CN116182950A - Multi-sensor detection fusion calculation method - Google Patents
Multi-sensor detection fusion calculation method Download PDFInfo
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- CN116182950A CN116182950A CN202310178288.0A CN202310178288A CN116182950A CN 116182950 A CN116182950 A CN 116182950A CN 202310178288 A CN202310178288 A CN 202310178288A CN 116182950 A CN116182950 A CN 116182950A
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention relates to the technical field of sensor detection, and particularly discloses a multi-sensor detection fusion calculation method, which comprises the following steps: step S1: acquiring measured values of a plurality of sensors at the current moment; step S2: and calculating detection fusion results of the plurality of sensors at the current moment according to the measured values of the plurality of sensors at the current moment. According to the multi-sensor detection fusion calculation method provided by the invention, detection fusion results of m historical moments before the current moment in the whole sampling period participate in the operation through the proximity matrix dimension expansion, so that m physical sensors are equivalently added, the confidence is increased, and meanwhile, the physical sensors are not added; because the degree of adhesion participates in the calculation of the weighting coefficient, even if a certain real sensor suddenly fails at a certain moment, the degree of adhesion is deviated from other sensors, so that the weighting coefficient is very small, the influence on a final calculated value is very small, and the accuracy of the measured value of the measured medium is improved.
Description
Technical Field
The invention relates to the technical field of sensor detection, in particular to a multi-sensor detection fusion calculation method.
Background
In process control systems such as temperature detection, pressure detection and flow detection, in order to eliminate measurement deviation of a single sensor, it is often necessary to measure the same physical quantity by a plurality of sensors at the same time, and in the conventional case, only mean value operation or median value operation is performed on measurement values of the plurality of sensors. However, in practical application, temperature gradient distribution, flow field distribution, pressure fluctuation and attenuation of the temperature cavity are involved, so that deviation of measured values of detection points where one or more sensors are located is easy to cause, and along with temperature rise, flow rate increase and pressure increase, the temperature field, the flow field and the pressure fluctuation are changed; the detection point at which the measurement is biased will also change.
Disclosure of Invention
Aiming at the defects and shortcomings in the prior art, the invention provides a multi-sensor detection fusion calculation method which can improve the accuracy of measured values of a measured medium.
As a first aspect of the present invention, there is provided a multi-sensor detection fusion calculation method including:
step S1: acquiring measured values of a plurality of sensors at the current moment;
step S2: and calculating detection fusion results of the plurality of sensors at the current moment according to the measured values of the plurality of sensors at the current moment.
Further, the method further comprises the following steps:
acquiring detection fusion results of a plurality of sensors at a historical moment, wherein the detection fusion results of the plurality of sensors at the historical moment are calculated according to the multi-sensor detection fusion calculation method, and the historical moment comprises any moment before the current moment;
and calculating to obtain fusion calculation results of the plurality of sensors at the current moment according to detection fusion results of the plurality of sensors at the historical moment and measurement values of the plurality of sensors at the current moment.
Further, the method further comprises the following steps:
setting the current time as k time, and obtaining n sensors in totalThe measurement value of each of the n sensors at k time is X i (k) The measurement value of the j-th sensor at the k moment is X j (k) Wherein i and j are any two of the n sensors;
calculating the measured value closeness s of the ith sensor and the jth sensor at k time ij(k) ，s ij(k) The calculation formula of (2) is as follows:
s ij(k) ＝min{X i (k),X j (k)}/max{X i (k),X j (k)}
then the measured value proximity matrix S of n sensors at time k n (k) The following are provided:
adding detection fusion results of m times before the k time as m hypothetical sensor measurement values into a measurement value closeness matrix of the k time to obtain a dimension-expanded closeness matrix S n+m (k) The dimension-expanded closeness matrix S n+m (k) The following are provided:
then the consistency measure r of the ith sensor measurement value at time k with the other sensor measurement values i (k) Is that
Further, the method further comprises the following steps:
from the consistency measure r of the ith sensor measurement at time k i (k) Respectively calculating a consistency mean value and a consistency variance of the i-th sensor measured value at the k moment;
consistency mean value of ith sensor measurement value at k moment
The calculation formula of (2) is as follows:
consistency variance of ith sensor measurements at time k
The calculation formula of (2) is as follows:
further, the method further comprises the following steps:
based on the consistent mean of the ith sensor measurements at time k
And consistency variance->
Calculating the weighting coefficient w of the i-th sensor measurement value at the k moment i (k)；
Weighting factor w of the i-th sensor measurement at time k i (k) The calculation formula of (2) is as follows:
for the weighting coefficient w i (k) Normalization processing is carried out to obtain a weighting coefficient W of fusion of the ith sensor measured value at the k moment i (k)：
Where i=1, 2 …, n+m.
Further, the method further comprises the following steps:
according to time kMeasurement X of the ith sensor i (k) And its corresponding fusion weighting coefficient W i (k) Calculating detection fusion results of n sensors at k time
The calculation formula of (2) is as follows:
the multi-sensor detection fusion calculation method provided by the invention has the following advantages:
1. the detection fusion results of m historical moments before the current moment in the whole sampling period participate in the operation through the dimension expansion of the proximity matrix, so that m physical sensors are equivalently added, and the physical sensors are not added while the confidence is increased;
2. because the degree of adhesion participates in the calculation of the weighting coefficient, even if a certain real sensor suddenly fails at a certain moment, the degree of adhesion is deviated from other sensors, so that the weighting coefficient is very small, the influence on a final calculated value is very small, and the accuracy of the measured value of the measured medium is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate the invention and together with the description serve to explain, without limitation, the invention.
Fig. 1 is a flowchart of a multi-sensor detection fusion calculation method provided by the invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this embodiment, a multi-sensor detection fusion calculation method is provided, and an example is taken as an illustration of a detected medium in which local non-uniformity exists and the positions of non-uniform positions of external factors are randomly changed in practical application, and fig. 1 is a flowchart of the multi-sensor detection fusion calculation method provided by the invention. As shown in fig. 1, the multi-sensor detection fusion calculation method includes:
step S1: acquiring measured values of a plurality of sensors at the current moment;
the same measured physical quantity is detected at different positions or directions of the measured medium by using a plurality of sensors with the same type and is recorded as n sensors;
step S2: and calculating detection fusion results of the plurality of sensors at the current moment according to the measured values of the plurality of sensors at the current moment.
Preferably, the method further comprises:
acquiring detection fusion results of a plurality of sensors at a historical moment, wherein the detection fusion results of the plurality of sensors at the historical moment are calculated according to the multi-sensor detection fusion calculation method, and the historical moment comprises any moment before the current moment;
and calculating to obtain fusion calculation results of the plurality of sensors at the current moment according to detection fusion results of the plurality of sensors at the historical moment and measurement values of the plurality of sensors at the current moment.
Preferably, the method further comprises:
setting the current moment as the k moment, and obtaining the measured value of each sensor in the n sensors at the k moment, wherein the measured value of the ith sensor at the k moment is X i (k) The measurement value of the j-th sensor at the k moment is X j (k) Wherein i and j are any two of the n sensors;
if X i (k) And X j (k) The difference between the two sensors is larger, which indicates that the measured values of the two sensors at the moment k have low mutual support degree; if X i (k) And X j (k) Very close, it shows that the measurement values of the two sensors at the k moment have high mutual support, and the authenticity of the two measurement values at the k moment is higher;
calculating the measured value closeness s of the ith sensor and the jth sensor at k time ij(k) ，s ij(k) The calculation formula of (2) is as follows:
s ij(k) ＝min{X i (k),X j (k)}/max{X i (k),X j (k)}
then the measured value proximity matrix S of n sensors at time k n (k) The following are provided:
for S n (k) For the i-th line element of (b)
Indicating that the measurement value of the ith sensor at the moment k is consistent with the measurement values of all sensors; if s ij (k) The value of (2) is greater indicating the i-th sensor at that timeThe measured value is relatively close to the measured values of all the sensors; conversely, the measurement value of the ith sensor deviates from the measurement values of the other sensors, and thus the degree of reliability of the measurement values thereof decreases.
In order to reflect the fusion estimation results before the whole sampling period to the observation closeness calculation at the moment, the detection fusion results at m times before the k time in the sampling period are taken as m hypothetical sensor measurement values to be added into a measurement value closeness matrix at the k time, so that the sensor measurement values are changed from n to n+m to obtain a dimension-expanded closeness matrix S n+m (k) I.e. the above proximity matrix S n (k) The last m rows and the last m columns of the matrix are added with m rows and m columns respectively, and the proximity matrix S after dimension expansion n+m (k) The following are provided:
then the consistency measure r of the ith sensor measurement value at time k with the other sensor measurement values i (k) Is that
Consistency measure r i (k) But reflects how close the measurement of sensor i is to all sensor measurements at a certain observation instant. The reliability of the sensor is shown by the consistency measurement at all observation moments, and in order to consider the reliability in the whole observation interval, the reliability information of the consistency measurement sequence implications at different moments is researched by applying two concepts of sample mean and variance in the statistical theory.
Preferably, the method further comprises:
from the consistency measure r of the ith sensor measurement at time k i (k) Respectively calculating a consistency mean value and a consistency variance of the i-th sensor measured value at the k moment;
at time k, consistent mean of ith sensor measurements
The calculation formula of (2) is as follows:
consistency variance of ith sensor measurement at time k
The calculation formula of (2) is as follows:
if the mean value of the consistency of a certain sensor is larger and the variance of the consistency is smaller, the sensor has higher reliability and higher weight in the data fusion process. The signal-to-noise ratio (the ratio of mean to variance) can be used to characterize a consistent reliability measure.
Preferably, the method further comprises:
weighting factor w of the ith sensor measurement i (k) Mean value of consistency
Positively correlated with variance->
And (5) negative correlation. According to the mean value of the consistency of the i-th sensor measurement at time k +.>
And consistency variance->
Calculating the weighting coefficient w of the i-th sensor measurement value at the k moment i (k)；
Weighting factor w of the i-th sensor measurement at time k i (k) The calculation formula of (2) is as follows:
for the weighting coefficient w i (k) Normalization processing is carried out to obtain a weighting coefficient W of fusion of the ith sensor measured value at the k moment i (k)：
Where i=1, 2 …, n+m.
Preferably, the method further comprises:
based on the measurement value X of the ith sensor at time k i (k) And its corresponding fusion weighting coefficient W i (k) Calculating detection fusion results of n sensors at k time
The calculation formula of (2) is as follows:
according to the multi-sensor detection fusion calculation method provided by the invention, detection fusion results of m historical moments before the current moment in the whole sampling period participate in the operation through the proximity matrix dimension expansion, so that m physical sensors are equivalently added, the confidence is increased, and meanwhile, the physical sensors are not added; because the degree of adhesion participates in the calculation of the weighting coefficient, even if a certain real sensor suddenly fails at a certain moment, the degree of adhesion is deviated from other sensors, so that the weighting coefficient is very small, the influence on a final calculated value is very small, and the accuracy of the measured value of the measured medium is improved.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (6)
1. The multi-sensor detection fusion calculation method is characterized by comprising the following steps of:
step S1: acquiring measured values of a plurality of sensors at the current moment;
step S2: and calculating detection fusion results of the plurality of sensors at the current moment according to the measured values of the plurality of sensors at the current moment.
2. The multi-sensor detection fusion calculation method according to claim 1, further comprising:
acquiring detection fusion results of a plurality of sensors at a historical moment, wherein the detection fusion results of the plurality of sensors at the historical moment are calculated according to the multi-sensor detection fusion calculation method, and the historical moment comprises any moment before the current moment;
and calculating to obtain fusion calculation results of the plurality of sensors at the current moment according to detection fusion results of the plurality of sensors at the historical moment and measurement values of the plurality of sensors at the current moment.
3. The multi-sensor detection fusion calculation method according to claim 2, further comprising:
setting the current moment as the k moment, and obtaining the measured value of each sensor in the n sensors at the k moment, wherein the measured value of the ith sensor at the k moment is X i (k) The measurement value of the j-th sensor at the k moment is X j (k) Wherein i and j are any two of the n sensors;
calculating the measured value closeness s of the ith sensor and the jth sensor at k time ij (k)，s ij (k) The calculation formula of (2) is as follows:
s ij (k)＝min{X i (k),X j (k)}/max{X i (k),X j (k)}
then the measured value proximity matrix S of n sensors at time k n (k) The following are provided:
adding detection fusion results of m times before the k time as m hypothetical sensor measurement values into a measurement value closeness matrix of the k time to obtain a dimension-expanded closeness matrix S n+m (k) The dimension-expanded closeness matrix S n+m (k) The following are provided:
then the consistency measure r of the ith sensor measurement value at time k with the other sensor measurement values i (k) Is that
4. The multi-sensor detection fusion calculation method according to claim 3, further comprising:
from the consistency measure r of the ith sensor measurement at time k i (k) Respectively calculating a consistency mean value and a consistency variance of the i-th sensor measured value at the k moment;
consistency mean value of ith sensor measurement value at k moment
The calculation formula of (2) is as follows:
consistency variance of ith sensor measurements at time k
The calculation formula of (2) is as follows:
5. the multi-sensor detection fusion calculation method of claim 4, further comprising:
based on the consistent mean of the ith sensor measurements at time k
And consistency variance->
Calculating the weighting coefficient w of the i-th sensor measurement value at the k moment i (k)；
Weighting factor w of the i-th sensor measurement at time k i (k) The calculation formula of (2) is as follows:
for the weighting coefficient w i (k) Normalization processing is carried out to obtain a weighting coefficient W of fusion of the ith sensor measured value at the k moment i (k)：
Where i=1, 2 …, n+m.
6. The multi-sensor detection fusion calculation method of claim 5, further comprising:
based on the measurement value X of the ith sensor at time k i (k) And its corresponding fusion weighting coefficient W i (k) Calculating detection fusion results of n sensors at k time
The calculation formula of (2) is as follows:
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