CN106468790A - A kind of sensor performance method for quantitatively evaluating based on Gauss distribution - Google Patents
A kind of sensor performance method for quantitatively evaluating based on Gauss distribution Download PDFInfo
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- CN106468790A CN106468790A CN201510519268.0A CN201510519268A CN106468790A CN 106468790 A CN106468790 A CN 106468790A CN 201510519268 A CN201510519268 A CN 201510519268A CN 106468790 A CN106468790 A CN 106468790A
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Abstract
The invention discloses a kind of sensor performance method for quantitatively evaluating based on Gauss distribution, including：Obtain the evaluating of tested sensor test data sampled point Gauss distribution under certain test condition, normal operating conditions；Set up the acceptance line of tested sensor based on canonical parameter under this certain test condition for the standard transducer；Judge whether tested sensor is qualified based on acceptance line and evaluating.The present invention can reduce the interference that the larger shake skew of the less waveform of probability of occurrence causes to test result, make data more conform to objective fact, can make quantitative assessment to different test environment lower sensor performances.
Description
Technical field
The present invention relates to earthquake-capturing equipment technology field, specifically, it is related to a kind of biography based on Gauss distribution
Sensor performance method for quantitatively evaluating.
Background technology
With the popularization of three-dimensional exploration, the requirement more and more higher to surveying accuracy, the collection essence to geophone
Degree requires also to improve therewith.Sensor is the core of geophone, to the precision of geophone, sensitivity
Play a decisive role etc. performance parameter.
At present, the response effect that the sensor that domestic independent research produces excites to external world is stable not enough, so
Need before use to be tested.For the result data of sensor test, it is mainly with test interface at this stage
Waveform reflects, and passes through paper trail, and data volume is big and fluctuation area is indefinite.This results in test interface reflection
Numerical value be instantaneous state it is impossible to the preset parameter as sensor is recorded.
Therefore when carrying out parameter comparison, the sensor only simultaneously detecting can be obtained by intuitively comparison of wave shape
To qualitatively conclusion, but because numerical value has floating not accomplish accurate quantification, especially when the sensor number of comparison and detection
When being worth close, test result is fuzzyyer.
Content of the invention
For solving problem above, the invention provides a kind of sensor performance quantitative assessment side based on Gauss distribution
Method, in order to make quantitative assessment to sensor performance, solves the problems, such as that test data is floated inaccurate so that surveying
Examination conclusion is objective, accurate.
According to one embodiment of present invention, there is provided a kind of sensor performance quantitative assessment based on Gauss distribution
Method, including：
Obtain tested sensor test data sampled point Gauss distribution under certain test condition, normal operating conditions
Evaluating；
The qualified of tested sensor is set up based on canonical parameter under described certain test condition for the standard transducer
Line；
Judge whether tested sensor is qualified based on described acceptance line and described evaluating.
According to one embodiment of present invention, described evaluating includes tested sensor in described certain test strip
The mathematical expectation of the test data under part.
According to one embodiment of present invention, described canonical parameter includes described standard transducer in a described location survey
The mathematical expectation of the test data under the conditions of examination and standard deviation square value.
According to one embodiment of present invention, the acceptance line setting up tested sensor further includes：
With the μ-n σ of described standard transducer as acceptance line, wherein, μ is the mathematical expectation of standard transducer,
σ is the standard deviation square value of standard transducer, and n is the required precision of tested sensor.
According to one embodiment of present invention, tested sensor is judged based on described acceptance line and described evaluating
Whether qualified further include：
Evaluating as the Gauss distribution of the test data of tested sensor is more than or equal to μ-n σ, then tested sensing
Device is qualified under described certain test condition；
Evaluating as the Gauss distribution of the test data of tested sensor is less than μ-n σ, then tested sensor exists
Unqualified under described certain test condition.
According to one embodiment of present invention, during described certain test condition difference, described acceptance line needs boundary again
Fixed.
According to one embodiment of present invention, tested sensor is surveyed under multiple different test conditions
Examination.
According to one embodiment of present invention, whether qualified under multiple difference test conditions for tested sensor
Result set up corresponding data base.
According to one embodiment of present invention, obtain tested sensor in certain test condition, normal operating conditions
The evaluating of lower test data sampled point Gauss distribution further includes：
Based on the repeatedly continuous random sampling for a long time of tested sensor, with the meansigma methodss of each continuous random sampling
Sample value as this time；
Tested sensor is calculated according to multiple sample value and tests number under certain test condition, normal operating conditions
Evaluating according to sampled point Gauss distribution.
According to one embodiment of present invention, obtain standard ginseng under described certain test condition for the standard transducer
Number further includes：
Based on standard transducer repeatedly continuous random sampling for a long time, with the meansigma methodss of each continuous random sampling
Sample value as this time；
Standard transducer is calculated according to multiple sample value and tests number under certain test condition, normal operating conditions
Canonical parameter according to sampled point Gauss distribution.
Beneficial effects of the present invention：
The present invention, by carrying out Regularity Analysis to collection sampling point, can reduce the less waveform of probability of occurrence larger
The interference that shake skew causes to test result, makes data more conform to objective fact, with the spy of Gauss distribution
Levy and solve the problems, such as that test data floating is inaccurate, it is fixed that different test environment lower sensor performances can be made
Amount is evaluated.
Other features and advantages of the present invention will illustrate in the following description, and, partly from description
In become apparent, or by implement the present invention and understand.The purpose of the present invention and other advantages can be passed through
In description, claims and accompanying drawing, specifically noted structure is realizing and to obtain.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment
Or required accompanying drawing does simple introduction in description of the prior art：
Fig. 1 is method flow diagram according to an embodiment of the invention；
Fig. 2 is standard transducer Gaussian distribution curve figure according to an embodiment of the invention；And
Fig. 3 is sensor Gaussian distribution curve figure to be measured according to an embodiment of the invention.
Specific embodiment
To describe embodiments of the present invention below with reference to drawings and Examples in detail, whereby to the present invention how
Application technology means are solving technical problem, and reach realizing process and fully understanding and real according to this of technique effect
Apply.As long as it should be noted that not constituting conflict, in each embodiment in the present invention and each embodiment
Each feature can be combined with each other, and the technical scheme being formed is all within protection scope of the present invention.
The test result of current sensor mainly with document form record transient data and waveform, is entered to synchronization
Qualitatively superior and inferior evaluating made by the sensor of row contrast test.Test data due to sensor has fluctuation area,
Therefore sensor transient data is unable to the performance parameter of accurate representation sensor.When needing to not same ring in the same time
When sensor under border is contrasted, the performance parameter reference value recording by the above process is limited, especially in quilt
The parameter value of survey sensor and standard transducer is more difficult to draw accurate conclusion when close.
Therefore, the invention provides a kind of sensor performance method for quantitatively evaluating based on Gauss distribution, by length
Time Continuous stochastical sampling and gaussian distribution characteristic are to define with reference to making to acceptance line with standard transducer, can
Quantitative assessment is made to sensor performance, solve the problems, such as test data float inaccurate so that test result
Objective, accurate.
It is illustrated in figure 1 a kind of sensor performance based on Gauss distribution according to an embodiment of the invention fixed
Amount evaluation methodology flow chart, below with reference to Fig. 1, the present invention is described in detail.
First, in step s 110, obtain tested sensor under certain test condition, normal operating conditions
The evaluating of test data sampled point Gauss distribution.
In this step, obtain this evaluating and include following several steps.First, specific at some
Under test condition, normal operation of sensor state, allow tested sensor carry out repeatedly continuous random for a long time and adopt
Sample；Test data due to tested sensor acquisition changes in certain fluctuation area, next, calculating every
The meansigma methodss of all sampled values in the secondary sampling time are as the sample value of this time；Finally, according to multiple
Sample value, calculates tested sensor under this test condition, normal operating conditions based on mathematical statistics method
The evaluating of test data sampled point Gauss distribution.This evaluating is used for evaluating commenting of tested sensor performance
Valency parameter, including the mathematical expectation of the test data sampled point Gauss distribution of tested sensor.
Survey can be improved by the sample value of the test data of the calculated tested sensor of above method
The inaccurate problem of examination data float is so that the evaluation of the Gauss distribution of calculated sample value test data is joined
Number is closer to the actual parameter of tested sensor.
Very big generally, due to the impact to tested sensor performance for the different test environments, for being correctly obtained difference
The performance parameter of tested sensor under test environment, need to enter to tested sensor under multiple different test environments
Row test.The frequency of the signal gathering when specifically, by the tested normal operation of sensor of change, amplitude, letter
The conditions such as ratio, temperature of making an uproar are changing the test condition of tested sensor.
Next, in the step s 120, built based on standard transducer canonical parameter under certain testing situations
Found the acceptance line of tested sensor.
In this step, the certain test condition in standard transducer canonical parameter under certain testing situations with
Certain test condition of the tested sensor in step S110 is identical.Because different sensors is in same test
Response effect under condition, the same external world excite is different, so, a calibrated sensor conduct need to be selected
Standard transducer, as the benchmark weighing other tested sensors.Compare tested sensor, standard transducer
Performance parameter is stable, accurate, meets the parameter request under this test condition.
After selected standard transducer, with test condition identical in step S110 under obtain this standard
The canonical parameter of measured sensor data Gauss distribution.This canonical parameter includes adopting of standard transducer test data
The mathematical expectation of sample value Gauss distribution and variance yields.
Specifically, in the mathematical expectation obtaining standard transducer sampling point value Gauss distribution and variance yields, it is
Reduce the impact to final result for its test data fluctuation area it is also desirable to carry out many vice-minister to standard transducer
The continuous random sampling of time, then using the meansigma methodss of all of continuous random sampling every time as the sample of this time
Value, calculates standard transducer under same certain test condition, normal operating conditions according to multiple sample value
The evaluating of test data sampled point Gauss distribution.
Then, the mathematical expectation based on standard transducer and standard deviation square value are plotted in certain Sigma σ precision
Parameter acceptance line μ-n σ under requiring, wherein, μ is the mathematical expectation of standard transducer, and σ senses for standard
The standard deviation square value of device, n is the required precision of tested sensor, and n value shows more greatly to tested sensor
Required precision is higher.Specifically, n value can be 3,4.5,6 etc..Acceptance line μ-n σ is in Gauss distribution in figure
It is the straight line parallel with the longitudinal axis, the corresponding value of its abscissa is μ-n σ.
Finally, in step s 130, judge whether tested sensor is qualified based on acceptance line and evaluating.
Specifically, in this step, based on tested sensor, evaluating under certain testing situations is same with this
Acceptance line under certain test condition determines whether this tested sensor is qualified.Test data as tested sensor
Gauss distribution evaluating be more than or equal to μ-n σ, then tested sensor is qualified under this certain test condition,
And the mathematical expectation of the Gauss distribution of the test data of tested sensor is closer to μ, its performance is better.As
The evaluating of the Gauss distribution figure of the test data of tested sensor be less than μ-n σ, then tested sensor this one
Determine unqualified under test condition.
Generally, whether the tested sensor of expression being clear and intuitive is qualified under certain testing situations, can draw
The Gauss distribution figure of tested sensor, the test data adopting during drafting can be by the side described in step S110
Method obtains.Then, draw in this Gauss distribution in figure and be based on the calculated acceptance line of step S120, then lead to
Cross this Gauss distribution figure and can judge whether tested sensor is suitable in this test condition clear and intuitively.
When tested sensor is tested under multiple different test conditions, under different test conditions, pass
The responsive state of sensor has very big difference, and therefore sensor test needs to test condition exhaustive division.For quilt
Survey sensor multiple difference test conditions under whether qualified result sets up corresponding data base, to difference
Test environment lower sensor performance makes quantitative assessment, and conveniently consults.
In the step s 120, using standard transducer, canonical parameter under certain testing situations sets up tested biography
During the acceptance line of sensor, for avoiding standard transducer all can not can meet this test strip under all of test condition
Parameter request under part, can select different standard transducers under different test conditions.Certainly, every
Standard transducer under individual test condition need to meet the parameter request of this test condition, and corresponding acceptance line needs again
Define.
The present invention, by carrying out Regularity Analysis to collection sampling point, can reduce the less waveform of probability of occurrence larger
The interference that shake skew causes to test result, makes data more conform to objective fact, with the spy of Gauss distribution
Levy and solve the problems, such as that test data floating is inaccurate, it is fixed that different test environment lower sensor performances can be made
Amount is evaluated.
The feasibility of the present invention is illustrated below by way of a specific embodiment.Be as shown in table 1
Sensor to be measured and standard transducer dominant frequency response intensity data is contrasted under 30Hz, 600mv shooting condition.
Table 1
| Standard transducer | Sensor to be measured | |
| 1 | 138216 | 138160 |
| 2 | 138346 | 137266 |
| 3 | 137513 | 137421 |
| 4 | 137879 | 137404 |
| 5 | 138387 | 137925 |
| 6 | 137925 | 137499 |
| 7 | 137806 | 137226 |
| 8 | 137566 | 137224 |
| 9 | 137732 | 137376 |
| 10 | 138046 | 137772 |
As shown in Table 1, sensor to be measured differ with the dominant frequency response intensity data of standard transducer less it is impossible to
Judge sensor to be measured whether in the range of prescription by the data of table 1.
Next using method of the present invention, sensor to be measured is judged.It is according to this as shown in Figure 2
The standard transducer Gaussian distribution curve figure of a bright embodiment, is according to the present invention as shown in Figure 3
The sensor Gaussian distribution curve figure to be measured of embodiment.As shown in Fig. 2 standard transducer arithmetic average
μ=137942, standard deviation sigma=305.4, then under same test condition, sensor acceptance line to be measured is
(μ -4.5 σ)=136567.7, sensor arithmetic average μ=137527 to be measured>136567.7, sensor to be measured thus full
Sufficient prescription.
While it is disclosed that embodiment as above, but described content is only to facilitate understand the present invention
And the embodiment adopting, it is not limited to the present invention.Technology people in any the technical field of the invention
Member, on the premise of without departing from spirit and scope disclosed in this invention, can be in the formal and details implemented
On make any modification and change, but the scope of patent protection of the present invention, still must be with appending claims institute
The scope defining is defined.
Claims (10)
1. a kind of sensor performance method for quantitatively evaluating based on Gauss distribution, including：
Obtain tested sensor test data sampled point Gauss distribution under certain test condition, normal operating conditions
Evaluating；
The qualified of tested sensor is set up based on canonical parameter under described certain test condition for the standard transducer
Line；
Judge whether tested sensor is qualified based on described acceptance line and described evaluating.
2. evaluation methodology according to claim 1 it is characterised in that described evaluating include tested
The mathematical expectation of test data under described certain test condition for the sensor.
3. evaluation methodology according to claim 2 it is characterised in that described canonical parameter include described
The mathematical expectation of test data under described certain test condition for the standard transducer and standard deviation square value.
4. evaluation methodology according to claim 3 is it is characterised in that set up the qualified of tested sensor
Line further includes：
With the μ-n σ of described standard transducer as acceptance line, wherein, μ is the mathematical expectation of standard transducer,
σ is the standard deviation square value of standard transducer, and n is the required precision of tested sensor.
5. evaluation methodology according to claim 4 is it is characterised in that based on described acceptance line and described
Evaluating judges whether tested sensor is qualified and further includes：
Evaluating as the Gauss distribution of the test data of tested sensor is more than or equal to μ-n σ, then tested sensing
Device is qualified under described certain test condition；
Evaluating as the Gauss distribution of the test data of tested sensor is less than μ-n σ, then tested sensor exists
Unqualified under described certain test condition.
6. evaluation methodology according to claim 5 is it is characterised in that described certain test condition is different
When, described acceptance line needs redefinition.
7. evaluation methodology according to claim 1 it is characterised in that to tested sensor multiple not
Tested under same test condition.
8. evaluation methodology according to claim 7 is it is characterised in that be directed to tested sensor multiple
Under different test conditions, whether qualified result sets up corresponding data base.
9. evaluation methodology according to claim 1 is it is characterised in that obtain tested sensor certain
Under test condition, normal operating conditions, the evaluating of test data sampled point Gauss distribution further includes：
Based on the repeatedly continuous random sampling for a long time of tested sensor, with the meansigma methodss of each continuous random sampling
Sample value as this time；
Tested sensor is calculated according to multiple sample value and tests number under certain test condition, normal operating conditions
Evaluating according to sampled point Gauss distribution.
10. evaluation methodology according to claim 1 is it is characterised in that obtain standard transducer described
Canonical parameter under certain test condition further includes：
Based on standard transducer repeatedly continuous random sampling for a long time, with the meansigma methodss of each continuous random sampling
Sample value as this time；
Standard transducer is calculated according to multiple sample value and tests number under certain test condition, normal operating conditions
Canonical parameter according to sampled point Gauss distribution.
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| CN109507751A (en) * | 2017-09-15 | 2019-03-22 | 中国石油化工股份有限公司 | The consistency quantitative evaluation method and system of seismic sensor |
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