CN112033479B - Ecological flow monitoring data acquisition and processing method - Google Patents
Ecological flow monitoring data acquisition and processing method Download PDFInfo
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Abstract
The invention discloses an ecological flow monitoring data acquisition and processing method, which relates to the technical field of flow monitoring and specifically comprises the following steps: collecting flow data through the corrected sensor unit in a sampling period T, collecting flow data for a plurality of times in each sampling period T, screening the flow data in each sampling period T, and calculating an average value to obtain a period value; calculating the average value of the period values of all sampling periods T in a preset statistical period to obtain a final credible flow value; according to the invention, by increasing the collection times in the sampling period and taking the average value obtained by counting in the counting period comprising a plurality of sampling periods as the credible flow value, the error in the data collection process is effectively eliminated, the accuracy of the data is improved, and the technical problem of low accuracy of the ecological flow monitoring data in the related technology is solved.
Description
Technical Field
The invention relates to the technical field of flow monitoring, in particular to an ecological flow monitoring data acquisition and processing method.
Background
Along with the implementation of the ground of the national ecological development strategy plan, the attention degree of the economic social development to the ecological environment is improved year by year, the construction of various hydraulic engineering requires to keep the stability of a river ecological system, the environmental problems caused by water storage of a power station and dehydration reduction of a dam lower stream section are very prominent, a certain ecological flow must be discharged and corresponding ecological flow discharge guarantee measures must be taken, so that adverse effects are reduced, water diversion type development and mixed type development in hydropower engineering, and dam type development of a reservoir initial water storage period and a power station participating in system peak shaving can cause water reduction or even flow interruption from a gate dam to a factory building stream section or a dam site lower stream section, so that the water environment capacity of the stream section is reduced, the water quality is influenced, the aquatic ecological system is damaged, and fish resources are influenced. The water level reduction and the water area reduction can also cause the vegetation degradation of the river valleys and the river banks, and have adverse effects on the industrial and agricultural water taking, shipping and landscape of the river reach.
In order to solve the problems, a let-down ecological flow monitoring system is installed in many hydropower stations, river channels and various water systems, so that the ecological flow condition of the river can be monitored and decision-making management can be facilitated. The accuracy of the data obtained by the ecological flow monitoring system provides a solid basis for decision management of an ecological environmental protection department, and because the ecological flow channels are communicated with natural water bodies, differences and instability exist, and the accuracy of the existing ecological flow monitoring data is difficult to guarantee, the data processing method capable of effectively eliminating errors in the data acquisition process is adopted, and the data processing method is particularly important for improving the accuracy of the data.
Disclosure of Invention
The invention provides an ecological flow monitoring data acquisition and processing method for improving the accuracy of data, and solves the technical problem that the accuracy of ecological flow monitoring data is not high in the related technology.
According to one aspect of the invention, an ecological flow monitoring data acquisition and processing method is provided, which comprises the following steps:
collecting flow data in a sampling period T, and collecting flow data for a plurality of times in each sampling period T;
screening the flow data in each sampling period T, and then calculating an average value to obtain a period value;
and calculating the average value of the period values of all sampling periods T in the preset statistical period to obtain a final credible flow value.
Further, the collecting flow data is a flow value collected and output by the sensor unit, and the flow characteristic function of the sensor unit is corrected before the flow data is collected, which comprises the following steps:
recording a default flow characteristic function f (x);
inputting water flow with a flow value Y in a preset range for multiple times in any channel, wherein the flow value Y of the water flow input each time is different, arranging a sensor unit to be corrected in the channel, recording the input flow value Y and an operation value x of the corresponding sensor unit to obtain an actual flow characteristic function F (x), wherein the operation value x of the sensor unit is obtained by multiplying an output value w of the sensor unit by a sensitivity coefficient z, the value range of the sensitivity coefficient z is [0,1], and the sensitivity coefficient z is set to be 1 initially;
adjusting a sensitivity coefficient z of the sensor unit, and giving the adjusted sensitivity coefficient z to the actual flow rate characteristic function F (x) to obtain a weighted actual flow rate characteristic function G (x), wherein the weighted actual flow rate characteristic function G (x) = zF (x) is such that the weighted actual flow rate characteristic function G (x) and the default flow rate characteristic function F (x) satisfy the following predetermined conditions:
when the operation value x of the sensor unit is smaller than the preset value Z, the weighted actual flow characteristic function G (x) and the default flow characteristic function f (x) are unitary linear functions;
giving a first weighting value n to a default flow rate characteristic function f (X) to obtain a first corrected flow rate characteristic function G (X), i.e., G (X) = nf (X), so that the first corrected flow rate characteristic function G (X) is the same as the weighted actual flow rate characteristic function G (X) when the calculated value X is smaller than a predetermined value Z;
adding the first corrected flow characteristic function g (x) to the correction function r (x) to obtain a final flow characteristic function h (x), namely h (r) = g (x) + r (x);
wherein, r (x) = (x-Z) a ×K1×z×(λ b -K2)
Wherein Z is the corrected sensitivity coefficient, Z is a preset value Z, λ is a constant, K1, K2, a, b are constants, the threshold values of a and b are [1,4], K1 and K2 are integers, and the threshold value is [1,10].
Further, the preset range of the water flow with the flow value Y in the preset range input for multiple times in any channel is 1-150 m 3 /h。
Further, λ is a fluid density value through the channel.
Further, the sensor unit is connected with a correction system, and the correction system comprises:
the flow recording unit is used for receiving and storing the operation value x output by the sensor unit, the flow value Y output by the sensor unit and the real flow value Y, and sending the operation value x, the flow value Y and the real flow value Y to the sensitivity adjusting unit and the first correction function output unit;
the sensor unit at least comprises a flowmeter, a sensitivity setting unit, a flow calculation unit and a function storage unit, wherein the flowmeter is arranged on a channel of flow to be measured and sends an output value W to the sensitivity setting unit, the sensitivity setting unit is used for storing a sensitivity coefficient z, multiplying the output value W and the sensitivity coefficient z to obtain an operation value x and sending the operation value x to the flow calculation unit, and the flow calculation unit calls a function stored in the function storage unit to calculate a flow value y;
a sensitivity adjustment unit for adjusting a sensitivity coefficient z, giving the adjusted sensitivity coefficient z to the actual flow rate characteristic function F (x) to obtain a weighted actual flow rate characteristic function G (x), transmitting the adjusted sensitivity coefficient z to the sensitivity setting unit and the correction function output unit of the sensor unit, and transmitting the weighted actual flow rate characteristic function G (x) to the first correction function output unit;
a first correction function output unit, configured to give a first weighting value n to the default flow characteristic function f (x) to obtain a first corrected flow characteristic function g (x), and send the first corrected flow characteristic function g (x) to the function synthesis unit;
a correction function output unit for generating a correction function r (x) and sending it to the function synthesis unit;
and the function synthesis unit is used for adding the first correction flow characteristic function g (x) and the correction function r (x) to obtain a final flow characteristic function h (x) and sending the final flow characteristic function h (x) to the function storage unit of the sensor unit.
Further, a function storage unit of the sensor unit initially stores a default flow characteristic function f (x).
Further, the sensitivity setting unit initially stores a sensitivity coefficient z =1.
Further, the screening processing of the traffic data in each sampling period T includes the following steps:
setting a confidence level p, the range of p being [0,1]]Looking up a normal distribution table according to the value of (1- (1-p)/2) to obtain a confidence value c, wherein the value range of the confidence value c is [0,3.9 ]]The corresponding confidence interval is { + -. C σ And reserving the flow data in the confidence interval.
Further, the formula for calculating the average value of the period values of all the sampling periods T in the predetermined statistical period to obtain the final trusted flow value is as follows:
C＝pJ+l
wherein C is a credible flow value, J is an average value calculated by period values of all sampling periods T, p is a preset deviation coefficient, and l is a preset zero error.
The invention has the beneficial effects that: according to the method, the error in the data acquisition process is effectively eliminated by increasing the acquisition times in the sampling period and taking the average value obtained by statistics in the statistical period comprising a plurality of sampling periods as the credible flow value;
the invention also screens the data in the sampling period by a random error elimination method and eliminates the error generated by counting the period by a system error elimination method, and the accuracy of the data is improved by comprehensive error elimination;
the invention also corrects the flow characteristic function of the sensor unit to ensure that the flow value acquired by the sensor unit is more accurate, thereby fundamentally eliminating the error of the originally acquired data and improving the accuracy of the data.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of the present invention for correcting a flow characteristic function of a sensor unit;
FIG. 3 is a schematic diagram of a calibration system of an embodiment of the present invention;
FIG. 4 is a distribution plot of standard deviation and measurements for an embodiment of the present invention.
In the figure: a calibration system 100, a flow rate recording unit 101, a sensitivity adjustment unit 102, a first calibration function output unit 103, a correction function output unit 104, and a function synthesis unit 105;
the flow meter comprises a sensor unit 200, a flow meter 201, a sensitivity setting unit 202, a flow calculation unit 203, and a function storage unit 204.
Detailed Description
The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand the subject matter described herein and are not intended to limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as needed. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with respect to some examples may also be combined in other examples.
As shown in fig. 1, a method for collecting and processing ecological traffic monitoring data includes the following steps:
and step 300, calculating an average value of the period values of all the sampling periods T in the preset statistical period to obtain a final credible flow value.
It should be noted that the manner of collecting the flow data in step 100 may be any manner in the prior art, and includes at least the following three manners:
river channels: direct mining with a flowmeter (electromagnetic, ultrasonic, etc.);
gates: a gate opening instrument and a water level meter;
water outlet: a water level gauge;
wherein, part of the acquisition modes need to introduce a corresponding hydraulic calculation formula into the acquired parameters to calculate the flow;
for example, for a valve opening obtained by a valve openmeter:
Q＝μ*A*(2*P/ρ)^0.5
wherein Q is flow, m ^ S =160m 3 /h
Mu-flow coefficient, related to the shape of the valve or tube;
a is the area, m ^2 is the cross-sectional area of the water flow path
P-pressure difference across the valve, in Pa,
rho is the density of the fluid medium, kg/m ^3;
wherein only P, the pressure difference before and after passing through the valve, is unknown, the pressure difference before and after is different when the opening degree of the valve is different, and the corresponding flow rate is different. The valve manufacturer provides a characteristic curve or formula of the opening degree and the flow of the valve port, and the pressure difference P between the front and the back of the valve is calculated according to the formula and the valve opening degree.
For example, for the direct sampling of the sensor, the sensor directly outputs the flow value, and a hydraulics calculation formula is not required to be introduced for calculation.
The step 200 of screening the traffic data in each sampling period T includes the following steps:
set the confidence level p, the range of p to [0,1]]Looking up a normal distribution table according to the value of (1- (1-p)/2) to obtain a confidence value c, wherein the value range of the confidence value c is [0,3.9 ]]The corresponding confidence interval is { + -. C σ And retaining the flow data in the confidence interval.
Random errors are errors that cancel each other out due to a series of small random fluctuations of the relevant factors during the measurement process. The random error reflects the degree of accuracy of the measurement. The accuracy is the dispersion degree of the distribution of repeated measurement results, and is usually expressed by standard deviation sigma. The standard deviation definition is the square root of the arithmetic mean of the standard values of the units of the population squared with their mean.
In the formula: c. C i -the measurement of the ith time within the sampling period T;
r-measuring the mean value;
the measurement results are usually fit to a normal distribution N (r, σ) 2 ). At this time, the standard deviation and the distribution of the measured values have a relationship as shown in fig. 4;
as can be seen from fig. 4, a sample data has a 68.2% probability of falling within a range of one σ from the mean. It is understood conversely that, if the number of samples is sufficiently large,there will be 68.2% of the samples that fall within one σ of the deviation from the mean. It is further understood that samples falling within a range of one σ from the mean can be considered to have a 68.2% confidence level. We define this confidence as the confidence level p and the corresponding range of deviation from the mean as the confidence interval { + -. C σ }。
Confidence level p and confidence interval { + -. C σ The relationship can be obtained by looking up the normal distribution table shown in the following table:
| c | 0 | 0.01 | 0.02 | 0.03 | 0.04 | 0.05 | 0.06 | 0.07 | 0.08 | 0.09 |
| 0 | 0.500 0 | 0.504 0 | 0.508 0 | 0.512 0 | 0.516 0 | 0.519 9 | 0.523 9 | 0.527 9 | 0.531 9 | 0.535 9 |
| 0.1 | 0.539 8 | 0.543 8 | 0.547 8 | 0.551 7 | 0.555 7 | 0.559 6 | 0.563 6 | 0.567 5 | 0.571 4 | 0.575 3 |
| 0.2 | 0.579 3 | 0.583 2 | 0.587 1 | 0.591 0 | 0.594 8 | 0.598 7 | 0.602 6 | 0.606 4 | 0.610 3 | 0.614 1 |
| 0.3 | 0.617 9 | 0.621 7 | 0.625 5 | 0.629 3 | 0.633 1 | 0.636 8 | 0.640 4 | 0.644 3 | 0.648 0 | 0.651 7 |
| 0.4 | 0.655 4 | 0.659 1 | 0.662 8 | 0.666 4 | 0.670 0 | 0.673 6 | 0.677 2 | 0.680 8 | 0.684 4 | 0.687 9 |
| 0.5 | 0.691 5 | 0.695 0 | 0.698 5 | 0.701 9 | 0.705 4 | 0.708 8 | 0.712 3 | 0.715 7 | 0.719 0 | 0.722 4 |
| 0.6 | 0.725 7 | 0.729 1 | 0.732 4 | 0.735 7 | 0.738 9 | 0.742 2 | 0.745 4 | 0.748 6 | 0.751 7 | 0.754 9 |
| 0.7 | 0.758 0 | 0.761 1 | 0.764 2 | 0.767 3 | 0.770 3 | 0.773 4 | 0.776 4 | 0.779 4 | 0.782 3 | 0.785 2 |
| 0.8 | 0.788 1 | 0.791 0 | 0.793 9 | 0.796 7 | 0.799 5 | 0.802 3 | 0.805 1 | 0.807 8 | 0.810 6 | 0.813 3 |
| 0.9 | 0.815 9 | 0.818 6 | 0.821 2 | 0.823 8 | 0.826 4 | 0.828 9 | 0.835 5 | 0.834 0 | 0.836 5 | 0.838 9 |
| 1 | 0.841 3 | 0.843 8 | 0.846 1 | 0.848 5 | 0.850 8 | 0.853 1 | 0.855 4 | 0.857 7 | 0.859 9 | 0.862 1 |
| 1.1 | 0.864 3 | 0.866 5 | 0.868 6 | 0.870 8 | 0.872 9 | 0.874 9 | 0.877 0 | 0.879 0 | 0.881 0 | 0.883 0 |
| 1.2 | 0.884 9 | 0.886 9 | 0.888 8 | 0.890 7 | 0.892 5 | 0.894 4 | 0.896 2 | 0.898 0 | 0.899 7 | 0.901 5 |
| 1.3 | 0.903 2 | 0.904 9 | 0.906 6 | 0.908 2 | 0.909 9 | 0.911 5 | 0.913 1 | 0.914 7 | 0.916 2 | 0.917 7 |
| 1.4 | 0.919 2 | 0.920 7 | 0.922 2 | 0.923 6 | 0.925 1 | 0.926 5 | 0.927 9 | 0.929 2 | 0.930 6 | 0.931 9 |
| 1.5 | 0.933 2 | 0.934 5 | 0.935 7 | 0.937 0 | 0.938 2 | 0.939 4 | 0.940 6 | 0.941 8 | 0.943 0 | 0.944 1 |
| 1.6 | 0.945 2 | 0.946 3 | 0.947 4 | 0.948 4 | 0.949 5 | 0.950 5 | 0.951 5 | 0.952 5 | 0.953 5 | 0.953 5 |
| 1.7 | 0.955 4 | 0.956 4 | 0.957 3 | 0.958 2 | 0.959 1 | 0.959 9 | 0.960 8 | 0.961 6 | 0.962 5 | 0.963 3 |
| 1.8 | 0.964 1 | 0.964 8 | 0.965 6 | 0.966 4 | 0.967 2 | 0.967 8 | 0.968 6 | 0.969 3 | 0.970 0 | 0.970 6 |
| 1.9 | 0.971 3 | 0.971 9 | 0.972 6 | 0.973 2 | 0.973 8 | 0.974 4 | 0.975 0 | 0.975 6 | 0.976 2 | 0.976 7 |
| 2 | 0.977 2 | 0.977 8 | 0.978 3 | 0.978 8 | 0.979 3 | 0.979 8 | 0.980 3 | 0.980 8 | 0.981 2 | 0.981 7 |
| 2.1 | 0.982 1 | 0.982 6 | 0.983 0 | 0.983 4 | 0.983 8 | 0.984 2 | 0.984 6 | 0.985 0 | 0.985 4 | 0.985 7 |
| 2.2 | 0.986 1 | 0.986 4 | 0.986 8 | 0.987 1 | 0.987 4 | 0.987 8 | 0.988 1 | 0.988 4 | 0.988 7 | 0.989 0 |
| 2.3 | 0.989 3 | 0.989 6 | 0.989 8 | 0.990 1 | 0.990 4 | 0.990 6 | 0.990 9 | 0.991 1 | 0.991 3 | 0.991 6 |
| 2.4 | 0.991 8 | 0.992 0 | 0.992 2 | 0.992 5 | 0.992 7 | 0.992 9 | 0.993 1 | 0.993 2 | 0.993 4 | 0.993 6 |
| 2.5 | 0.993 8 | 0.994 0 | 0.994 1 | 0.994 3 | 0.994 5 | 0.994 6 | 0.994 8 | 0.994 9 | 0.995 1 | 0.995 2 |
| 2.6 | 0.995 3 | 0.995 5 | 0.995 6 | 0.995 7 | 0.995 9 | 0.996 0 | 0.996 1 | 0.996 2 | 0.996 3 | 0.996 4 |
| 2.7 | 0.996 5 | 0.996 6 | 0.996 7 | 0.996 8 | 0.996 9 | 0.997 0 | 0.997 1 | 0.997 2 | 0.997 3 | 0.997 4 |
| 2.8 | 0.997 4 | 0.997 5 | 0.997 6 | 0.997 7 | 0.997 7 | 0.997 8 | 0.997 9 | 0.997 9 | 0.998 0 | 0.998 1 |
| 2.9 | 0.998 1 | 0.998 2 | 0.999 2 | 0.998 3 | 0.998 4 | 0.998 4 | 0.998 5 | 0.998 5 | 0.998 6 | 0.998 6 |
| C | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 |
| 3 | 0.998 7 | 0.999 0 | 0.999 3 | 0.999 5 | 0.999 7 | 0.999 8 | 0.999 8 | 0.999 9 | 0.999 9 | 1.000 0 |
assuming a confidence level of p, the corresponding confidence interval range is { ± c σ }, denoted as c = f (p). p ranges from 0 to 1, c ranges from 0 to 3.9. According to the value of (1- (1-p)/2), a table lookup can be performed to obtain the value of c.
Example (c): p =0.9, (1- (1-0.9)/2) =0.95, and a table lookup may find the value of c between 1.64 and 1.65, assuming c =1.645. I.e. confidence interval of { + -1.645 σ }
During the data processing process, different confidence levels can be selected to screen the sampling data, and the measured value with larger deviation from the average value is removed, so that the measurement accuracy is improved. Such as setting upWith a confidence of 0.99, the deviation from the mean will be greater than 2.575 σ After the measured values are eliminated, the average values are recalculated as final sampling data. Note that here a high confidence does not mean that the data is "good", and that data with a large deviation from the mean is also true with a certain probability. It is generally believed that: the sampling error of 100 samples is investigated to be +/-10%; the sampling error of 500 samples is + -5%; the sampling error at 1200 samples was ± 3%.
In step 300, when the average value of the period values of all sampling periods T in the predetermined statistical period is calculated to obtain the final trusted flow value, the system error is eliminated,
assuming that the average value calculated by the period values of all the sampling periods T and the credible flow value keep a linear relation, namely:
C＝pJ+l
in the formula: c- - -a trusted flow value, J- - -an average value calculated by period values of all sampling periods T, p- - -a deviation coefficient, l- - -a zero point error;
the systematic error can be revised by adjusting the values of p and l.
The above method is only applicable to systematic errors that satisfy a linear relationship.
As an example, the parameters may be set in the present embodiment with reference to the following table;
the parameters associated with data acquisition and processing are summarized below. Each parameter should be settable.
As shown in fig. 2, for the flow data collection mode in which the collected flow data is the flow value collected and outputted by the sensor unit,
the method for correcting the flow characteristic function of the sensor unit before collecting the flow data comprises the following steps:
the default flow characteristic function f (x) is set by a conventional default, which is actually provided by a manufacturer of the sensor unit 200 and is a reference function obtained by the manufacturer according to parameter estimation and experimental tests of the sensor unit 200; when the predetermined calculation value X is smaller than the predetermined value Z, the default flow characteristic function f (X) is a simple unitary linear function, and the flow value calculated in this range is accurate.
Inputting water flow with a flow value Y in a preset range for multiple times in any channel, wherein the flow value Y of the water flow input each time is different, arranging a sensor unit 200 to be corrected in the channel, and recording the input flow value Y and a corresponding operation value x of the sensor unit 200 to obtain an actual flow characteristic function F (x);
for the ecological flow monitoring applied in the embodiment, it is appropriate to take the predetermined range of 1-150 m3/h, and the ecological flow monitoring within the application range can be embodied by inputting water with the flow value Y within the predetermined range into any channel, and the source of the water should be the water body to which the sensor unit 200 is applied.
The default flow characteristic function F (x) mentioned above is set by default, so that there is a deviation in actual use, i.e. the actual flow characteristic function F (x) and the default flow characteristic function F (x) do not coincide within the range of the predetermined value Z, and the actual flow characteristic functions F (x) are not all unitary linear functions.
The type of the sensor unit 200 in this embodiment may be various, and may be selected but not limited to: electromagnetic flow meter, ultrasonic flow meter, thermal flow meter; the types of the calculated value x are different from each other in the various sensor cells 200 described above, and the calculated value x, that is, the types of the corresponding sensor cells 200, is classified into a differential pressure, a time difference, a voltage difference, and the like.
The present embodiment specifically describes a case where the electromagnetic flowmeter 201 is applied to the sensor unit 200:
referring to the electromagnetic flowmeter 201 described above, the predetermined value Z is 8V, and the electromagnetic flowmeter 201 is used as the sensor unit 200 in the case where the description is not given below.
the first corrected flow rate characteristic function G (x) is represented on the same function image as the weighted actual flow rate characteristic function G (x), that is, the image of the function of the two is completely superimposed.
Since the weighted actual flow characteristic function G (x) and the first corrected flow characteristic function G (x) have the same starting point (which should be 0), the first weighting value n only needs to adjust the slopes of the function images of the weighted actual flow characteristic function G (x) and the first corrected flow characteristic function G (x) to be consistent, and calculation can be performed by using any one of the calculated values x as a reference, so that the flow value of the weighted actual flow characteristic function G (x) under the calculated value x is the same as the flow value of the first corrected flow characteristic function G (x).
The first corrected flow characteristic function G (x) is the same as the weighted actual flow characteristic function G (x), and at this time, within the predetermined value Z, the first corrected flow characteristic function G (x) can obtain an accurate flow value through calculation, and the deviation between the output flow value and the actual flow value is small.
Referring to step 120, when the predetermined value Z is 8V, the first corrected flow rate characteristic function g (x) can calculate the same flow rate value as the actual flow rate within 8V, but the calculation result may be deviated when the predetermined value is exceeded, and if x = Z, the calculation result may be deviatedThe flow rate value corresponds to 80m 3 The cross section area of a channel corresponding to one correction is 0.3 square meter, the difference between the cross section area of the channel corresponding to the correction and the actual cross section area of the channel corresponding to the correction is not too large, and the difference also means that if the cross section of the channel is reduced, the cross section of the channel is 80m 3 If the calculated value x corresponding to/h exceeds 8V, the accuracy cannot be ensured during measurement, and if the first corrected flow rate characteristic function g (x) exceeds the predetermined value Z, the flow rate characteristic function g (x) is not a unitary linear function, and 80m needs to be measured for such a case 3 The flow/h needs to be further corrected, as described in step 140.
k1, K2, a, b are all constants, the threshold values of a and b are [1,4], K1 and K2 are integers, and the threshold value is [1,10].
The sensor unit 200 calculates and outputs a flow rate value with the final corrected flow rate characteristic function h (x).
By further correction of the above formula, when the calculated value is larger than the predetermined value Z, calculation of a flow rate value of the passage smaller than the passage sectional area at the time of correction is obtained by addition of the correction function r (x).
The flow rate value can be calculated within a predetermined calculation value range by the method, the flow rate characteristic function within the predetermined calculation value range is a unitary linear function, and a linear function curve represents the reduction of an error and the accuracy of the output flow rate value.
The calculated value x of the sensor unit 200 is obtained by multiplying the output value w of the sensor unit 200 by the sensitivity coefficient z, the value range of the sensitivity coefficient z is [0,1], the sensitivity coefficient z is initially set to 1, and the sensitivity coefficient z given to the actual flow rate characteristic function F (x) is corrected by replacing the sensitivity coefficient z initially set to 1 with the sensitivity coefficient z given to the actual flow rate characteristic function F (x), and therefore, it is necessary to correct the sensitivity coefficient z in synchronization with the calculated value x of the final corrected flow rate characteristic function h (x) so as to calculate an accurate flow rate value.
As shown in fig. 3, the present embodiment additionally provides a correction system 100 based on the above method for correcting the flow characteristic function of the sensor unit 200, including:
the flow recording unit 101 is configured to receive and store the operation value x output by the sensor unit 200, the flow value Y output by the sensor unit 200, and the real flow value Y, and send the operation value x, the flow value Y, and the real flow value Y to the sensitivity adjusting unit 102 and the first correction function output unit 103;
the sensor unit 200 at least comprises a flow meter 201, a sensitivity setting unit 202, a flow calculation unit 203 and a function storage unit 204, wherein the flow meter 201 is arranged on a channel of a flow to be measured and sends an output value W to the sensitivity setting unit 202, the sensitivity setting unit 202 is used for storing a sensitivity coefficient z, multiplying the output value W and the sensitivity coefficient z to obtain an operation value x and sending the operation value x to the flow calculation unit 203, and the flow calculation unit 203 calls a function stored in the function storage unit 204 to calculate a flow value y.
The real flow value Y is an output parameter provided by an external device, such as a water pump, and is an input parameter for inputting water into the channel during correction.
A sensitivity adjustment unit 102 configured to adjust a sensitivity coefficient z, obtain a weighted actual flow rate characteristic function G (x) by applying the adjusted sensitivity coefficient z to the actual flow rate characteristic function F (x), transmit the adjusted sensitivity coefficient z to the sensitivity setting unit 202 and the correction function output unit 104 of the sensor unit 200, and transmit the weighted actual flow rate characteristic function G (x) to the first correction function output unit 103;
the first correction function output unit 103 is configured to assign a first weighting value n to the default flow characteristic function f (x) to obtain a first corrected flow characteristic function g (x), and send the first corrected flow characteristic function g (x) to the function synthesis unit 105;
the correction function output unit 104 is configured to generate a correction function r (x) and send the correction function r (x) to the function synthesis unit 105;
the function synthesis unit 105 adds the first corrected flow characteristic function g (x) and the correction function r (x) to obtain a final flow characteristic function h (x), and sends the final flow characteristic function h (x) to the function storage unit 204 of the sensor unit 200; and taking the final flow characteristic function h (x) as a basis for calculating the flow value y.
The function storage unit 204 of the sensor unit 200 initially stores a default flow characteristic function f (x);
the sensitivity coefficient z =1 initially stored by the sensitivity setting unit 202;
the adjusted sensitivity coefficient z needs to satisfy the following preset conditions: when the operation value x of the sensor unit 200 is smaller than the predetermined value Z, the weighted actual flow rate characteristic function G (x) and the default flow rate characteristic function f (x) are both unitary linear functions.
Claims (7)
1. An ecological flow monitoring data acquisition and processing method is characterized by comprising the following steps:
collecting flow data in a sampling period T, and collecting flow data for a plurality of times in each sampling period T;
screening the flow data in each sampling period T, and then calculating an average value to obtain a period value;
calculating the average value of the period values of all sampling periods T in a preset statistical period to obtain a final credible flow value;
the screening processing of the flow data in each sampling period T comprises the following steps:
set the confidence level p, the range of p to [0,1]]Looking up a normal distribution table according to the value of (1- (1-p)/2) to obtain a confidence value c, wherein the value range of the confidence value c is [0,3.9 ]]The corresponding confidence interval is { + -. C σ Reserving flow data in a confidence interval, wherein sigma is a standard deviation; the calculation formula for calculating the average value of the period values of all the sampling periods T in the preset statistical period to obtain the final credible flow value is as follows:
C＝pJ+l
wherein C is a credible flow value, J is an average value calculated by period values of all sampling periods T, p is a preset deviation coefficient, and l is a preset zero error.
2. The ecological flow monitoring data collecting and processing method according to claim 1, wherein the collected flow data is a flow value collected and output by the sensor unit, and the flow characteristic function of the sensor unit is corrected before the flow data is collected, comprising the following steps:
recording a default flow characteristic function f (x);
inputting water flow with a flow value Y in a preset range for multiple times in any channel, wherein the flow value Y of the water flow input each time is different, arranging a sensor unit to be corrected in the channel, recording the input flow value Y and an operation value x of the corresponding sensor unit to obtain an actual flow characteristic function F (x), wherein the operation value x of the sensor unit is obtained by multiplying an output value w of the sensor unit by a sensitivity coefficient z, the value range of the sensitivity coefficient z is [0,1], and the sensitivity coefficient z is set to be 1 initially;
adjusting a sensitivity coefficient z of the sensor unit, and giving the adjusted sensitivity coefficient z to the actual flow rate characteristic function F (x) to obtain a weighted actual flow rate characteristic function G (x), wherein the weighted actual flow rate characteristic function G (x) = zF (x) is such that the weighted actual flow rate characteristic function G (x) and the default flow rate characteristic function F (x) satisfy the following predetermined conditions:
when the operation value x of the sensor unit is smaller than the preset value Z, the weighted actual flow characteristic function G (x) and the default flow characteristic function f (x) are unitary linear functions;
giving a first weighting value n to a default flow rate characteristic function f (X) to obtain a first corrected flow rate characteristic function G (X), i.e., G (X) = nf (X), so that the first corrected flow rate characteristic function G (X) is the same as the weighted actual flow rate characteristic function G (X) when the calculated value X is smaller than a predetermined value Z;
adding the first corrected flow characteristic function g (x) to the correction function r (x) to obtain a final flow characteristic function h (x), namely h (r) = g (x) + r (x);
wherein, r (x) = (x-Z) a ×K1×z×(λ b -K2)
Wherein Z is the corrected sensitivity coefficient, Z is a preset value Z, λ is a constant, K1, K2, a, b are constants, the threshold values of a and b are [1,4], K1 and K2 are integers, and the threshold value is [1,10].
3. The ecological flow monitoring data acquisition and processing method as claimed in claim 2, wherein the predetermined range of the water flow with the flow value Y of the predetermined range input for a plurality of times in any channel is 0-150 m 3 /h。
4. The ecological flux monitoring data collecting and processing method as claimed in claim 2, wherein λ is a density value of a fluid passing through the channel.
5. The ecological flow monitoring data collecting and processing method according to claim 2, wherein the sensor unit is connected to a calibration system, and the calibration system comprises:
the flow recording unit is used for receiving and storing the operation value x output by the sensor unit, the flow value Y output by the sensor unit and the real flow value Y, and sending the operation value x, the flow value Y and the real flow value Y to the sensitivity adjusting unit and the first correction function output unit;
the sensor unit at least comprises a flowmeter, a sensitivity setting unit, a flow calculation unit and a function storage unit, wherein the flowmeter is arranged on a channel of the flow to be measured and sends an output value W to the sensitivity setting unit, the sensitivity setting unit is used for storing a sensitivity coefficient z, multiplying the output value W and the sensitivity coefficient z to obtain an operation value x and sending the operation value x to the flow calculation unit, and the flow calculation unit calls a function stored in the function storage unit to calculate a flow value y;
a sensitivity adjustment unit for adjusting a sensitivity coefficient z, giving the adjusted sensitivity coefficient z to the actual flow rate characteristic function F (x) to obtain a weighted actual flow rate characteristic function G (x), transmitting the adjusted sensitivity coefficient z to the sensitivity setting unit and the correction function output unit of the sensor unit, and transmitting the weighted actual flow rate characteristic function G (x) to the first correction function output unit;
a first correction function output unit, configured to give a first weighting value n to the default flow characteristic function f (x) to obtain a first corrected flow characteristic function g (x), and send the first corrected flow characteristic function g (x) to the function synthesis unit;
a correction function output unit for generating a correction function r (x) and sending it to the function synthesis unit;
and the function synthesis unit is used for adding the first correction flow characteristic function g (x) and the correction function r (x) to obtain a final flow characteristic function h (x) and sending the final flow characteristic function h (x) to the function storage unit of the sensor unit.
6. The ecological traffic monitoring data collecting and processing method according to claim 5, wherein the function storage unit of the sensor unit initially stores a default traffic characteristic function f (x).
7. The ecological traffic monitoring data collecting and processing method according to claim 5, wherein the sensitivity setting unit initially stores a sensitivity coefficient z =1.
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