponding to time sequences of the same length of a water layer to be detected, calculating an average value sequence of all the third flow rate sequences, and calculating the similarity of a fourth flow rate sequence to be compared and the average value sequence, wherein the time sequences of the fourth flow rate sequence and the third flow rate sequence have the same length; the calculation mode of the quality factor of the layered echo intensity is as follows: 1 and the difference value of the quotient of the echo intensity of the water layer to be detected and the echo intensity threshold value; the calculation mode of the water level relevance quality factor is as follows: acquiring a fifth flow rate sequence of a water layer to be detected and a corresponding water level sequence, taking a negative value for the water level sequence to obtain a negative water level sequence, wherein the fifth flow rate sequence has the same sequence length as the water level sequence and a time sequence corresponding to the negative water level sequence, and acquiring a smaller similarity value of the fifth flow rate sequence, the water level sequence and the negative water level sequence;
step S4: and summarizing the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor and the periodic quality factor to obtain a comprehensive quality factor, wherein the comprehensive quality factor evaluation is in inverse proportion to the quality of the flow measurement hierarchical data.
2. The method for evaluating the quality of flow measurement data according to claim 1, wherein the flow measurement instrument installation data at least comprises a flow measurement instrument water depth and a flow measurement instrument installation posture, and in step S2, if the flow measurement instrument water depth is smaller than a set depth threshold, the flow measurement stratification data is invalid, and if either the roll or pitch of the flow measurement instrument installation posture is larger than a set posture threshold, the flow measurement stratification data is invalid.
3. The method for evaluating the quality of flow measurement data according to claim 1, wherein the river data at least includes a river section width, and in step S2, if the distance between the water layers to be measured is greater than the river section width, the flow measurement hierarchical data is invalid.
4. The method for evaluating the quality of lateral flow data according to claim 1, wherein the hierarchical self-similarity quality factor is calculated as follows: acquiring a first flow rate sequence and a second flow rate sequence corresponding to adjacent time sequences of a water layer to be detected, wherein the time sequences of the first flow rate sequence and the second flow rate sequence are the same in length, and calculating the similarity of the first flow rate sequence and the second flow rate sequence.
5. The method of claim 1, wherein the hierarchical noise quality factor is calculated as follows: if the maximum absolute value of the flow velocity sequence is larger than 1, normalizing the flow velocity sequence to obtain a normalized sequence, and then calculating the standard deviation of the normalized sequence; and if the maximum absolute value of the flow velocity sequence is not more than 1, directly calculating the standard deviation of the flow velocity sequence.
6. The method for evaluating the quality of lateral flow data according to claim 1, wherein the periodic quality factor is calculated as follows: and acquiring a sixth flow rate sequence of the first period and a seventh flow rate sequence of the second period of the water layer to be detected, wherein the period lengths of the first period and the second period are the same, and calculating the similarity between the sixth flow rate sequence and the seventh flow rate sequence.
7. A flow measurement data quality evaluation apparatus, comprising:
the data acquisition unit is used for acquiring river channel data, flow meter installation data and at least one flow measurement hierarchical data corresponding to at least one water layer, wherein the flow measurement hierarchical data at least comprises a flow measurement time sequence, a flow measurement flow velocity sequence, a flow measurement water level sequence and echo intensity corresponding to the water layer;
the external factor evaluation unit is used for judging whether the flow measurement hierarchical data of the water layer to be measured is effective or not based on the flow meter installation data and the river channel data;
the quality factor unit is used for calculating and obtaining a layering self-similarity quality factor, a layering mutual similarity quality factor, a layering echo intensity quality factor, a layering noise quality factor, a water level correlation quality factor and a periodic quality factor of the water layer to be measured based on the flow measurement layering data;
wherein the quality factor of the layered self-similarity is the similarity of two adjacent flow velocity sequences with the same time length of the water layer to be detected; the layered mutual similarity quality factor is the similarity between flow rate sequences of the water layer to be detected in the same time length; the quality factor of the layered echo intensity represents the echo intensity condition of the water layer to be detected; the quality factor of the layered noise is the standard deviation of a flow velocity sequence of a water layer to be detected in a certain time length; the water level correlation quality factor represents the similarity between the flow rate sequence and the water level sequence of the water layer to be detected; the periodic quality factor represents the similarity between periodic flow rate sequences of the water layer to be detected; the calculation mode of the hierarchical mutual similarity quality factor is as follows: acquiring a plurality of third flow rate sequences corresponding to time sequences of the same length of a water layer to be detected, calculating an average value sequence of all the third flow rate sequences, and calculating the similarity between a fourth flow rate sequence to be compared and the average value sequence, wherein the time sequences of the fourth flow rate sequence and the third flow rate sequence are the same in length; the calculation mode of the quality factor of the layered echo intensity is as follows: 1 and the difference value of the quotient of the echo intensity of the water layer to be detected and the echo intensity threshold value; the calculation mode of the water level relevance quality factor is as follows: acquiring a fifth flow rate sequence of a water layer to be detected and a corresponding water level sequence, taking a negative value for the water level sequence to obtain a negative water level sequence, wherein the sequence lengths of the fifth flow rate sequence, the water level sequence and a time sequence corresponding to the negative water level sequence are the same, and the smaller value of the similarity of the fifth flow rate sequence, the water level sequence and the negative water level sequence is acquired;
and the evaluation unit is used for summarizing the hierarchical self-similarity quality factor, the hierarchical mutual similarity quality factor, the hierarchical echo intensity quality factor, the hierarchical noise quality factor, the water level correlation quality factor and the periodic quality factor to obtain a comprehensive quality factor, wherein the comprehensive quality factor evaluation is in inverse proportion to the quality of the flow measurement hierarchical data.
8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the method of assessing the quality of lateral flow data according to any one of claims 1 to 6.
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